A HYBRID VEHICLE AND A METHOD FOR ENERGY MANAGEMENT OF A HYBRID VEHICLE

Abstract

A hybrid vehicle includes a drive train, an electrical energy source coupled to the drive train and electrically connected to an electric energy storage device having a state-of-charge, and a non-electrical energy source coupled to the drive-train. A convexification model for the vehicle is used for determining at least one control parameter for operating the vehicle. By applying a convex approach for forming the at least one control parameter it is possible to be sure that the at least one control parameter in fact is a presently optimized parameter. Furthermore, the convex approach minimizes the computational resources necessary for determining the at least one control parameter. The use of a minimal amount of computational resources is specifically desirable in relation to a vehicle on-board solution, typically implementing real-time, continuous, calculations of the at least one control parameter. A corresponding method and computer program product are also provided.

Claims

1. A hybrid vehicle, comprising: a drive train; an electrical energy source coupled to the drive train and electrical connected to an electric energy storage device having a state-of-charge; a non-electrical energy source coupled to the drive-train; processing circuitry arranged in the hybrid vehicle and adapted to in real-time determine at least one control parameter for operating the vehicle over a defined trip route, wherein the control parameter is provided for controlling the transfer of energy from the electrical energy source and the non-electrical energy source to the drive train, and a control system configured to receive the at least one control parameter from the processing circuitry and to control the transfer of energy from the electrical energy source and the non-electrical energy source to the drive train based on the at least one control parameter, wherein the processing circuitry for determining the at least one control parameter is configured to: receive information relating to the operation of the vehicle, comprising at least information relating to the defined trip route, the state-of-charge of the electric energy storage device, and an operational speed of the vehicle; determine the at least one control parameter based on the vehicle's kinetic energy, wherein the vehicle's kinetic energy is determined using a convexification model for the vehicle applying a speed to energy transformation and based on the information relating to the operation of the vehicle, and provide the at least one control parameter to the control system.

2. The hybrid vehicle according to claim 1, wherein the at least one control parameter comprises at least one of a state and a costate to be used with a control policy applied by the control system, wherein the state is configured to define a control variable and the costate is configured to define the cost of using the control variable.

3. The hybrid vehicle according to claim 2, wherein the state comprises at least one of a vehicle speed, a state of charge for the electrical energy storage device, a state of health for the energy storage device, an operational state for the electrical energy source, and an operational state for the non-electrical energy source.

4. The hybrid vehicle according to claim 1, wherein the convexification model is adapted to predict an optimized use of the electrical energy source and the non-electrical energy source for the defined trip route.

5. The hybrid vehicle according to claim 1, wherein the information relating to the operation of the vehicle further comprise at least one of information as to a current kinetic energy of the vehicle and a topographic profile of the defined trip route.

6. The hybrid vehicle according to claim 1, wherein the operational speed of the vehicle comprises at least one of a current speed of the vehicle, a desired average speed of the vehicle, a speed interval for the vehicle and a speed limit for the defined trip route.

7. The hybrid vehicle according to claim 1, wherein the at least one control parameter is further determined based on a predetermined cruise gear for the vehicle.

8. The hybrid vehicle according to claim 1, wherein the at least one control parameter is further determined based on a cost for operating the vehicle, including a comparison between operating the vehicle using the electrical energy source and operating the vehicle using the non-electrical energy source.

9. The hybrid vehicle according to claim 1, wherein the at least one control parameter is defined over a time period, forming a reference trajectory to be applied by the control system.

10. The hybrid vehicle according to claim 9, wherein the control system is further configured to receive the reference trajectory and a current operational state of the vehicle and output an adapted state reference to be applied by the control system.

