GB2569351A

GB2569351A - Whole journey predictive energy optimisation

Info

Publication number: GB2569351A
Application number: GB1720903.2A
Authority: GB
Inventors: Plianos Alex; Kumar Pavan; Antonin Cancel Laurentiu; Hancock Matthew; Molnar Csaba
Original assignee: Jaguar Land Rover Ltd
Current assignee: Jaguar Land Rover Ltd
Priority date: 2017-12-14
Filing date: 2017-12-14
Publication date: 2019-06-19
Anticipated expiration: 2037-12-14
Also published as: DE102018220572A1; GB201720903D0; GB2569351B

Abstract

A hybrid vehicle, has a traction battery (Fig.1,106), an electric traction motor (Fig.1,108) and a combustion engine (Fig.1,104). The system involves prediction means 208 for predicting power use of the vehicle over a journey or trip; and scheduling means 210 for, in dependence on the prediction, scheduling when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode. The first mode may be a charge depletion mode, permitting increased depletion of traction battery energy than is permitted by the second mode. The scheduling means is configured to require the traction battery energy or State of Charge (SOC) to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey. The scheduling means may determine a threshold based on the prediction, around which the vehicle switches from the first and second modes. The threshold may be based on driving environment, speed etc. The journey may be split into several segments for prediction purposes.

Description

WHOLE JOURNEY PREDICTIVE ENERGY OPTIMISATION

TECHNICAL FIELD

The present disclosure relates to whole journey predictive energy optimisation. In particular, but not exclusively it relates to whole journey predictive energy optimisation in a vehicle.

Aspects of the invention relate to a controller, a control method and a computer program.

BACKGROUND

It is known for a hybrid vehicle to comprise a combustion engine, and an electric traction motor or motors powered by a traction battery. The combustion engine and electric traction motor are for providing tractive torque. The combustion engine and electric traction motor may be in a parallel hybrid configuration so that the vehicle can be driven with the electric traction motor only and with the combustion engine only, or by both the electric traction motor and the combustion engine.

It is common for a hybrid vehicle to exclusively use its electric traction motor at the beginning of a journey, until the electrical charge stored in the traction battery has been depleted. Then, the combustion engine is switched on for the remainder of the journey.

SUMMARY OF THE INVENTION

It is an aim of the present invention to address disadvantages of the prior art.

Aspects and embodiments of the invention provide a controller, a control method and a computer program as claimed in the appended claims.

According to an aspect of the invention there is provided a controller for a vehicle, the vehicle comprising a traction battery, an electric traction motor and a combustion engine, the controller comprising: prediction means (predicter) configured to predict power use of the vehicle over a journey; and scheduling means (scheduler) configured to, in dependence on the prediction, schedule when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode, wherein the first mode permits increased depletion of traction battery energy (charge) than is permitted by the second mode, and wherein the scheduling means is configured to require the traction battery energy to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey.

The first mode may be a ‘charge depletion’ mode in which the combustion engine is used less than in the second mode, increasing a reliance on usage of traction battery energy. The second mode may be a ‘charge sustain’ mode in which the combustion engine is used more than in the first mode, reducing a reliance on usage of traction battery energy.

This provides the advantage of reduced energy consumption. This is because the controller maximises use of charge depletion mode but only deploys charge depletion mode when it is most efficient to, for example during urban driving at the beginning and end of a journey in low speed and/or urban environments.

In some examples, the level of permitted depletion in the first mode is sufficient for a torque demand to be satisfied wholly by depletion of traction battery energy (reduction of state of charge), without contribution by the combustion engine, unless the torque demand cannot be satisfied wholly by depletion of traction battery energy while a constraint is satisfied. The constraint may be a capability limit of the electric traction motor, a capability (e.g. power) limit of the traction battery, or the like. In some examples, the second mode implements a setpoint amount of traction battery energy (state of charge), such that if a current amount of traction battery energy is below the setpoint the amount of traction battery energy must be increased towards the setpoint.

