System, computer-implemented method and storage medium comprising a software program for operating a hybrid or electric vehicle

Abstract

The present disclosure relates to a system (100) for operating a hybrid or electric vehicle (1), comprising: - a temperature control module (110) for thermally conditioning a drive energy storage device (10) of the hybrid or electric vehicle (1); - a prediction module (120) which designed to predict a temperature of the drive energy storage device (10) on the basis of a thermal model (TM) of the drive energy storage device (10), said prediction module (120) being designed to adapt at least one model parameter of the thermal model (TM) using an adaptation algorithm, wherein the adaptation algorithm uses historical data relating to the drive energy storage device (10); and - a control module (130) for controlling the temperature control module (110) in such a way that the thermal conditioning of the drive energy storage device (10) takes place on the basis of the predicted temperature of the drive energy storage device (10).

Description

[0001] 24-0128

[0002] System for operating a hybrid or electric vehicle

[0003] The present disclosure relates to a system for operating a hybrid or electric vehicle, a vehicle with such a system, a method for operating a hybrid or electric vehicle, and a storage medium for exporting the method. The present disclosure relates in particular to a prediction of the temperature of a drive energy storage device using a thermal model of the drive energy storage device.

[0004] State of the art

[0005] In light of the growing demands on mobility technology, particularly regarding the efficiency of energy storage systems in hybrid and electric vehicles, the development of high-performance thermal management has become a top priority. As part of thermal management, a process called preconditioning of the drive energy storage system can be implemented. Preconditioning involves preheating or precooling the drive energy storage system before the vehicle is started or charged. This measure ensures that the drive energy storage system operates at optimal temperatures, thus improving both its performance and lifespan.

[0006] Disclosure of the invention

[0007] It is an object of the present disclosure to specify a system for operating a hybrid or electric vehicle, a vehicle with such a system, a method for operating a hybrid or electric vehicle, and a storage medium for carrying out the method, which enable improved prediction of the temperature of a drive energy storage device. In particular, it is an object of the present disclosure to enable improved thermal conditioning of the drive energy storage device.

[0008] 26.08.2024 24-0128 24-0128

[0009] This problem is solved by the subject matter of the independent claims. Advantageous embodiments are specified in the dependent claims.

[0010] According to an independent aspect of the present disclosure, a system for operating a hybrid or electric vehicle is specified. The system comprises a temperature control module configured to thermally condition a drive energy storage device of the hybrid or electric vehicle; a prediction module configured to predict a temperature of the drive energy storage device based on a thermal model of the drive energy storage device, wherein the prediction module is configured to adapt at least one model parameter of the thermal model by means of an adaptation algorithm, the adaptation algorithm using historical data relating to the drive energy storage device; and a control module configured to control the temperature control module such that the thermal conditioning of the drive energy storage device is carried out based on the predicted temperature of the drive energy storage device.

[0011] According to the invention, geometric and time-dependent thermal parameters of a thermal model are adapted using an adaptation algorithm. Such model adaptation enables the reduction of model errors and a more precise adaptation to the specific conditions of the drive energy storage system, which may not have been fully considered in the original model. This allows for the use of a more accurate model for prediction, improving the efficiency and accuracy of forecasting the drive energy storage system's temperature. As a result, improved thermal conditioning of the drive energy storage system, e.g., before a charging process, is possible.

[0012] The prediction module and the control module may include software components / algorithms that are set up to run on at least one processor and thereby perform the functionalities of the respective module.

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[0014] The prediction module and the control module can be implemented in a single software and / or hardware module. Alternatively, the prediction module and the control module can each be implemented in separate software and / or hardware modules.

[0015] Depending on the specific design, the hybrid or electric vehicle can be a pure electric vehicle (BEV) or a plug-in hybrid vehicle (PHEV). The term "vehicle" includes cars, trucks, buses, motorhomes, motorcycles, etc., used for the transport of persons, goods, etc. In particular, the term includes motor vehicles for passenger transport.

[0016] The drive energy storage system in a hybrid or electric vehicle is an energy storage system that stores electrical energy in the form of high-voltage direct current. This stored energy is used to power at least one of the vehicle's electric motors. Typically, these drive energy storage systems are composed of lithium-ion cells or modules configured in a battery pack. The drive energy storage system can also be referred to as a high-voltage storage system or battery.

[0017] The drive energy storage system can be connected to a charging station via the charging device of the hybrid or electric vehicle. Various charging technologies can be used to charge the electric drive energy storage system of such hybrid or electric vehicles. With AC charging, the device that converts the alternating current to direct current for charging the electric drive energy storage system is located in the vehicle. With DC charging, however, the conversion of the alternating current takes place directly in the charging station.

