Power flows for vehicle combinations

Abstract

A computer system for representing a power flow of an autonomously or semi-autonomously controllable vehicle combination, the computer system comprising processing circuitry configured to acquire information regarding energy system components and power flows of one or more units of the vehicle combination, represent power flows of the vehicle combination as a directed graph comprising a plurality of vertices connected by a plurality of edges, wherein the plurality of vertices represent the energy system components and / or zero-sum junctions and the plurality of edges represent power flows between the vertices, and represent the directed graph as a matrix.

Description

POWER FLOWS FOR VEHICLE COMBINATIONSTECHNICAL FIELD

[0001] The disclosure relates generally to vehicle control. In particular aspects, the disclosure relates to power flows 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] Energy management for multi-unit vehicle combinations is challenging due to the complexity of coordinating multiple vehicle units together. Unlike single-unit vehicles, vehicle combinations require careful consideration of each vehicle unit’s configuration and the interaction between them. An energy management system for a multi-unit vehicle combination should also be able to adapt to vehicle combinations that change units, even the number of units, as well as the current operating conditions. For example, the energy management system should be able to model power flows of the vehicle combination when a new vehicle configuration is provided in order to provide an optimal energy utilisation of the vehicle.

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

[0004] This disclosure provides systems, methods and other approaches for representing a power flow of a vehicle combination. Information regarding energy system components and power flows of the units of the vehicle combination is acquired, for example including power supplied, power consumed, power losses, and power capabilities. This information is represented as a directed graph, where vertices represent the energy system components of the vehicle combination and / or zero-sum junctions, and edges represent power flows between the vertices. The directed graph is then represented as a matrix that can be transmitted to an automated driving system of the vehicle combination.

[0005] According to a first aspect of the disclosure, there is provided a computer system for representing a power flow of an autonomously or semi-autonomously controllable vehicle combination, the computer system comprising processing circuitry configured to acquire information regarding energy system components and power flows of one or more units of the vehicle combination, represent power flows of the vehicle combination as a directed graph comprising a plurality of vertices connected by a plurality of edges, wherein the plurality of vertices represent the energy system components and / or zero-sum junctions and the plurality of edges represent power flows between the vertices, and represent the directed graph as a matrix.

[0006] The first aspect of the disclosure may seek to provide a computer system for improved energy management for multi-unit vehicle combinations, which takes into consideration each vehicle unit’s configuration and the complex interactions between the units. By modelling the power flows of the vehicle combination, the energy management system for a multi-unit vehicle combination is able to adapt to vehicle combinations that change units, even the number of units, as well as the current operating conditions. Representing these power flows as a matrix enables them to be communicated in an efficient manner. A technical benefit may include optimal energy utilisation of the vehicle via flexible and efficient representation of the vehicle’s power flows.

[0007] Optionally in some examples, including in at least one preferred example, the energy system components include one or more of an electrical machine, a battery, a wheel, a set of service brakes, an auxiliary mechanical system, and an auxiliary electrical system. A technical benefit may include improved energy management, as these energy system components are the components of the vehicle combination that provide and / or consume energy and thus are an integral part in providing an optimal energy utilisation of the vehicle.

[0008] Optionally in some examples, including in at least one preferred example, the power flows between the vertices comprise one or more of a power supplied, a power consumed, and a power loss. A technical benefit may include improved energy management, as each of a power supplied, a power consumed, and a power loss are factors that affect the vehicle unit’s and the interactions between units and consequently an integral part in representing the power flows of the vehicle combination and providing an optimal energy utilisation.

[0009] Optionally in some examples, including in at least one preferred example, the matrix is an incidence matrix or an adjacency matrix. A technical benefit may include that the complex power flow model represented by the graph can be stored and transmitted between components of a control system for a vehicle combination in an efficient manner, as incidencematrices or adjacency matrices have a relatively low memory and bandwidth requirement and are flexible and scalable dependent on different power flow architectures of different configurations for vehicle combinations.

[0010] Optionally in some examples, including in at least one preferred example, in the incidence matrix, a unidirectional edge is represented by 1 or -1 and a bidirectional edge is represented by 2. A technical benefit may include that power flows in two directions may be summarised in the matrix, providing a more concise and efficient representation of the power flows of the vehicle combination.

[0011] Optionally in some examples, including in at least one preferred example, the matrix is a sparse matrix. A technical benefit may include a computationally effective computer system with improved memory utilisation.

[0012] Optionally in some examples, including in at least one preferred example, the processing circuitry is further configured to transmit the matrix to an automated driving system of the vehicle combination. A technical benefit may include improved energy management, as the automated driving system may then determine and send requests for power and energy management based on the received matrix, enabling predictive energy management to optimise range and mission performance.

[0013] Optionally in some examples, including in at least one preferred example, the processing circuitry is further configured to transmit capabilities associated with the edges to the automated driving system. A technical benefit may include that capabilities such as minimum power flow and maximum power flow dependent on the configuration of the energy system components may be taken into consideration as well as constraints such as a maximum power difference between the powers of two edges, enabling improved vehicle stability and performance.