11. A computer implemented method for determining at least one control parameter for operating a hybrid vehicle, the hybrid vehicle comprising: a drive train; an electrical energy source coupled to the drive train and electrically connected to an electric energy storage device having a state-of-charge; a non-electrical energy source coupled to the drive-train, and a control system configured to control the transfer of energy from the electrical energy source and the non-electrical energy source to the drive train based on the at least one control parameter, wherein the control parameter is provided for controlling the transfer of energy from the electrical energy source and the non-electrical energy source to the drive train, wherein the method comprises the steps of: receiving information relating to the operation of the vehicle, comprising at least information relating to the defined trip route, the state-of-charge of the electric energy storage device, and an operational speed of the vehicle; determining the at least one control parameter based on the vehicle's kinetic energy, wherein the vehicle's kinetic energy is determined using a convexification model for the vehicle applying a speed to energy transformation and based on the information relating to the operation of the vehicle, and providing the at least one control parameter to the control system.

12. The method according to claim 11, wherein the at least one control parameter comprises at least one of a state and a costate to be used with a control policy applied by the control system, wherein the state is configured to define a control variable and the costate is configured to define the cost of using the control variable.

13. The method according to claim 12, wherein the state comprises at least one of a vehicle speed, a state of charge for the electrical energy storage device, a state of health for the energy storage device, an operational state for the electrical energy source, and an operational state for the non-electrical energy source.

14. The method according to claim 11, wherein the convexification model is adapted to predict an optimized use of the electrical energy source and the non-electrical energy source for the defined trip route.

15. The method according to claim 11, wherein the information relating to the operation of the vehicle further comprise at least one of information as to a current kinetic energy of the vehicle and a topographic profile of the defined trip route.

16. The method according to claim 11, wherein the operational speed of the vehicle comprises at least one of a current speed of the vehicle, a desired average speed of the vehicle, a speed interval for the vehicle and a speed limit for the defined trip route.

17. The method according to claim 11, wherein the at least one control parameter is further determined based on a predetermined cruise gear for the vehicle.

18. The method according to claim 11, wherein the at least one control parameter is further determined based on a cost for operating the vehicle, including a comparison between operating the vehicle using the electrical energy source and operating the vehicle using the non-electrical energy source.

19. The method according to claim 11, wherein the at least one control parameter is defined over a time period, forming a reference trajectory to be applied by the control system.

20. The method according to claim 19, further comprising the step of receiving the reference trajectory and a current operational state of the vehicle, and output an adapted state reference to be applied by the control system.

21. Computer program product comprising a non-transitory computer readable medium having stored thereon a computer program for determining at least one control parameter for operating a hybrid vehicle, the hybrid vehicle comprising a drive train, an electrical energy source coupled to the drive train and electrically connected to an electric energy storage device having a state-of-charge, a non-electrical energy source coupled to the drive-train, and a control system configured to control the transfer of energy from the electrical energy source and the non-electrical energy source to the chive train based on the at least one control parameter, wherein the control parameter is provided for controlling the transfer of energy from the electrical energy source and the non-electrical energy source to the drivetrain, wherein the computer program product comprises: code for receiving information relating to the operation of the vehicle, comprising at least information relating to the defined trip route, the state-of-charge of the electric energy storage device, and an operational speed of the vehicle; code for determining the at least one control parameter based on the vehicle's kinetic energy, wherein the vehicle's kinetic energy is determined using a convexification model for the vehicle applying a speed to energy transformation and based on the information relating to the operation of the vehicle, and code for providing the at least one control parameter to the control system.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The various aspects of the invention, including its particular features and advantages, will be readily understood from the following detailed description and the accompanying drawings, in which:

[0031] FIG. 1a illustrates a hybrid vehicle equipped with an on-board control unit for determining at least one control parameter in accordance to a currently preferred embodiment of the invention;

[0032] FIG. 1b shows an exemplary drive train structure adapted in accordance to an embodiment of the invention;

[0033] FIG. 2 provides an illustration of an on-board control arrangement according to a currently preferred embodiment of the invention; and

[0034] FIG. 3 depicts method steps applied by the on-board control arrangement shown in FIG. 2.

DETAILED DESCRIPTION

[0035] The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which currently preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided for thoroughness and completeness, and fully convey the scope of the invention to the skilled addressee. Like reference characters refer to like elements throughout.

