Trajectory planning for vehicle combinations

Abstract

A computer system for determining an input relating to a manoeuvre for a battery electric vehicle, BEV, or hybrid electric vehicle, HEV, combination, the computer system comprising processing circuitry configured to acquire parameters of the vehicle combination, including one or more vehicle capabilities that are functions of capability parameters, update a vehicle model based on the acquired parameters, and determine an input relating to a manoeuvre for the vehicle combination using the updated vehicle model.

Description

TRAJECTORY PLANNING FOR VEHICLE COMBINATIONSTECHNICAL FIELD

[0001] The disclosure relates generally to vehicle control. In particular aspects, the disclosure relates to trajectory planning for vehicle combinations. The disclosure can be applied in heavy-duty vehicles, such as trucks, buses, and construction equipment. In particular, the disclosure can be applied in multi-unit vehicle combinations with distributed propulsion and energy storage. Although the disclosure may be described with respect to a particular vehicle, the disclosure is not restricted to any particular vehicle.BACKGROUND

[0002] In vehicle motion management, accurate trajectory planning for multi -unit battery electric vehicle (BEV) or hybrid electric vehicle (HEV) combinations is challenging due to the complexity of coordinating and manoeuvring multiple vehicle units together. Unlike singleunit vehicles, vehicle combinations require careful consideration of each vehicle unit’s dynamic and the interaction between them. These multi-unit configurations often exhibit complex interactions, such as inter-unit communication, varying states, and dynamic behaviours.

[0003] It is therefore desired to develop a solution for vehicle motion management that addresses or at least mitigates some of these issues.SUMMARY

[0004] This disclosure provides systems, methods and other approaches for determining an input relating to a manoeuvre for a BEV or HEV combination in order to control motion of the vehicle combination autonomously. In particular, dynamic information, including one or more vehicle capabilities, is received from the vehicle combination and used to implement a vehicle model. The vehicle model outputs an input relating to a manoeuvre in the form of a trajectory for the vehicle combination, which can be used by control system to control motion of the vehicle combination.

[0005] According to a first aspect of the disclosure, there is provided a computer system for determining an input relating to a manoeuvre for a battery electric vehicle, BEV, or hybridelectric vehicle, HEV, combination, the computer system comprising processing circuitry configured to acquire parameters of the vehicle combination, including one or more vehicle capabilities that are functions of capability parameters, update a vehicle model based on the acquired parameters, and determine an input relating to a manoeuvre for the vehicle combination using the updated vehicle model.

[0006] The first aspect of the disclosure may seek to provide a computer system for accurate trajectory planning for a multi -unit vehicle combination. A technical benefit may include that the current configurations and conditions of each unit in the vehicle combination can be taken into consideration by using an up-to-date vehicle model to determine an input relating to a manoeuvre for the vehicle combination. In this way, accurate trajectory planning can be ensured.

[0007] Optionally in some examples, including in at least one preferred example, the vehicle capabilities comprise at least one of a maximum range capability, a maximum operational time capability, a longitudinal acceleration minimum, a longitudinal acceleration maximum, a longitudinal acceleration rate minimum, a longitudinal acceleration rate maximum, a longitudinal velocity minimum, a longitudinal velocity maximum, a longitudinal distance minimum, a longitudinal distance maximum, a yaw rate minimum, a yaw rate maximum, a yaw acceleration minimum, a yaw acceleration maximum, a longitudinal velocity maximum for uphill slopes, and a longitudinal velocity maximum values for downhill slopes. A technical benefit may include that the vehicle model can be updated based on a range of vehicle capabilities, which themselves may be based on dynamic information relating to the vehicle combination. This enables the vehicle model to determine an input relating to a manoeuvre for the vehicle combination in a precise and accurate manner.

[0008] Optionally in some examples, including in at least one preferred example, the longitudinal acceleration minimum and / or the longitudinal acceleration maximum are a function of one or more of a longitudinal velocity of the vehicle combination, a mass of the vehicle combination, a lateral acceleration of the vehicle combination, a turning radius of the vehicle combination, a longitudinal force provided by one or more electrical machines of the vehicle combination, a thermal property of one or more electrical machines of the vehicle combination, and / or a thermal property of one or more batteries of the vehicle combination. A technical benefit may include that the longitudinal acceleration minimum and / or maximum will be determined based on current states and properties of the vehicle combination

[0009] Optionally in some examples, including in at least one preferred example, the parameters of the vehicle combination further comprise one or more structural parameters ofthe vehicle combination and / or one or more dynamic parameters of the vehicle combination. A technical benefit may include that the vehicle model is updated according to the current status of the vehicle, such that the output of the model will be based on real-time conditions accurate to the vehicle combination that is to be controlled.

[0010] Optionally in some examples, including in at least one preferred example, the structural parameters of the vehicle combination comprise at least one of a type of the vehicle combination, a number of units in the vehicle combination, a number of axles in each unit, a tyre type in each axle group, a distance of each axle of each unit to the first axle and coupling points of the unit, the number of steered axles in each unit, the number of propelled axles in each unit, the number of liftable axles in each unit, nominal wheel diameters, a track of each axle, a mass of the unladen vehicle combination, and a centre of gravity of the unladen vehicle combination. A technical benefit may include that the vehicle model can be updated based on a range of structural parameters of the current configuration of the vehicle combination, meaning the complex behaviors and interactions between vehicle units in a vehicle combination can be properly modelled. By taking these structural parameters of the vehicle into consideration, more precise trajectory planning for the vehicle combination may be achieved.

[0011] Optionally in some examples, including in at least one preferred example, the dynamic parameters of the vehicle combination comprise at least one of a mass of each unit, a load on each axle, an inertia of each unit, a lumped cornering stiffness of each axle, a rolling resistance of each axle, a distance of a dynamic centre of gravity from the first axle of each unit, and an air drag property. A technical benefit may include that the vehicle model can be updated based on a range of dynamic parameters, meaning the complex behaviors and interactions between vehicle units in a vehicle combination can be properly modelled. In this way, more accurate and precise trajectory planning for the vehicle combination may be achieved.

[0012] Optionally in some examples, including in at least one preferred example, the processing circuitry is configured to determine the input using the vehicle model according to an operational design domain and / or a safe operating envelope. A technical benefit may include that instabilities such as rollover, jack-knife, and / or an unsafe swept path width can be avoided.

[0013] Optionally in some examples, including in at least one preferred example, the vehicle capabilities are determined based on current values of the capability parameters. A technical benefit may include that the vehicle model is updated base on real-time conditions,ensuring that the trajectory for the vehicle combination is determined in an accurate and up-to- date manner.

[0014] Optionally in some examples, including in at least one preferred example, the determined input comprises a trajectory for the vehicle combination including at least one of an acceleration and a path curvature for the vehicle combination. A technical benefit may include that the input can provide an accurate and up-to-date trajectory to control motion of the vehicle combination autonomously.

[0015] Optionally in some examples, including in at least one preferred example, the processing circuitry is further configured to provide the determined input to a control system configured to determine control inputs for the vehicle combination. A technical benefit may include that motion of the vehicle combination may be controlled autonomously in a safe and accurate manner based on the determined input.

[0016] According to a second aspect of the disclosure, there is provided a vehicle comprising the computer system of any preceding claim. The second aspect of the disclosure may seek to provide a vehicle capable of accurate trajectory planning for a multi -unit vehicle combination. A technical benefit may include that the current configurations and conditions of each unit in the vehicle combination can be taken into consideration by using an up-to-date vehicle model to determine an input relating to a manoeuvre for the vehicle combination. In this way, accurate trajectory planning can be ensured.