[0018] Preferably, the temperature control module is configured to thermally precondition the drive energy storage device before a charging process. The term "thermal preconditioning," as used in the present disclosure, refers to setting the temperature of the drive energy storage device to a

[0019] 26.08.2024 24-0128 24-0128

[0020] Target temperature or setpoint temperature achieved through heating or cooling. This corresponds to a temperature that is optimal for charging the drive energy storage system, for example, with regard to charging time, charging power, and / or the load on the drive energy storage system during the charging process.

[0021] Preferably, the charging process is a fast charging process. The term "fast charging process," as used in the present disclosure, refers to DC charging in which a fast charging station itself converts the alternating current into direct current and transmits the direct current to the drive energy storage system of the hybrid or electric vehicle with minimal loss.

[0022] In some designs, the fast charging station can implement the so-called IU charging method. Here, charging initially takes place with a constant current until approximately 80% of the charging capacity is reached. Afterward, the fast charging station uses a constant voltage. This enables a short charging time and / or high charging power.

[0023] Preferably, the control module is configured to control the temperature control module in such a way that the drive energy storage system has a target temperature at a specific time (e.g., an expected arrival time at a charging station or an expected departure time of the parked vehicle) and / or at a specific geolocation (e.g., a charging station). The thermal conditioning can, for example, be carried out such that the temperature of the drive energy storage system has reached the target temperature upon arrival at the charging station, meaning that optimal charging with regard to charging time, charging power, and / or load on the drive energy storage system can be performed upon arrival at the charging station.

[0024] The estimated time of arrival at the charging station and / or the estimated departure time of the parked vehicle can be determined using suitable means. For example, route and navigation functions can be used to predict the duration, distance, route profile, speeds, etc., until the charging process begins.

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[0026] Preferably, the temperature control module comprises at least one heating device configured to heat the drive energy storage device for thermal conditioning, for example, to the target temperature. The at least one heating device may include electric heating elements, a liquid-based heater, a heat pump, an internal resistance heater, and / or an induction heater; however, the present disclosure is not limited to these.

[0027] The prediction module is designed to predict the temperature of the drive energy storage system based on its thermal model. A thermal model of the drive energy storage system is a mathematical and physical representation of heat generation and distribution within the system. This model considers various physical and chemical processes that occur within the drive energy storage system, for example, during operation and charging, and how these processes affect the system's temperature. Components of the thermal model include, but are not limited to, heat sources, heat conduction, and heat dissipation.

[0028] The thermal model allows for a prediction of the drive energy storage temperature. The temperature of the drive energy storage system is determined by one or more of the following aspects:

[0029] 1. Passive cooling through convection,

[0030] 2. Active cooling through heat extraction, e.g. through a heat pump,

[0031] 3. Active heating, e.g., by trimming, heating, etc., and

[0032] 4. Internal heating through power input and output.

[0033] Preferably, the prediction module is configured to predict the temperature of the drive energy storage system in a discrete-time manner. In particular, the thermal model can be a discrete-time thermal model. With discrete-time prediction, the temperature is predicted only at specific, discrete points in time. This means that the temperature values ​​are recorded at fixed intervals (e.g., every second, every minute).

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[0035] Preferably, the prediction module is configured to predict the temperature of the drive energy storage system in a time-discrete manner as follows (the formula can be derived, for example, based on Newton's law of cooling using suitable approximations):

[0036] Here, t denotes time.

[0037] T n+1 the predicted temperature of the drive energy storage at time t( n +i)

[0038] T n the predicted temperature of the drive energy storage at time t( n )

[0039] The ambient temperature

[0040] / the current through the drive energy storage a a the heat transfer coefficient of free convection to the outside

[0041] X the (temperature-dependent) thermal conductivity of the drive energy storage system s the length of the drive energy storage system

[0042] - A a surface of the drive energy storage cp the specific heat capacity of the drive energy storage m the mass of the drive energy storage the internal resistance of the drive energy storage (e.g. SoC, C-rate etc.) a and b represent at least one model parameter of the thermal model, which includes geometric and time-dependent influencing factors and is adapted by means of the adaptation algorithm based on historical data.

[0043] Preferably, the thermal model, such as the discrete-time thermal model, is in output error (OE) form. The term "output error" refers to the difference (error) between the predicted output of the model and the actual result.

[0044] 26.08.2024 24-0128 24-0128 the actually measured output. The aim is to minimize this error by appropriately selecting the model parameters.

[0045] Preferably, the OE model shape is defined as follows:

[0046] Y - <p T■ 0 + E with

[0047] Y represents the temperature of the drive energy storage system. <t> T The historical data (e.g., measured values), 0 the model parameters, and E the error. 0 contains so-called adaptation variables and f2, which modify the previous or original model parameters a and b. In particular, the error E can be determined using the adaptation variables. and f2 is minimized.