[0036] Referring now to the drawings and to FIGS. 1a and 1b in particular, there is depicted an exemplary hybrid vehicle, here illustrated as a truck 100. The truck 100 is provided with a first 102 and a second 104 source of motive power for propelling the truck 100 via a driveline 106 connecting the power sources 102, 104 to a set of wheels 108. The first power source is constituted by an internal combustion engine (ICE) 02 in the form of a diesel engine connected to a fuel tank 110. It will in the following, for ease of presentation, be referred to as an internal combustion engine 102. The second power source is constituted by an electrical motor 104, powered by an electric energy storage device, such as a rechargeable battery 112. Those skilled in the art will recognize a variety of battery configurations that may be employed within the scope of the claimed invention, such as lead-acid, lithium-ion, nickel-cadmium, nickel-metal hydride, etc. As used herein, a “battery” may include multiple batteries or cells operatively interconnected, e. g., in series or in parallel, to supply electrical energy.

[0037] The truck 100 further comprises a generator (not explicitly shown). The generator is selectively connected to the drivetrain 106 to drive the generator, which causes the generator to generate electrical energy, as understood by those skilled in the art. The generator is operatively connected to the battery 112 to supply electrical energy thereto for recharging the battery 12. An on-board control arrangement 114 controls the transfer of energy from the energy sources 102, 104 to the drivetrain 106, as well as the flow of energy between the drivetrain 106 and the generator for recharging the battery 112, depending on at least one control command provided by the on-board control arrangement, etc.

[0038] In the exemplifying embodiment illustrated in FIG. 1 b, the truck 100 employs a parallel drivetrain structure, optionally configured to allow the battery 112 to be rechargeable by an off-board electrical source (such as the electric grid). In addition, it should be understood that the hybrid truck 100 also may employ regenerative braking, meaning that for example the electrical motor 104 may be employed to act as a generator for charging the battery 112, for example when breaking the truck 100 when travelling down a steep hill, etc. Within the scope of the claimed invention, the hybrid truck 100 may alternatively be configured to employ a series drivetrain structure.

[0039] The on-board control arrangement 114 may include a general purpose processor, an application specific processor, a circuit containing processing components, a group of distributed processing components, a group of distributed computers configured for processing, etc. The processor may be or include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory. The memory may be one or more devices for storing data and/or computer code for completing or facilitating the various methods described in the present description. The memory may include volatile memory or non-volatile memory. The memory may include database components, object code components, script components, or any other type of information structure for supporting the various activities of the present description. According to an exemplary embodiment, any distributed or local memory device may be utilized with the systems and methods of this description. According to an exemplary embodiment the memory is communicably connected to the processor (e. g., via a circuit or any other wired, wireless, or network connection) and includes computer code for executing one or more processes described herein.

[0040] The on-board control arrangement 114 may also be connected to e. g. a communication interface (such as e. g. a CAN bus or similar, or a dedicated communication interface) of the truck 100, preferably for allowing control of elements of the truck 100, such as for example to control gear shifting, braking and gas pedals and the on/off state of the internal combustion engine (ICE) of the truck 100.

[0041] Turning now to FIG. 2 which provides a detailed illustration of the on-board control arrangement 114, provided for controlling the energy transfer for the hybrid vehicle shown in FIGS. 1a and 1b. The on-board control arrangement 114 comprises processing circuitry 202 and a control system 204, working together for controlling the transfer of energy from the ICE 102 and the electrical motor 104 to the drive train 106. Generally, the processing circuitry 202 determines at least one control parameter and the control system 204 adapts the at least one control parameter based on a feedback signal defining a current operational state of the truck 100. Thus, a constant adjustment may be made to the at least one control parameter to better correspond to the current operation of the truck 100.

