A system and method for the management of regenerative-braking for battery recharging in a hybrid electric vehicle

Abstract

A control system for a vehicle comprising a powertrain comprising a plurality of energy sources including a battery. The control system comprising a simulation module configured to provide model predi

Description

Route Descriptors Distance Distance along the selected route Terrain One dimensional sequence of altitude along the route. Altitude distance pairs Slopes the gradient - array of gradients given as distance slope pairs Initial Gradient spot estimate of gradient at current position Current Slope spot estimate of gradient at current position Drive Cycle Velocity / Time graphs as set velocity time pairs Drive Cycle Name Name assigned to standard drive cycles Velocity Vehicle velocity Requested velocity - (either from driver or drive cycle in simulation) Set Velocity - target velocity as set by SEMAS controller Current Actual Velocity Maximum velocity as set by speed limiter Acceleration Acceleration / deceleration of the vehicle acceleration requested by driver or drive cycle acceleration set acceleration current maximum acceleration as a limit braking or retardation - live acceleration Force Force Front Rolling resistance expressed as Force in Newtons-measured as normal to ground Rear Rolling resistance expressed as Force in Newtons measured as normal to ground Aerodynamic drag in Newtons Climbing resistance due to gravity (+ve) or downhill force (-ve) Force produced by the powertrain gravitational constant density of air Cross Sectional Area Height of CoG above ground plane wheel base distance of CoG in front of rear wheels Power Power usually expressed in kW in the model Power produced by the powertrain at wheel Power output of the Fuel Cell at input to DC / DC convertor Power input or output to the Battery system at the battery terminals Power requested at the wheels by the driver or drive cycle Power losses in the DC / DC convertor Power losses in the balance of the transmission Sum of Power used in the auxiliary systems (pumps, fans, AC and others) Power input to the Fuel Cell expressed as kW Power input to the Fuel Cell expressed as kg / s of hydrogen It will be appreciated that the SEMAS system of the present disclosure may employ further parameters and does not have to use all the parameters listed in the table above; this table is provided as an exemplary embodiment only. Examples of other signals that can be measured by 5 the SEMAS control system may include one or more of: Vehicle air speed, Current Vehicle speed, requested vehicle Speed, Current Power to wheels, Current Regeneration, Battery Power Flow, Mode Maximum allowable battery pack discharge current, Mode Maximum allowable battery pack charge current from regenerative braking, Mode Max Battery, Pack Temp, Mode Min Battery Pack Temp, ModeMax SOC, ModeMin SOC, ModeMaxFCOutput, ModeMinFC Output, FC Power out, FC Stack Temperature, Mode FC Max Up ROC, Mode FCMax Down ROC. The SEMAS system of the present disclosure may also be provided with a vision system (not shown) that preferably comprises a fusion of radar and LIDAR modules. These are part of the sensors and data for the traffic module 170 As an example, the vision system can measure or provide one or more of: distance to vehicle in front, tracking vehicle in front speed (within limits), warning on lane drift, emergency braking, 360-degree vision. These signals may be used to provide within the drive module 150 a request velocity profile in the form of an enhanced cruise control data into the SEMAS controller. This will not necessarily be a constant speed request but could also take account of traffic awareness to maximise use of slip steam drag reduction and smooth out velocity changes. The drive profile requested may also be used to provide an anticipatory drive mode, where vehicle speed or acceleration adjustments are made to anticipate obstacles or slowing down of traffic, for example to minimise ramp rates within the vehicle subsystems. The SEMAS system of the present disclosure uses machine learning to further optimize vehicle performance and reduce total cost of ownership. A machine learning system can compare the current progress over the route with historical progress over the same route. This is useful especially for the LGV or HGV sector where vehicles often repeat the same route multiple times per week. On-board data logging of the parameters can be used to refine the library terrain maps and GPS route data and the historical power demand curve to provide improvement in the power demand projection and so reduce ramp rates and loads on the powertrain subsystems on a continual improvement basis. The SEMAS system can learn how to optimise control of the various subsystems to meet driver demands while minimising fuel consumption. The GPS (or other satellite navigation sensor) and on-board data logger provide the opportunity to send high spatial resolution data of the powertrain system components linked to the geoposition. This, in turn, enables the use of machine learning techniques to refine the vehicle settings for that element of the route in future while taking into account variable parameters including the vehicle load, and environmental and traffic conditions. It is then possible to monitor battery state of charge and adjust fuel cell power output at a low rate to ensure that rate limitations are met, and that battery power output is available for when it is required. The SEMAS system may also provide an improved fuel gauge, which may be used for measuring gaseous or multi-phase materials hydrogen. With conventional liquid fuels it is usual to use a liquid level gauge of some form and infer quantity of fuel remaining by calibration of the level against the known geometry of the fuel tank. For a gaseous fuel held at high pressure, the simplest measurement is to measure the current gas pressure and from this infer how much hydrogen is within the fuel storage tanks from the remaining useable pressure. This is an approximately linear relationship as hydrogen behaves similar to an ideal gas. However, with available pressure gauges this is a relatively inaccurate measure, particularly within an LGV or HGV where multiple tanks are coupled together and the accuracy with which pressure is measured does not provide an accurate measure of the available gas. It is also important to know the rate of consumption of hydrogen. This can be inferred from the fuel cell output and the fuel cell efficiency map. Coriolis meters are available to measure mass flow but are expensive and ideally suited to static use. Not only is it technically difficult to accurately measure mass flow of hydrogen; there is no readily available method to produce an accurate hydrogen fuel gauge. The hydrogen metrology problem has implications for accurately establishing the range of the vehicles and means that a reserve tank or buffer amount must be provided. This in turn means that the vehicle is unlikely to achieve the full range available from the on-board hydrogen store. The present disclosure provides adapted cylinder mounts that allow for a direct measurement of the mass of hydrogen, by measuring the gross weight of the hydrogen and the storage device. Calibration against the empty weight will provide a direct measurement of on-board mass of hydrogen. Accurate hydrogen metrology is necessary to reduce the margin of error on the range prediction and reduce fluctuating estimates that cause range anxiety. For dispensing hydrogen gas, existing standards (such as SAE J2601) already require communication between the vehicle and hydrogen refuelling system (HRS) to ensure connection is made and to measure temperature and pressure the mass dispensed can then be measured. As the fuel gauge electronics will record the empty mass of each cylinder, the instantaneous mass of hydrogen remaining can then be calculated from the gross weight of each cylinder. As noted, on board flow measurement of hydrogen is also difficult. A simple differentiation of the mass with time will allow a mass flow estimate. The system will correlate several parameters and power measurements on the output of the fuel cell to give instantaneous fuel cell efficiency. The efficiency measure and residual fuel inventory can be used by onboard telematics to calculate the residual range available, both dynamically and accurately. The SEMAS controller of the present disclosure can use this information, together with vehicle destination information, to dynamically adjust the power output of the fuel cell and manage the energy balance between battery and fuel cell subsystems to ensure that the vehicle reaches its destination, albeit with a temporary performance limitation. The embodiments of the present disclosure provide a vehicle and powertrain simulation which provides a high-fidelity hybrid FCEV simulation tool to optimise system integration and demonstrate performance against a very wide range of vehicle duty cycles, using real world terrain maps in combination with other factors, such as, operational constraints on the specified powertrain components. Add-on modules provide for operational cost assessment, environmental assessment and modelling the operation of the refuelling infra-structure at the depot level. Each of the system modules of the SEMAS control system can be provided as stand-alone modules with a well-defined input / output interface. Data can be transferred in intermediate data files that are in text format and can be opened and read with any text editor. This provides a great deal of flexibility in using the SEMAS system and facilitates the compilation of an extensive customised library of performance and operational data. Thus, making it is possible to swap out and replace powertrain system components. For example, one can swap out a fuel cell subsystem, change the data map and re-run the whole vehicle simulation of the refurbished vehicle against the same terrain and duty cycle. Figure 16 shows a further detailed schematic of the SEMAS modelling suite illustrating the relationship of the model to a physical embodiment as illustrated in the control system 902 showing its subsidiary components. The SEMAS control system 902 comprises an abstraction of vehicle powertrain simulator 1600 that provides output to a total cost of ownership cost model suite 1602. The powertrain simulator 1600 also receives inputs from a vehicle dynamic simulation module 1604 via power demand time series libraries 1606 and from visualisation analysis and reporting module 1608. Further input to the vehicle powertrain simulator 1200 is provided by a hydrogen site-based refilling station 1610. A system controller module 1612 provides a simulation of the onboard SEMAS control system 902. Here resides the central control algorithm of the SEMAS control system for controlling the hybrid energy subsystem making decisions on how to best meet the power demand of the powertrain. Its inputs include such parameters as: the power demand, route, load, available capacity of each of the powertrain components, the fuel supply, and the route to complete; and it provides an output of system controller state data. The control signals 1614 and the energy flows in the power train 1636 are represented in the software model. The control signal module 1614 holds the data representing the control signals as time series areas over the whole journey. This means that they are accessible so that a component module can run independently for testing and development. Similarly, the power output / input of each of the modules representing power train components is collected as an array of time series data. The power output times series 1636 can be summed and compared with the power demand time series 1606 to see if demand is met by the SEMAS MPC model 1612 under test. This architecture means that models of power train components can be modified individually and independently simulated within the context of the whole vehicle simulation model. It also allows simulation of the entire powertrain, with the exception of a real component to facilitate hardware in the loop testing where the control signals are fed to a real component and its outputs are captured and fed back into the model. The SEMAS control system 902 comprises a thermal module 1616 which handles the thermal output from the fuel cell and the onboard routing of thermal energy to provide battery heating and cooling and environmental control of the vehicle cab, and cargo as may be required. This module 1616 also handles ancillary systems, additional heat and cooling as heat or onboard stationary power. The thermal module 1616 is provided with inputs comprising components [specifics] that make up the thermal and ancillary electrical systems of the vehicle, such as lights and heating, and provides outputs comprising state data of the ancillary heat, power, and cooling systems. A hydrogen system 1618 comprises three subsystems: a model of the onboard hydrogen metering system 1620, an model of onboard hydrogen storage device 1622 and onboard hydrogen fuelling system module 1624. In combination, these subsystems simulate the operation of the onboard integrated hydrogen system 1618. This includes hydrogen storage states including quantity, pressure, and vessel temperature. Inputs to the hydrogen system 1618 comprise the hydrogen system operational parameters and limits; and outputs comprise state data of the hydrogen supplies, including quantity, temperature, and pressure. While this is a model representation of a gaseous hydrogen storage system, it is also possible to model and alternative hydrogen storage systems such as cryogenic liquid hydrogen, hydride storage or ammonia storage and convertor. A fuel cell model 1626 is provided that uses fuel cell operational maps to transform input to output and tracks its internal state data. Its inputs are provided as fuel cell efficiency maps and it provides outputs of state data such as fuel cell heat, power output and internal temperature. A battery object 1628 is provided to simulate performance of the battery or battery bank of the vehicle of the present disclosure. It is provided with battery efficiency maps (typically provided by the manufacturers of the battery) and it tracks the battery's inputs, outputs and rate of charge or discharge. It provides as an output the state of charge, energy inflow and outflow and the internal temperature of the battery, among other things. A power converter object 1630 contains state data and efficiency maps of the power conversion models including DC-DC converters and AC-DC converters. Its inputs include power converter efficiency maps and rate limits of the power conversion subsystems, and it provides state and converter outputs. A motor object 1632 represents the main e-axle component which provides the motor generator and conversion to and from electrical power to motive force. This module 1632 comprises the efficiency maps of the various components that make up the e-axle. Its inputs comprise the full e-axle module maps and it provides outputs of electrical to kinetic power conversions and back again. These functions may be provided by a separate motor object 1633 and generator object 1634. A system power bus 1636 exchanges data with the objects 1626 through 1634 and provides a data conversion function translating energy and power demand to voltage and current at the levels necessary for the various powertrain subsystems. Its inputs include energy flows through the various powertrain subsystems and power routing information, which it provides as outputs power routes and energy balance. The model visualisation module 1608 provides statistical analysis and visual outputs such as graphing functions and simulation reports. It is provided with an output file from the powertrain simulator 1600 and provides a simulation readable report as its output. As mentioned above, the vehicle powertrain simulator 1604 is a dynamic simulation that receives power demand time series for the physical vehicle and the selected traffic duty operational duty and drive cycles. These as provided by duty cycle generator 1649, terrain generators 1642 and vehicle physical characteristics module 1644. The duty cycle generator 1640 comprises a library of standard drive cycles. These may preferably include drive cycle specifications, such as TRL PPR 3541, but may also include customer derived cycles relating to typical reference routes and traffic data. This duty cycle generator 1640 allows preparation of custom drive cycle by sampling, scaling and containing drive cycles to provide a 1 TRL is a wholly owned subsidiary of the Transport Research Foundation (TRF), a non-profit distributing company limited by guarantee, and established for the impartial furtherance of transport and related research, consultancy, and expert advice. They publish standard duty cycles, specifically a reference Book of Driving Cycles for use in the measurement of road vehicle emissions, such as Published Project Report PPR354 test cycle adapted to customer specification or for a specific test such as a downhill endurance test. It may be provided with standard library of drive cycles and customer derived drive cycles, and it provides an output of velocity time traffic profiles 1646 which comprise vehicle test drive cycles expressed as velocity time series. The terrain generators 1642 comprise two alternative terrain generation modules. The first terrain generator may provide for development of realistic test routes based on map data for a route under investigation. Real routes can be used as the basis for creating artificial routes; for example, if the data for real routes (say a drive cycle over a hill) is obtained, then that data could be segmented and the vehicle could then be presented with a series of hills to test the control algorithms over a more challenging terrain. It can also use samples of synthesised route data from the other terrain generator sub-component. The module 1642 receives as inputs GPS terrain data and map data and provides as an output elevation, distance and slope distance maps for the selected route. The second terrain generator module provided as part of module 1642 may be used to provide synthetic or idealised routes. These are used for stress testing the design and may include, for example, extended gradients or a series of upward and downward slops to test the energy flows for energy storage regeneration and peak power demands. It receives as inputs a series of desired route parameters, such as maximum gradient or number of flat or hill sections and provides as outputs a set of elevation slope journey files 1648 which provide elevation / distance and slope / distance maps for the selected synthetic route. The physical vehicle characteristic module 1644 is used to prepare a parameterised physical model of the vehicle under test. Parameters may include weight, wheel loading, load carried and drag factor. As its inputs, it receives a series of vehicle parameters and it provides structured output file 1650, with input and calculated parameters describing the physical model of the vehicle. Therefore, with these inputs, the vehicle dynamic simulator 1604 module provides a dynamic simulation of the physical vehicle with the selected drive cycle against the route selected and terrain and produces a time series energy demand at the vehicle wheels. It is provided with output files from the other components 1640, 1642, 1644 and provide as outputs a power demand time series library 1606. The SEMAS control system 902 may also be provided with a hydrogen production pathways model 1652. This model 1652 provides a well to tank analysis of the fuel cell electric vehicle to determine overall hydrogen demands, system efficiency at fleet and vehicle level and carbon intensity taking into account a hydrogen source pathway and fleet vehicle inventory and duty cycle, hydrogen storage capacity and flow rates. This model 1652 provides an output for the hydrogen site based refilling station 1610 comprising the filling demand model, wait times and number of vehicles serviced. Here, the "hydrogen source pathway" refers to how the hydrogen is produced, stored transported and dispensed - it is useful to classify hydrogen according to the source pathway. The pathway may define the primary energy source (fossil gas biomethane, electrolysis, renewable or non-renewable electricity)-each of these pathwayswill have different carbon contributions and so the carbon intensity of the hydrogen will be different. This is not the actual carbon in the hydrogen, but rather the carbon emitted in making the hydrogen. The cost and environmental module 1602 provides a total cost of ownership and environmental comparative data for specific fuel cell electric vehicles and their diesel equivalents running similar duty cycles. It is provided with inputs of fuel cost projections, Capital Expenditure (CapEx) and Operational Expenditure (OpEx) cost models and fuel carbon intensity factors, and it provides outputs of TCO and environmental savings. An input to the cost module suite 1602 is provided by output files 1654 which are output from the hydrogen site-based refilling station module 1610 and comprising hydrogen quality, carbon intensity and projected cost per kilogram data. The vehicle powertrain simulator 1600 is a simulation model of the vehicle's powertrain. This contains a system description of the vehicle and runs the main simulation of the energy flow of the vehicle. The module 1600 allows a user to select the powertrain subsystems and configure them to work together. The components themselves are parameterised objects which contain state data. The module 1600 steps through the energy demand time series input from the time series library files 1606 and calculates the I / O and state of each of the connected objects described elsewhere. The inputs for the module 1600 include the power demand time series data 1606 for the physical vehicle, and the selected traffic duty, operational duty, and drive cycle. The module 1600 then outputs a times series of the parameters / state and alarm of each of the powertrain subsystems of the module 1600. It also shows where the required duty cycle is not met and can also calculate the operational margin of the powertrain - when knowing the whole operating envelope of the powertrain subsystems, this high fidelity SEMAS model of the present disclosure can compare the actual output of each to the operational maximum and derive the available margin or headroom on each subsystem. Relationship between TCO and Least Cost Total cost of owner ship is often used to compare different vehicles. To do this the calculation basis needs to be the same. Therefore, fuel consumption is measured using standard drive cycles under similar conditions. We prefer the term least cost optimisation. Because with SEMAS we aim to optimise at all system levels to control variable factors that affect TCO from a system energy management perspective. These factors will include route selection, fuelling strategy, condition monitoring (as part of planned preventive maintenance), and controlling energy and power flows to enhance durability of the subsystems and the primary energy demand of the vehicle. Relationship between SEMAS and Autonomous Driving 6 different Autonomous driving levels have been defined by the SAE. As shall be described, the SEMAS system of the present invention provides different functions at different levels of autonomy. The SEMAS Controller is a supervisor controller that takes data input from a wide range of sensors and subsystem states to manage energy flows between the various sub systems. At autonomy level 0 SEMAS is transparent to the driver it is working to optimise the balance of energy between the FC and ESS to ensure that the FC and ESS are in a state to maximise regenerative braking or to work in parallel with the FC to meet the peak power demands of the driver. The predictive element is taken from the set route and the calculation of likely power profile given the performance setting of the vehicle (max performance, normal or eco mode set by driver or fleet manager for this route). Driver information is limited to range remaining and predicted time to destination. At autonomy level 1 SEMAS will limit power available at points on the route to deliver a least cost power profile while maintaining the drive characteristics that the driver (or fleet manager) sets. It will also inform the driver of status and warn of condition and provides guidance on ways to improve the energy performance. At autonomy levels 2- 5 SEMAS will take control of the longitudinal progress of the vehicle with a form of adaptive cruise control and use the EBS system. Rather than maintaining speed it will cuts acceleration and increase / decrease speed in response to the terrain and traffic. SEMAS will switch from level 0 to level 1 to provide range extension if the route changes and range is recalculated where the pervious performance level for calculated for the route can no longer be met to reach the next fuelling point or destination. Commercial vehicles may be classified for tax and other purposes according to their gross combination mass and / or a range of other factors such as intended use, construction, engine, type of fuel and emissions. Terminology can vary between different licensing and taxation regimes. For the purposes of the present disclosure, a Light Goods Vehicle (LGV) is defined as being commercial carrier vehicles with a gross combination mass under 3,500 kg, and a Heavy Goods Vehicle (HGV) as having a gross combination mass of 3,500 kg or greater. As a non-exhaustive list of examples, a typical LGV may be a pick-up truck or a van, while a typical HGV may be a dry and consumer goods truck, a flatbed truck, curtain sider, tanker, transporter. It will be appreciated that unless specifically stated otherwise, the present disclosure is not limited to any vehicle classification, that is, the principles disclosed herein can apply generally to HGV, LGV or even to domestic vehicles. Various regulations and licenses apply depending on the expected gross weight. For HGVs, hydrogen fuel cell systems and / or drivetrains can offer a viable zero emission alternative to diesel-powered systems and, in addition, can be more viable as compared with battery electric vehicles because a fuel cell electric vehicle set up is able to pull heavy loads, has a long duty cycle and range capability and offers a quick refuelling time. The systems and processes discussed above are intended to be illustrative and not limiting. One skilled in the art would appreciate that the actions of the processes discussed herein may be omitted, modified, combined, and / or rearranged, and any additional actions may be performed without departing from the scope of the invention. More generally, the above disclosure is meant to be exemplary and not limiting. Only the claims that follow are meant to set bounds as to what the present disclosure includes. Furthermore, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and examples relating to one embodiment may be combined with any other embodiment appropriately, done in different orders, or done in parallel. In addition, the systems and methods described herein may be performed in real-time. It should also be noted that the systems and / or methods described above may be applied to, or used in accordance with, other systems and / or methods. All the features disclosed in this specification (including any accompanying claims, abstract, and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract, and drawings), may be replaced by alternative features serving the same, equivalent, or similar purpose unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features. The invention is not restricted to the details of any foregoing embodiments. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract, and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed. The claims should not be construed to cover merely the foregoing embodiments, but also any embodiments which fall within the scope of the claims. Throughout the description and claims of this specification, the words "comprise" and "contain" and variations of them mean "including but not limited to", and they are not intended to (and do not) exclude other moieties, additives, components, integers, or steps. Throughout the description and claims of this specification, the singular encompasses the plural unless the context otherwise requires it. Where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise. All the features disclosed in this specification (including any accompanying claims, abstract, and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. The disclosure is not restricted to the details of any foregoing embodiments. The disclosure extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract, and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed. It will be appreciated that various modifications can be made to the above without departing from the scope of the disclosure. For example, while references have been made to a "driver" herein, it will be appreciated that the disclosure also applies to autonomous vehicles which either have driver assistance technologies, or no driver at all.

Claims

1. A control system for a vehicle comprising a powertrain comprising a plurality of energy sources, the plurality of energy sources comprising a battery, the control system comprising:a simulation module configured to provide model predictive control to the control system;such that the control system is configured to actively monitor, control and optimise power recapture from regenerative-braking in the vehicle.

2. The control system of claim 1 configured to provide one or more control signals to the powertrain, thereby controlling the regenerative-braking in the vehicle3. The control system of claim 1 or claim 2, configured to maximise overall power efficiency from the regenerative-braking.

4. The control system of any preceding claim, wherein the vehicle is a fuel cell electric vehicle and the plurality of energy sources comprises a fuel cell.

5. The control system of claim 4, wherein the vehicle comprises a fuel cell subsystem comprising the fuel cell.

6. The control system of claim 5, wherein the fuel cell comprises a hydrogen fuel cell.

7. The control system of any preceding claim, wherein the vehicle is a zero-emission hybridised heavy goods vehicle.

8. The control system of any preceding claim comprising:monitoring circuitry configured to monitor the power recapture from regenerative-braking; wherein:the control system is configured to:determine an optimal power recapture; andadjust the power recapture from regenerative-braking to the optimal level, thereby providing optimised power recapture from regenerative-braking.

9. The control system of any proceeding claim comprising one or more interfaces configured to receive inputs, the control and optimisation of the power recapture being dependent on the received inputs.

10. The control system of claim 9, wherein at least one of the one or more interfaces is a wireless communications interface.

11. The control system of claim 9 or claim 10, wherein the inputs comprise one or more of data from a driver of the vehicle, route data, traffic data, Global Positioning System data, terrain data, temperature data, route data, status of component data, parasitic load data, power flows in one or more subsystems of the vehicle data, DC / DC convertors and the two way DC / AC controller of the power axle data, vehicle speed and driver demand for change in speed data, temperature in fuel cell stack data, battery temperature data, current hydrogen inventory data, current battery state of charge data, current ramp rate on fuel cell data, water management data or cargo weight.

12. The control system of claim 11, wherein the data relates to current status and / or rate of change.

13. The control system of any of preceding claim, wherein the simulation module configured to provide a simulation model of the vehicle, the control and optimisation of the power recapture being dependent on the simulation model.

14. The control system of claim 13, wherein the simulation module is configured to model one or more of the following in the generation of the simulation model of the vehicle: thermal management, a hydrogen fuel cell; fuel cell cooling, a high voltage DC-DC converter; a HVAC subsystem, a power distribution subsystem, a PDU and powertrain controller, an energy storage subsystem, a high voltage battery, a E-drive subsystem, an inverter, an e-axle, a hydrogen subsystem, one or more hydrogen tanks, a hydrogen supply system, hydrogen refuelling, hydrogen de-fuelling, a hydrogen fuel cell subsystem, a DC-DC converter, parasitic loads, a cabin heater, an e-stop, a low voltage battery, an axle-wheel-tyre subsystem, and cargo weight.

15. The control system of claims 13 or 14, wherein the simulation module is configured to generate a multivariant optimization model for controlling and optimising power recapture from regenerative braking.

16. The control system of claims 15 configured to:derive a model predictive control algorithm;define, using the derived model predictive control algorithm, a cost function to enable optimisation of the power recapture; andapply a control scheme based to optimise the power recapture based on the cost function.

17. The control system of any of claims 15 or 16 configured to:detect information relating to a current state of charge of the battery;provide the information relating to the current state of charge of the battery to the simulation model; anduse model predictive control to control and optimise the power recapture from regenerative braking using the detected information relating to the current state of charge of the battery.

18. The control system of any preceding claim, configured to control the powertrain based on the ideal operating range of components of the powertrain.

19. The control system of any preceding claim configured to be operable in one of a plurality of 5 control modes comprising a performance mode, a balanced mode, a life extension mode, a fuel efficiency mode, a dynamic range adjust mode, a range extend mode, and a driver assist mode.

20. The control system of any preceding claim comprising a ramp rate module configured to implement a control algorithm to limit the ramp rate of one of the energy sources.1021. The control system of claim 20, wherein one of the energy sources comprises a hydrogen fuel cell, the control algorithm being used to limit the ramp rate of the hydrogen fuel cell.

22. A method of actively monitoring, controlling and optimising power recapture from15 regenerative braking in a vehicle using the control system of any preceding claim.

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