[0017] According to a third aspect of the disclosure, there is provided a computer- implemented method for determining an input relating to a manoeuvre for an autonomously or semi-autonomously controllable vehicle a battery electric vehicle battery electric vehicle, BEV, or hybrid electric vehicle, HEV, combination, the method comprising acquiring, by processing circuitry of a computer system, parameters of the vehicle combination, including one or more vehicle capabilities that are functions of capability parameters, updating, by the processing circuitry, a vehicle model based on the acquired parameters, and determining, by the processing circuitry, an input relating to a manoeuvre for the vehicle combination using the updated vehicle model.

[0018] The third aspect of the disclosure may seek to provide a computer-implemented method for accurate trajectory planning for a multi -unit vehicle combination. A technical benefit may include that the current configurations and conditions of each unit in the vehicle combination can be taken into consideration by using an up-to-date vehicle model to determine an input relating to a manoeuvre for the vehicle combination. In this way, accurate trajectory planning can be ensured.

[0019] According to a fourth aspect of the disclosure, there is provided a computer program product comprising program code for performing, when executed by processing circuitry, the computer-implemented method. The fourth aspect of the disclosure may seek to enable new vehicles and / or legacy vehicles to be conveniently configured, by software installation / update, to perform accurate trajectory planning for a multi -unit vehicle combination. A technical benefit may include that the current configurations and conditions of each unit in the vehicle combination can be taken into consideration by using an up-to-date vehicle model to determine an input relating to a manoeuvre for the vehicle combination. In this way, accurate trajectory planning can be ensured.

[0020] According to a fifth aspect of the disclosure, there is provided a non-transitory computer-readable storage medium comprising instructions, which when executed by processing circuitry, cause the processing circuitry to perform the computer-implemented method. The fifth aspect of the disclosure may seek to enable new vehicles and / or legacy vehicles to be conveniently configured, by software installation / update, to perform accurate trajectory planning for a multi -unit vehicle combination. A technical benefit may include that the current configurations and conditions of each unit in the vehicle combination can be taken into consideration by using an up-to-date vehicle model to determine an input relating to a manoeuvre for the vehicle combination. In this way, accurate trajectory planning can be ensured.

[0021] The disclosed aspects, examples (including any preferred examples), and / or accompanying claims may be suitably combined with each other as would be apparent to anyone of ordinary skill in the art. Additional features and advantages are disclosed in the following description, claims, and drawings, and in part will be readily apparent therefrom to those skilled in the art or recognized by practicing the disclosure as described herein.

[0022] There are also disclosed herein computer systems, control units, code modules, computer-implemented methods, computer readable media, and computer program products associated with the above discussed technical benefits.BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Examples are described in more detail below with reference to the appended drawings.

[0024] FIG. 1A schematically shows a side view of a vehicle combination according to an example of the disclosure.

[0025] FIG. IB schematically shows a top view of a vehicle combination according to an example of the disclosure.

[0026] FIG. 2 schematically shows, in terms of functional blocks, a control system for a vehicle according to an example of the disclosure.

[0027] FIG. 3 is a flow chart of a computer-implemented method according to an example.

[0028] FIG. 4 is a schematic diagram of a computer system for implementing examples disclosed herein.

[0029] Like reference numerals refer to like elements throughout the description.DETAILED DESCRIPTION

[0030] The detailed description set forth below provides information and examples of the disclosed technology with sufficient detail to enable those skilled in the art to practice the disclosure.

[0031] Accurate trajectory planning for multi -unit battery electric vehicle (BEV) or hybrid electric vehicle (HEV) combinations is challenging due to the complexity of coordinating and manoeuvring multiple vehicle units together. Unlike single-unit vehicles, vehicle combinations require careful consideration of each vehicle unit’s dynamic and the interaction between them. These multi-unit configurations often exhibit complex interactions, such as inter-unit communication, varying states, and dynamic behaviours.

[0032] To remedy this, systems and methods are proposed for determining an input relating to a manoeuvre for a BEV or HEV combination in order to control motion of the vehicle combination autonomously. In particular, dynamic information, including one or more vehicle capabilities, is received from the vehicle combination and used to implement a vehicle model. The vehicle model outputs an input relating to a manoeuvre in the form of a trajectory for the vehicle combination, which can be used by control system to control motion of the vehicle.

[0033] FIG. 1A schematically shows a side view of an example vehicle combination 100 of the type considered in this disclosure. The vehicle combination 100 comprises a number of units 110, including a tractor unit and at least one trailing unit. Each unit 110 may be given an index z, and the total number of units 110 in a vehicle combination 100 is designated n. Whilst two trailing units are shown, it will be appreciated that the vehicle combination 100 may comprise more or fewer trailing units connected to each other. This gives rise to different types and designations of vehicle combinations.

[0034] A tractor unit, such as the tractor unit 110-1, is generally the foremost unit in a vehicle combination 100, and may comprise the cabin for the driver, including steering controls, dashboard displays and the like. Generally, the tractor unit 110-1 is used to provide propulsion power for the vehicle combination 100. In the example of FIG. 1A, the tractor unit 110-1 may also be used to store goods that are being transported by the vehicle combination 100

[0035] A trailing unit, such as the trailing units 110-i, 110-n, is generally used to store goods that are being transported by the vehicle combination 100. A trailing unit may be a truck, trailer, dolly and the like. A trailing unit may also provide propulsion to the vehicle combination 100. A trailing unit without a front axle, such as the trailing units 110-i, 110-n, is known as a semi-trailer. In vehicle combinations such as that shown in FIG. 1 A, vehicle motion management is available on a unit level to receive requests from a manual or virtual driver to coordinate the propulsion, braking and steering.

[0036] Whilst three tractor axles and two axles per trailer are shown, it will be appreciated that any suitable number of axles may be provide on the respective units 110. It will also be appreciated that any number of the tractor axles and / or trailer axles may be driven axles, including zero (i.e. one of the units may include at least one driven axle while the other does not).

[0037] The vehicle combination 100 may comprise one or more sources or propulsion. For example, on or more of the units 110 may comprise one or more electrical machines 120 such as electric motors. Each unit 110 may comprise one or more batteries 130 configured to provide power to the electrical machines 120. A vehicle combination 100 that uses only battery power is a BEV. In some examples, for example in the case of an HEV, a unit 110, most often a tractor unit 110-1, may also include another source of propulsion, for example an internal combustion engine (ICE). The vehicle combination 100 also comprises a drivetrain (not shown) to deliver mechanical power from the propulsion source (the electrical machines 120 or the ICE) to the wheels 140. All units 110 may provide propulsion to the vehicle combination 100. In the examples discussed herein, the vehicle combination 100 may be a BEV or an HEV.

[0038] The electrical machines 120 are configured to drive, e.g. provide torque and / or steering to, one or more axles or individual wheels 140 of the unit 110. The electrical machines 120 of a unit 110 can supply either a positive (propulsion) or negative (braking) force. In some examples, electric motors may also be operated as generators, in order for the electric motors to generate braking force when required. The use of electrical machines 120 to supply a negative force is known as regenerative braking. The energy recovered from regenerativebraking can be stored in the batteries 130, and so regenerative braking is generally preferred over using service brakes 150.

[0039] Furthermore, each unit 110 may comprise one or more sets of service brakes 150. The service brakes 150 of a unit 110 can supply a negative (braking) force. The service brakes 150 may be, for example, frictional brakes such as pneumatic brakes. Pneumatic brakes use a compressor to fill the brake with air, which may be powered by the batteries 130. In some examples, the brakes may be electro-mechanical brakes or hydraulic brakes.

[0040] The vehicle combination 100 may also comprise one or more auxiliary systems (not shown). The auxiliary systems may include auxiliary mechanical systems, such as alternators, power take-off (PTO) systems, and an air compressors, and auxiliary electrical systems, such as steering pumps, headlights, other light systems, ignition systems, audio systems, and air conditioning systems.