This provides the advantage of reduced energy consumption and urban emissions by avoiding use of the combustion engine for urban low-energy driving. However, in some examples the ability to implement ‘torque assist’ for higher torque demands, and the ability to implement electric traction motor-only driving for lower torque demands is not necessarily inhibited. In some examples, the torque assist mode is used when torque demand exceeds the capability of the currently active one of the electric traction motor or the combustion engine, so torque from the active one of the combustion engine or electric traction motor is increased by the combined torque from the combustion engine and the electric traction motor to equal the torque demand.

In some examples, the scheduling means is configured to determine a threshold dependent on the prediction, to require the vehicle to be controlled in the first mode while the threshold is not exceeded, and to require the vehicle to be controlled in the second mode while the threshold is exceeded.

This provides the advantage of reduced energy consumption because the scheduled use of the modes is tailored to each specific journey. Further, a threshold is a quantitative condition so can be automatically determined based on quantitative information about the journey, without the need for user input. Further, the use of the single (whole journey) threshold is computationally efficient therefore allowing regular updates to the schedule during driving, to account for real-world variations from the original predictions.

In some examples, the threshold distinguishes a low-speed and/or urban driving environment from a high-speed and/or extra-urban driving environment.

This provides the advantage of reduced energy consumption because the combustion engine is favoured for high power driving and the electric traction motor is favoured for low power driving.

In some examples, the threshold is a power threshold. In some examples, the power threshold is dependent on at least one of the following (predicted) variables: vehicle speed; speed limit; road gradient; vehicle mass; road curvature; road type; auxiliary electrical loading. In some examples, the power threshold is dependent on a combination of the variables. In some examples, the power threshold is implemented as a vehicle speed threshold.

This provides the advantage of more accurate scheduling because the scheduled use of the modes is tailored to the quantitative characteristics of each specific journey.

In some examples, the scheduling means is configured to control a value of the threshold such that the traction battery energy is a defined amount lower at the end of the journey than at the beginning of the journey.

This provides the advantage of reduced energy consumption because the threshold favours maximum use of the electric traction motor in parts of the journey where charge sustain mode is inefficient.

In some examples, the controller is configured such that during driving while the vehicle is controlled in the first mode, exceedance of the threshold is not alone sufficient to cause the engine to switch to an on state from an off state for satisfying torque demand.

This provides the advantage that the schedule cannot be easily manually overridden. In some examples, the only way to use the combustion engine during the first mode is to increase torque demand until torque assist mode is activated. In torque assist mode, torque from the combustion engine is increased by the electric traction motor to increase vehicle acceleration.

In some examples, the controller is configured such that during driving while the vehicle is controlled in the second mode, non-exceedance of the threshold is not alone sufficient to cause the engine to switch to an off state from an on state for satisfying torque demand.

This provides the advantage that the schedule cannot be easily manually overridden. In some examples, the only way to use the electric traction motor to deplete the traction battery during the second mode is to activate torque assist mode.

In some examples, the journey comprises a plurality of segments and wherein the prediction means is configured to predict a value indicative of power consumption of the vehicle for each segment. In some examples, the total number of segments does not exceed 1500.

This provides the advantage of improved computational efficiency because, where the/each parameter indicative of power use is a constant value for each segment, the level of detail used by the scheduling means is reduced allowing faster processing and therefore regular scheduling updates during driving.

In some examples, the controller comprises aggregation means (an aggregator) configured to aggregate, from the segments, information indicative of the predicted power use, wherein the scheduling means is configured to determine the threshold in dependence on the aggregation. In some examples, the aggregation is for data binning in a plurality of dimensions. In some examples, determining the threshold comprises calculating energy use over each segment.

This provides the advantage of improved computational efficiency and reduced memory use because rather than calculating and storing the energy for each segment, energy is calculated for each bin within which information has been aggregated. In the determination of the power threshold, the calculation iterations reduce from n1 = number of segments to n2 = number of bins. Memory is reduced as n1 segments and their corresponding distances along the journey route are no longer required to be stored in memory. This allows regular updates to the schedule during driving, to account for real-world variations from the original predictions.

In some examples, the aggregation is performed using no more than 2000 bins.

This provides the advantage of improved computational efficiency therefore allowing regular scheduling updates during driving, because the number of computational cycles required to perform operations on the binned data is minimal.

In some examples, the controller comprises update means (updater) configured to, during the journey, update a prediction of power use over the remainder of the journey, and in dependence on the updated prediction, update a schedule of when during the remainder of the journey the vehicle is to be controlled in the first mode, and when during the remainder of the journey the vehicle is to be controlled in the second mode.

This provides the advantage that less accurate information is needed for the initial prediction, because real-world variations from the original predictions will be regularly accounted for.

In some examples, the prediction means is configured to predict the power use in dependence on information indicative of the predicted power use, acquired by a vehicle navigation system and/or predicted from machine learning.

This provides the advantage of reduced driver disturbance because the controller functions can be performed automatically and can be hidden from the driver.

In some examples, the information indicative of the predicted power use, comprises one or more of: a distance-dependent parameter; a speed-dependent parameter; a gradientdependent parameter.

This provides the advantage that information indicative of the predicted power use can be acquired from existing vehicle subsystems such as a navigation system.

In some examples, the scheduling means is configured to permit switching between the first mode and the second mode a plurality of times during the journey.

This provides the advantage that energy consumption can be minimised for journeys that vary several times between high power and low power consumption.

According to an aspect of the invention there is provided a controller for a vehicle, the vehicle comprising a traction battery, an electric traction motor and a combustion engine, the controller comprising at least one electronic processor; and at least one electronic memory device electrically coupled to the electronic processor and having instructions (a computer program) stored therein, the at least one electronic memory device and the instructions configured to, with the at least one electronic processor, perform: predicting power use of the vehicle over a journey; in dependence on the prediction, scheduling when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode, wherein the first mode permits increased depletion of traction battery energy than is permitted by the second mode, and wherein the schedule is configured to require the traction battery energy to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey.

According to another aspect of the invention there is provided a control method for a vehicle, the vehicle comprising a traction battery, an electric traction motor and a combustion engine, the control method comprising: predicting power use of the vehicle over a journey; in dependence on the prediction, scheduling when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode, wherein the first mode permits increased depletion of traction battery energy than is permitted by the second mode, and wherein the scheduling requires the traction battery energy to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey.

According to another aspect of the invention there is provided a computer program that, when run on at least one electronic processor, causes at least: predicting power use of a vehicle over a journey, the vehicle comprising a traction battery, an electric traction motor and a combustion engine; in dependence on the prediction, scheduling when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode, wherein the first mode permits increased depletion of traction battery energy than is permitted by the second mode, and wherein the scheduling requires the traction battery energy to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey.

According to a further aspect of the present invention there is provided a (non-transitory) computer readable medium comprising the computer program.

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:

Fig 1 illustrates an example of a vehicle;

Fig 2A illustrates an example of a controller and Fig 2B illustrates an example of a computerreadable storage medium;

Fig 3 illustrates an example method;

Fig 4A illustrates an example of information indicative of predicted power use and an example of a threshold, and Fig 4B illustrates an example of a schedule of the first and second modes.

DETAILED DESCRIPTION

Fig 1 illustrates an example of a vehicle 100 in which embodiments of the invention can be implemented. In some, but not necessarily all examples, the vehicle 100 is a passenger vehicle, also referred to as a passenger car or as an automobile. Passenger vehicles generally have kerb weights of less than 5000 kg. In other examples, embodiments of the invention can be implemented for other applications, such as industrial vehicles, air or marine vehicles.

The vehicle 100 of Fig 1 comprises a combustion engine 104. The combustion engine 104 may be a reciprocating piston internal combustion engine. The vehicle 100 is a hybrid vehicle because the vehicle 100 further comprises a traction battery 106 and at least one electric traction motor 108. The traction battery 106 is an electrical energy source for the electric traction motor 108. In some examples, the electric traction motor 108 is a motorgenerator, while in other examples a separate generator is provided. In some examples, the electric traction motor 108 is a starter-generator, operable to perform the function of a starter motor. The purpose of the generator is to recharge the traction battery 106, for example by implementing a regenerative braking function.

The vehicle 100 is a parallel hybrid vehicle, such that the electric traction motor 108 and combustion engine 104 can be operated in a variety of modes.

In a ‘charge depletion’ mode (first mode), the electric traction motor 108 is in an on state to produce tractive torque. This depletes electrical charge stored in the traction battery 106. If torque demand is high in charge depletion mode, 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 106 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 100.

In a ‘charge sustain’ mode (second mode), the combustion engine 104 is in an on state to produce tractive torque, and the electric traction motor 108 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.

In charge sustain mode, if electric traction motor-only driving in response to low torque demand is permitted, X may be any value from the range 0-40% in an example. In some examples, a condition (such as X) for switching to electric traction motor-only driving also depends on another parameter. The another parameter may include one or more of: the state of charge of the traction battery; road gradient; the temperature of an exhaust gas after-treatment apparatus, such as a catalyst temperature; the temperature of an engine system, such as a coolant temperature; or vehicle speed.

In some, but not necessarily all examples, torque assist mode is available within charge sustain mode while the current state of charge of the traction battery 104 is above the setpoint. In some examples, torque assist mode may also be available within charge sustain mode even if the current state of charge of the traction battery 104 is below the setpoint.

In some, but not necessarily all examples torque assist mode in charge sustain mode is implemented if torque demand cannot be satisfied wholly by use of the combustion engine 104 while a constraint is satisfied. In charge sustain mode, the constraint may take the form of a capability limit of the combustion engine 104. The capability of the combustion engine 104 may be expressed as a threshold torque or in any other suitable way.

Charge depletion mode permits increased depletion of traction battery energy than is permitted by the charge sustain mode, because the charge depletion mode does not require a constant setpoint state of charge. In fact, the level of permitted depletion in the charge depletion mode is sufficient for low to mid-range torque demands (e.g. within range 0-Y%) to be satisfied wholly by depletion of traction battery energy, without contribution by the combustion engine 104. For example, a maximum rate of depletion could be permitted. Torque assist mode is still available within charge depletion mode if torque demand exceeds a constraint such as the capability of the electric traction motor 108. The capability of the electric traction motor 108 may be expressed as a threshold torque or in any other suitable way. The threshold may be the same as or different from that of the combustion engine 104. The capability of the electric traction motor 108 may correspond to a torque demand of Y% where Y may be 100% torque demand, or any other value from the range 70%-100% in other examples.

Additionally, or alternatively, the or another constraint may take the form of a capability limit of the traction battery 106 such as a power limit. The power limit may act as a bottleneck so that the electric traction motor 108 cannot be used to its full capability. The power limit may depend, for example, on temperature because temperature affects battery performance. If this constraint is not satisfied, the combustion engine 104 may be used to satisfy, at least in part, the torque demand, such that the constraint remains satisfied.

Accordingly, the different behavior between charge sustain mode and charge depletion 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.

In some, but not necessarily all examples the vehicle 100 comprises a vehicle navigation system 102. The vehicle navigation system 102 may be a satellite navigation system, for example the vehicle navigation system 102 may be configured as a Global Positioning System (GPS) navigation system.

The vehicle 100 comprises a controller 200 in which embodiments of the invention can be implemented. Any existing vehicle controller could be used to implement the functionality disclosed herein. In other examples to that shown in Fig 1 the controller 200 could be the vehicle navigation system 102.

Fig 2A illustrates an example of a controller 200 such as the controller 200 illustrated in Fig 1. The controller 200 comprises means to cause any one or more of the methods described herein to be performed.

The controller 200 of Fig 2A includes at least one electronic processor 202; and at least one electronic memory device 204 electrically coupled to the electronic processor 202 and having instructions 206 (e.g. a computer program) stored therein, the at least one electronic memory device 204 and the instructions 206 configured to, with the at least one electronic processor 202, cause any one or more of the methods described herein to be performed. In Fig 2A, but not necessarily all examples, the instructions 206 comprise prediction means 208 (a predicter), scheduling means 210 (a scheduler), aggregation means 212 (an aggregator), and update means 214 (an updater), the functions of which will be described below.

Fig 2A also illustrates an example of a computer program that, when run on at least one electronic processor 202, causes one or more of the methods described herein to be performed.

Fig 2B illustrates an example of a computer-readable storage medium 216 comprising the computer program 206.

Fig 3 illustrates an example of a control method 300 (‘method’) in accordance with aspects of the present invention. The method 300 may be performed by the controller 200.

The method 300 comprises, at block 302, predicting power use of the vehicle 100 over a journey. This is a function of the prediction means 208.

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 102. In this example, the starting location, destination and waypoints may be deterministic because they are specified by user inputs to the vehicle navigation system 102. Therefore, the route is deterministic.

In another example, the route is predicted from machine learning. The machine learning could indicate where the vehicle 100 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 100 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 100 is predicted for the journey following that route from any available information indicative of power use. Particularly useful variables include a distance-dependent parameter such as distance, a speed-dependent parameter such as speed or legal speed limit, 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 100, and auxiliary electrical loads (e.g. lighting, heating, cooling, engine accessories) can be used to account for losses therefrom.

In some, but not necessarily all examples a driving style identification algorithm is implemented to predict the aggressiveness of the driver. For example, a stored database of drivers is matched to particular key identifiers. When a particular key fob or other device interacts with (e.g. unlocks) the vehicle 100, the driver’s record is interrogated to determine who will drive the vehicle 100, and determine a modifier of the predicted power that varies in dependence on the driver’s relative driving style compared to other drivers. The modifier may depend on driving parameters including one or more of torque demand, use of brakes, use of energy-consuming vehicle accessory subsystems. The modifier could raise or lower the predicted power to account for driving style as indicated by the driving parameters. The driving parameters may be determined dynamically via monitoring and stored against that driver’s record, in the manner of machine learning.

In an example, information indicative of power use for the journey can be published by the vehicle navigation system 102. For example, speed limits and/or average traffic speeds, and elevation points, can be provided.

Additionally or alternatively, information indicative of power use for the journey can be determined from machine learning. For example, the machine learning may determine the change of state of charge of the traction battery 106 with respect to time over a past route identical to the route to be followed in the present journey.

Fig 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.

in some, but not necessarily in all examples, the journey comprises a plurality of segments. In Fig 4A, but not necessarily all examples, the journey comprises 14 segments. In other 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 routefinding algorithm such as Dijkstra’s algorithm implemented in the vehicle navigation system 102, 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 prediction has been made at block 302, the method 300 progresses to block 304, which schedules when during the journey the vehicle 100 is to be controlled in the first mode (charge depletion mode), and when during the journey the vehicle 100 is to be controlled in the second mode (charge sustain mode).

A first target of the scheduling means 210 is to schedule use of the charge depletion mode and the charge sustain mode such that the traction battery energy (state of charge) is lower at the end of the journey relative to the traction battery energy at the beginning of the journey (e.g. 100% charge). In particular, but not exclusively, the target is for the state of charge to be at a particular value at the end of the journey, for example a value from the range 0% to 30%.

A second target of the scheduling means 210 is to schedule use of the charge depletion mode and the charge sustain mode such that the vehicle 100 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 100 is in use and undertaking the journey. During the use of the vehicle 100, the controller is configured to implement the output condition such that the combustion engine

104 and electric traction motor 108 are controlled to switch between the modes as required by the output condition. In some examples, periodic updates from the vehicle navigation system 102 ensure that the implementation of the schedule is synchronized with the actual journey which may, for example, be subject to delays.

In the following paragraphs, a beneficial scheduling approach for block 304 is disclosed which involves calculation of a threshold as the output condition. During the use of the vehicle 100, 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 (e.g. power, 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 Fig 4A, to require the vehicle 100 to be controlled in the charge depletion mode while the threshold 402 is not exceeded, and to require the vehicle 100 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 is 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 abovementioned value from the range 0% to 30%. A method for calculating the threshold 402 will be discussed in more detail below.

Fig 4B illustrates the effect of applying the threshold 402. Fig 4B illustrates the state of charge of the traction battery 106 on the y-axis, and the x-axis is as defined for Fig 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 Fig 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 102. 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 Fig 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, not limited to a vehicle speed threshold. The threshold 402 could represent at least one of, or a combination of the following predicted variables: vehicle speed (or speed limit), 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 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.

A technical effect of using a vehicle speed threshold is ease of monitoring whether the threshold is exceeded while the vehicle 100 is being driven. 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 108 are used only when they are at their most efficient.

During driving, it is important that the preferred state (on or off) of the combustion engine 104 and electric traction motor 108 as determined by the mode scheduled in block 304 should not be too easy to override by driver input, because this could cause excessive depletion or under-utilisation of the traction battery energy. It is envisaged that during driving while the vehicle 100 is controlled in the charge depletion mode, exceedance of the threshold 402 is not alone sufficient to cause the engine to switch to an on state from an off state for satisfying torque demand. Exceedance of another stricter threshold may be required. Similarly, while the vehicle 100 is controlled in the charge sustain mode, nonexceedance of the threshold 402 is not alone sufficient to cause the engine to switch to the off state. For example, if the torque demand approaches 100% (e.g. in range Y%-100%), this indicates a significantly higher power requirement than is associated with the threshold 402, and therefore represents exceedance of a stricter threshold than the threshold 402. Only then would a torque assist function be employed in which both the combustion engine 104 and the electric traction motor 108 are used to increase tractive torque.

An example algorithm for calculating the threshold 402 quickly will now be described, performed by the aggregation means 212 of the controller 200 for example in block 304. 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	0km	0 km

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. No more than 2000 bins in total is computationally efficient, i.e. the array is smaller than 64x64.

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. In other examples, the calculation may utilize different and/or additional variables associated with predicted energy consumption.

The result of the accumulation can be mapped to a particular threshold 402 in the electronic memory device. For example, with reference to Fig 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 100 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. Further, other forms of threshold may be applied other than a vehicle speed threshold, for example by controlling how the vector of bins is classified in the summing operation. Further, if additional variables are to be taken into account requiring the use of additional arrays, an optimization algorithm may be incorporated to find the optimum threshold 402. In another example, arrays may not be used and the threshold 402 could be guessed and iterated via simulation. However, the method described above is particularly computationally efficient, allowing the prediction and scheduling blocks 302 and 304 to be repeated during a journey to account for changing realworld driving conditions.

The update means 214 is responsible for, during the journey, repeating block 302 to update a prediction of power use over the remainder of the journey, and in dependence on the updated prediction, repeating block 304 to update a schedule of when during the remainder of the journey the vehicle 100 is to be controlled in the charge depletion mode, and when during the remainder of the journey the vehicle 100 is to be controlled in the charge sustain mode. In an example, the schedule consists of the threshold 402, therefore updating the schedule means updating the value of the threshold 402. The update may be performed periodically to repeat multiple times during the journey (e.g. repeating within the order of milliseconds or seconds), and/or in response to manual input, and/or in response to feedback indicating, for example, deviations from the expected state of charge, a change from the planned route of the journey, or other driver deviations from predicted behaviour. In an example implementation, if the vehicle 100 has consumed more traction battery energy than originally planned, the threshold 402 will be lowered by the update means 214 so that a greater proportion of the remaining journey is completed in charge sustain mode.

For purposes of this disclosure, it is to be understood that the controller(s) 200 described herein can each comprise a control unit or computational device having one or more electronic processors. A vehicle 100 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 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 ad EEPROM); flash memory; or electrical or other types of medium for storing such information/instructions.

The blocks illustrated in Fig 3 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.

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 endeavoring 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

1. A controller for a vehicle, the vehicle comprising a traction battery, an electric traction motor and a combustion engine, the controller comprising:

prediction means configured to predict power use of the vehicle over a journey; and scheduling means configured to, in dependence on the prediction, schedule when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode, wherein the first mode permits increased depletion of traction battery energy than is permitted by the second mode, and wherein the scheduling means is configured to require the traction battery energy to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey.

2. The controller of claim 1, wherein the level of permitted depletion in the first mode is sufficient for a torque demand to be satisfied wholly by depletion of traction battery energy, without contribution by the combustion engine, unless the torque demand cannot be satisfied wholly by depletion of traction battery energy while a constraint is satisfied.

3. The controller of claim 1 or 2, wherein the second mode implements a setpoint amount of traction battery energy, such that if a current amount of traction battery energy is below the setpoint the amount of traction battery energy must be increased towards the setpoint.

4. The controller of any preceding claim, wherein the scheduling means is configured to determine a threshold dependent on the prediction, to require the vehicle to be controlled in the first mode while the threshold is not exceeded, and to require the vehicle to be controlled in the second mode while the threshold is exceeded.

5. The controller of claim 4, wherein the threshold distinguishes a low-speed and/or urban driving environment from a high-speed and/or extra-urban driving environment.

6. The controller of claim 5, wherein the threshold is a power threshold.

7. The controller of claim 6, wherein the power threshold is dependent on at least one of the following variables: vehicle speed; speed limit; road gradient; vehicle mass; road curvature; road type; auxiliary electrical loading.

8. The controller of claim 7, wherein the power threshold is dependent on a combination of the variables.

9. The controller of claim 7, wherein the power threshold is implemented as a vehicle speed threshold.

10. The controller of any one of claims 5 to 9, wherein the scheduling means is configured to control a value of the threshold such that the traction battery energy is a defined amount lower at the end of the journey than at the beginning of the journey.

11. The controller of any of claims 5 to 10, configured such that during driving while the vehicle is controlled in the first mode, exceedance of the threshold is not alone sufficient to cause the engine to switch to an on state from an off state for satisfying torque demand.

12. The controller of any of claims 5 to 11, configured such that during driving while the vehicle is controlled in the second mode, non-exceedance of the threshold is not alone sufficient to cause the engine to switch to an off state from an on state for satisfying torque demand.

13. The controller of any preceding claim, wherein the journey comprises a plurality of segments, and wherein the prediction means is configured to predict a value indicative of power consumption of the vehicle for each segment.

14. The controller of claim 13, comprising aggregation means configured to aggregate, from the segments, information indicative of the predicted power use, and wherein the scheduling means is configured to determine the threshold in dependence on the aggregation.

15. The controller of claim 14, wherein the aggregation is performed using no more than 2000 bins.

16. The controller of any preceding claim, comprising update means configured to, during the journey, update a prediction of power use over the remainder of the journey, and in dependence on the updated prediction, update a schedule of when during the remainder of the journey the vehicle is to be controlled in the first mode, and when during the remainder of the journey the vehicle is to be controlled in the second mode.

17. The controller of any preceding claim, wherein the prediction means is configured to predict the power use in dependence on information indicative of the predicted power use, acquired by a vehicle navigation system and/or predicted from machine learning.

18. The controller of claim 17, wherein the information indicative of the predicted power use, comprises one or more of: a distance-dependent parameter; a speed-dependent parameter; a gradient-dependent parameter.

19. The controller of any preceding claim, wherein the scheduling means is configured to permit switching between the first mode and the second mode a plurality of times during the journey.

20. A control method for a vehicle, the vehicle comprising a traction battery, an electric traction motor and a combustion engine, the control method comprising:

predicting power use of the vehicle over a journey;

in dependence on the prediction, scheduling when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode, wherein the first mode permits increased depletion of traction battery energy than is permitted by the second mode, and wherein the scheduling requires the traction battery energy to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey.

21. A computer program that, when run on at least one electronic processor, causes at least:

predicting power use of a vehicle over a journey, the vehicle comprising a traction battery, an electric traction motor and a combustion engine;

in dependence on the prediction, scheduling when during the journey the vehicle is to be controlled in a first mode, and when during the journey the vehicle is to be controlled in a second mode, wherein the first mode permits increased depletion of traction battery energy than is 5 permitted by the second mode, and wherein the scheduling requires the traction battery energy to be lower at the end of the journey relative to the traction battery energy at the beginning of the journey.