[0042] The processing circuitry 202 is configured to receive general operational information relating to the truck 100. Such information includes information relating to a defined trip route to be taken by the truck 100, a state-of-charge of the battery 108, and an operational speed of the truck 100. This information may be received, from for example, a GPS receiver (not shown) and from information received from a navigation system (not shown) provided with the truck 100. The navigation system may be connected to the processing circuitry 202 using the CAN bus. It should be noted that the processing circuitry 202 and the control system 204 both may be implemented as one entity, in software, hardware and a combination thereof. It may also be possible to implement the processing circuitry 202 and the control system 204 as separate entities, in communication with each other. The processing circuitry 202 and the control system 204 may be implemented, e. g. distributed within the truck 100, such as within one or a plurality of engine control units (ECU) of the truck 100.

[0043] Information received from the navigation system may for example comprise topographical data for the defined trip, including information as to altitude variations over the defined trip, as well as information relating to curves, road surfaces, etc. Further information may typically be included, such as speed limits for segments of the defined trip route. The term speed limit should be interpreted broadly and may include both a maximum and a minimum speed for the different road segments. Accordingly, the expression “operational speed of the vehicle” may be defined as the desired speed of the vehicle at a segment of the defined trip route.

[0044] The processing circuitry 202 implements an abstraction of a vehicle model that accurately represents the vehicle mode! about a given set speed, e. g. defined by the information provided by the navigation system, and cruise gear for use in propelling the truck 100. The vehicle model applied by the control architecture is a convex model that has been adapted to work with the vehicle's kinetic energy rather that being based on the speed of the truck 100. In addition, the vehicle model samples based on distance traveled by the truck 100 rather than based on time.

[0045] With further reference to FIG. 3, the vehicle model receives, S1, the general operational information for the truck 100 and determined, S2, the at least one control parameter. The at least one control parameter may in turn typically comprise a state and a costate, expressions that may be recognized from the technical area of optimal control, in relation to the present invention, the state and the costate provided from the vehicle model are defined over a time period, forming a reference trajectory for each of the state and the costate.

[0046] The processing circuitry 202 typically also comprises a feedback module to allow adjustments of the reference trajectories for each of the state and the costate based on feedback from control system 204 controlling the truck 100. The feedback from the control system 204 typically comprise information relating to the current operational state of the truck 100, such as the current speed of the truck 100, the current SoC for the battery 112, aging level of the battery 112, speed limit for the current road segment, etc.

[0047] It should be noted that the processing circuitry 202 and the control system 204 may be allowed to operate on “different time scales”. For example, the processing circuitry 202 and the control system 204 may apply different sampling frequencies. In addition, it may in accordance to the invention be possible to allow the processing circuitry 202 and the control system 204 to sample based on different scales, such as allowing the processing circuitry to sample (form an updated reference trajectory for each of the state and the costate) based on a distance traveled for the truck 100, such as forming new parameters once every kilometre traveled by the truck 100, while the control system 204 samples as a time scale, such as with a 20 Hz sampling rate. The sampling rates (distance/time) as presented above are just examples and other sampling rates are possible and within the scope of the invention.

[0048] The state and the costate having been adjusted by the feedback module are typically adjusted for providing, S3, a state reference and a costate reference to be applied by the control system 204. The state reference may be seen as a set-point for a control policy applied by the control system 204. Thus, the control system 204 may act independently of the processing circuitry 202. The costate reference will typically indicate a cost relating to the state reference. As such, the control system 204 may also take this information into account when controlling the operation of the truck 100.

[0049] Furthermore, by dividing the control problem handled by the on-board control arrangement 114 into two layers (i. e. the processing circuitry 202 and the control system 204) that operate with different update frequencies and prediction horizons, the top layer (i. e. the processing circuitry 202) may be adapted to plan the kinetic and electric energy as a convex optimization problem. In addition, in order to avoid a mixed-integer problem, the gear and the switching decision between hybrid and pure electric mode may be optimized in the lower layer (i. e. the control system 204) in a dynamic program.

[0050] With further reference to the vehicle model forming part of the processing circuitry 202, a plurality of steps and approximations are performed in order to obtain a convex optimization problem, including constraining the trip time to not be greater than a given travel time, or by constraining kinematic energy to not be less than a given mean energy.

