Automotive calibration optimisation

Abstract

A method for optimising vehicle calibration parameters, including: providing a representative real world drive cycle to a physical unit under test (100); generating performance data for the physical u

Description

FIELD The invention relates to the optimisation of the calibration of automotive vehicles, for example to maximise energy efficiency. BACKGROUND Modern vehicles are increasingly complex, with numerous interconnected systems and controllers. To maximise vehicle performance, the various systems must be calibrated appropriately. Optimising the calibration of the vehicle systems can lead to significant improvements in performance, such as in energy efficiency. However, traditional methods for calibrating vehicle systems can be time-consuming, inefficient, and expensive. BRIEF DESCRIPTION OF THE INVENTION Disclosed is a method for optimising vehicle calibration parameters, including: providing a representative real world drive cycle to a physical unit under test; generating performance data for the physical unit under test as the physical unit under test traverses the drive cycle; uploading the performance data as training data to generate a digital twin of a system, sub-system or vehicle in which the physical unit under test is configured to be used; providing vehicle performance prediction data from the digital twin to a calibration optimiser; and optimising one or more vehicle calibration parameters using the calibration optimiser. The method may further include providing the optimised vehicle calibration parameters to the physical unit under test, and validating that the optimised vehicle calibration parameters improve the performance of the unit under test. The method may further include generating the representative real world drive cycle. The drive cycle may be a synthetic drive cycle generated using one or more of map data, GNSS data, and / or representative traffic flow data from traffic simulator platforms. The drive cycle may include one or more of a velocity or speed profile, a gradient profile, an elevation profile, a weather profile, and a traffic simulation profile. The physical unit under test may be a powertrain or part thereof. The physical unit under test may be provided on an XiL rig or instrumented vehicle which contains the physical unit under test to determine the performance data. The vehicle performance data may include simulated performance data for a simulated vehicle system. The simulated vehicle system may be different to the physical unit under test. The method may further comprise sending the optimised vehicle calibration parameters to a vehicle to recalibrate the vehicle’s vehicle calibration parameters over the lifetime of the vehicle. Also disclosed is a method for generating journey-specific optimised vehicle calibration parameters for a vehicle, including: generating a drive cycle for the journey; simulating vehicle performance for the journey using a digital twin of the vehicle and a baseline set of vehicle calibration parameters; and optimising the vehicle calibration parameters for the journey using a calibration optimiser. The method may further include inputting the journey to a navigation module, and generating the drive cycle for the journey using a drive cycle generator which uses map data from the navigation module and representative traffic flow data from traffic simulator platforms to generate the drive cycle. The method may further include recalibrating the vehicle with the journey-specific optimised vehicle calibration parameters. If the vehicle is determined to have deviated from the generated drive cycle during the journey, the vehicle may be recalibrated back to the baseline vehicle calibration parameters. The drive cycle may include one or more of a velocity or speed profile, a gradient profile, an elevation profile, a weather profile, and a traffic simulation profile. Simulating the vehicle performance for the journey may include determining a predicted vehicle energy consumption for the journey. The vehicle calibration parameters may include one or more of a torque management parameter, a thermal management parameter, and an energy management parameter. The torque management parameter may include a torque distribution parameter between an internal combustion engine and an electric motor. Also disclosed is a method for reducing friction brake usage in a vehicle, including: generating a predicted drive cycle for a journey; simulating vehicle performance over the drive cycle using a digital twin of the vehicle and a baseline set of vehicle calibration parameters to generate a brake force profile for the journey; generating a battery state-of-charge profile for an electrical and / or thermal battery of the vehicle for the journey; determining an amount of energy that would be generated if all of the brake force in the brake force profile was provided by a regenerative braking system; determining if the electrical and / or thermal battery has sufficient capacity at each stage of the journey to receive all of the energy generated by the regenerative braking system; and generating an energy discharge profile for the journey indicating an energy to be discharged from an electrical battery of the vehicle or a thermal battery of the vehicle such that the electrical or thermal battery has capacity to receive all of the regenerated energy. Also disclosed is a method for reducing energy consumption in a vehicle, comprising: generating a drive cycle for a journey to be driven by the vehicle; simulating the performance of the vehicle over the journey by providing the drive cycle to a digital twin of the vehicle; determining an optimum acceleration, deceleration and coasting profile for the journey to minimise energy consumption; and providing the optimum acceleration profile to a driver of the vehicle in real time as the driver completes the journey. When linked to ACC controller, with the knowledge of expected velocity profile, gradient profile, optimum deceleration and coasting profile are generated for the journey to minimise energy consumption; and providing the optimum deceleration and coasting profile to a driver of the vehicle in real time as the driver completes the journey. BRIEF DESCRIPTION OF THE FIGURES In order that the present disclosure may be more readily understood, preferable embodiments thereof will now be described, by way of example only, with reference to the accompanying drawings, in which: Fig. 1 is a schematic view of a system according to the present disclosure; Fig. 2 is a schematic view of vehicles according to the present disclosure; Fig. 3 is a schematic view of a vehicle according to the present disclosure; and Figs. 4-14 illustrate various aspects of the system of the present disclosure. DETAILED DESCRIPTION OF THE DISCLOSURE Disclosed are systems and methods for optimising the calibration of automotive vehicles. The systems and methods disclosed herein may be used to optimise the calibration of one or more systems or sub-systems of the automotive vehicle, for example, or to optimise the calibration of the vehicle as a whole. The vehicle 3 may be a land vehicle such as a car, truck, lorry, motorbike, or similar. The vehicle 3 may include a powertrain 30. The vehicle 3 may include one or more wheels 301 which may engage a ground surface (e.g. via a tyre), and the wheels may be considered part of the powertrain 30. The powertrain may include a power unit 302. The power unit 302 may include an internal combustion engine, a hybrid power unit comprising an internal combustion engine and an electric motor, or an electric motor, for example. As such, the vehicle 3 may be an electric vehicle in some versions, such as a battery electric vehicle, fuel cell electric vehicle, hybrid electric vehicle or plugin hybrid electric vehicle. The vehicle 3 may include a fuel supply 304 for the power unit 302, which may include a fuel tank such as a fossil fuel (e.g. petrol / gasoline, diesel, eFuel, etc.) tank (e.g. for vehicles 3 having an internal combustion engine), or a hydrogen tank (e.g. for vehicles 3 having a hydrogen fuel cell). The vehicle 3 may include a power supply 305 for the power unit 302, such as an electric battery. The powertrain 30 may include one or more of a transmission(s), drive shaft(s), torque converter(s), and differential(s). The vehicle 3 may include a heating, ventilation and air conditioning system (HVAC) 32. The vehicle 3 may include one or more user input devices, such as a brake pedal, accelerator or gas pedal, steering wheel, and so on. The user input devices may enable the user to control the operation of the vehicle 3. The vehicle 3 may include one or more controllers 31. For example, the vehicle 3 may include one or more domain controllers such as one or more of a battery management system (BMS) 311, powertrain electronic control unit (PT ECU) or powertrain control module 312, thermal electronic control unit (thermal ECU) 313, adaptive cruise control unit (ACC) 314. The vehicle 3 may include one or more zonal controllers such as one or more of a zonal powertrain controller 315 or zonal thermal controller 316. For example, the battery management system 311 may monitor and control the operation of the power supply 305 (e.g. electric battery). For example, the battery management system 311 may monitor one or more of a battery voltage, battery temperature, battery current, battery health, battery coolant flow (if applicable), or state of balance of the battery cells. The BMS 311 may also control battery operation by determining one or more of a minimum or maximum cell voltage, charge current limit, discharge current limit, and so on. The powertrain ECU 312 may monitor and / or control a torque demand, such as a torque demand at a wheel 301. For example, in hybrid vehicles 3, the powertrain ECU 312 may determine a torque demand to be met by the internal combustion engine and / or electric motor. The powertrain ECU 312 may determine a fraction of a torque demand to be met by the internal combustion engine and a fraction of a torque demand to be met by the electric motor, for example, and may control the distribution of torque between the internal combustion engine and the electric motor. The powertrain ECU 312 may also determine a torque demand for a given user input, such as a position of the accelerator. Each controller may include one or more calibration parameters governing the operation of the controller. The performance of the vehicle may be optimised by optimising the calibration parameters which govern the operation of the controllers. A system 1 and method of optimising the calibration parameters is disclosed herein. The system 1 may include one or more real components 10 and one or more virtual components 20. In particular, the system 1 may include a unit under test 100 (which may be real). The system 1 may include a software defined vehicle or a connected vehicle 101 (which may be real). The system 1 may include a real world representative drive cycle or scenario generator 200 (which may be a virtual module). The system 1 may include a digital twin 201 (which may be a virtual module). The system 1 may include a calibration optimiser 202 (which may be a virtual module). The method may include generating a drive cycle, and the drive cycle may be generated by a drive cycle generator 200. The drive cycle generator 200 may be implemented as a software module, for example. The drive cycle may represent a set of driving conditions to be provided to a unit under test 100, for example in order to assess the performance of the unit under test 100. The drive cycle may include one or more of a velocity or speed profile, a gradient profile, an elevation profile, a weather profile, and a traffic simulation profile. The velocity or speed profile may specify a velocity or speed over time or distance. The gradient profile may specify a gradient over time or distance. The elevation profile may specify an elevation over time or distance. The weather profile may specify weather conditions over time or distance, such as ambient temperature, wind speed and direction, or precipitation. The traffic simulation profile may specify traffic flow conditions over time, such as a congestion parameter. For example, the congestion parameter may indicate a reduction in speed compared to a baseline value, and the baseline value may represent no congestion. The traffic simulation profile may therefore represent the impact of traffic flow or congestion on the drive cycle. The traffic simulation profile may be an input to the velocity or speed profile and may thus influence the output of the velocity or speed profile. In some versions, the velocity or speed profile may be generated separately from the traffic simulation profile, and the two may then be combined to generate a modified velocity or speed profile. The traffic simulation profile may be based on historic traffic flow data for a given route or location, for example. In some versions the drive cycle may be generated by gathering real-world drive cycle data, and may therefore be referred to as a “real” drive cycle. For example, the data may be gathered by driving a route in the real world and measuring one or more of the vehicle speed, elevation, gradient, ambient weather conditions (e.g. ambient temperature, wind speed, wind direction, precipitation), and traffic flow. The real-world data may be recorded and may thus form a drive cycle which can be provided to the unit under test 100. In some versions the drive cycle may be a synthetic drive cycle. The synthetic drive cycle may be generated by a synthetic drive cycle generator. The drive cycle may be considered “synthetic” in the sense that it is not generated by driving a route in the real world and recording measurements along the route; instead, the synthetic drive cycle may be generated virtually. For example, the synthetic drive cycle may be generated based on input data. The input data may include map data and / or representative traffic flow data from traffic simulator platforms. The map data may include road speed limits, elevation, gradient, and / or typical traffic conditions at a given time, for example. The input data may include weather data, which may be provided by weather stations for example. The weather data may be live weather data or historic weather data. The weather data may include the typical weather conditions for a given location at a given time (and this may include one or more of temperature, wind speed and direction, and precipitation). The elevation and / or gradient profile(s) may be generated by an elevation service module. The elevation service module may use map data to generate the elevation and / or gradient profile(s). The velocity or speed profile may be generated by a routing service module. The routing service module may use map data to generate the velocity or speed profile. The drive cycle (whether synthetic or real) may be provided to a unit under test 100. The unit under test 100 may be the vehicle 3 or a part thereof, for example a system or subsystem of the vehicle 3. For example, the unit under test 100 may be the powertrain 30 or a part thereof such as the power supply 305. The unit under test 100 may be a physical unit, and may be provided with instrumentation to measure relevant performance data for the unit under test 100. For example, if the unit under test 100 is the power supply 305, the measured performance data may include battery current, battery voltage, battery temperature, state of charge, depth of discharge, state of health, and so on. If the unit under test 100 is a powertrain 30 or part thereof, measured performance data may include torque, speed, temperature, DC bus voltage, dyno load, dyno speed, horsepower, torque demand, torque output (which may therefore be compared with the torque demand to identify lag in the system, for example), mechanical power, electrical power demand, coolant temperature, torque ramp up rate, torque ramp down rate, rate of change of torque, or any other performance data. It will be appreciated that relevant performance data will depend on the specific unit under test 100. The performance data may be obtained by incorporating the unit under test 100 into an XiL rig. XiL may also be known as “everything-in-the-loop”, and may encompass one or more of software-in-the-loop (SiL), hardware-in-the-loop (HiL), driver-in-the-loop (DiL), and vehicle-in-the-loop (ViL). In particular, in an XiL rig, the unit under test 100 may be a physical (real) unit, and at least a part of the vehicle 3 may be simulated (virtually). The XiL rig may therefore combine real and simulated parts of the vehicle 3 to provide performance data for the unit under test 100 in the context of the vehicle 3 - to establish how the unit under test 100 would perform if incorporated into the particular vehicle 3, for example. This can allow the performance of the same unit under test 100 (for example, the power supply 305) to be evaluated for different vehicles 3 (for example, a first car compared to a second car, which may be heavier, for example). This reduces the number of real physical components needed to assess the performance of the unit under test 100, and can therefore reduce costs, reduce material use, and speed up development times. The XiL rig may include sensors to monitor the performance characteristics of the unit under test 100, so as to generate the performance data. The sensors may differ depending on the particular unit under test 100. For example, if the unit under test 100 is the power supply 305, the sensors may include current sensors, voltage sensors, temperature sensors, and so on. If the unit under test 100 is the powertrain 30, the sensors may include current sensors, voltage sensors, temperature sensors, torque sensors, speed sensors, power sensors, and so on. The XiL rig may include one or more replicators to replicate one or more inputs influencing the performance of the unit under test 100. For example, the XiL rig may include a dyno (dynamometer) to replicate road conditions, for example by road load replication (which may also be known as road load simulation). The XiL rig may include one or more temperature controllers (such as a thermal emulator, heater and / or cooler) to replicate temperature conditions, such as the temperature conditions that would be experienced by the unit under test 100 during the generated drive cycle. These temperature conditions could include the ambient temperature indicated in the weather profile and / or temperatures associated with the operation of the vehicle 3, and may include a combination of the ambient temperature and the temperatures associated with the operation of the vehicle 3. In some versions the unit under test 100 may be incorporated into a sample vehicle or sample vehicle fleet for testing. Performance data may therefore be gathered by driving the sample vehicle on the generated drive cycle, which may be done in the real world by driving the vehicle on a test route, or may be done by testing the sample vehicle on a dyno, or through following an impeding vehicle subjected to the test route on a proving ground for example. The unit under test 100 may be controlled so as to simulate or emulate the generated drive cycle (e.g. to simulate or emulate the effect on the unit under test 100 of the vehicle 3 traversing the generated drive cycle). For example, if the unit under test 100 is the powertrain 30 or part thereof, it may be controlled so as to output the required velocity / speed indicated by the velocity or speed profile. If the unit under test 100 is the power supply 305, for example, then it may be controlled to simulate or emulate the charge and / or discharge parameters corresponding to the generated drive cycle (e.g. to provide the necessary power to meet the velocity / speed specified in the velocity / speed profile, and / or to receive power generated by regenerative braking throughout the drive cycle). The system 1 may include the digital twin 201, which may be a digital twin 201 of the vehicle 3 or a part thereof, such as the unit under test 100. The performance data may be uploaded to the digital twin 201. The digital twin 201 may be configured to simulate the performance of the vehicle 3. The digital twin 201 may therefore include one or more digital systems corresponding to real systems of the vehicle 3 - such as a digital powertrain corresponding to the powertrain 30, for example. The digital twin may include one or more of a thermal system digital twin, a HV-battery digital twin, and / or an EDU / e-motor digital twin, for example. The digital twin 201 may be generated by inputting the performance data into a machine learning module configured to simulate the performance of the unit under test 100 and / or the vehicle 3. The digital twin 201 may, therefore, be used to predict the performance of the vehicle 3 for a given drive cycle, such as to predict the energy usage of the vehicle 3 for the drive cycle. The calibration optimiser 202 may receive input data from the drive cycle generator 200 and / or the digital twin 201. The calibration optimiser 202 may receive the generated drive cycle from the drive cycle generator 200. The calibration optimiser 202 may receive simulated vehicle performance data from the digital twin 201, and may receive one or more calibration parameters from the digital twin 201. The calibration optimiser 202 may optimise one or more of the calibration parameters. In some versions the calibration optimiser 202 may store the calibration parameters and may provide the calibration parameters to the digital twin 201, and the digital twin 201 may determine simulated vehicle performance data based on the received calibration parameters. The calibration optimiser 202 may receive the generated drive cycle from the drive cycle generator 200 and the simulated vehicle performance data from the digital twin 201, and may optimise the calibration parameters so as to minimise or maximise one or more vehicle performance parameters, and this may be done using one or more cost functions. For example, the calibration optimiser 202 may minimise energy consumption (which may include electrical power consumption and / or fossil fuel consumption, for example) for the vehicle 3 over the course of the generated drive cycle. The calibration optimiser 202 may minimise CO2 emissions for the vehicle 3 over the course of the drive cycle. The calibration optimiser 202 may minimise heat generation and / or irreversible losses over the course of the drive cycle. In some versions a number of cost functions may be combined so as to perform global optimisation for the vehicle 3. The calibration optimiser 202 may generate optimised calibration parameters. The optimised calibration parameters may be outputted to the digital twin or simulated control strategies for validation (and this may therefore be virtual validation). For example, the digital twin may simulate the performance of the vehicle 3 or the unit under test 100 for the same generated drive cycle, and the simulated vehicle performance data under the optimised calibration parameters may be compared with the previous vehicle performance data to verify that the vehicle performance has improved. Additionally or alternatively, the optimised calibration parameters may be provided to the unit under test 100 for physical validation. The unit under test 100 may, therefore, traverse the generated drive cycle again under the optimised calibration parameters, and the performance data associated with the optimised calibration parameters may be compared with the previous performance data to verify that the performance of the unit under test 100 or vehicle 3 has improved. The process may be iterative, with the calibration optimiser iteratively optimising the calibration parameters. The calibration parameters may govern vehicle torque management, vehicle thermal management, and / or vehicle energy management, for example. The calibration parameters may be provided as calibration maps. The optimisation of the calibration parameters may include wheel torque request optimisation, HV-battery BMS optimisation, and / or thermal controller optimisation, for example. The optimisation process may be performed for a series of different drive cycles. These drive cycles may each be generated by the drive cycle generator and may be representative of real world drive cycles expected to be encountered by the vehicle 3. The calibration optimiser 202 may therefore generate a series of optimised calibration parameters, and these optimised calibration parameters may be averaged to provide globally optimised calibration parameters for the vehicle 3 or the unit under test 100. The optimised calibration parameters or the globally optimised calibration parameters may be provided to a software-defined vehicle 101, for example by an over-the-air download. The software-defined vehicle 101 may therefore be updated with the optimised or globally optimised calibration parameters, and this may improve the performance of the software-defined vehicle 101 over its lifetime. Performance data from the software-defined vehicle 101 may be uploaded to the digital twin 201 periodically or continuously, and this may improve the accuracy of the digital twin. The vehicle 3 may include a high-performance computer. The digital twin 201 may be hosted remotely from the vehicle 3 or the software-defined vehicle 101, for example on the cloud, or may be downloaded to the high-performance computer. The software-defined vehicle 101 may be considered an example of the vehicle 3. The drive cycle generator 200, digital twin 201 and calibration optimiser 202 may enable journeyspecific calibration optimisation for the vehicle 3. The vehicle 3 may include a navigation module, or may be linked to an external navigation module (such as a smartphone app). A user may input a journey to the navigation module. The drive cycle generator may generate a drive cycle for the input journey, including one or more of the profiles described previously (e.g. using map data). The generated drive cycle may be input to the calibration optimiser 202, which may optimise the calibration parameters for the vehicle 3 for the input journey based on the simulated vehicle performance data provided by the digital twin 201. The vehicle 3 may then be recalibrated with the optimised calibration parameters for the input journey. The optimised calibration parameters may be determined on the cloud and downloaded to the vehicle 3, or may be determined locally at the vehicle 3. The optimised journey-specific calibration parameters may include, for example, one or more of optimised adaptive cruise control parameters, optimised predictive torque management parameters, optimised predictive thermal management parameters, optimised vehicle preconditioning parameters, and optimised battery management parameters. The optimised parameters may be downloaded to the relevant controller or controllers, for example to an energy management VCU, vehicle VCU, fuel cell controller, adaptive cruise control controller, powertrain ECU, thermal ECU, and / or battery management system. In some versions the optimised parameters may be downloaded to the relevant zonal controller, such as a powertrain zonal ECU or thermal zonal ECU. The disclosed optimisation method and system 1 may be used to minimise the total cost of ownership of the vehicle 3. For example, the drive cycle generator 200 and digital twin 201 may be used to simulate the performance of the vehicle 3 for a given journey. The performance data may include energy consumption data for example, such as predicted battery state-of-charge or depth-of-discharge data, or predicted fuel consumption. This can be used to estimate the energy / fuel required to complete the journey, and / or the predicted energy / fuel remaining in the vehicle 3 at the end of the journey. This predicted performance data can be used to optimise the refuelling or recharging of the vehicle 3, or a fleet of vehicles 3. For example, the amount of energy / fuel needed for the vehicle 3 to complete the journey can be predicted, and the vehicle 3 can then be provided with that amount of energy / fuel (rather than simply filling the vehicle 3 to its maximum energy / fuel capacity). Additionally, if the journey start time is known, the vehicle 3 charging schedule can be optimised to minimise carbon intensity for the input electrical power. For example, the vehicle 3 may be charged and / or thermally pre-conditioned with energy provided by a grid supply. Thermal preconditioning may include warming or cooling a vehicle cabin, for example, and / or prewarming an engine or battery of the vehicle 3. The carbon intensity, and / or forecast carbon intensity, of the grid energy may be known (for example measured in grams of CO2 per kWh). The calibration optimiser 202 may then determine an optimum charging time and / or duration for the vehicle 3 in order to minimise the carbon intensity of the input energy. The methods disclosed herein may also be used to provide real-time driver coaching, for example to minimise energy consumption. As described previously, a user journey may be input to a navigation module. The drive cycle generator 200 may generate a drive cycle for that journey and the digital twin 201 may be used to predict the vehicle performance for that journey. The calibration optimiser 202 may be used to determine the optimum calibration parameters for that journey, for example to minimise energy consumption. However, instead of providing the optimised calibration parameters directly to the vehicle 3, the parameters may instead be provided to a driver, for example visually, such as via a driver coaching app configured to be displayed on a smartphone or integrated vehicle display. For example, the calibration optimiser 202 may determine a recommended acceleration and / or recommended braking for a given gradient and speed / velocity based on vehicle location (e.g. to optimise energy consumption for the journey), and may display the recommendation to the driver, for example as an indicator of how much to accelerate and / or brake in real time throughout the journey. The calibration optimisation may also be used to minimise friction brake usage for a given journey. The vehicle 3 may include a brake system 33 including a regenerative braking system 330 and a friction brake system 331, for example, and the regenerative braking system 330 may be configured to provide energy to an electrical battery (such as the power supply 305), load bank, and / or thermal battery 303 (which may also be referred to as a heat battery and may include a material such as a phase change material). The vehicle 3 may therefore include a brake system controller 317 to control the distribution of braking force between the regenerative and friction brake systems 330,331. The user may input a journey to a navigation module, and the drive cycle generator 200 may generate a drive cycle for the input journey. The digital twin 201 may simulate the vehicle 3 driving the journey including the simulation of braking events. This may be used to generate a brake force profile for the journey, for example (which may be a profile of required brake force over distance or time). The calibration optimiser 202 may use the drive cycle including the brake force profile to determine the expected regenerative braking output throughout the journey, and the associated charge levels of the electrical battery 305 and / or thermal battery 303, for example. If the calibration optimiser 202 determines that the output regenerative braking energy exceeds the ability of the electrical battery 305 and / or thermal battery 303 to receive the output energy at any point in the journey, the calibration optimiser 202 may generate a corresponding energy discharge profile for the electrical battery and / or thermal battery 303. The energy discharge profile may ensure that the electrical battery 305 and / or thermal battery 303 has capacity to receive energy from the regenerative braking system 330, which may reduce the need to use friction braking during the journey. The calibration optimiser 202 may, therefore, output braking calibration parameters for the journey, and the vehicle 3 may control the operation of the braking system 33 according to the braking calibration parameters. The braking calibration parameters may include a distribution of braking force between the regenerative and friction brake systems 330,331, and may include charge / discharge parameters for the electrical battery 305 and / or thermal battery 303. Discharge parameters for the electrical battery 305 may indicate a required output power, for example, and the output power may be distributed between the powertrain 30 (e.g. electric motor) and auxiliary loads, such as the HVAC system 32. Discharge parameters for the thermal battery 303 may indicate a required heat output, for example, and the output heat may be distributed between the powertrain 30 (e.g. to warm the internal combustion engine) and auxiliary loads such as the HVAC system 32 (e.g. to warm the cabin). Excess heat may be discharged through the vehicle 3 radiator / cooling system. For example, if the journey includes a downhill section (which would be indicated in the elevation or gradient profile), the calibration optimiser 202 may generate optimised calibration parameters indicating that power or heat output from the electrical battery 305 or thermal battery 303 should be increased before the downhill section so that capacity is available to recharge the respective batteries via regenerative braking on the downhill section. The calibration optimiser 202 may generate optimised calibration parameters indicating predictive thermal conditioning of the electrical battery 305 prior to a high power demand event such as climbing steep gradient, thereby preventing performance degradation due to electrical battery 305 cell temperatures. When used in this specification and claims, the terms "comprises" and "comprising" and variations thereof mean that the specified features, steps or integers are included. The terms are not to be interpreted to exclude the presence of other features, steps or components. The invention may also broadly consist in the parts, elements, steps, examples and / or features referred to or indicated in the specification individually or collectively in any and all combinations of two or more said parts, elements, steps, examples and / or features. In particular, one or more features in any of the embodiments described herein may be combined with one or more features from any other embodiment(s) described herein. Protection may be sought for any features disclosed in any one or more published documents referenced herein in combination with the present disclosure. Although certain example embodiments of the invention have been described, the scope of the appended claims is not intended to be limited solely to these embodiments. The claims are to be construed literally, purposively, and / or to encompass equivalents. 5

Claims

1. A method for optimising vehicle calibration parameters, including:providing a representative real world drive cycle to a physical unit under test;generating performance data for the physical unit under test as the physical unit under test traverses the drive cycle;uploading the performance data as training data to generate a digital twin of a system, subsystem or vehicle in which the physical unit under test is configured to be used;providing vehicle performance prediction data from the digital twin to a calibration optimiser; andoptimising one or more vehicle calibration parameters using the calibration optimiser.

2. A method according to claim 1, further including providing the optimised vehicle calibration parameters to the physical unit under test, and validating that the optimised vehicle calibration parameters improve the performance of the unit under test.

3. A method according to any preceding claim, further including generating the drive cycle.

4. A method according to claim 3, wherein the drive cycle is a synthetic drive cycle generated using map data and / or representative traffic flow data from traffic simulator platforms.

5. A method according to any preceding claim, wherein the drive cycle includes one or more of a velocity or speed profile, a gradient profile, an elevation profile, a weather profile, and a traffic simulation profile.

6. A method according to any preceding claim, wherein the physical unit under test is a powertrain or part thereof.

7. A method according to any preceding claim, wherein the physical unit under test is provided on an XiL rig or instrumented vehicle which contains the physical unit under test to determine the performance data.

8. A method according to any preceding claim, wherein the vehicle performance data includes simulated performance data for a simulated vehicle system.

9. A method according to claim 8, wherein the simulated vehicle system is different to the physical unit under test.

10. A method according to any preceding claim, further comprising sending the optimised vehicle calibration parameters to a vehicle to recalibrate the vehicle’s vehicle calibration parameters over the lifetime of the vehicle.

11. A method for generating journey-specific optimised vehicle calibration parameters for a vehicle, including:generating a drive cycle for the journey;simulating vehicle performance for the journey using a digital twin of the vehicle and a baseline set of vehicle calibration parameters; andoptimising the vehicle calibration parameters for the journey using a calibration optimiser.

12. A method according to claim 11, further including inputting the journey to a navigation module, and generating the drive cycle for the journey using a drive cycle generator which uses map data from the navigation module and representative traffic flow data from traffic simulator platforms to generate the drive cycle.

13. A method according to claim 11 or 12, further including recalibrating the vehicle with the journey-specific optimised vehicle calibration parameters.

14. A method according to any of claims 11-13, wherein if the vehicle is determined to have deviated from the generated drive cycle during the journey, the vehicle is recalibrated back to the baseline vehicle calibration parameters.

15. A method according to any of claims 11-14, wherein the drive cycle includes one or more of a velocity or speed profile, a gradient profile, an elevation profile, a weather profile, and a traffic simulation profile.

16. A method according to any of claims 11-15, wherein simulating the vehicle performance for the journey includes determining a predicted vehicle energy consumption for the journey.

17. A method according to any of claims 11-16, wherein the vehicle calibration parameters include one or more of a torque management parameter, a thermal management parameter, and an energy management parameter.

18. A method according to claim 17, wherein the torque management parameter includes a torque distribution parameter between an internal combustion engine and an electric motor.

19. A method for reducing friction brake usage in a vehicle, including: generating a predicted drive cycle for a journey;simulating vehicle performance over the drive cycle using a digital twin of the vehicle and a baseline set of vehicle calibration parameters to generate a brake force profile for the journey;generating a battery state-of-charge profile for an electrical and / or thermal battery of the vehicle for the journey;determining an amount of energy that would be generated if all of the brake force in the brake force profile was provided by a regenerative braking system;determining if the electrical and / or thermal battery has sufficient capacity at each stage of the journey to receive all of the energy generated by the regenerative braking system; andgenerating an energy discharge profile for the journey indicating an energy to be discharged from an electrical battery of the vehicle or a thermal battery of the vehicle such that the electrical or thermal battery has capacity to receive all of the regenerated energy.

20. A method for reducing energy consumption in a vehicle, comprising:generating a drive cycle for a journey to be driven by the vehicle;simulating the performance of the vehicle over the journey by providing the drive cycle to a digital twin of the vehicle;determining an optimum acceleration profile for the journey to minimise energy consumption; andproviding the optimum acceleration, deceleration and coasting profile to a driver of the vehicle in real time as the driver completes the journey.

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