[0041] The ICE, electrical machines 120 and service brakes 150 are considered as actuators of the vehicle combination 100. Other actuators may also be present. For example, steering actuators 150, such as steering servo arrangements, may be provided, and may be implemented as electro-hydraulic actuators. Each actuator in a given unit 110 may be given an index k, and the total number of actuators in a given unit 110 is designated m. It will be appreciated that each axle and / or wheel 140 may have an associated electrical machine 130, set of service brakes 150, and / or set of steering actuators 150.

[0042] The vehicle combination 100, or indeed one or more (e.g. each) units 110, can be considered to comprise two systems: a propulsion system comprising the components that are involved in propulsion of the vehicle combination 100, and a braking system comprising the components that are involved in braking of the vehicle combination 100. As such, the propulsion system can be considered to comprise one or more of the ICE, electrical machines 120, the drivetrain, and batteries 130 of the vehicle combination 100, while the braking system can be considered to comprise the ICE, the electrical machines 120, the drivetrain, the batteries 130, and the service brakes 150. As such, there is some overlap between the propulsion system and the braking system.

[0043] FIG. IB schematically shows a top view of an example vehicle combination 100 of the type considered in this disclosure. Similarly to the example of FIG. 1A, the vehicle combination 100 comprises a number of units 110, including a tractor unit and a plurality of trailing units. FIG. IB also shows the requested global forces of the vehicle combination 100 as a whole. Examples of requested global forces of the vehicle combination 100 as a whole may e.g. include a total longitudinal / axial force Fx.tot a total lateral / radial force Fy, tot, and / or oneor more yaw moments Mz,i for the respective vehicle units 110. In order to control motion of a vehicle combination 100, the requested global forces of the vehicle combination 100 must be determined and resolved. This may be achieved by a control system 200 (shown in FIG. 2) of the vehicle combination 100 that determines control signals based on a requested reference input and certain operating conditions of the vehicle combination 100.

[0044] In the example of FIG. IB, the vehicle combination 100 includes a combination control allocator 210 and a plurality of unit control allocators 212. The combination control allocator 210 and the various unit specific control allocators 212 together form a distributed control allocation system for the vehicle combination 100. In this system, the control allocation may be performed on multiple levels, i.e. first on a level of the vehicle combination 100 as a whole, and then on a level of each vehicle unit 110 individually. The combination control allocator 210 may be provided (as shown) as part of the tractor unit 110-1, while the unit control allocators 212 are provided as part of each individual unit 110. It will be appreciated that the combination control allocator 210 may be provided as part of any unit 110 of the vehicle combination 100.

[0045] FIG. 2 schematically shows, in terms of functional blocks, an example control system 200 for a vehicle, such as the vehicle combination 100. The control system 200 serves to perform various functions of the vehicle combination 100, such as power management and motion coordination. The control system 200 comprises a tactical layer 202, a target generator 204, a state estimator 206, an energy manager 208, a combination control allocator 210 and a plurality of unit control allocators 212. The combination of the target generator 204, the state estimator 206, and the energy manager 208, may be referred to as a vehicle motion controller (VMC) of the vehicle combination 100. The various modules may e.g. be implemented as code running on a processing circuitry, or similar. The various modules may comprise processing circuitry configured to implement various operations disclosed below. The various modules may include a memory storing instructions that, when executed by the processing circuitry, cause the processing circuitry to perform the various operations. The various modules may be communicatively connected or connectable to each other, for example as known in the art.

[0046] The tactical layer 202 is responsible for ensuring that the trajectory for the whole combination 100 is obstacle free and collision free. The tactical layer 202 may also be referred to as an automated driving system (ADS) of the vehicle combination 100. For example, the tactical layer 202 may determine a trajectory for the vehicle combination 100 that ensures that a swept path of the vehicle combination 100 and the individual units 110 is safe and achievable. To this end, the tactical layer 202 may provide an input rads relating to a manoeuvre in anautonomous driving case. The input rads may include requests such as target distance, velocity, acceleration, and curvature (steering) for the vehicle combination 100. These may be scalar values or vectors with evolutions for a given prediction horizon. The tactical layer 202 may also send determined future performance limits for the vehicle combination 100. Optionally, the tactical layer 202 may also send requests for power and energy management to optimize range and mission performance. For example, the tactical layer 202 may also include predictive energy management, including battery targets, capabilities and statuses that determine how the energy sources of the vehicle combination 100 should be used for a whole mission.

[0047] In some examples, the tactical layer 202 comprises a vehicle model 203. The vehicle model 203 is a model of the vehicle combination 100 intended to plan trajectories of the vehicle combination 100. As such, the vehicle model 203 can be used to determine the input rads. The vehicle model 203 may include different parameters of the vehicle combination 100 such as capabilities, structural parameters, and dynamic parameters of the vehicle combination 100, and be capable of determining the forces acting on the vehicle combination 100. The vehicle model 203 can be any suitable model, for example a model known in the art. The vehicle model 203 can be based on real tests, computer model simulations, a machine-learning model, or other suitable means known in the art. The vehicle model 203 may be, for example, a single-track model (i.e., left and right wheels on a given axle are considered together), such as a bicycle model. The vehicle model 203 may alternatively be a more complex model such as a dual track model (i.e., left and right wheels on a given axle are considered separately). The real units can have axle groups with several axles, but in the model they may be considered together. A tyre model can be used in combination with the vehicle model 203. The tyre model may take into account the cornering stiffness of the tyres of the vehicle combination 100. The vehicle model 203 may be configured to operate within an agreed operational design domain (ODD) and a specified safe operating envelope (SOE) for the vehicle combination 100. The vehicle model 203 may therefore include vehicle motion management logic that includes capabilities of the vehicle combination 100 and the SOE to avoid instabilities such as rollover, jack-knife, and / or an unsafe swept path width.

[0048] The vehicle model 203 may be time-invariant or time variant, based on certain parameters of the vehicle combination 100. To this end, the tactical layer 202 may receive parameters yi of the vehicle combination 100 from the vehicle combination 100 and / or the individual units 110. The parameters yi may include capabilities, structural parameters, and / or dynamic parameters of the vehicle combination 100.

[0049] The vehicle capabilities comprise at least one of a maximum range capability, a maximum operational time capability, a longitudinal acceleration minimum, a longitudinal acceleration maximum, a longitudinal acceleration rate minimum, a longitudinal acceleration rate maximum, a longitudinal velocity minimum, a longitudinal velocity maximum, a longitudinal distance minimum, a longitudinal distance maximum, a yaw rate minimum, a yaw rate maximum, a yaw acceleration minimum, a yaw acceleration maximum, a longitudinal velocity maximum for uphill slopes, and a longitudinal velocity maximum values for downhill slopes. While the maximum range capability relates to total distance that the vehicle can travel, the longitudinal distance minimum / maximum refers to a relatively short distance, for example for shunting in a logistic context for moving a vehicle in a yard, or for a safe stop.

[0050] In some examples, the capabilities are functions of capability parameters. For example, the longitudinal acceleration minimum and / or the longitudinal acceleration maximum may be a function of one or more of a longitudinal velocity of the vehicle combination 100, a mass of the vehicle combination 100, a lateral acceleration of the vehicle combination 100, a turning radius of the vehicle combination 100, a longitudinal force provided by the electrical machines 120, and / or a thermal property of one or more batteries 130. In some examples, the longitudinal force provided by the electrical machines 120 is a function of thermal properties of the electrical machines 120, as the power capabilities of the the electrical machines 120, and consequently the longitudinal force capabilities, will be a function of motor temperature. Similarly, the capability of the batteries 130 depends on thermal properties of the batteries 130. Furthermore, the thermal properties of the batteries 130 may limit performance of the electrical machines 120 in the case that the battery power limits the electrical machine power and the electrical machines 120 can only provide a certain torque. The vehicle capabilities may also be influenced by a thermal mode requested by the tactical layer 202, as discussed further below.