[0051] This may typically be defined as:

[00001]$\underset{k=1}{\overset{N}{.Math.}}.Math.\frac{s_d}{\sqrt{2.Math.E\left(k\right)/m}}\le T_{\mathrm{trip}}.Math.\left(\mathrm{Nonlinear}.Math..Math.\mathrm{convex}.Math..Math.\mathrm{constraint}\right),\mathrm{or}$$\underset{k=1}{\overset{N}{.Math.}}.Math.E\left(k\right)\ge E_{\mathrm{total}}.Math.\left(\mathrm{Linear}.Math..Math.\mathrm{convex}.Math..Math.\mathrm{constraint}\right)$

where E(k) is the vehicle kinematic energy, m is the vehicle mass, sd is the sampling distance,k is the sample index, N is the total number of samples, Ttrip the required time to travel the distance Nsd (being a product), and Etotal is the total kinematic energy when travelling at the average speed required to keep the trip time.

[0052] Furthermore, it is desirable to: [0053] Approximate the power limits of the powertrain components (engine, electric machine, battery, etc.) as affine force limits in kinetic energy obtained by linearizing around the set speed, for example provided by a driver of the vehicle (the average speed required to keep the trip time). [0054] Model the longitudinal force as a sum of three contributions:

[0055] I. Force delivered on cruise gear constrained by the engine maximum force on cruise gear.

[0056] II. Remaining force delivered from the engine for any lower admissible gear, constrained by the engine maximum power that can be delivered at the set speed.

[0057] III. Force of the hybrid system constrained by the total maximum force, engine and electric machines, on cruise gear. [0058] Approximate engine losses with two distinct constant marginal efficiencies, one for the force that can be delivered on cruise gear and one lower marginal efficiency for the remaining force delivered at lower gears. This is a convex model that encourages operation at cruise gear. Friction losses are modeled as convex in kinetic energy. [0059] Model losses of powertrain components (electric machine, battery, etc.), except the engine, as convex functions in delivered force and vehicle's kinetic energy. Non-convex component models are approximated as convex about the set speed, and [0060] Relax component losses by letting powertrain components throw away energy.

[0061] Further relaxations and approximations in regards to the convex vehicle model is possible and within the scope of the invention. In addition, within the scope of the invention it is desirable to emphasis on the fact that the above disclosed hierarchical decentralized control scheme with corresponding predictive control algorithms for coordinated control of the kinetic and electric energy buffers in a hybrid vehicle is made possible based on the vehicle modelling steps that enable the use of convex optimization. The convex optimization allows for fuel saving potential of coordinated use of the kinetic and electric energy buffer, whereas the low computational requirements mean that the control scheme can be implemented on currently available electronic control units.

[0062] Being able to plan the kinetic and electric energy buffers in a convex optimization problem opens up the possibility of expanding the predictive controller with even more dynamic states. This may for example allow for control schemes with models of surrounding traffic as well as control schemes and convex optimizations to minimize the fuel consumption of a vehicle platoon with several conventional and hybrid vehicles.

[0063] In summary, the present invention relates to hybrid vehicle, comprising a drive train, an electrical energy source coupled to the drive train and including an electric energy storage device having a state-of-charge, a non-electrical energy source coupled to the drive-train. In accordance to the invention, a convexification model for the vehicle is used for determining at least one control parameter for operating the vehicle. By applying a convex approach for forming the at least one control parameter it is possible to be sure that the at least one control parameter in fact is a presently optimized parameter. Furthermore, the convex approach minimizes the computational resources necessary for determining the at least one control parameter. The use of a minimal amount of computational resources is specifically desirable in relation to a vehicle on-board solution, typically implementing realtime, continuous, calculations of the at least one control parameter.

[0064] The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor.

[0065] By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

[0066] Although the figures may show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps. Additionally, even though the invention has been described with reference to specific exemplifying embodiments thereof, many different alterations, modifications and the like will become apparent for those skilled in the art.

[0067] Variations to the disclosed embodiments can be understood and effected by the skilled addressee in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. Furthermore, in the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality.