GB2629578A - Power threshold determination - Google Patents

Abstract

A control system (208) for computing a power threshold for activating and deactivating an electric propulsion mode of a hybrid electric vehicle (10) is described. The control system (208) is configured to receive route information concerning a journey planned to be undertaken by the vehicle (10), determine, from the route information, a driving profile for the journey, the driving profile comprising an expected vehicle speed for each of a plurality of segments of the journey, and determine an adjusted vehicle speed for each segment in dependence on an adjustment factor, the adjustment factor being based on historical driving data for the vehicle (10). The power threshold is then determined for the vehicle (10) based on the adjusted vehicle speeds for each segment of the journey; and a signal is output in dependence on the determined power threshold. In this way, the vehicle speed threshold calculation is able to consider the manner in which the vehicle (10) has previously been driven, by applying an adjustment factor to more generic vehicle speed information, the adjustment factor being based on historical driving data. This improves the utilisation of the electric propulsion mode.

Description

POWER THRESHOLD DETERMINATION

TECHNICAL FIELD

The present disclosure relates to a system and method for determining a power threshold for activating and deactivating an electric-only mode of operation of a hybrid electric vehicle.

Aspects of the invention relate to a system, a method of determining a power threshold, a computer program and a vehicle.

BACKGROUND

Hybrid Electric Vehicles (HEVs) are propelled both by an internal combustion engine (ICE) and an electric motor powered by electrical power stored in batteries. These vehicles may be operated in a variety of modes, including a ICE only mode in which the vehicle is propelled entirely by the internal combustion engine (and during which the vehicle batteries may be recharged, for example using the engine, or regenerative braking), an electric-only mode in which the vehicle is propelled entirely by the electric motor(s), and in which the ICE may be switched off, and optionally a dual-propulsion mode in which the powertrain is driven by both the ICE and the electric motors in parallel. To minimise emissions, and (assuming electric charging occurs at low cost) to minimise journey cost, it is desirable to operate the vehicle in an electric-only mode for as much of a journey as possible, but also to arrange that the portions of the journey during which the electric-only mode is operated are those portions for which electric-only operation is most efficient. In practice, this means that it is preferable to operate the vehicle in the electric-only mode at relatively low speeds, and to utilise the ICE at relatively high speeds.

It is possible to determine, for a given journey, a speed threshold above which the vehicle should be propelled using the ICE and below which the vehicle should be propelled in the electric-only mode, and which will make maximum use of the battery charge (preferably while retaining a reserve energy to permit the final portion of the journey to be carried out in the electric-only mode). This speed threshold is determined in part based on probable energy costs for each segment of a journey. The electric mode of vehicle operation can be selected for those segments for which it is most efficient.

Inaccuracies in the computations resulting from the driving profile can be resolved by using information about the journey ahead coming from the navigation system, including the distance to destination, the road gradient, average speed on sections of the road ahead, and speed limits. However, this approach has some limitations because the average speed on the road is based on an aggregated dataset from multiple users (that is, how other drivers have driven on those segments), which may not reflect the probable average speed of the instant driver.

This average speed error directly impacts the magnitude of the aerodynamic drag force applied on the vehicle which then introduces a bias into the energy cost prediction.

It is an aim of the present invention to address one or more of the disadvantages associated with the prior art.

SUMMARY OF THE INVENTION

Aspects and embodiments of the invention provide a control system, a method of controlling vehicle steering, a computer program and a vehicle, as claimed in the appended claims.

In one aspect, there is provided a control system for computing a power threshold for activating and deactivating an electric propulsion mode of a hybrid electric vehicle, the control system comprising one or more controller, the control system configured to: receive route information concerning a journey planned to be undertaken by the vehicle; determine, from the route information, a driving profile for the journey, the driving profile comprising an expected vehicle speed for each of a plurality of segments of the journey; determine an adjusted vehicle speed for each segment in dependence on an adjustment factor, the adjustment factor being based on historical driving data for the vehicle; and determine the power threshold for the vehicle based on the adjusted vehicle speeds for each segment of the journey; and output a signal in dependence on the determined power threshold.

Reference to the control system being configured to' is to be understood to mean 'the one or more controllers of the control system are collectively configured to'. In this way, the vehicle speed threshold calculation is able to take into account the manner in which the vehicle has previously been driven, by applying an adjustment factor to more generic vehicle speed information, the adjustment factor being based on historical driving data (for the vehicle). This improves the utilisation of the electric propulsion mode, and may reduce emissions (by reducing the extent to which the ICE is used to propel the vehicle). Viewed slightly differently, the power threshold may be considered to be a threshold below which the electric propulsion system is used in preference to the internal combustion engine, and above which the internal combustion engine is used in preference to the electric propulsion system. The power threshold may also be considered to be a threshold below which the vehicle is operated in a first propulsion mode having a first set of conditions for activating and deactivating the electric propulsion system and/or the internal combustion engine, and above which the vehicle is operated in a second propulsion mode having a second (different) set of conditions for activating and deactivating the electric propulsion system and/or the internal combustion engine.

The power threshold is preferably a speed threshold, and the electric propulsion mode is deactivated when the speed threshold is exceeded. This provides a straightforward metric for triggering the change between ICE and electric propulsion. In other examples the power threshold is not a pure speed threshold, but is partly dependent on vehicle speed. This may provide a more refined measure, but at the cost of increased complexity.

The control system may be configured to compute a reserve energy for a final portion of the journey based on the adjusted vehicle speeds for segments corresponding to the final portion of the journey. In this way, the amount of energy to be set aside for the final portion of the journey may be more accurately estimated.

The control system may be configured to monitor a driven speed of the vehicle for a journey segment, compare the driven speed of the vehicle with an expected vehicle speed for that journey segment, and compute the adjustment factor based on a difference between the driven speed and the expected speed.

These steps of monitoring, comparing and determining may be based on the current journey, or a previous journey. It will be appreciated that this process may be ongoing, with each journey providing new data as to how the vehicle is driven, thereby improving the accuracy of the historical driving data, and improving the accuracy of the resulting adjustment factor. In other words, the historical data may be built up over time, including based on the current journey. In the latter case the resulting adjustment factors may be used immediately in updating the power threshold. It will further be appreciated that the adjustment factor(s) and/or the historical data will typically be stored in a memory, and updated in the memory over time as the monitoring progresses.

In any case, in this way, historical driving data may be obtained, and accumulated over time.

The historical driving data may in this case be a speed offset (e.g. +/-X kph) from an expected vehicle speed (the expected vehicle speed being based either on an aggregated data set for multiple drivers, or a speed based on other considerations such as a speed limit, road type or gradient for example). Where the expected vehicle speed is based on an aggregated data set for multiple drivers, this may be derived from a separate source of driving data from that accumulated using the present technique and/or from other drivers operating a vehicle carrying out the present technique. In other words, the historical driving data used with the present technique to determine and refine the adjustment factors may further be used as, or to generate, expected vehicle speed information in an aggregated data set used by other drivers (and in practice the instant driver too). Where the aggregated data set is derived from a separate source of driving data, this may simply be any form of measurement of actual vehicle speeds as vehicle traverse a particular road segment. A baseline expected vehicle speed for a particular road segment may for example simply be the actual average speed of all vehicles measured traversing that segment.

The control system may be configured to compare the driven speed of the vehicle with an expected vehicle speed for each of a plurality of speed ranges, and to determine an adjustment factor for each speed range. The speed range is preferably a range of expected vehicle speed, but in some implementations may be a range of driven speed. In this way, speed ranges may be provided with adjustment factors as and when sufficient historical driving data is acquired to determine them. The system may acquire adjustment factors for some speed ranges before others. Until sufficient data is obtained to enable an adjustment factor to be accurately determined in relation to a particular speed range, the expected vehicle speed may be used for that speed range instead, or an assumed adjustment factor may be generated based on historical driving data for other speed ranges (for example neighbouring speed ranges), or an assumed adjustment factor may be generated in another way.

The control system may be configured to monitor the driven speed of the vehicle, compare the driven speed of the vehicle with an expected vehicle speed, and compute the adjustment factor based on the difference, during the planned journey, and to use the newly computed adjustment factor to refine the power threshold. In this way, the vehicle speed threshold may be refined as a journey progresses, taking into account the driver's most recent driving behaviour (during the current journey itself).

The control system may be configured to compute the power threshold by predicting an electrical energy consumption for each of the plurality of journey segments, each journey segment representing a constant expected vehicle speed and road gradient.

The control system may be configured to compute the power threshold by aggregating segments by vehicle speed and additionally by road gradient, and computing an electrical energy consumption for each aggregated group of segments. In this way, it is possible to make the computation more accurate in cases where different drivers may react (drive) differently on different road gradients. For example, a particular driver may be quite sensitive to differing road gradients, for example driving relatively carefully (and thus more slowly) on higher gradients (uphill or downhill) compared with on flat roads. Another driver may not differ in driving style significantly as a function of road gradient.

In some simple implementations a single adjustment factor may be applied, based on the historical driving data for the vehicle, irrespective of vehicle speed. This might for example be a percentage uplift or reduction from the expected vehicle speed by +1-X%. However, more advanced implementations take into account that a particular driver may vary from an average driver by a greater or lesser extent at different (expected) vehicle speeds. For example, a particular driver may drive at similar speeds to an average driver at low speeds around built up areas, but may drive significantly more aggressively than average at higher speeds on motorways. To account for this, the control system may be configured to select an adjustment factor for computing the adjusted vehicle speed in dependence on the expected vehicle speed for a segment of the journey.

The expected speed may be based on an aggregated data set for multiple drivers.

If an amount of data collected and generated by the monitoring and comparing steps is less than a threshold amount for a given expected vehicle speed or speed range, the control system may be configured to compute the power threshold using the expected vehicle speed rather than an adjusted vehicle speed for that expected vehicle speed or speed range.

The control system may be configured to determine a driving style based on the historical driving data, and to set the adjustment factor based on the determined driving style. In a simple case, this could be (as referenced above) simply applying a single adjustment factor independently of vehicle speed. Alternatively though, the adjustment factor applied for a given driving style may vary as a function of (expected vehicle speed).

The control system may be configured to use an adjustment factor computed based on driven speeds for a speed range corresponding to the current segment if available, and to use an adjustment factor based on determined driving style otherwise. In this case, driving style is used as a mechanism to provide a coarse estimate of what the adjustment factor could be expected to be before speed-relevant data becomes available.

It will be appreciated that, in some cases, more than one individual may have use of (drive) the vehicle. Each individual may drive differently. To cater for this, the control system may be configured to receive a signal indicative of the identity of a driver of the vehicle, and to select the adjustment factor in dependence on the identified driver.

Various ways of achieving this are known -such as facial recognition using a camera in the vehicle cabin, the presence of a particular portable electronic device (for example a smartphone) or fob, or entry or selection of a driver via an infotainment system of the vehicle. In this case, the control system may be configured to compute the adjustment factor in relation to the identified driver based on historical driving data associated with the identified driver.

The control system may be configured to activate and deactivate the electric propulsion mode of the vehicle in dependence on whether the power threshold is exceeded.

According to another aspect there is provided a vehicle comprising a control system as described above.

According to another aspect of the invention, there is provided a method for computing a power threshold for activating and deactivating an electric propulsion mode of a hybrid electric vehicle, the method comprising: receiving route information concerning a journey planned to be undertaken by the vehicle; determining, from the route information, a driving profile for the journey, the driving profile comprising an expected vehicle speed for each of a plurality of segments of the journey; determining an adjusted vehicle speed for each segment in dependence on an adjustment factor, the adjustment factor being based on historical driving data for the vehicle; and determining the power threshold for the vehicle based on the adjusted vehicle speeds for each segment of the journey; and outputting a signal in dependence on the determined power threshold.

In another aspect, there is provided computer readable instructions which, when executed by a computer, are arranged to perform a method according to the above.

Within the scope of this application it is expressly intended that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. That is, all embodiments and/or features of any embodiment can be combined in any way and/or combination, unless such features are incompatible. The applicant reserves the right to change any originally filed claim or file any new claim accordingly, including the right to amend any originally filed claim to depend from and/or incorporate any feature of any other claim although not originally claimed in that manner.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which: Figure 1 shows a schematic representation of a vehicle having a battery, an electric propulsion system, a control system, and a display; Figures 2A and 2B schematically illustrate a control system and a group of vehicle controllers; Figure 3 schematically illustrates control system components for estimating a vehicle range; Figures 4A and 4B schematically illustrate how a journey can be divided into segments, and how propulsion modes can be allocated to those segments; Figure 5 schematically illustrates a set of differing speed profiles dependent on driving style; Figure 6 schematically illustrates one embodiment of a predictive energy optimisation system; Figure 7 schematically illustrates another embodiment of a predictive energy optimisation system; and Figure 8 shows a schematic flow diagram of the control method.

DETAILED DESCRIPTION

FIG. 1 illustrates an example of a vehicle 10 in which embodiments of the invention can be implemented. In some, but not necessarily all examples, the vehicle is a passenger vehicle, also referred to as a passenger car or as an automobile. In other examples, embodiments of the invention can be implemented for other applications, such as commercial vehicles.

The vehicle 10 comprises a plurality of systems including an internal combustion engine (ICE) 104, electric propulsion system 12, control system 208, and energy storage means 22 for powering the electric propulsion system 12, such as one or more batteries, for example one or more high voltage batteries. The combustion engine 104 may be a reciprocating piston internal combustion engine. The electric propulsion system 12 may be a traction motor, and the energy storage means 22 may be a traction battery for powering the electric traction motor.

In some examples, the electric traction motor is a motor-generator, while in other examples a separate generator is provided. In some examples, the electric traction motor is a starter-generator, operable to perform the function of a starter motor. The purpose of the generator is to recharge the traction battery, for example by implementing a regenerative braking function.