[0051] The structural parameters of the vehicle combination 100 comprise at least one of a type of the vehicle combination 100, a number of units 110 of the vehicle combination 100, a number of axles in each unit 110, a tyre type in each axle group, a distance of each axle of each unit 110 to the first axle and coupling points of the unit 110, the number of steered axles in each unit 110, the number of propelled axles in each unit 110, the number of liftable axles in each unit 110, nominal diameters of the wheels 140, a track of each axle, a mass of the unladen vehicle combination 100, and a centre of gravity of the unladen vehicle combination 100. The type of the vehicle combination 100 may be defined by different types of coupling used in the vehicle combination 100. The tyre type may be defined by a tyre stiffnesses, a peak friction / slip parameter of the tyre, and / or other parameters used in known tyre models such as the Pacejka Magic Formula or a brush model.

[0052] The dynamic parameters of the vehicle combination 100 comprise at least one of a mass of each unit 110, a load on each axle, an inertia of each unit 110, a lumped cornering stiffness of each axle, a rolling resistance of each axle, a distance of a dynamic centre of gravity from the first axle of each unit 110, and an air drag property. The inertia may be expressed in three directions, although the vertical direction is most relevant for trajectory planning as it represents the yaw moment of inertia, which is relevant for the yaw-plane motion of the vehicle combination 100. The air drag property may include am effective surface of the vehicle combination 100 for different wind directions.

[0053] Based on these received parameters yi of the vehicle combination 100, the vehicle model 203 can be updated to reflect the current state of the vehicle combination 100. This can be advantageous in autonomous driving of multi-unit vehicle combinations, as it may enable safe and precise trajectory planning, which is not trivial due to the complexity in their dynamics and interactions between units 110. For instance, an updated vehicle model 203 can enable a swept path of both the vehicle combination 100 and individual units 110 to be maintained within a safe range. Other typical use cases for the vehicle model 203 include overtake situations on uphill for the vehicle combination 100, where the vehicle model 203 can determine whether the vehicle combination 100 has sufficient motion capabilities for a successful overtake. Additionally, the vehicle model 203 can be applied to assess rough timing, determining how long the vehicle combination 100 can be used.

[0054] In some examples, the tactical layer 202 can decide on state of charge (SoC) targets for the batteries 130 of the vehicle combination 100 as a function of distance, in some cases considering slope changes, etc. For example, the tactical layer 202 can request the battery 130 of a unit 110 having a higher SoC be drained for an uphill slope, as it can foresee that batteries 130 of all units 110 can be charged fully with regenerative braking at a following downhill slope. In some examples, an SoC controller (not shown) can calculate weighting factors for SoC targets. In some examples, the tactical layer 202 can send targets for the state of energy rate (SoE) directly to the combination control allocator 210.

[0055] Furthermore, the tactical layer 202 can request the transfer of energy from one unit 110 to another by means of propulsion in one unit 110 and regenerative braking in the other (as explained in WO 2021 / 180300 Al in the name of Volvo Truck Corporation). In another example, the tactical layer 202 requests the battery 130 of a unit 110 be drained faster thananother based on the number of available chargers in a following charge station or due to equalizing the charging time of all units 110 or minimizing the total charging time at the charging station.

[0056] The tactical layer 202 can also be used to select an operating mode (otherwise known as a thermal management mode) for the vehicle combination 100. A vehicle combination 100 may be capable of operating in a number of different modes dependent on desired performance. It is advantageous to provide smart electric vehicle units that can provide different settings or automatically detect which operating mode is most suitable for durable and / or efficient driving. The tactical layer 202 can select an operating mode based on factors such as current traffic situation, road types, GPS signals, weather conditions, or a vehicle usage preference (a preferred driving scenario for example long distance, short distance usage, etc.). The tactical layer 202 can also select an operating mode based on real time data from the vehicle sensors, or vehicle-to-vehicle / infrastructure communication data. For example, if it is determined that a quick acceleration or high performance is needed based on this data (e.g. due to changes in traffic conditions), the tactical layer 202 can select an operating mode accordingly. The operating modes may include an “Eco” mode or “Range” mode, in which acceleration and top speed of the vehicle combination 100 can be limited to optimise energy efficiency and maximise range, an “Endurance” mode, intended to enable a vehicle combination 100 to operate for a long duration, a “Performance” mode, configured to provide maximum acceleration and top speed, and an “I-know” mode, in which pre-set configurations for the vehicle combination 100 can be adjusted appropriate to desired performance.

[0057] The tactical layer 202 can interface with vehicle motion management components of the control system 202, in particular the target generator 204. As discussed above, the tactical layer 202 may provide an input rads relating to a manoeuvre to the target generator 204. In some instances, the input rads may be determined by the vehicle model 203 based on the current parameters yi received from the vehicle combination 100. This interface ensures that motion in a reference coordinate system can be requested by the tactical layer 202 within the capabilities of the vehicle combination 100 to ensure safe and efficient motion control. This enables fully automated driving with redundancy and vehicle safety.

[0058] The purpose of the target generator 204 is to determine a requested reference input rreqand a requested combination control input Vcomb.req for the vehicle combination 100. The requested reference input rreqis determined based on an input related to a manoeuvre for the vehicle combination 100, for example the input rads from the vehicle model 203 of the tactical layer 202, and represents a requested movement of the vehicle combination 100. The requestedcombination control input Vcomb.req can be determined based on the requested reference input rreqand / or the input rads. The requested combination control input Vcomb.req can also be determined based on a motion capability Vcomb.cap for the vehicle combination 100. The target generator 204 comprises a path planner / controller 214 and a force generator 216.

[0059] In particular, the target generator 204 may receive an input related to a manoeuvre for the vehicle combination 100. The manoeuvre may be, for example, straight-line driving, cornering, braking and the like. The target generator 204 may receive data from, for example, a steering wheel and / or gas / brake pedal of the combination 100, indicating that the driver (or some other system of the vehicle combination 100) wants to change the direction and / or the speed of the vehicle combination 100 in a certain way. This may be the case in a semi- autonomous driving scenario. In some examples, the input may originate from elsewhere, for example any other system that may provide some indication of how the overall forces of the vehicle combination 100 are to be influenced (e.g. steered, propelled or braked). For example, the data may originate from a lane assist system, a lane following system, an emergency steering system, an emergency braking system, an automated or semi-automated drive system. In one particular example, the target generator 204 may receive the input rads from the vehicle model 203 of the tactical layer 202. This may be the case in a fully autonomous driving scenario. Based on this input, the target generator 204 may output a requested reference input rreq. In particular, the path planner / controller 214 determines the requested reference input rreq. The requested reference input rreqmay comprise at least one of a longitudinal acceleration axof the vehicle combination 100 as a whole or of a unit 110 of the vehicle combination 100 (for example the unit 110 comprising the combination control allocator 210), a longitudinal velocity vxiof a tractor unit 110-1, a lateral velocity vyiof the tractor unit 110-1, a yaw rate cozt of at least one unit 110 of the vehicle combination 100, and a steering angle yreqof the tractor unit 110-1. In some examples, the target generator 204 may also receive determined future performance limits for the vehicle combination 100.

[0060] The requested combination control input Vcomb.req is determined by the force generator 216. The requested combination control input Vcomb.req can be determined based on the requested reference input rreq, or based on the input rads directly. In the latter case, the path planner / controller 214 can be used to determine a requested reference input rreqfor shorter term motion, for example by up-sampling the requests rads from the tactical layer 202 that may be sent infrequently (e.g. every second or so). The requested combination control input V comb, req may include requested motion parameters for the vehicle combination 100. In particular, the forces Ftot.req and / or moments Mz, tot, req that need to be applied to the vehicle combination 100as a whole in order to follow the requested reference input rreqare determined. The requested motion parameters included in the requested combination control input Vcomb.req of the vehicle combination 100 may comprise at least one of a requested longitudinal force Fx, tot, req of the vehicle combination 100, a requested lateral force Fy,tot,req of the vehicle combination 100, a requested longitudinal coupling force F ext, req between consecutive units 110, and a requested lateral coupling force Fcyt.req between consecutive units 110. These make up the total requested force to be applied Ftot,req for the vehicle combination 100. The motion parameters included in the requested combination control input Vcomb.req of the vehicle combination 100 may also comprise a requested yaw moment Mz,t,req for one or more units 110.

[0061] The requested combination control input Vcomb.req may also be determined based on state information j’2 from the different units 110 of the vehicle combination 100 and a motion capability Vcomb.cap for the vehicle combination 100. The state information y2 may include information from sensors of the vehicle combination 100 such as wheel speed sensors, inertial measurement units, articulation angle sensors and the like. The motion capability Vcomb.cap of the vehicle combination 100 may describe the limits of motion parameters for safe operation of the vehicle combination 100. The motion capability Vcomb.cap may comprise at least one of a longitudinal force capability Fx.tot,cap of the vehicle combination 100, a lateral force capability Fy. tot, cap of the vehicle combination 100, and a yaw moment capability Mz,t,cap for one or more units 110. The state information y2 may also include structural parameters of the vehicle combination 100 as discussed above in relation to parameters yi .

[0062] The requested combination control input Vcomb.req may be determined based on a vehicle model. The vehicle model can be any suitable model, for example a model known in the art. The model can be based on real tests, computer model simulations, a machine-learning model, or other suitable means known in the art. The vehicle model may provide motion prediction of the vehicle combination 100 by looking at previous steering input and acceleration input. The prediction may include instabilities such as understeer or rollover risk, for example within a one second horizon. The model may be, for example, a single-track model, i.e., left and right wheels on a given axle are considered together. The real units can have axle groups with several axles, but in the model they are considered together. A tyre model can be used in combination with the vehicle model. The tyre model may take into account the cornering stiffness of the tyres of the vehicle combination 100.

[0063] The state estimator 206 is responsible for processing state information ys from the different units 110 of the vehicle combination 100. For example, the state estimator 206 may receive information from sensors of the vehicle combination 100 such as wheel speedsensors, inertial measurement units, articulation angle sensors and the like and use this information to determine states for the vehicle combination 100 and the various units. The state estimator 206 may then output unit-specific state information xPto the energy manager 208 and unit-specific state information xcto the combination control allocator 210.

[0064] The energy manager 208 determines a power split between the different units 110 of the vehicle combination 100. The energy manager 208 may also determine a power split within each unit 110, meaning how the power demand is divided between the actuators (for example, the ICE, the electrical machines 120, service brakes 150, and / or steering actuators) of the unit 110. Inputs to the energy manager 208 include the requested reference input rreqfrom the target generator 204 and the statuses SoX of the batteries 130 of the vehicle combination 100. The energy manager 208 determines a power allocation and an associated power allocation input Ucomb,des. The power split may be determined based on the state of energy rate (SoE) for each unit 110 and / or the longitudinal part of the requested force for the unit’s propulsion system Fxpi.req. The energy manager 208 may consider factors that affect long-term energy consumption, such as road slopes, SoC states, charger locations, and the like, and determine power behavior as a function of the energy over time. The energy manager 208 may also be configured as a power manger. For example when a time horizon is considered, it may handle energy. When instantaneous values are considered, it may handle power.

[0065] Based on these values, the control allocators 210, 212 may determine control data that meets the requested global forces of the vehicle combination 100 to meet certain constraints, such as power management (optimising battery usage) and safety constraints (ensuring that the trajectory for the whole combination 100 is obstacle free and collision free). In particular, the control allocators 210, 212 determine how various actuators (for example, the ICE, the electrical machines 120, service brakes 150, and / or steering actuators) of the vehicle combination 100 are to be controlled in order to generate requested global forces of the vehicle combination 100 as a whole. The combination control allocator 210 and the various unit specific control allocators 212 together form a distributed control allocation system for the vehicle combination 100. In this system, the control allocation is performed on multiple levels, i.e. first on a level of the vehicle combination 100 as a whole, and then on a level of each vehicle unit 110 individually.

[0066] The combination control allocator 210 transforms the requested combination control input Vcomb.req from the target generator 204 into an allocated combination control input Ucomb for the vehicle combination 100, describing appropriate motion parameters for each unit110. The allocated combination control input uCOmb of the vehicle combination 100 comprises the forces F and / or moments AT to be applied for the vehicle combination 100. The allocated combination control input Ucomb comprises allocated unit control inputs m describing the forces and / or moments that each respective unit 110 is to produce in order to provide the allocated combination control input Ucomb of the vehicle combination 100. The allocated unit control inputs ut may comprise a force control input for the unit’s propulsion system FPi, and a force control input for the unit’s braking system Fbt.

[0067] The unit control allocators 212 comprise a specific control allocator 212 for each unit 110 of the vehicle combination 100. The unit-specific allocated control inputs m that are output from the combination control allocator 210 are transformed into actuator-specific allocated control inputs Uk, describing actual actuator commands by the unit-specific control allocators 212. For example, the unit-specific control allocators 212 map the forces and moments of each unit 110 into the steering and drive / brake torques to be applied at the wheels of each unit 110. To do this, the unit control allocators 212 may determine a requested force control input for the unit’s propulsion system FPiand a requested force control input for the unit’s braking system Fbt. The unit control allocators 212 then determine the actuator-specific allocated control inputs Uk accordingly, which comprise allocated force control inputs for the individual actuators of the unit’s different systems: FPk for the actuators of the propulsion system, and Fbk for the actuators of the braking system.

[0068] In some examples, each unit 110 may be capable of estimating its own capabilities Ui,cap, e.g. how much and / or how fast the unit 110 can move at a current time instant. The unit capabilities comprise a force capability for its propulsion system FPi,capand a force capability for its braking system Fbt,caP. This may be based on an actuator capability uk,caPfor each actuator, e.g. how much and / or how fast the actuator can move at a current time instant. The actuator capabilities comprise a force capability for the actuators FPk,caPduring propulsion and a force capability for the actuators Fbk,caPduring braking. The actuators of each unit 110 may provide an actuator capability Uk,caPto the respective unit control allocator 212-i, which provides a unit capability Ui,capto the combination control allocator 210. The unit capabilities Ui,capmay also comprise capabilities of the power input / output of the batteries 130.

[0069] Each unit 110 may also be capable of estimating its own power losses Pi, loss. The unit power losses Pi, loss comprise a power loss for its propulsion system PPi,iossand a power loss for its braking system Pbi.ioss. This may be based on an actuator power losses Pk,ioss,i for each actuator in the unit 110 as well as other power losses in the unit 110, such as power losses in the batteries and the drivetrain. The actuator power losses Pk,ioss,i comprise a power loss forpropulsion actuators Ppk,ioss,i (e.g. electrical machines 120, ICE, and / or other propulsion sources) and a power loss for braking actuators Pbk,ioss,i (e.g. electrical machines 120 and / or service brakes 150) The actuators of each unit 110 may provide the actuator power losses Pk,ioss,i to the respective unit control allocator 212-i, which provides unit power losses Pi, loss to the combination control allocator 210.

[0070] FIG. 3 is a flow chart of a computer-implemented method 300 according to an example. The method 300 is for determining an input relating to a manoeuvre for a BEV or HEV combination, such as the vehicle combination 100. The method 300 enables accurate trajectory planning for a vehicle combination 100 by updating a vehicle model that determines the trajectory based on current parameters of the vehicle combination 100. The method 300 may be implemented by processing circuitry of a computer system (e.g., the control system 200 described in relation to FIG. 2, and in particular the tactical layer 202).