[0014] Optionally in some examples, including in at least one preferred example, the processing circuitry is further configured to transmit functions associated with the edges to the automated driving system. A technical benefit may include that a power flow associated with an edge may be described in terms of one or more parameters, e.g. torque, speed, voltage, current or temperature, providing a more accurate representation of the power flows of the vehicle combination.

[0015] Optionally in some examples, including in at least one preferred example, the processing circuitry is further configured to transmit current states of the vehicle combination to the automated driving system. A technical benefit may include improved energy management that is able to adapt according to current operating conditions.

[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 improved energy management for multi-unit vehicle combinations, which takes into consideration each vehicle unit’s configuration and the complex interactions between the units. By modelling the power flows of the vehicle combination, the energy management system for a multi-unit vehicle combination is able to adapt to vehicle combinations that change units, even the number of units, as well as the current operating conditions. Representing these power flows as a matrix enables them to be communicated in an efficient manner. A technical benefit may include optimal energy utilisation of the vehicle via flexible and efficient representation of the vehicle’s power flows.

[0017] According to a third aspect of the disclosure, there is provided a battery electric vehicle combination or hybrid electric vehicle combination comprising the computer system of any preceding claim. The third aspect of the disclosure may seek to provide a battery electric vehicle capable of improved energy management for multi-unit vehicle combinations, which takes into consideration each vehicle unit’s configuration and the complex interactions between the units. By modelling the power flows of the vehicle combination, the energy management system for a multi -unit vehicle combination is able to adapt to vehicle combinations that change units, even the number of units, as well as the current operating conditions. Representing these power flows as a matrix enables them to be communicated in an efficient manner. A technical benefit may include optimal energy utilisation of the vehicle, which may be a particular advantage for a battery electric vehicle combination or hybrid electric vehicle combination.

[0018] According to a fourth aspect of the disclosure, there is provided a computer- implemented method for representing a power flow of an autonomously or semi-autonomously controllable vehicle combination, the method comprising acquiring, by processing circuitry of a computer system, information regarding energy system components and power flows of one or more units of the vehicle combination, representing, by the processing circuitry, power flows of the vehicle combination as a directed graph comprising a plurality of vertices connected by a plurality of edges, wherein the plurality of vertices represent the energy system components of the vehicle combination and / or zero-sum junctions and the plurality of edges represent power flows between the vertices, and representing, by the processing circuitry, the directed graph as a matrix.

[0019] The fourth aspect of the disclosure may seek to improve energy management for multi -unit vehicle combinations, by taking into consideration each vehicle unit’s configuration and the complex interactions between the units. By modelling the power flows of the vehiclecombination, the energy management system for a multi-unit vehicle combination is able to adapt to vehicle combinations that change units, even the number of units, as well as the current operating conditions. Representing these power flows as a matrix enables them to be communicated in an efficient manner. A technical benefit may include optimal energy utilisation of the vehicle, which may be a particular advantage for a battery electric vehicle combination or hybrid electric vehicle combination.

[0020] According to a fifth 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 fifth aspect of the disclosure may seek to enable new vehicles and / or legacy vehicles to be conveniently configured, by software installation / update, to model the power flows of the vehicle combination such that each vehicle unit’s configuration and the complex interactions between the units may be taken into consideration in the energy management of the vehicle. A technical benefit may include that the energy management system able to adapt to vehicle combinations that change units as well as current operating conditions, resulting in optimal energy utilisation of the vehicle.

[0021] According to a sixth 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 sixth aspect of the disclosure may seek to enable new vehicles and / or legacy vehicles to be conveniently configured, by software installation / update, to model the power flows of the vehicle combination such that each vehicle unit’s configuration and the complex interactions between the units may be taken into consideration in the energy management of the vehicle. A technical benefit may include that the energy management system able to adapt to vehicle combinations that change units as well as current operating conditions, resulting in optimal energy utilisation of the vehicle.

[0022] 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.

[0023] 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

[0024] Examples are described in more detail below with reference to the appended drawings.

[0025] FIG. 1A schematically shows a side view of a vehicle combination according to an example of the disclosure.

[0026] FIG. IB schematically shows a top view of a vehicle combination according to an example of the disclosure.

[0027] FIG. 2 schematically shows, in terms of functional blocks, a control system for a vehicle according to an example of the disclosure.

[0028] FIG. 3 schematically shows a power flow model for a vehicle combination according to an example of the disclosure.

[0029] FIG. 4 shows a directed graph for representing a power flow model according to an example of the disclosure.

[0030] FIG. 5 is a flow chart of a computer-implemented method according to an example.

[0031] FIG. 6 is a schematic diagram of a computer system for implementing examples disclosed herein.

[0032] Like reference numerals refer to like elements throughout the description.DETAILED DESCRIPTION

[0033] 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.

[0034] Energy management for multi-unit vehicle combinations is challenging due to the complexity of coordinating multiple vehicle units together. Unlike single-unit vehicles, vehicle combinations require careful consideration of each vehicle unit’s configuration and the interaction between them. An energy management system for a multi-unit vehicle combination should also be able to adapt to vehicle combinations that change units, even the number of units, as well as the current operating conditions. For example, the energy management system should be able to model power flows of the vehicle combination when a new vehicle configuration is provided in order to provide an optimal energy utilisation of the vehicle.

[0035] To remedy this, systems and methods are proposed for representing a power flow of a vehicle combination. Information regarding energy system components and power flows of the units of the vehicle combination is acquired, for example including power supplied,power consumed, power losses, and power capabilities. This information is represented as a directed graph, where vertices represent the energy system components of the vehicle combination and / or zero-sum junctions, and edges represent power flows between the vertices. The directed graph is then represented as a matrix that can be transmitted to an automated driving system of the vehicle combination.

[0036] 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.

[0037] 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

[0038] 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.

[0039] 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).

[0040] 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 poweris 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.

[0041] 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 regenerative braking can be stored in the batteries 130, and so regenerative braking is generally preferred over using service brakes 150.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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 thecomponents 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.

[0046] 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 one or more yaw moments Mz,t 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.

[0047] 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.

[0048] 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.

[0049] 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 an autonomous 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.

[0050] 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. To this end, the tactical layer 202 may receive a model of the power flows of the vehicle combination 100 from the VMC, in particular the energy manager 208, as will be discussed below.

[0051] 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 trackmodel (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 rolloverjack-knife, and / or an unsafe swept path width.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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 than another 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.

[0060] 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.

[0061] 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.

[0062] 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 requested combination control input Vcomb.req can be determined based on the requested reference input rreq and / 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.

[0063] 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 rad 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 (forexample 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 y req of the tractor unit 110-1. In some examples, the target generator 204 may also receive determined future performance limits for the vehicle combination 100.

[0064] 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 inputor 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 100 as 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.

[0065] 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 F 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,caPfor 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 .

[0066] 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.

[0067] The target generator 204 may also be configured to send stability information ys to the tactical layer 202. The stability information ys may include constraints associated with an SOE of the vehicle combination 100 to avoid instabilities such as rollover, jack-knife, and / or an unsafe swept path width.

[0068] The state estimator 206 is responsible for processing state information y4 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 speed sensors, 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.

[0069] 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, anddetermine power behaviour 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.

[0070] The energy manager 208 may also be configured to send power information ys to the tactical layer 202. The power information ys may include a representation of the power flows of the vehicle combination 100. The representation may be determined using a directed graph that represents power flows between the different energy system components of the vehicle combination 100, such as the electrical machines 120, batteries 130, wheels 140, service brakes 150, auxiliary mechanical systems, and auxiliary electrical systems. This may then be represented as a matrix, for example an incidence matrix. The power information ys may include capabilities associated with the power flows. In this way, the energy manager 208 may provide a flexible and scalable power flow model of the vehicle combination 100 to the tactical layer 202. The determination of the representation of the power flows of the vehicle combination 100 will be discussed in more detail in relation to FIGs. 3 to 5. The power information ys may also include structural parameters received from the target generator 204 and statuses SoX of the batteries 130.

[0071] 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.

[0072] 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 unit 110. 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 forcesand / 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 Ui may comprise a force control input for the unit’s propulsion system FPi, and a force control input for the unit’s braking system Fbi.

[0073] 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 Fbi. 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.

[0074] 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.

[0075] 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 for propulsion 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 lossesPk,ioss,i to the respective unit control allocator 212-i, which provides unit power losses Pt, loss to the combination control allocator 210.

[0076] FIG. 3 shows an example power flow model 300 for a vehicle combination 100 that includes the power losses present across the combination. The power flow model 300 divides the total power demand Pveh required for the vehicle combination 100 to perform a certain manoeuvre across the various units 110. The power demand Pveh may be determined by multiplying the overall requested force F tot, req by the current velocity of the vehicle combination 100

[0077] The power demand Punit.i for a unit 110-i is split into the power Pw,t to be delivered at the wheels 140-i of the unit 110-i and the power losses P resist, t due to resistive forces such as air resistance, friction, and the like. For the purposes of this disclosure, the power losses Presist.i due to resistive force are not considered as part of the power losses of the unit 110. Furthermore, the model considers only power delivered by the electrical machines 120, though it will be appreciated that power may delivered by an ICE, which would have associated losses.

[0078] The power Pw,i to be delivered at the wheels 140-i of each unit 110-i is split into the mechanical power Pm,em,i delivered by the electrical machines 120-i and the power Psb.i delivered by the service brakes 150-i during braking. The power Pm,em,i delivered by the electrical machines 120-i may have associated capabilities, for example for minimum and maximum power delivery. These may be scalar values and / or functions of properties such as temperature, state of heat capacity, or time. The power Psb.i delivered by the service brakes 150-i during braking may also have associated capabilities, for example limits for maximum and minimum power.

[0079] The power Pm,em,i delivered by the electrical machines 120-i of each unit 110-i is split into the electrical power Pe.em i delivered from the batteries 130-i, power losses Pioss.em.i from the electrical machines 120-i, including losses from inverters coupled to the electrical machines 120-i, and power losses from the drivetrain Pioss, dt,i. The power losses Pioss.em.i from the electrical machines 120-i and the power losses from the drivetrain Pioss, dt.i may be comprised of losses from a brake resistor used to dissipate energy instead of sending it to the batteries 130-i, for example when the batteries 130-i are charged. These power losses may have associated capabilities, for example desired values for maximum and minimum losses. The power of the brake resistor may also have a power limit that may be a function of the coolant temperature of the brake resistor.

[0080] The electrical power Pe,em,i delivered from the batteries 130-i of each unit 110-i is split into the actual power Pbatt.i delivered from the batteries 130-i and power losses Pioss, batt, ifrom the batteries 130-i including any converter. The actual power Pbatt.i may be comprised of two components: a power Pbatt.i, p>o delivered from the batteries 130-i during propulsion, and a power Pbatt,i,p<o recovered by the batteries 130-i during regenerative braking. The power Pbatt.i delivered from the batteries 130-i may have associated capabilities, for example for minimum and maximum power delivery. These may be scalar values and / or functions of properties such as temperature, state of heat capacity, or time. The power losses Pioss.batt.i from the batteries 130-i may be comprised of ohmic losses due to the internal resistance of the batteries 130-i. Therefore, power may be limited by a cooling circuit and a maximum temperature that is desired for the batteries 130-i. The power losses Pioss. batt.i from the batteries 130-i may also have associated capabilities, for example desired values for maximum and minimum losses.

[0081] The power Psb.i delivered by the service brakes 150-i of each unit 110-i is split into the thermal recovery power Prec.sb.i from the service brakes 150-i, and power losses Pioss.sb.i from the service brakes 150-i. The power losses Pioss.sb.i from the service brakes 150-i are principally heat losses. With pneumatic brakes, there may also be losses from the compressor. The thermal recovery power Prec.sb.i from the service brakes 150-i is a result of regenerative braking, where the kinetic energy of a braking vehicle that would otherwise be lost as heat is converted into a useful form. In current systems, the thermal recovery power Prec.sb.i from the service brakes 150-i is often zero.

[0082] Further losses that are not shown in FIG. 3 but may be present in a power model of the vehicle combination 100 may include losses associated with a fuel cell such as heat rejection from internal ohmic losses of the fuel cell, and losses of the auxiliary systems, such the consumption of a compressor for air, blower of hydrogen, and the like.

[0083] A detailed description of the power flow model 300 and its use for determining a power allocation for the vehicle combination 100 is disclosed in PCT patent applications PCT / EP2022 / 082326, PCT / EP2022 / 08237, and PCT / EP2022 / 082338, which were filed in the name of Volvo Truck Corporation on 17 November 2022.

[0084] The inventors have appreciated that a power flow model for a vehicle combination 100, such as the power flow model 300 shown in FIG. 3, can be represented as a directed graph. A directed graph contains a set of vertices (or nodes) connected by directed edges (or arcs). The edges have a direction associated with them, meaning that the relationship between vertices may be asymmetrical, i.e. an edge from A to B does not necessarily imply the presence of an edge from B to A.

[0085] According to the present disclosure, the vertices of a directed graph for a power flow model represent the energy system components of the vehicle combination 100 and / orzero-sum junctions. Energy system components are the components of the vehicle combination 100 that provide and / or consume energy. These components may also be called transducers, as they convert one form of energy to another. The components of the vehicle combination 100 include the electrical machines 120, batteries 130, wheels 140, service brakes 150, and any auxiliary mechanical or electrical systems. Zero-sum junctions are vertices where the sum of power flowing into the vertex is equal to the sum of power flowing out of the vertex. These include representations of energy that is present in the model, such as kinetic energy, potential energy losses, and battery (potential) energy.

[0086] According to the present disclosure, the edges of a directed graph for a power flow model represent power flows between the vertices. Power flows between the vertices may comprise a power supplied by a given component, a power consumed by a given component, and / or a power loss. The edges can be classified according to predefined types, such as power for a particular unit 110-i, power losses from electrical machines 120-i, power losses from batteries 130-i, and power losses due to resistive forces on a unit 110-i.

[0087] An example of a directed graph 400 for a power flow model is shown in FIG. 4. The directed graph 400 comprises a plurality of vertices 402 connected by a plurality of edges 404. The directed graph 400 represents the power flow model 300 shown in FIG. 3.

[0088] The vertex 402a is a zero-sum junction that represents the kinetic energy used by the vehicle combination 100. As such, it is connected to the vertex 402b, which represents the vehicle combination 100, by bidirectional edge 404a. The edge 404a represents the total power demand Pveh for the vehicle combination 100, which could be positive during propulsion or negative during braking.

[0089] The vertex 402b is connected to the vertices 402c, 402d, and 402e, which respectively represent the units 110-1, 110-i, and 110-n. The vertex 402b is connected to the vertices 402c, 402d, and 402e, by respective bidirectional edges 404b, 404c, and 404d. The edges 404b, 404c, and 404d represent the power demand Pumt for each unit 110-i, which could be positive (propulsion) or negative (braking).

[0090] The vertex 402e is connected to vertex 402f, which is a zero-sum junction that represents the potential energy lost by the vehicle combination 100. The vertex 402e is connected to vertex 402f by bidirectional edge 404e, which represents the power losses P resist, i due to resistive forces such as air resistance, friction, and the like. Here, the edge 404e may also include a power flow due to gravitational effects, which could be positive during uphill travel or negative during downhill travel.

[0091] The vertex 402e is also connected to vertex 402g, which represents the wheels 140- i, of unit 110-i, by bidirectional edge 404f. The edge 404f represents the power Pw,i to be delivered at the wheels 140-i, which could be positive during propulsion or negative during braking.

[0092] The vertex 402g is connected to vertex 402h, which represents the service brakes 150-i of unit 110-i, by unidirectional edge 404g. The edge 404g represents the power Psb.i delivered by the service brakes 150-i of the unit 110-i. As discussed above, this is split into the thermal recovery power Prec.sb.i from the service brakes 150-i and power losses Pioss.sb.i from the service brakes 150-i. As the thermal recovery power Prec.sb.i is often zero, the edge 404g can be considered as only power losses Pioss.sb.i, and is therefore unidirectional.

[0093] The vertex 402g is also connected to vertex 402i, which represents the electrical machines 120-i of unit 110-i, by bidirectional edge 404h. The edge 404h represents the power Pm,em,i delivered by the electrical machines 120-i, which could be positive during propulsion when the electrical machines 120-i are acting as motors, or negative during braking when the electrical machines 120-i are acting as generators.

[0094] The vertex 402i is connected to vertex 402j, which is a zero-sum junction that represents lumped losses from a cooling system associated with the electrical machines 120-i and the batteries 130-i, by unidirectional edge 404j. The edge 404j represents power losses P loss, em, i from the electrical machines 120-i and power losses from the drivetrain Pioss.dt.t and is therefore unidirectional.

[0095] The vertex 402i is also connected to vertex 402k, which represents the batteries 130-i of unit 110-i, by bidirectional edge 404i. The edge 404i represents the electrical power P e,em,i delivered from the batteries 130-i, which could be positive during discharging or negative during charging.

[0096] The vertex 402k is also connected to vertex 402j, by unidirectional edge 404k. The edge 404k represents power losses Pioss.batt.t from the batteries 130-i and is therefore unidirectional.

[0097] The vertex 402k is also connected to vertex 4021, which is a zero-sum junction that represents the battery energy, by bidirectional edge 4041. The edge 4041 represents the actual power Pbatt.i delivered from the batteries 130-i, which could be positive (discharging) or negative (charging).

[0098] A directed graph, such as the directed graph 400, can be represented in a table or matrix. For example, an adjacency matrix or an incidence matrix can be used to represent the vertices and edges of the directed graph. An incidence matrix is a generally a matrix of 0s andIs (and -Is when the directed graph is oriented), whose rows represent vertices and whose columns represent edges. A 7 in row i and column j of the incidence matrix means that the edge j is connected to the vertex z and has a direction away from vertex i. A -1 in row z and column j means that the edge j is connected to the vertex z and has a direction towards vertex z. A 0 row z and column j means that the edge j is not connected to the vertex z. The use of Is and -Is represents unidirectional edges. This can be extended by using 2s to represent bidirectional edges. The resulting matrix may be a sparse matrix (a matrix in which most of the elements are 0).

[0099] An example of such a matrix for the directed graph 400 is shown below:Such a representation of a directed graph is advantageous, as a sparse adjacency matrix or incidence matrix has a relatively low memory and bandwidth requirement. In this way, the complex power flow model represented by the graph can be stored and transmitted between components of a control system for a vehicle combination in an efficient manner. This is also flexible and scalable dependent on different power flow architectures of different configurations for vehicle combinations.

[0100] FIG. 5 is a flow chart of a computer-implemented method 500 according to an example. The method 500 is for representing a power flow of an autonomously or semi- autonomously controllable vehicle combination, such as the vehicle combination 100. The method 500 enables complex power flow models to be stored and transmitted in an efficient manner. The method 500 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 part of a VMC, for example the energy manager 208).

[0101] At 502, information is acquired regarding energy system components and power flows of one or more units 110 of the vehicle combination 100. As discussed above, the energy system components of may comprise electrical machines 120, batteries 130, wheels 140, service brakes 150, auxiliary mechanical systems, and / or auxiliary electrical systems of the vehicle combination 100. This information may include a number of units 110 of the vehicle combination 100, a number of energy system components in each unit 110, power relationships between each component, and the like. The power flows between the vertices comprise one or more of a power supplied, a power consumed, and a power loss. This information may be compiled from the energy system components themselves.

[0102] At 504, the power flows of the vehicle combination 100 are represented as a directed graph, such as the directed graph 400. As discussed above, the directed graph comprises a plurality of vertices representing the energy system components of the vehicle combination 100 and / or zero-sum junctions. The vertices are connected by a plurality of edges that represent the power flows between the vertices.

[0103] At 506, the directed graph is represented as a matrix. The matrix may be an incidence matrix or an adjacency matrix, in particular a sparse incidence or adjacency matrix. In the incidence matrix, a unidirectional edge, such as edge 404g, may be represented by 1 or -1, and a bidirectional edge, such as edge 404a, may be represented by 2. It will be appreciated that other matrix notations to convey the directionality of the power flows may also be used.

[0104] At 508, the matrix may be transmitted to an ADS of the vehicle combination 100 (e.g. the tactical layer 202). The ADS may then send requests for power and energy management based on the received representation of the power flows of the vehicle combination 100. For example, the tactical layer 202 may provide 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. This can be used to optimise range and mission performance. As the power flows are represented in a matrix, they can be transmitted in an efficient manner. The matrix may be transmitted as part of power information ys, as discussed above.

[0105] In addition to the matrix representation of the power flows of the vehicle combination 100, the VMC may also transmit further information related to the vertices and edges of the directed graph. This may include a type of component represented by a vertex, dynamic parameters of a component represented by a vertex, capabilities (performance limits) of component represented by a vertex or of an edge (for example as functions of parameters), and current values of properties of a component represented by a vertex.

[0106] For example, at 510, capabilities may be transmitted to the ADS. In particular, power capabilities associated with the edges of the directed graph may be transmitted, such as a minimum and / or maximum power flow associated with a particular edge. These capabilities may be scalar values. These capabilities may be dependent on the configuration of the energy system components and their couplings. These capabilities may be transmitted as part of the power information ys. Vehicle stability constraints may also be transmitted. These capabilities could indicate other constraints to avoid vehicle instability, such as a maximum power difference between the powers of two edges. These capabilities may be transmitted as part of the stability information ys, as discussed above. Other capabilities may also be transmitted, for example minimum and / or maximum electric traction force, and minimum and / or maximum service brake force.

[0107] At 512, functions associated with the edges of the directed graph may be transmitted. For example, the power flow associated with a particular edge may be a function of properties such as temperature, state of heat capacity, time (decay of components), electrical machine speed, road slope, road curvature, and the like. For example, when the power flow associated with a particular edge is related to losses (for instance, power losses of a battery, and power losses of an EM, transmission and converter), the VMC can send a function to the ADS that describes the power losses in terms of one or more parameters (e.g. torque speed voltage, current, and temperature) of the connected vertex or vertices. These functions may be transmitted as part of the power information ys.

[0108] At 514, current states of the vehicle combination 100 may be transmitted. These may be current values of properties of the vertices of the graph (or functions such as polynomial functions), for example temperature of a component, heat capacity of a cooling system, velocity of one or more units 110 of the vehicle combination 100, internal resistance and open circuit voltages of one or more batteries 130, capacity of one or more batteries 130, SoC of one or more batteries 130, state of energy (SoE) of one or more batteries 130, state of health (SoH) of one or more batteries 130, fuel cell ohmic resistance and Nemst voltage.

[0109] The method 500 enables improved energy management for multi-unit vehicle combinations, which takes into consideration each vehicle unit’s configuration and the complex interactions between the units. By modelling the complex power flows of the vehicle combination in a directed graph, the energy management system for a multi-unit vehicle combination is able to adapt to vehicle combinations that change units, even the number of units, as well as the current operating conditions. Representing these power flows as a matrixenables them to be communicated in an efficient manner. This flexible and efficient representation of the vehicle’s power flows enables optimal energy utilisation of the vehicle.

[0110] FIG. 6 is a schematic diagram of a computer system 600 for implementing examples disclosed herein. The computer system 600 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 600 may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. While only a single device is illustrated, the computer system 600 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.

[0111] The computer system 600 may comprise at least one computing device or electronic device capable of including firmware, hardware, and / or executing software instructions to implement the functionality described herein. The computer system 600 may include processing circuitry 602 (e.g., processing circuitry including one or more processor devices or control units), a memory 604, and a system bus 606. The computer system 600 may include at least one computing device having the processing circuitry 602. The system bus 606 provides an interface for system components including, but not limited to, the memory 604 and the processing circuitry 602. The processing circuitry 602 may include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory 604. The processing circuitry 602 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 toperform the functions described herein. The processing circuitry 602 may further include computer executable code that controls operation of the programmable device.

[0112] The system bus 606 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 604 may be one or more devices for storing data and / or computer code for completing or facilitating methods described herein. The memory 604 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 604 may be communicably connected to the processing circuitry 602 (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 604 may include non-volatile memory 608 (e.g., read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.), and volatile memory 610 (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 602. A basic input / output system (BIOS) 612 may be stored in the non-volatile memory 608 and can include the basic routines that help to transfer information between elements within the computer system 600.

[0113] The computer system 600 may further include or be coupled to a non-transitory computer-readable storage medium such as the storage device 614, 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 614 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.

[0114] 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 614 and / or in the volatile memory 610, which may include an operating system 616 and / or one or more program modules 618. All or a portion of the examples disclosed herein may be implemented as a computer program 620 stored on a transitory or non- transitory computer-usable or computer-readable storage medium (e.g., single medium ormultiple media), such as the storage device 614, which includes complex programming instructions (e.g., complex computer-readable program code) to cause the processing circuitry 602 to carry out actions described herein. Thus, the computer-readable program code of the computer program 620 can comprise software instructions for implementing the functionality of the examples described herein when executed by the processing circuitry 602. In some examples, the storage device 614 may be a computer program product (e.g., readable storage medium) storing the computer program 620 thereon, where at least a portion of a computer program 620 may be loadable (e.g., into a processor) for implementing the functionality of the examples described herein when executed by the processing circuitry 602. The processing circuitry 602 may serve as a controller or control system for the computer system 600 that is to implement the functionality described herein.

[0115] The computer system 600 may include an input device interface 622 configured to receive input and selections to be communicated to the computer system 600 when executing instructions, such as from a keyboard, mouse, touch-sensitive surface, etc. Such input devices may be connected to the processing circuitry 602 through the input device interface 622 coupled to the system bus 606 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 600 may include an output device interface 624 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 600 may include a communications interface 626 suitable for communicating with a network as appropriate or desired.

[0116] 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.

[0117] According to certain examples, there is also disclosed:

[0118] Example 1 : A computer system (200, 208, 600) for representing a power flow of an autonomously or semi-autonomously controllable vehicle combination (100), the computer system (200, 208, 600) comprising processing circuitry (602) configured to: acquire information regarding energy system components (120, 130, 140, 150) and power flows (300)of one or more units (110) of the vehicle combination (100); represent power flows (300) of the vehicle combination (100) as a directed graph (400) comprising a plurality of vertices (402) connected by a plurality of edges (404), wherein the plurality of vertices (402) represent the energy system components (402b-e, 402g-i, 402k) and / or zero-sum junctions (402a, 402f, 402j, 4021) and the plurality of edges (404) represent power flows between the vertices (402); and represent the directed graph (400) as a matrix.

[0119] Example 2: The computer system (200, 208, 600) of example 1, wherein the energy system components include one or more of an electrical machine (120), a battery (130), a wheel (140), a set of service brakes (150), an auxiliary mechanical system, and an auxiliary electrical system.

[0120] Example 3: The computer system (200, 208, 600) of example 1 or 2, wherein the power flows between the vertices (402) comprise one or more of a power supplied, a power consumed, and a power loss.

[0121] Example 4: The computer system (200, 208, 600) of any preceding example, wherein the matrix is an incidence matrix or an adjacency matrix.

[0122] Example 5: The computer system (200, 208, 600) of example 4, wherein, in the incidence matrix, a unidirectional edge (404g, 404j, 404k) is represented by 1 or -1 and a bidirectional edge (404a-f, 404h-i, 4041) is represented by 2.

[0123] Example 6: The computer system (200, 208, 600) of any preceding example, wherein the matrix is a sparse matrix.

[0124] Example 7: The computer system (200, 208, 600) of any preceding example, wherein the processing circuitry (602) is further configured to transmit the matrix to an automated driving system (202) of the vehicle combination (100).

[0125] Example 8: The computer system (200, 208, 600) of example 7, wherein the processing circuitry (602) is further configured to transmit capabilities associated with the edges (404) to the automated driving system (202).

[0126] Example 9: The computer system (200, 208, 600) of example 7 or 8, wherein the processing circuitry (602) is further configured to transmit functions associated with the edges (404) to the automated driving system (202).

[0127] Example 10: The computer system (200, 208, 600) of any of examples 7 to 9, wherein the processing circuitry (602) is further configured to transmit current states of the vehicle combination (100) to the automated driving system (202).

[0128] Example 11 : A vehicle (100) comprising the computer system (200, 208, 600) of any preceding example.

[0129] Example 12: A battery electric vehicle combination (100) or hybrid electric vehicle combination (100) comprising the computer system (200, 208, 600) of any preceding example.

[0130] Example 13 : A computer-implemented method (500) for representing a power flow of an autonomously or semi-autonomously controllable vehicle combination (100), the method (500) comprising: acquiring (502), by processing circuitry (602) of a computer system (200, 208, 600), information regarding energy system components (120, 130, 140, 150) and power flows (300) of one or more units (110) of the vehicle combination (100); representing (504), by the processing circuitry (602), power flows (300) of the vehicle combination (100) as a directed graph (400) comprising a plurality of vertices (402) connected by a plurality of edges (404), wherein the plurality of vertices (402) represent the energy system components (402b- e, 402g-i, 402k) of the vehicle combination (100) and / or zero-sum junctions (402a, 402f, 402j, 4021) and the plurality of edges (404) represent power flows between the vertices (402); and representing (506), by the processing circuitry (602), the directed graph (400) as a matrix.

[0131] Example 14: The computer-implemented method (500) of example 13, wherein the energy system components include one or more of an electrical machine (120), a battery (130), a wheel (140), a set of service brakes (150), an auxiliary mechanical system, and an auxiliary electrical system.

[0132] Example 15: The computer-implemented method (500) of example 13 or 14, wherein the power flows between the vertices (402) comprise one or more of a power supplied, a power consumed, and a power loss.

[0133] Example 16: The computer-implemented method (500) of example 13 to 15, wherein the matrix is an incidence matrix or an adjacency matrix.

[0134] Example 17: The computer-implemented method (500) of example 16, wherein, in the incidence matrix, a unidirectional edge (404g, 404j, 404k) is represented by 1 or -1 and a bidirectional edge (404a-f, 404h-i, 4041 ) is represented by 2.

[0135] Example 18: The computer-implemented method (500) of example 13 to 17, wherein the matrix is a sparse matrix.

[0136] Example 19: The computer-implemented method (500) of example 13 to 18, further comprising transmitting (508), by the processing circuitry (602), the matrix to an automated driving system (202) of the vehicle combination (100).

[0137] Example 20: The computer-implemented method (500) of example 19, further comprising transmitting (510), by the processing circuitry (602), capabilities associated with the edges (404) to the automated driving system (202).

[0138] Example 21 : The computer-implemented method (500) of example 19 or 20, further comprising transmitting (512), by the processing circuitry (602), functions associated with the edges (404) to the automated driving system (202).

[0139] Example 22: The computer-implemented method (500) of example 19 to 21, further comprising transmitting (514), by the processing circuitry (602), current states of the vehicle combination (100) to the automated driving system (202).

[0140] Example 23: A computer program product comprising program code for performing, when executed by processing circuitry (602), the computer-implemented method (500) of example 13 to 22.

[0141] Example 24: A non-transitory computer-readable storage medium comprising instructions, which when executed by processing circuitry (602), cause the processing circuitry to perform the computer-implemented method (500) of example 13 to 22.

[0142] 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.

[0143] 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.

[0144] 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 referredto as being "directly connected" or "directly coupled" to another element, there are no intervening elements present.

[0145] 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.

[0146] 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, 208, 600) for representing a power flow of an autonomously or semi-autonomously controllable vehicle combination (100), the computer system (200, 208, 600) comprising processing circuitry (602) configured to: acquire information regarding energy system components (120, 130, 140, 150) and power flows (300) of one or more units (110) of the vehicle combination (100); represent power flows (300) of the vehicle combination (100) as a directed graph (400) comprising a plurality of vertices (402) connected by a plurality of edges (404), wherein the plurality of vertices (402) represent the energy system components (402b-e, 402g-i, 402k) and / or zero-sum junctions (402a, 402f, 402j, 4021) and the plurality of edges (404) represent power flows between the vertices (402); and represent the directed graph (400) as a matrix.

2. The computer system (200, 208, 600) of claim 1, wherein the energy system components include one or more of an electrical machine (120), a battery (130), a wheel (140), a set of service brakes (150), an auxiliary mechanical system, and an auxiliary electrical system.

3. The computer system (200, 208, 600) of claim 1 or 2, wherein the power flows between the vertices (402) comprise one or more of a power supplied, a power consumed, and a power loss.

4. The computer system (200, 208, 600) of any preceding claim, wherein the matrix is an incidence matrix or an adjacency matrix.

5. The computer system (200, 208, 600) of claim 4, wherein, in the incidence matrix, a unidirectional edge (404g, 404j, 404k) is represented by 1 or -1 and a bidirectional edge (404a-f, 404h-i, 4041) is represented by 2.

6. The computer system (200, 208, 600) of any preceding claim, wherein the matrix is a sparse matrix.

7. The computer system (200, 208, 600) of any preceding claim, wherein the processing circuitry (602) is further configured to transmit the matrix to an automated driving system (202) of the vehicle combination (100).

8. The computer system (200, 208, 600) of claim 7, wherein the processing circuitry (602) is further configured to transmit capabilities associated with the edges (404) to the automated driving system (202).

9. The computer system (200, 208, 600) of claim 7 or 8, wherein the processing circuitry (602) is further configured to transmit functions associated with the edges (404) to the automated driving system (202).

10. The computer system (200, 208, 600) of any of claims 7 to 9, wherein the processing circuitry (602) is further configured to transmit current states of the vehicle combination (100) to the automated driving system (202).

11. A vehicle (100) comprising the computer system (200, 208, 600) of any preceding claim.

12. A battery electric vehicle combination (100) or hybrid electric vehicle combination (100) comprising the computer system (200, 208, 600) of any preceding claim.

13. A computer-implemented method (500) for representing a power flow of an autonomously or semi-autonomously controllable vehicle combination (100), the method (500) comprising: acquiring (502), by processing circuitry (602) of a computer system (200, 208, 600), information regarding energy system components (120, 130, 140, 150) and power flows (300) of one or more units (110) of the vehicle combination (100); representing (504), by the processing circuitry (602), power flows (300) of the vehicle combination (100) as a directed graph (400) comprising a plurality of vertices (402) connected by a plurality of edges (404), wherein the plurality of vertices (402) represent the energy system components (402b-e, 402g-i, 402k) of the vehicle combination (100) and / or zero-sum junctions (402a, 402f, 402j, 4021) and the plurality of edges (404) represent power flows between the vertices (402); andrepresenting (506), by the processing circuitry (602), the directed graph (400) as a matrix.

14. A computer program product comprising program code for performing, when executed by processing circuitry (602), the computer-implemented method (500) of claim 13.

15. A non-transitory computer-readable storage medium comprising instructions, which when executed by processing circuitry (602), cause the processing circuitry to perform the computer-implemented method (500) of claim 13.

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