The vehicle 10 is a parallel hybrid vehicle, such that the electric traction motor 208 and combustion engine 104 can be operated in a variety of modes. Generally speaking, the present technique is concerned with selecting between a first propulsion mode in which the vehicle is solely or primarily propelled using electric propulsion, and a second propulsion mode in which the vehicle is solely or primarily propelled using the internal combustion engine. Within the first propulsion mode the internal combustion engine may optionally be used for short periods, for example when torque demand is high, to provide additional assistance torque. Similarly, within the second propulsion mode the electric motor may optionally be used for short periods, for example when torque demand is high, to provide additional assistance torque. Examples of various propulsion modes are set out below.

The propulsion modes may include a 'charge depletion' mode (example of a first mode) in which the electric traction motor 12 is in an on state to produce tractive torque. This depletes electrical charge stored in the traction battery 22. If torque demand is high in charge depletion mode, then in some implementations the combustion engine 104 may be operated in an on state simultaneously to provide a 'torque assist' function. In a 'charge attain' mode, the traction battery 22 is recharged by the generator when the combustion engine produces torque in excess of driver torque demand. In an example, in charge attain mode, a regenerative braking function is operable which could request the combustion engine 104 in an off state to maximise power recuperation during braking of the vehicle 10.

In a 'charge sustain' mode (example of a second mode), the combustion engine 104 is in an on state to produce tractive torque, and the electric traction motor 12 can be used to provide torque assist or short periods of electric traction motor-only driving, however this must be compensated for by attaining charge to maintain (sustain) a target (setpoint) state of charge, for example using regenerative braking or shifting combustion engine load to a higher point. For example, the target state of charge can be a constant value for the whole time that charge sustain mode is in operation. The level of permitted depletion in the charge sustain mode is insufficient for mid-range to high-range torque demands (e.g. within range X-100%, where 100% is equivalent to open throttle) to be satisfied wholly by depletion of traction battery energy, without contribution by the combustion engine 104. Therefore, the combustion engine 104 will primarily be in its on state during the charge sustain mode. The torque demand 'X' corresponds to either zero torque demand or to a threshold torque demand greater than zero, below which electric traction motor-only driving is permitted.

Accordingly, the different behaviour between charge sustain (second) mode and charge depletion (first) mode is particularly apparent while a mid-range torque demand is applied, e.g. a torque demand value from the range 40%-70%. For this torque demand, the combustion engine 104 would, in accordance with some but not necessarily all examples, be used in charge sustain mode but not used in charge depletion mode.

The control system 208 is configured to implement any one or more of the methods described herein. FIG. 2A illustrates how the control system 208 may be implemented. The control system 208 of FIG. 2A illustrates a controller 200. In other examples, the control system 208 may comprise a plurality of controllers 200 onboard and/or off board the vehicle 10. In examples any suitable control system 208 can be used.

The controller 200 of FIG. 2A includes at least one processor 202; and at least one memory device 204 electrically coupled to the electronic processor 202 and having instructions 206 (for example a computer program) stored therein, the at least one memory device 204 and the instructions 206 configured to, with the at least one processor 202, cause any one or more of the methods described herein to be performed.

FIG. 2A therefore illustrates a control system 208, wherein the one or more electronic controllers 200 collectively comprise: at least one electronic processor 202 having an electrical input for receiving information associated with energy storage control; and at least one electronic memory device 204 electrically coupled to the at least one electronic processor 202 and having instructions 206 stored therein; and wherein the at least one electronic processor 202 is configured to access the at least one memory device 204 and execute the instructions thereon so as to cause the control system 208 to perform and/or cause performance of any one or more of the methods described herein.

Also illustrated in the example of FIG. 2A are one or more vehicle systems 226. In examples, the vehicle system(s) 226 can comprise any suitable vehicle system(s). For example, the vehicle system(s) 226 can comprise any suitable vehicle system(s) 226 from which the control system 208 can receive and/or to which the control system 208 can transmit, directly or indirectly, one or more signals 20, for example to control an energy storage means 22 of a vehicle 10. In examples, the one or more vehicle systems 226 comprise one or more systems involved in control of an energy storage means of the vehicle 10. In examples, the one or more vehicle systems 226 comprise one or more systems involved in control of state of charge of an energy storage means 22 of the vehicle 10. For example, one or more vehicle systems 226 can comprise any suitable system or systems 226 of the vehicle configured to provide energy to and/or draw energy from energy storage means 22 of the vehicle 10. For example, the one or more vehicle systems 226 can comprise one or more energy recovery systems and/or one or more electric motors and so on. In examples, the one or more vehicle systems 226 can comprise one or more systems configured to report on the present state and/or present usage of energy by the energy storage means 22 of the vehicle 10. In examples, the one or more vehicle systems 226 can comprise one or more systems configured to provide information to allow a determination of a predicted destination 12 for the vehicle 10 and/or an associated confidence value. In examples, the one or more systems 226 comprise a powertrain controller module and/or an infotainment system.

FIG. 2B illustrates a non-transitory computer readable storage medium 218 comprising the instructions 206 (computer software). Accordingly, FIG. 2B illustrates a non-transitory computer readable medium 218 comprising computer readable instructions 206 that, when executed by a processor 202, cause performance of at least the method of one or more of FIG. 7 and/or as described herein.

In Figure 3, several of the vehicle systems 226 are shown, including a Plug-In-Vehicle Infotainment system (PIVI) 226a, a gateway (GVV) module 226b and a powertrain control module, which in the present implementation corresponds to the control system 208. The PIVI 226a carries out navigation functionality, and outputs eHorizon data to the gateway module 226b over an ethernet connection. The eHorizon data comprises route information concerning a journey planned to be undertaken by the vehicle. The gateway module 226b provides requested information to the powertrain control module (control system 208). The requested information includes, for each of the segments of the planned journey, a segment offset (0609), a segment gradient (Ss.,), and a segment speed (V609). The segment offset is in effect a length/distance of the segment (since subtracting the offset of a preceding segment from a particular segment will provide the length of the particular segment), the segment gradient is a road gradient for the segment. This may be an average gradient for the portion of the road corresponding to that segment. Since segments need not be all of the same length, generally new segments will be defined whenever the road gradient changes significantly, with the result that the gradient is at least generally uniform for the portion of road corresponding to any given segment. The segment speed is an assumed average speed for the segment, based on historical traffic data from multiple users. It will be appreciated that different users may traverse the segment at different speeds, and the segment speed is an average of these, collected by any suitable method. The powertrain control module 208 has a function of selecting which of the propulsion modes should be used, and in particular selects between the first mode and the second mode in dependence on a power threshold, as will be described below.

With the present technique, the power threshold is dependent on a journey planned to be carried out by the vehicle. More specifically, the power threshold is dependent on a total expected power usage for the journey, and a manner in which the power usage is distributed across different segments of the journey. In some, but not necessarily all examples, the journey is a route between a starting location and a destination, optionally via one or more waypoints. In one example, the route is published by the vehicle navigation system. In this example, the starting location, destination and waypoints may be deterministic because they are specified by user inputs to the vehicle navigation system. Therefore, in this example, the route is deterministic. In another example, the route is predicted from machine learning. The machine learning could indicate where the vehicle 10 has previously been driven and at which times. This enables the route to be determined probabilistically. For example, at 8am on a weekday the driver normally drives to work, which trains a predictive algorithm to determine that when the driver enters their vehicle 10 at 8am on a weekday, they are going to follow a particular route.

In one example, the journey over which power use is predicted extends up to the destination. In another example, the prediction extends only as far as a waypoint.

Once the route is known, the power use of the vehicle 10 is predicted for the journey following that route. The present technique uses vehicle speed, but may additionally use other useful variables including a distance-dependent parameter such as distance, and a gradient-dependent parameter such as road gradient or elevation points from which gradient can be determined. Other useful parameters include vehicle mass, aerodynamic drag coefficient(s), road curvature (e.g. curve radius-dependent or a number of bends), road type (e.g. road classification, number of lanes). The controller 200 may be configured to perform a force analysis of the available parameters to predict the power to be used for the vehicle journey, in accordance with Newton's second laws of motion. In some examples, a model of the drivetrain and/or powertrain of the vehicle 10, and auxiliary electrical loads (e.g. lighting, heating, cooling, engine accessories) can be used to account for losses therefrom.

Figure 4A shows two graphs illustrating two types of information indicative of predicted power use. The x-axis represents time or distance. The y-axis of the upper graph represents vehicle speed. The y-axis of the lower graph represents road gradient, which may be positive (uphill) or negative (downhill), and could be expressed as a percentage or any other suitable form.

Figure 4A represents the journey as a plurality of segments. In Figure 4A, but not necessarily all examples, the journey comprises 14 segments. In other (more realistic) examples the journey may comprise up to 1500 segments or greater. The speed, gradient and other information is quantized by the controller 200 to a constant value over each segment. The number of segments into which the journey is divided corresponds to a degree of spatial and/or temporal resolution. The width of each segment may correspond to a particular time and/or distance (width on the x-axis), and may be different from the width of at least one other segment. In an example, each segment represents a line between two nodes on a graph representative of a road network. The graph may be that used by a route-finding algorithm such as Dijkstra's algorithm implemented in the vehicle navigation system, for finding the route. The node-to-node spacing is variable because each node may correspond to one of a real road junction or to a helper node for improving spatial resolution (e.g. accounting for road curves). Therefore the segment widths are variable. The segmentation may occur in the vehicle navigation system 102 for the purposes of route calculation, prior to receipt of the information, or in other examples the controller 200 may perform the segmentation.

Once the planned journey has been identified, the method schedules when during the journey the vehicle 10 is to be controlled in the first mode (charge depletion mode), and when during the journey the vehicle 10 is to be controlled in the second mode (charge sustain mode). One of the targets of this scheduling process is to schedule use of the charge depletion mode and the charge sustain mode such that the vehicle 10 is controlled in the charge depletion mode during any portions (e.g. segments) of the journey in which the predicted power use is low, and to be controlled in the charge sustain mode during any other portions (e.g. segments) of the journey in which the predicted power use is high.

The schedule provides an output condition for switching between the modes while the vehicle 10 is in use and undertaking the journey. During the use of the vehicle 10, the controller is configured to implement the output condition such that the combustion engine 104 and electric traction motor 12 are controlled to switch between the modes as required by the output condition, which is based on comparing the current speed of the vehicle with a threshold. In some examples, periodic updates from the vehicle navigation system ensure that the implementation of the schedule is synchronized with the actual journey which may, for example, be subject to delays.

During the use of the vehicle 10, a mode switching operation to switch from the first mode to the second mode is performed whenever a continually measured variable on which the threshold is based (vehicle speed, or a variable which is at least in part dependent on vehicle speed) exceeds the threshold. The mode switching operation switches from the second mode to the first mode whenever the continuously measured variable falls below the threshold. In this example, mode switching is not performed on segment boundaries, but it could be in other examples. In some, but not necessarily all examples the threshold takes a constant value for the whole predicted journey.

The scheduling approach calculates a threshold 402 as shown in Figure 4A, to require the vehicle 10 to be controlled in the charge depletion mode while the threshold 402 is not exceeded, and to require the vehicle 10 to be controlled in the charge sustain mode while the threshold 402 is exceeded. The threshold 402 is dependent on the predicted power use as described above, therefore the threshold 402 is different for each different journey. The value of the threshold 402 may be defined such that the traction battery energy is the required amount lower at the end of the journey than at the beginning of the journey, for example the threshold 402 ensures that the state of charge at the end of the journey is at the above-mentioned value from the range 0% to 30%. A method for calculating the threshold 402 will be discussed in more detail below.

Figure 4B illustrates the effect of applying the threshold 402. Figure 4B illustrates the state of charge of the traction battery 22 on the y-axis, and the x-axis is as defined for Figure 4A. The predicted vehicle speed over journey segments 1, 2, 3, 10, 12, 13 and 14 is below the threshold 402, so charge depletion mode will be used throughout those segments if the vehicle speed during the journey is according to prediction, as can be seen on those areas on Figure 4B labelled 'CD' (charge depletion mode) aligned under those segments, where the state of charge is decreasing. The vehicle speed during the journey may be represented by a current vehicle speed obtained from a speed sensor, and/or an estimated or predicted vehicle speed obtained from the navigation system. The predicted vehicle speed over journey segments 4- 9 and 11 is above the threshold 402 so charge sustain mode will be used throughout those segments if the vehicle speed during the journey is according to prediction, as can be seen on those areas on Figure 4B labelled CS' (charge sustain mode) aligned under those segments, where the state of charge is a constant setpoint.

In the example of Figs 4A-4B, the threshold 402 is a vehicle speed threshold, therefore a journey segment is above the threshold 402 if the representative constant vehicle speed (or speed limit) for that segment is above the vehicle speed threshold. However, in other examples, the threshold 402 could be any power threshold which is at least partly dependent on vehicle speed. In addition to vehicle speed, the threshold 402 could be dependent on at least one of, or a combination of the following predicted variables -road gradient, vehicle mass, road curvature, road type, or auxiliary electrical loading, or any other variables of the above described information indicative of predicted power use. For example, the power threshold could represent a value in kilojoules/second, wherein a journey segment is above that threshold 402 if the power derived from the force analysis for that segment is above the threshold 402.

Further, the vehicle speed threshold could be better able to distinguish between urban driving and extra-urban driving, for example in urban areas which are hilly and therefore require high energy consumption. This means that electric-motor-only driving is favoured in urban areas, so vehicle emissions are moved away from urban areas. A technical effect of a power threshold that accounts for a combination of the above power variables is that energy consumption is reduced because the combustion engine 104 and electric traction motor 22 are used only when they are at their most efficient.

An example algorithm for calculating the threshold 402 quickly will now be described. The calculation accounts for vehicle speed (or speed limit), road gradient, and segment distance, however additional or fewer variables associated with predicted power use can be used in

other examples.