[0071] At 302, parameters yi of the vehicle combination 100 are acquired. The parameters yi include one or more vehicle capabilities that are functions of capability parameters. As discussed above, the vehicle capabilities comprise at least one of a maximum range capability, a maximum operational time capability, a longitudinal acceleration minimum, a longitudinal acceleration maximum, a longitudinal acceleration rate minimum, a longitudinal acceleration rate maximum, a longitudinal velocity minimum, a longitudinal velocity maximum, a longitudinal distance minimum, a longitudinal distance maximum, a yaw rate minimum, a yaw rate maximum, a yaw acceleration minimum, a yaw acceleration maximum, a longitudinal velocity maximum for uphill slopes, and a longitudinal velocity maximum values for downhill slopes. The parameters yi may be current parameters of the vehicle combination 100 determined based on current values of the capability parameters, or may be predicted and / or modelled values of parameters.

[0072] Capability parameters that may influence the vehicle capabilities may include a longitudinal velocity of the vehicle combination 100, a mass of the vehicle combination 100, a lateral acceleration of the vehicle combination 100, a turning radius of the vehicle combination 100, a longitudinal force provided by the electrical machines 120, a thermal property of one or more electrical machines 120, and / or a thermal property of one or more batteries 130.

[0073] In some examples, the parameters yi of the vehicle combination 100 further include one or more structural parameters of the vehicle combination 100. The structural parameters of the vehicle combination 100 comprise at least one of a type of the vehicle combination 100, a number of units 110 of the vehicle combination 100, a number of axles in each unit 110, a tyre type in each axle group, a distance of each axle of each unit 110 to the first axle and couplingpoints of the unit 110, the number of steered axles in each unit 110, the number of propelled axles in each unit 110, the number of liftable axles in each unit 110, nominal diameters of the wheels 140, a track of each axle, a mass of the unladen vehicle combination 100, and a centre of gravity of the unladen vehicle combination 100.

[0074] In some examples, the parameters yi of the vehicle combination 100 further include one or more dynamic parameters of the vehicle combination 100. The dynamic parameters of the vehicle combination 100 comprise at least one of a mass of each unit 110, a load on each axle, an inertia (e.g. a vertical inertia or yaw moment of inertia) of each unit 110, a lumped cornering stiffness of each axle, a rolling resistance of each axle, a distance of a dynamic centre of gravity from the first axle of each unit 110, and an air drag property.

[0075] At 304, a vehicle model 203 is updated based on the acquired parameters yi. The vehicle model 203 is a model of the vehicle combination 100 intended to plan trajectories of the vehicle combination 100. As such, the model is updated to reflect the status of the vehicle combination 100 defined by the parameters yi. In this way, the output of the model can be ensured to be accurate to the vehicle combination 100 that is to be controlled. The vehicle model 203 can be any suitable model, for example a model known in the art. The vehicle model 203 may be configured to operate within an agreed ODD and a specified SOE for the vehicle combination 100. The vehicle model 203 may therefore include vehicle motion management logic that includes capabilities of the vehicle combination 100 and the SOE to avoid instabilities.

[0076] At 306, an input rads relating to a manoeuvre for the vehicle combination 100 is determined using the updated vehicle model 203. As discussed above, the tactical layer 202 can use the vehicle model 203 to determine the input rads. The input rads includes a trajectory for the vehicle combination 100 that includes requests such as acceleration and curvature for the vehicle combination 100. The trajectory is determined such that a swept path of the vehicle combination 100 and the individual units 110 is safe and achievable. That is to say, the swept path is within road boundaries and can be achieved by the propulsion system. The vehicle model 203 can determine the input rads online and / or in real-time. As the model 203 has been updated to reflect the status of the vehicle combination 100 defined by the parameters yi, the tactical layer 202 will have the capability to make informed decisions based on real-time conditions and it can be ensured that the trajectory for the vehicle combination 100 is determined in an accurate and up-to-date manner. This can be advantageous in autonomous driving of multi -unit vehicle combinations, as it may enable safe and precise trajectoryplanning, which is not trivial due to the complexity in their dynamics and interactions between units 110.

[0077] At 308, the determined input rads may be provided to the target generator 204. Based on this input, the target generator 204 may output a requested combination control input Vcomb.req. In particular, the requested combination control input vCOmb,req can then be determined based on the determined input rads by the force generator 216 so that the vehicle combination 100 can be controlled in an appropriate manner. In some examples, the path planner / controller 214 can be used to determine a requested reference input rreqfor shorter term motion.

[0078] The method 300 enables accurate trajectory planning for a vehicle combination 100 by updating a vehicle model that determines the trajectory based on current parameters of the vehicle combination 100. The current configurations and conditions of each unit in the vehicle combination can be taken into consideration by using an up-to-date vehicle model to determine an input relating to a manoeuvre for the vehicle combination. In this way, accurate trajectory planning can be ensured.

[0079] FIG. 4 is a schematic diagram of a computer system 400 for implementing examples disclosed herein. The computer system 400 is adapted to execute instructions from a computer-readable medium to perform these and / or any of the functions or processing described herein. The computer system 400 may be connected (e.g., networked) to other machines in a LAN (Local Area Network), LIN (Local Interconnect Network), automotive network communication protocol (e.g., FlexRay), an intranet, an extranet, or the Internet. While only a single device is illustrated, the computer system 400 may include any collection of devices that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Accordingly, any reference in the disclosure and / or claims to a computer system, computing system, computer device, computing device, control system, control unit, electronic control unit (ECU), processor device, processing circuitry, etc., includes reference to one or more such devices to individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. For example, control system may include a single control unit or a plurality of control units connected or otherwise communicatively coupled to each other, such that any performed function may be distributed between the control units as desired. Further, such devices may communicate with each other or other devices by various system architectures, such as directly or via a Controller Area Network (CAN) bus, etc.

[0080] The computer system 400 may comprise at least one computing device or electronic device capable of including firmware, hardware, and / or executing software instructions toimplement the functionality described herein. The computer system 400 may include processing circuitry 402 (e.g., processing circuitry including one or more processor devices or control units), a memory 404, and a system bus 406. The computer system 400 may include at least one computing device having the processing circuitry 402. The system bus 406 provides an interface for system components including, but not limited to, the memory 404 and the processing circuitry 402. The processing circuitry 402 may include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory 404. The processing circuitry 402 may, for example, include a general-purpose processor, an application specific processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a circuit containing processing components, a group of distributed processing components, a group of distributed computers configured for processing, or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processing circuitry 402 may further include computer executable code that controls operation of the programmable device.

[0081] The system bus 406 may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of bus architectures. The memory 404 may be one or more devices for storing data and / or computer code for completing or facilitating methods described herein. The memory 404 may include database components, object code components, script components, or other types of information structure for supporting the various activities herein. Any distributed or local memory device may be utilized with the systems and methods of this description. The memory 404 may be communicably connected to the processing circuitry 402 (e.g., via a circuit or any other wired, wireless, or network connection) and may include computer code for executing one or more processes described herein. The memory 404 may include non-volatile memory 408 (e.g., read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.), and volatile memory 410 (e.g., random-access memory (RAM)), or any other medium which can be used to carry or store desired program code in the form of machineexecutable instructions or data structures and which can be accessed by a computer or other machine with processing circuitry 402. A basic input / output system (BIOS) 412 may be stored in the non-volatile memory 408 and can include the basic routines that help to transfer information between elements within the computer system 400.

[0082] The computer system 400 may further include or be coupled to a non-transitory computer-readable storage medium such as the storage device 414, which may comprise, for example, an internal or external hard disk drive (HDD) (e.g., enhanced integrated drive electronics (EIDE) or serial advanced technology attachment (SATA)), HDD (e.g., EIDE or SATA) for storage, flash memory, or the like. The storage device 414 and other drives associated with computer-readable media and computer-usable media may provide nonvolatile storage of data, data structures, computer-executable instructions, and the like.

[0083] Computer-code which is hard or soft coded may be provided in the form of one or more modules. The module(s) can be implemented as software and / or hard-coded in circuitry to implement the functionality described herein in whole or in part. The modules may be stored in the storage device 414 and / or in the volatile memory 410, which may include an operating system 416 and / or one or more program modules 418. All or a portion of the examples disclosed herein may be implemented as a computer program 420 stored on a transitory or non- transitory computer-usable or computer-readable storage medium (e.g., single medium or multiple media), such as the storage device 414, which includes complex programming instructions (e.g., complex computer-readable program code) to cause the processing circuitry 402 to carry out actions described herein. Thus, the computer-readable program code of the computer program 420 can comprise software instructions for implementing the functionality of the examples described herein when executed by the processing circuitry 402. In some examples, the storage device 414 may be a computer program product (e.g., readable storage medium) storing the computer program 420 thereon, where at least a portion of a computer program 420 may be loadable (e.g., into a processor) for implementing the functionality of the examples described herein when executed by the processing circuitry 402. The processing circuitry 402 may serve as a controller or control system for the computer system 400 that is to implement the functionality described herein.

[0084] The computer system 400 may include an input device interface 422 configured to receive input and selections to be communicated to the computer system 400 when executing instructions, such as from a keyboard, mouse, touch-sensitive surface, etc. Such input devices may be connected to the processing circuitry 402 through the input device interface 422 coupled to the system bus 406 but can be connected through other interfaces, such as a parallel port, an Institute of Electrical and Electronic Engineers (IEEE) 1394 serial port, a Universal Serial Bus (USB) port, an IR interface, and the like. The computer system 400 may include an output device interface 424 configured to forward output, such as to a display, a video display unit (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)).The computer system 400 may include a communications interface 426 suitable for communicating with a network as appropriate or desired.

[0085] The operational actions described in any of the exemplary aspects herein are described to provide examples and discussion. The actions may be performed by hardware components, may be embodied in machine-executable instructions to cause a processor to perform the actions, or may be performed by a combination of hardware and software. Although a specific order of method actions may be shown or described, the order of the actions may differ. In addition, two or more actions may be performed concurrently or with partial concurrence.

[0086] According to certain examples, there is also disclosed:

[0087] Example 1 : A computer system (200, 202, 400) for determining an input relating to a manoeuvre for a battery electric vehicle, BEV, or hybrid electric vehicle, HEV, combination (100), the computer system (200, 202, 400) comprising processing circuitry (402) configured to: acquire parameters of the vehicle combination (100), including one or more vehicle capabilities that are functions of capability parameters; update a vehicle model (203) based on the acquired parameters; and determine an input relating to a manoeuvre for the vehicle combination (100) using the updated vehicle model (203).

[0088] Example 2: The computer system (200, 202, 400) of example 1, wherein the vehicle capabilities comprise at least one of a maximum range capability, a maximum operational time capability, a longitudinal acceleration minimum, a longitudinal acceleration maximum, a longitudinal acceleration rate minimum, a longitudinal acceleration rate maximum, a longitudinal velocity minimum, a longitudinal velocity maximum, a longitudinal distance minimum, a longitudinal distance maximum, a yaw rate minimum, a yaw rate maximum, a yaw acceleration minimum, a yaw acceleration maximum, a longitudinal velocity maximum for uphill slopes, and a longitudinal velocity maximum values for downhill slopes.

[0089] Example 3: The computer system (200, 202, 400) of example 2, wherein the longitudinal acceleration minimum and / or the longitudinal acceleration maximum are a function of one or more of a longitudinal velocity of the vehicle combination (100), a mass of the vehicle combination (100), a lateral acceleration of the vehicle combination (100), a turning radius of the vehicle combination (100), a longitudinal force provided by one or more electrical machines (120) of the vehicle combination (100), a thermal property of one or more electrical machines (120) of the vehicle combination (100), and / or a thermal property of one or more batteries (130) of the vehicle combination (100).

[0090] Example 4: The computer system (200, 202, 400) of any preceding example, wherein the parameters of the vehicle combination (100) further comprise one or more structural parameters of the vehicle combination (100) and / or one or more dynamic parameters of the vehicle combination (100).

[0091] Example 5: The computer system (200, 202, 400) of example 4, wherein the structural parameters of the vehicle combination (100) comprise at least one of a type of the vehicle combination (100), a number of units (110) in the vehicle combination (100), a number of axles in each unit (110), a tyre type in each axle group, a distance of each axle of each unit to the first axle and coupling points of the unit, the number of steered axles in each unit, the number of propelled axles in each unit (110), the number of liftable axles in each unit (110), nominal wheel diameters, a track of each axle, a mass of the unladen vehicle combination (100), and a centre of gravity of the unladen vehicle combination (100).

[0092] Example 6: The computer system (200, 202, 400) of example 4 or 5, wherein the dynamic parameters of the vehicle combination (100) comprise at least one of a mass of each unit (110), a load on each axle, an inertia of each unit (110), a lumped cornering stiffness of each axle, a rolling resistance of each axle, a distance of a dynamic centre of gravity from the first axle of each unit (110), and an air drag property.

[0093] Example 7: The computer system (200, 202, 400) of any preceding example, wherein the processing circuitry (402) is configured to determine the input using the vehicle model (203) according to an operational design domain and / or a safe operating envelope.

[0094] Example 8: The computer system (200, 202, 400) of any preceding example, wherein the vehicle capabilities are determined based on current values of the capability parameters.

[0095] Example 9: The computer system (200, 202, 400) of any preceding example, wherein the determined input comprises a trajectory for the vehicle combination (100) including at least one of an acceleration and a path curvature for the vehicle combination (100).

[0096] Example 10: The computer system (200, 202, 400) of example 9, wherein the trajectory comprises a safe and / or achievable swept path for the vehicle combination (100).

[0097] Example 11 : The computer system (200, 202, 400) of any preceding example, wherein the processing circuitry (402) is further configured to provide the determined input to a control system (204) configured to determine control inputs for the vehicle combination (100).

[0098] Example 12: A vehicle (100) comprising the computer system (200, 202, 400) of any preceding example.

[0099] Example 13: A computer-implemented method (300) for determining an input relating to a manoeuvre for an autonomously or semi-autonomously controllable vehicle a battery electric vehicle battery electric vehicle, BEV, or hybrid electric vehicle, HEV, combination (100), the method (300) comprising: acquiring (302), by processing circuitry (402) of a computer system (200, 202, 400), parameters of the vehicle combination (100), including one or more vehicle capabilities that are functions of capability parameters; updating (304), by the processing circuitry (402), a vehicle model (203) based on the acquired parameters; and determining (306), by the processing circuitry (402), an input relating to a manoeuvre for the vehicle combination (100) using the updated vehicle model (203).

[0100] Example 14: The computer-implemented method (300) of example 13, wherein the vehicle capabilities comprise at least one of a maximum range capability, a maximum operational time capability, a longitudinal acceleration minimum, a longitudinal acceleration maximum, a longitudinal acceleration rate minimum, a longitudinal acceleration rate maximum, a longitudinal velocity minimum, a longitudinal velocity maximum, a longitudinal distance minimum, a longitudinal distance maximum, a yaw rate minimum, a yaw rate maximum, a yaw acceleration minimum, a yaw acceleration maximum, a longitudinal velocity maximum for uphill slopes, and a longitudinal velocity maximum values for downhill slopes.

[0101] Example 15: The computer-implemented method (300) of example 14, wherein the longitudinal acceleration minimum and / or the longitudinal acceleration maximum are a function of one or more of a longitudinal velocity of the vehicle combination (100), a mass of the vehicle combination (100), a lateral acceleration of the vehicle combination (100), a turning radius of the vehicle combination (100), a longitudinal force provided by one or more electrical machines (120) of the vehicle combination (100), a thermal property of one or more electrical machines (120) of the vehicle combination (100), and / or a thermal property of one or more batteries (130) of the vehicle combination (100).

[0102] Example 16: The computer-implemented method (300) of any of examples 13 to 15, wherein the parameters of the vehicle combination (100) further comprise one or more structural parameters of the vehicle combination (100) and / or one or more dynamic parameters of the vehicle combination (100).

[0103] Example 17: The computer-implemented method (300) of any of examples 13 to 18, wherein the structural parameters of the vehicle combination (100) comprise at least one of a type of the vehicle combination (100), a number of units (110) in the vehicle combination (100), a number of axles in each unit (110), a tyre type in each axle group, a distance of each axle of each unit to the first axle and coupling points of the unit, the number of steered axles ineach unit, the number of propelled axles in each unit (110), the number of liftable axles in each unit (110), nominal wheel diameters, a track of each axle, a mass of the unladen vehicle combination (100), and a centre of gravity of the unladen vehicle combination (100).

[0104] Example 18: The computer-implemented method (300) of example 16 or 17, wherein the dynamic parameters of the vehicle combination (100) comprise at least one of a mass of each unit (110), a load on each axle, an inertia of each unit (110), a lumped cornering stiffness of each axle, a rolling resistance of each axle, a distance of a dynamic centre of gravity from the first axle of each unit (110), and an air drag property.

[0105] Example 19: The computer-implemented method (300) of any of examples 13 to18, wherein the processing circuitry (402) is configured to determine the input using the vehicle model (203) according to an operational design domain and / or a safe operating envelope.

[0106] Example 20: The computer-implemented method (300) of any of examples 13 to19, wherein the vehicle capabilities are determined based on current values of the capability parameters.

[0107] Example 21 : The computer-implemented method (300) of any of examples 13 to20, wherein the determined input comprises a trajectory for the vehicle combination (100) including at least one of an acceleration and a path curvature for the vehicle combination (100).

[0108] Example 22: The computer-implemented method (300) of example 21, wherein the trajectory comprises a safe and / or achievable swept path for the vehicle combination (100).

[0109] Example 23: The computer-implemented method (300) of any of examples 13 to 22, further comprising providing (308), by the processing circuitry (402), the determined input to a control system (204) configured to determine control inputs for the vehicle combination (100).

[0110] Example 24: A computer program product comprising program code for performing, when executed by processing circuitry (402), the computer-implemented method (300) of any of examples 13 to 23.

[0111] Example 25: A non-transitory computer-readable storage medium comprising instructions, which when executed by processing circuitry (402), cause the processing circuitry to perform the computer-implemented method (300) of any of examples 13 to 23.

[0112] The terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. It will be further understood that the terms "comprises,""comprising," "includes," and / or "including" when used herein specify the presence of stated features, integers, actions, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, actions, steps, operations, elements, components, and / or groups thereof.

[0113] It will be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the present disclosure.

[0114] Relative terms such as "below" or "above" or "upper" or "lower" or "horizontal" or "vertical" may be used herein to describe a relationship of one element to another element as illustrated in the Figures. It will be understood that these terms and those discussed above are intended to encompass different orientations of the device in addition to the orientation depicted in the Figures. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present.

[0115] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0116] It is to be understood that the present disclosure is not limited to the aspects described above and illustrated in the drawings; rather, the skilled person will recognize that many changes and modifications may be made within the scope of the present disclosure and appended claims. In the drawings and specification, there have been disclosed aspects for purposes of illustration only and not for purposes of limitation, the scope of the disclosure being set forth in the following claims.

Claims

CLAIMSWhat is claimed is:

1. A computer system (200, 202, 400) for determining an input relating to a manoeuvre for a battery electric vehicle, BEV, or hybrid electric vehicle, HEV, combination (100), the computer system (200, 202, 400) comprising processing circuitry (402) configured to: acquire parameters of the vehicle combination (100), including one or more vehicle capabilities that are functions of capability parameters; update a vehicle model (203) based on the acquired parameters; and determine an input relating to a manoeuvre for the vehicle combination (100) using the updated vehicle model (203).

2. The computer system (200, 202, 400) of claim 1, wherein the vehicle capabilities comprise at least one of a maximum range capability, a maximum operational time capability, a longitudinal acceleration minimum, a longitudinal acceleration maximum, a longitudinal acceleration rate minimum, a longitudinal acceleration rate maximum, a longitudinal velocity minimum, a longitudinal velocity maximum, a longitudinal distance minimum, a longitudinal distance maximum, a yaw rate minimum, a yaw rate maximum, a yaw acceleration minimum, a yaw acceleration maximum, a longitudinal velocity maximum for uphill slopes, and a longitudinal velocity maximum values for downhill slopes.

3. The computer system (200, 202, 400) of claim 2, wherein the longitudinal acceleration minimum and / or the longitudinal acceleration maximum are a function of one or more of a longitudinal velocity of the vehicle combination (100), a mass of the vehicle combination (100), a lateral acceleration of the vehicle combination (100), a turning radius of the vehicle combination (100), a longitudinal force provided by one or more electrical machines (120) of the vehicle combination (100), a thermal property of one or more electrical machines (120) of the vehicle combination (100), and / or a thermal property of one or more batteries (130) of the vehicle combination (100).

4. The computer system (200, 202, 400) of any preceding claim, wherein the parameters of the vehicle combination (100) further comprise one or more structural parameters of the vehicle combination (100) and / or one or more dynamic parameters of the vehicle combination (100).

5. The computer system (200, 202, 400) of claim 4, wherein the structural parameters of the vehicle combination () comprise at least one of a type of the vehicle combination (100), a number of units (110) in the vehicle combination (), a number of axles in each unit (110), a tyre type in each axle group, a distance of each axle of each unit to the first axle and coupling points of the unit, the number of steered axles in each unit, the number of propelled axles in each unit (110), the number of liftable axles in each unit (110), nominal wheel diameters, a track of each axle, a mass of the unladen vehicle combination (100), and a centre of gravity of the unladen vehicle combination (100).

6. The computer system (200, 202, 400) of claim 4 or 5, wherein the dynamic parameters of the vehicle combination (100) comprise at least one of a mass of each unit (110), a load on each axle, an inertia of each unit (110), a lumped cornering stiffness of each axle, a rolling resistance of each axle, a distance of a dynamic centre of gravity from the first axle of each unit (110), and an air drag property.

7. The computer system (200, 202, 400) of any preceding claim, wherein the processing circuitry (402) is configured to determine the input using the vehicle model (203) according to an operational design domain and / or a safe operating envelope.

8. The computer system (200, 202, 400) of any preceding claim, wherein the vehicle capabilities are determined based on current values of the capability parameters.

9. The computer system (200, 202, 400) of any preceding claim, wherein the determined input comprises a trajectory for the vehicle combination (100) including at least one of an acceleration and a path curvature for the vehicle combination (100).

10. The computer system (200, 202, 400) of claim 9, wherein the trajectory comprises a safe and / or achievable swept path for the vehicle combination (100).

11. The computer system (200, 202, 400) of any preceding claim, wherein the processing circuitry (402) is further configured to provide the determined input to a control system (204) configured to determine control inputs for the vehicle combination (100).

12. A vehicle (100) comprising the computer system (200, 202, 400) of any preceding claim.

13. A computer-implemented method (300) for determining an input relating to a manoeuvre for an autonomously or semi-autonomously controllable vehicle a battery electric vehicle battery electric vehicle, BEV, or hybrid electric vehicle, HEV, combination (100), the method (300) comprising: acquiring (302), by processing circuitry (402) of a computer system (200, 202, 400), parameters of the vehicle combination (100), including one or more vehicle capabilities that are functions of capability parameters; updating (304), by the processing circuitry (402), a vehicle model (203) based on the acquired parameters; and determining (306), by the processing circuitry (402), an input relating to a manoeuvre for the vehicle combination (100) using the updated vehicle model (203).

14. A computer program product comprising program code for performing, when executed by processing circuitry (), the computer-implemented method () of claim 13.

15. A non-transitory computer-readable storage medium comprising instructions, which when executed by processing circuitry (), cause the processing circuitry to perform the computer-implemented method () of claim 13.

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