First, a data binning operation is performed in which each segment is assigned to a particular bin in a multi-dimensional array. Each bin along a first dimension (e.g. columns) represents an interval of a first variable, for example vehicle speed. Each bin along a second dimension (e.g. rows) represents an interval of a second variable, for example road gradient. The segment distance is assigned to the bin. If two segments are identified that belong to a particular bin, their distances are added (aggregated) in the bin. An example binning array is shown in Table 1, wherein kph represents kilometres per hour, km represents kilometres, and % represents road gradient in percent: >90kph 30-90kph 0-30kph >5% 0.2km 0km 0.2km -5% to 5% 3km 5km 4km <-5% 0.1km Okm 0km Table 1: Binning array In a practical implementation, the number of data bins may vary from the example of Table 1. Tens, hundreds or thousands of bins could be used, with corresponding smaller intervals.

In one example implementation, the speed threshold is determined by calculating a value of total energy consumption for each bin. The energy consumption calculation in this example is a function of vehicle speed, gradient and distance.

The result of the accumulation can be mapped to a particular threshold 402 in the electronic memory device. For example, with reference to Figure 4A, a vehicle speed threshold 402 of 15kph is determined. In an example implementation, each row (or column) of bins is summed up into a single scalar value, for example resulting in a vector of total energy consumption bins classified only by speed (not by speed and gradient). The vehicle speed threshold is determined by accumulating the total energy consumption values in vehicle speed bins, starting from the lowest vehicle speed bin and moving towards the highest vehicle speed bin, until the available traction battery energy matches the cumulative energy. This process ensures the vehicle speed threshold is optimised such that the vehicle 10 will utilize charge depletion mode as much as possible, at the times when charge depletion mode is most efficient (low speed driving), and with the desired level of depletion by the end of the journey.

It would be appreciated that the vehicle speed threshold 402 can be calculated using alternative methods to the method shown above.

Returning to Figure 3, the requested information provided to the powertrain control module, to estimate the energy usage of the various segments of the journey, may be considered to be a driving profile for the journey. The driving profile therefore comprises an expected vehicle speed for each of a plurality of segments of the journey (each segment having a particular gradient, and a length). Using the driving profile, and in particular these three parameters, along with a set of constants C1, C2, C3, it is possible to estimate an amount of energy which will be consumed during traversal of each segment, according to the following Equation (1): E Reg mentin ---C1 Vsegn: Ls inert Lin] In equation (1), Esegment [J] is the amount of energy (in Joules) consumed by traversal of the segment (output), Vsegment is the segment speed, m is the mass of the vehicle (kg), g is the gravitational constant, Ssegment is the gradient of the segment, and Lsegment is the length of the segment (in metres).

The energy consumption for the entirety of the planned journey can be estimated by aggregating the estimates for the individual segments.

Carrying out this estimate of energy consumption on a per-segment basis may be computationally expensive. It is therefore preferable if this computation can be handled more quickly.

One way to achieve this is to aggregate like-segments together (that is, segments having the same gradient and the same segment speed). The length of these like-segments can then be added together, to define a group of segments having a combined length. Equation (1) above can then be carried out on each group of segments, and the estimates for each group aggregated to give the estimate for the entire journey.

In this case, the segment information may be provided in matrix form, and comprises a matrix offset (Omar) (combined length of all segments in a group), matrix gradient (Smat) (common gradient for group), and matrix speed (Vmat) (common speed for group). Then, the matrix offset, matrix gradient and matrix speed may be substituted for the segment offset, segment gradient and segment speed in equation (1) above, and the Energy consumption aggregated for each group of segments.

Generally, acceleration/deceleration events are not taken into account for energy calculation, and it may be expected that the impact of this is limited due to the acceleration and deceleration events cancelling each other out (for example due to regenerative braking).

Conventionally, it is assumed that the driver of the vehicle is following the average speed profile from the eHorizon data. However, this assumption may often not be correct, leading to inaccuracies in the assumption of vehicle speed used in the determination of energy consumption for each segment. In particular, the average speed profile, defining the expected speeds, is based on an aggregated data set for multiple drivers. The actual driver may handle the vehicle differently, deviating from the expected speeds, resulting in energy costs for the various segments of the journey which are either too high or too low. Since the vehicle speed threshold for triggering switching between the first and second propulsion modes is calculated in dependence on those expected speeds, the speed threshold may also be inaccurately determined, leading to either overutilisation or underutilisation of the EV-only mode of propulsion.

The present technique improves on this by determining an adjusted vehicle speed for each segment in dependence on an adjustment factor, where the adjustment factor is based on historical driving data for the vehicle (or in some cases for the driver, or the driver and vehicle in combination).

As a result, the speed threshold for triggering switching into and out of the EV-only mode may be determined based on the adjusted vehicle speeds for each segment of the journey, which can be expected to give rise to a better utilisation of the EV only mode.

In addition to this, the adjusted vehicle speed (rather than an expected vehicle speed) may also be used in a calculation of an amount of reserve energy to be set aside for a final portion of the journey. This reserve energy may be computed using Equation (1) above, aggregated for those segments which make up the final portion of the journey. The reserve energy may then be used as the required lower amount referenced above, for example.

The historical driving data may be obtained by monitoring a driven speed of the vehicle for a journey/road segment, and comparing the driven speed of the vehicle with an expected vehicle speed for that journey/road segment (that is, the segment speed referred to above). This may have been carried out over one or more prior journeys (although data from the present journey may also be used). In a simple case, the adjustment factor may be determined based on a difference between the driven speed and the expected speed. Generally though, the monitoring and comparing will take place in relation to multiple segments (having the same or similar gradient, and/or the same or similar speed), with the differences in speed being averaged over those multiple segments to obtain a more robust history of how the vehicle is being driven by the current driver.

An adjustment factor may be determined for each of a plurality of speed ranges (of the segment speed). More specifically, each segment has a segment speed, as described above, which is an average speed for multiple users. When a segment speed is being compared with an actual driven speed for the segment, an adjustment factor relating the actual driven speed to the segment speed is determined and associated with a speed range which contains the segment speed. Over multiple driven segments, the comparison of the driven speed of the vehicle with an expected vehicle speed is thus carried out (preferably multiple times) for each of a plurality of speed ranges. Ultimately, this will result in adjustment factors being determined for each speed range between zero and the maximum speed of the vehicle.

In some cases the segments may be grouped by gradient as well as speed, which might be beneficial if the driver's driving style should differ from "average" by a differing amount dependent on gradient. However, this is optional. If this feature is implemented, the resulting data set would give rise to adjustment factors as a function of segment speed and segment gradient. In use, the vehicle controllers would therefore identify, for each segment on the planned journey, the segment gradient and the segment speed, and look up the resulting adjustment factor (for example from a lookup table). The energy consumption calculation would then take place using the segment length, the segment gradient and the adjusted speed value.

Prior to the commencement of the journey, the historical driving data is based on previous journeys undertaken by the vehicle (and not necessarily of the same route -the adjustment factors can be determined based on any prior journeys). If no previous journeys have been carried out (or insufficient journeys to build up reliable speed data), there will be no historical driving data to use in generating adjustment factors. In this case the unadjusted data (raw segment speed) is used instead.

During the planned journey itself, the system monitors the driven speed of the vehicle, and compares this with an expected vehicle speed (for the segment being traversed). The adjustment factor is then determined (or refined) based on the difference, during the planned journey. The newly determined adjustment factor is then used to refine the vehicle speed threshold for transitioning to and from the EV-only mode. If no historical driving data existed prior to commencement of the journey, then the adjustment factor may be based entirely on the driven speed of the vehicle (and its comparison with expected vehicle speeds) during the course of the current journey. This will mean that at the outset of the journey the vehicle speed threshold used to switch to and from the EV-only mode may be based only on the generic driving data which is unadjusted, but that as new driving data is generated from the current journey (for the various speed ranges), adjustment factors will be applied for those speed ranges for which adequate driving data has been accumulated. This may mean that at least a predetermined number of segments of a particular speed range (of expected vehicle speed) are traversed by the vehicle/driver before an adjustment factor is used for that range. In some embodiments an adjustment factor may not be used until it is determined to be sufficiently robust or reliable. For example, an adjustment factor may not be used until the differences between the driven speed and the expected speed within a given speed range have stabilised at a value, or are based on sufficient driven speed data, or have a variance less than a predetermined amount. The skilled person will be aware of other techniques for determining the stability or reliability of data.

Optionally, the historical driving data used to determine the adjustment factors may be the most recent driving data, or the adjustment factors may be determined based on a weighted average of historical driving data in which more recent driving data is awarded extra weight.

This may be particularly important when the vehicle is new, since a driver may drive more cautiously until they become used to the handling of the vehicle.

In one simple implementation, a driving style for the driver is determined based on the historical driving data, and a speed adjustment factor (or factors) is selected based on the determined driving style. This driving style might for example be "aggressive", "default" or "economical". For example, a driven speed of the vehicle may be monitored, and compared with an expected vehicle speed, and the driving style determined based on the magnitude and direction of the difference. The differences may be aggregated over time, and may either be absolute values, or proportional (for example percentage over or under the expected vehicle speed). If the driven speed is determined, on average, to be less than the expected speed by a first threshold amount, the driving style may be classified as "economical". If the driven speed is determined, on average, to be greater than the expected speed by a second threshold amount, the driving style may be classified as "aggressive". Otherwise, the driving style may be classified as "default". The adjustment factor is then set based on the determined driving style. For example, if the driving style has been classified as "economical", a first adjustment factor may be applied, which is less than 1, and which results in an adjusted vehicle speed which is less than the expected vehicle speed. If the driving style has been classified as "aggressive", a second adjustment factor may be applied, which is greater than 1, and which results in an adjusted vehicle speed which is greater than the expected vehicle speed. If the driving style has been classified as "default", an adjustment factor of 1 may be applied, resulting in no adjustment to the expected vehicle speed. The first and second thresholds may have the same magnitude, or may have different magnitudes. In one example a single uniform adjustment factor is applied for all segment speeds. In another example different adjustment factors are applied to different ranges of segment speed, but still dependent on the determined driving style.

Figure 5 shows how the different driving styles may be represented on a graph of speed profile (in kph) versus vehicle offset (distance into journey). The top line "A" shows the "aggressive" driving style, which can be seen to be at a fixed (and positive) speed offset with respect to a default driving style "B" (which corresponds to the "average" vehicle speed provided in the eHorizon data) which is shown in the middle line of Figure 5. The bottom line "C" shows the "economical" driving style, which can be seen to be at a fixed (and negative) speed offset with respect to the default driving style. In alternative embodiments the offset may be variable (that is, not the same magnitude for a given speed range).

Referring to Figure 6, a functional representation of a predictive energy optimisation control system is shown. The segment offset Oseg, segment gradient Sseg and segment speed Vseg are provided both to a driver characterisation function 510 and also to an energy calculation function 520. The driver characterisation function 510 also receives a driven speed Vdrh, of the vehicle for a road segment currently being traversed by the vehicle. The driver characterisation function 510 also receives (or otherwise has access to) a set of speed adjustment factors which are based on historical driving data (that is, based on comparisons between driven (actual) speeds and expected speeds for the driver). The driver characterisation function 510 adjusts the segment speed Vseg using the relevant speed adjustment factor, and outputs the corrected (adjusted) segment speed Vadj_seg to the energy calculation function 520. It also (or alternatively) outputs an indication C of whether a corrected segment speed is available. The energy calculation function 520 then calculates the energy consumption for the journey, and resulting vehicle speed threshold, based on the corrected segment speeds, as described above (if the corrected segment speed is available). If the corrected segment speed is not available (which may be because there is either no data, insufficient data (less than a threshold amount), or unreliable data relating to how the vehicle is being driven at the relevant speed), then the unadjusted value of segment speed may be used instead. The driver characterisation function 510 also outputs an indication of the driving style Dstyle of the driver (economical, normal or sport/aggressive), again based on the historical driving data for the driver. In parallel with the above functions of the driver characterisation function 510 and energy calculation function 520, the driver characterisation function 510 compares the driven speed Vdrh, with the segment speed Vseg for a current segment, and uses this to determine or refine a speed adjustment value, and/or the driving style of the driver. As an alternative to the above (but shown also on Figure 5), the matrix offset Omat, matrix gradient Smdt and matrix speed Vmat (as described above) are provided to both the driver characterisation function and the energy calculation function. These are used in the same manner as the segment-based equivalents, but at lower computational cost, and result in an adjusted matrix speed Vadjeme being generated and output from the driver characterisation function 510 to the energy calculation function 520.

Figure 7 is similar to Figure 6. However, in this case the output of a driver characterisation function 510a to an energy calculation function 520b (which correspond to the functions 510, 520 of Figure 5) is simply a speed correction factor Cv, and a driver type Dtype (economical, normal, or sport/aggressive). In this case the driver's driving style is simply characterised as one of a plurality of profiles (in this case 3), each with a corresponding speed correction factor.

The energy calculation function 520a takes the speed correction factor supplied from the driver characterisation function 510a, and adjusts the segment speed accordingly. The energy calculation is then carried out, by the energy calculation function, using the adjusted speed values, and the vehicle speed threshold based thereon.

In Figures 6 and 7, the eHorizon data (segment information, including segment speed, gradient and offset) is sent to both the driver characterisation function 510, 510a and the energy calculation function 520, 520a. Until the corrected speed information becomes available, the predictive energy optimisation feature will use the eHorizon speed data without adjustment. The speed profile correction will happen in parallel (in the background) based on driver's driving style. Once the driver characterisation has achieved good enough robustness of the adjustment factor(s), the speed profile will be corrected and sent to energy demand calculation function 520, 520a, every time there is a eHorizon data transmission. The corrected speed data should preferably have the same data format as the eHorizon data. The driver type (discrete values) may be sent independently of the corrected speed values.

Table 2, below, indicates an example adjustment factor for each of a plurality of (segment) speed values: Speed 0-34 35-54 55-64 65-74 75-84 85-94 95- 105- 125+ (kph) 104 124 Adjustment 0.85 0.85 0.93 0.97 1.05 1.07 1.10 1.10 1.05 Factor As can be seen from Table 2, the driver represented by this table typically drives at a slower speed than an average driver at lower vehicle speeds (below 75kph) and at a higher speed than an average driver at higher vehicle speeds (75kph+). It will be appreciated that this kind of profile (where the driver is not uniformly aggressive or economical compared with an average driver at all vehicle speeds) would be difficult to replicate with the driving style based implementation above.

The two implementations of determining an adjustment factor for each (expected) speed range based on driving data obtained corresponding to that speed range, and of determining an adjustment factor in dependence of a driving style, may be used together. Generally, driving style might be determined more quickly than the adjustment factor at certain speed ranges, since the latter may only be obtained on certain road types. For example, if a journey starts out in a residential area, the vehicle will not be driven at motorway speeds. This may enable the adjustment factors for low speed ranges to be fully determined, but will provide no data at motorway speeds. However, the low speed data may be sufficient to determine the driving style of the driver, which may be used to predict how the driver may handle the vehicle at higher speeds too. As a result, the control system may be configured to use an adjustment factor determined based on driven speeds for a speed range corresponding to the current segment if available, and to use an adjustment factor based on determined driving style otherwise. In this example, the driving style may be relied on heavily towards the beginning of a journey, but may be relied on less and less as driving data is accumulated for different speed ranges.

The above techniques work well assuming that the vehicle is always being driven by the same driver. However, in the case that a vehicle is being driven by more than one driver, the range estimates will be less accurate (due to historical driving data being based on multiple drivers -although this may still be expected to be more accurate than using an estimate based on a large number of drivers as is conventionally the case). One way of addressing this issue is to identify the driver of the vehicle -both in generating the historical driving data, and in selecting the adjustment factors suitable for the driver. Accordingly, the control system may be configured to receive a signal indicative of the identity of a driver of the vehicle, and to select the adjustment factor in dependence on the identified driver. The control system may also be configured to determine the adjustment factor in relation to the identified driver based on historical driving data associated with the identified driver. Any suitable way of identifying the driver may be used, such as by detecting the presence of an electronic identifier (for example a dedicated fob, or a driver's smartphone), facial recognition using a face camera, or by a user entering a code or otherwise identifying themselves to the vehicle, including by fingerprint or voice signature. In such embodiments the techniques described above are equally applicable, but the generation of the historical driving data will be carried out separately per driver, and the selection of adjustment factor will be carried out by determining the identity of the driver, determining the adjustment factor based on historical driving data for the identified driver, and then carrying out the energy consumption calculation and range estimation as per the above.

Referring to Figure 8, a method for determining a vehicle speed threshold for selecting between two different propulsion modes (including one EV-only mode). The method can be seen to comprise two stages, these being a learning stage in which historical driving data is accumulated which can be used to determine adjustment factors, and a vehicle speed threshold determining stage in which the range is estimated using the determined adjustment factors. In practice the two stages may be carried out in parallel, with vehicle driven speeds being compared to expected speeds during a journey, and the adjustment factors being refined based thereon to improve the accuracy of the estimate. The first stage of the method can be seen to comprise a step S1 of monitoring a driven speed of the vehicle for a journey segment, and a step S2 of receiving an expected vehicle speed for that journey segment. Then, at a step S3, the driven speed is compared with the expected speed and a difference identified. At a step S4, the difference is stored in association with a speed range within which the expected speed falls. The process returns to the step S1 to continue monitoring, and also to a step S5 where the speed differences stored at the step S4 are used to generate adjustment factors. The adjustment factor may for example be a factor which can be multiplied by an expected speed value to result in an adjusted speed value which is substantially an average of the driven speeds previously applied by the user for that expected speed value.

The second stage of the method can be seen to comprise a step S6 of initiating a new route. This step may be manually carried out by the driver in selecting a destination in a navigation system, or may be carried out automatically by the system based on a current day and time, and previous journeys carried out by the driver. The step of initiating a new route may for example comprise determining a predicted destination for the vehicle 10. In examples, determining a predicted destination for the vehicle 10 can be performed in any suitable way using any suitable method. In examples, the predicted destination for the vehicle 10 can be determined in dependence on any suitable information/data determined in any suitable way.

In examples, the information/data can be determined from any suitable source(s), for example information can be received from one or more vehicle systems 226 and/or received from one or more sources external to the vehicle and/or received from one or more user devices and/or retrieved from memory 204 and so on. For example, the predicted destination may be determined in dependence on at least one of: time of day, day of week, week of month, month of year, a present location of the vehicle 10, number of times the vehicle 10 has stopped at a present location of the vehicle 10, number of times the vehicle 10 has travelled to a possible destination, number of times the vehicle 10 has travelled from a present location of the vehicle 10 to a possible destination, and one or more occupants of the vehicle 10. For example, it can be determined that in the past the vehicle 10 has travelled from a present location of the vehicle 10 to a particular destination at the present time of day of the present day of the week a number of times, allowing a predicted destination 12 for the vehicle 10 to be determined. In other examples, one or more destinations can be associated with a user or users of a vehicle 10. For example, a user can indicate destinations such as 'home' or 'work' and such indicated/labelled/predetermined destinations can influence the determination of a predicted destination. Based on the determined destination, a route to be travelled by the vehicle 10 can be determined in dependence on the predicted destination. In examples, determining a route to be travelled by the vehicle 10 in dependence on the predicted destination can be performed in any suitable way using any suitable method. For example, any suitable route determining algorithm can be used to determine a route to be travelled by the vehicle 10 in dependence on the predicted destination. As used herein, determining a route is intended to include, in some examples, processing information/data such as the present location of the vehicle 10, the predicted destination and map information to determine the route and also to include receiving the route to be travelled by the vehicle 10. In examples, determining a route can be considered determining a route to allow the vehicle 10 to travel and/or traverse from the present location of the vehicle 10 to a predicted destination.

At a step S7, route information concerning the journey planned to be undertaken by the vehicle is received. At a step S8, a driving profile is determined, from the route information, for the journey, the driving profile comprising an expected vehicle speed for each of a plurality of segments of the journey. At a step S9, an adjusted vehicle speed is determined for each segment in dependence on the adjustment factor(s), for example by multiplying the vehicle speed for each segment by an adjustment factor associated with a vehicle speed range for the expected vehicle speed. Then, at a step 510, the energy consumption for the vehicle is estimated based on the adjusted vehicle speeds for each segment of the journey, for example based on Equation (1) above. At a step 511, the energy consumption estimates are used to determine a vehicle speed threshold for controlling switching between the two modes of propulsion.

It will be appreciated that the step S1 to S5 may be carried out in advance of the planned journey, for example on previous journeys by the vehicle/driver. The steps S6 to 511 may be carried out, for example, when the vehicle is switched on and the journey planned. Then, the step S1 to S5 may be carried out again while the vehicle is undertaking the planned journey, leading to refined values for the adjustment factors. Then, as the adjustment factor is refined, the step S7 to 511 may be repeated (as shown in Figure 8), to result in the range estimate being refined. In this way, the vehicle speed threshold becomes more and more effective at optimising utilisation of the EV only mode as the vehicle is being driven, and the journey progresses.

As used herein "for" should be considered to also include "configured or arranged to". For example, "a control system for" should be considered to also include "a control system configured or arranged to".

For purposes of this disclosure, it is to be understood that the controller(s) described herein can each comprise a control unit or computational device having one or more electronic processors, the one or more processors collectively configured to perform the control system functionality set out in the control system claims. A vehicle and/or a system thereof may comprise a single control unit or electronic controller or alternatively different functions of the controller(s) may be embodied in, or hosted in, different control units or controllers. A set of instructions could be provided which, when executed, cause said controller(s) or control unit(s) to implement the control techniques described herein (including the described method(s)). The set of instructions may be embedded in one or more electronic processors, or alternatively, the set of instructions could be provided as software to be executed by one or more electronic processor(s). For example, a first controller may be implemented in software run on one or more electronic processors, and one or more other controllers may also be implemented in software run on one or more electronic processors, optionally the same one or more processors as the first controller. It will be appreciated, however, that other arrangements are also useful, and therefore, the present disclosure is not intended to be limited to any particular arrangement. In any event, the set of instructions described above may be embedded in a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) that may comprise any mechanism for storing information in a form readable by a machine or electronic processors/computational device, including, without limitation: a magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto optical storage medium; read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or electrical or other types of medium for storing such information/instructions.

It will be appreciated that various changes and modifications can be made to the present invention without departing from the scope of the present application. The blocks illustrated in the FIG. 7 may represent steps in a method and/or sections of code in the computer program 206. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some steps to be omitted.

As used herein, the term "determining" (and grammatical variants thereof) can include, not least; calculating, computing, processing, deriving, investigating, looking up (for example, looking up in a table, a database or another data structure), ascertaining and the like. Also, "determining" can include receiving (for example, receiving information), accessing (for example, accessing data in a memory) and the like. Also "determining" can include resolving, selecting, choosing, establishing, and the like.

Although embodiments of the present invention have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the invention as claimed.

Features described in the preceding description may be used in combinations other than the combinations explicitly described. Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not. Although features have been described with reference to certain embodiments, those features may also be present in other embodiments whether described or not. Whilst endeavouring in the foregoing specification to draw attention to those features of the invention believed to be of particular importance it should be understood that the Applicant claims protection in respect of any patentable feature or combination of features hereinbefore referred to and/or shown in the drawings whether or not particular emphasis has been placed thereon.

Claims (19)

1. CLAIMS1. A control system for computing a power threshold for activating and deactivating an electric propulsion mode of a hybrid electric vehicle, the control system comprising one or more controller, the control system configured to: Receive route information concerning a journey planned to be undertaken by the vehicle; determine, from the route information, a driving profile for the journey, the driving profile comprising an expected vehicle speed for each of a plurality of segments of the 10 journey; determine an adjusted vehicle speed for each segment in dependence on an adjustment factor, the adjustment factor being based on historical driving data for the vehicle; determine the power threshold for the vehicle based on the adjusted vehicle speeds for each segment of the journey; and output a signal in dependence on the determined power threshold.
2. 2. The control system of claim 1, wherein the power threshold is a speed threshold, and the electric propulsion mode is deactivated when the speed threshold is exceeded.
3. 3. The control system of claim 1, configured to compute a reserve energy for a final portion of the journey based on the adjusted vehicle speeds for segments corresponding to the final portion of the journey, the reserve energy being an amount of electrical energy or state of charge of a vehicle battery.
4. 4. The control system of claim 1, configured to: Monitor a driven speed of the vehicle for a journey segment; compare the driven speed of the vehicle with an expected vehicle speed for that journey segment; and compute the adjustment factor based on a difference between the driven speed and the expected speed.
5. 5. The control system of claim 4, configured to compare the driven speed of the vehicle with an expected vehicle speed for each of a plurality of speed ranges, and to compute an adjustment factor for each speed range.
6. 6. The control system of claim 3 or claim 4, configured to monitor the driven speed of the vehicle, compare the driven speed of the vehicle with an expected vehicle speed, and compute the adjustment factor based on the difference, during the planned journey, and to use the newly computed adjustment factor to refine the power threshold.
7. 7. The control system of any preceding claim, configured to compute the power threshold by predicting an electrical energy consumption for each of the plurality of journey segments, each journey segment representing a constant expected vehicle speed and road gradient.
8. 8. The control system of any preceding claim, configured to compute the power threshold by aggregating segments by vehicle speed and road gradient, and computing an electrical energy consumption for each aggregated group of segments.
9. 9. The control system of any preceding claim, configured to select an adjustment factor for computing the adjusted vehicle speed in dependence on the expected vehicle speed for a segment of the journey.
10. 10. The control system of any preceding claim, wherein the expected speed is based on an aggregated data set for multiple drivers.
11. 11. The control system of any preceding claim, wherein if an amount of data collected and generated by the monitoring and comparing steps is less than a threshold amount for a given expected vehicle speed or speed range, the control system is configured to compute the power threshold using the expected vehicle speed rather than an adjusted vehicle speed for that expected vehicle speed or speed range.
12. 12. The control system of any preceding claim, configured to determine a driving style based on the historical driving data, and to set the adjustment factor based on the determined driving style.
13. 13. The control system of claim 5 and claim 12, configured to use an adjustment factor computed based on driven speeds for a speed range corresponding to the current segment if available, and to use an adjustment factor based on determined driving style otherwise.
14. 14. The control system of any preceding claim, configured to receive a signal indicative of the identity of a driver of the vehicle, and to select the adjustment factor in dependence on the identified driver.
15. 15. The control system of claim 14, configured to compute the adjustment factor in relation to the identified driver based on historical driving data associated with the identified driver.
16. 16. The control system of any preceding claim, configured to activate and deactivate the electric propulsion mode of the vehicle in dependence on whether the power threshold is exceeded.
17. 17. A vehicle comprising the control system of claims 1 to 16.
18. 18. A method for computing a power threshold for activating and deactivating an electric propulsion mode of a hybrid electric vehicle, the method comprising: Receiving route information concerning a journey planned to be undertaken by the vehicle; determining, from the route information, a driving profile for the journey, the driving profile comprising an expected vehicle speed for each of a plurality of segments of the journey; determining an adjusted vehicle speed for each segment in dependence on an adjustment factor, the adjustment factor being based on historical driving data for the vehicle; determining the power threshold for the vehicle based on the adjusted vehicle speeds for each segment of the journey; and outputting a signal in dependence on the determined power threshold.
19. 19. Computer readable instructions which, when executed by a computer, are arranged to perform a method according to claim 18.