[0048] Preferably, the adaptation algorithm is configured to adjust the at least one adaptation variable using a Recursive Least Squares (RLS) algorithm such that the error of the output error model form is minimized. An RLS algorithm is an adaptive filtering algorithm that can be used to adjust coefficients to minimize the error between the actual and the desired outcome of the prediction.

[0049] According to another independent aspect of the present disclosure, a hybrid or electric vehicle, in particular a motor vehicle, is specified. The hybrid or electric vehicle comprises the system for operating a hybrid or electric vehicle according to the embodiments of the present disclosure.

[0050] 26.08.2024 24-0128 24-0128

[0051] According to another independent aspect of the present disclosure, a computer-implemented method for operating a hybrid or electric vehicle is specified. The method comprises predicting, by means of a prediction module, the temperature of a drive energy storage device based on a thermal model of the drive energy storage device using Newton's law of cooling, wherein the Newtonian law of cooling includes a one-dimensional heat conduction term in its exponent; and controlling, by means of a control module, a temperature control module such that thermal conditioning of the drive energy storage device is carried out based on the predicted temperature of the drive energy storage device.

[0052] The procedure for operating a hybrid or electric vehicle may implement aspects of the system for operating a hybrid or electric vehicle described in this document.

[0053] According to another independent aspect of the present disclosure, a software (SW) program is specified. The SW program can be configured to run on one or more processors and thereby execute the method described in this document for operating a hybrid or electric vehicle.

[0054] According to another independent aspect of the present disclosure, a storage medium is specified. The storage medium may include a software program configured to run on one or more processors and thereby execute the method described in this document for operating a hybrid or electric vehicle.

[0055] According to another independent aspect of the present disclosure, software with program code is specified. The software is designed to carry out the method for operating a hybrid or electric vehicle when the software runs on one or more software-controlled devices.

[0056] 26.08.2024 24-0128 24-0128

[0057] According to another independent aspect of the present disclosure, a system is specified. The system comprises one or more processors; and at least one memory connected to the one or more processors and containing instructions that can be executed by the one or more processors to carry out the method described in this document for operating a hybrid or electric vehicle.

[0058] A processor or processor module is a programmable computing unit, i.e., a machine or an electronic circuit that controls other elements according to given instructions and thereby advances an algorithm (process).

[0059] Brief description of the drawings

[0060] Examples of the manifestation of the revelation are shown in the figures and are described in more detail below. They show:

[0061] Figure 1 schematically shows a hybrid or electric vehicle with a system for operating a hybrid or electric vehicle according to embodiments of the present disclosure, and

[0062] Figure 2 shows a flowchart of a method for operating a hybrid or electric vehicle according to embodiments of the present disclosure.

[0063] Implementations of the revelation

[0064] Unless otherwise noted, the same reference symbols are used for identical and equivalent elements in the following.

[0065] Figure 1 schematically shows a hybrid or electric vehicle 1 with a system 100 for operating a hybrid or electric vehicle according to embodiments of the present

[0066] 26.08.2024 24-0128 24-0128

[0067] Disclosure. The hybrid or electric vehicle 1 can, according to embodiments, be a pure electric vehicle (BEV) or a plug-in hybrid vehicle (PHEV).

[0068] The hybrid or electric vehicle 1 includes a drive energy storage device 10, which can be connected to a charging station and charged via a charging device of the hybrid or electric vehicle 1. In order to optimize the charging of the drive energy storage device 10, for example with regard to charging time, charging power and / or load on the drive energy storage device 10, the drive energy storage device 10 can be thermally preconditioned to a target temperature by heating or cooling before the charging process.

[0069] For example, the drive energy storage device 10 can be thermally preconditioned such that it has essentially a target temperature at a specific time (e.g., the expected arrival time at a charging station or the expected departure time of the parked vehicle) and / or at a specific geolocation (e.g., a charging station). The thermal conditioning can be carried out, for example, such that the temperature of the drive energy storage device 10 has reached the target temperature upon arrival at the charging station, meaning that optimal charging with regard to charging time, charging power, and / or load on the drive energy storage device 10 can be performed upon arrival at the charging station.

[0070] For such thermal management of the drive energy storage device 10, the temperature of the drive energy storage device 10 can be predicted based on a thermal model of the drive energy storage device 10. Predicting the temperature of the drive energy storage device 10 enables precise and reliable thermal conditioning, such as setting an optimal target temperature of the drive energy storage device 10 before a charging process.

[0071] The following section explains in detail the system 100 for the thermal management of the drive energy storage unit 10.

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[0073] The system 100 comprises a temperature control module 110, which is configured to thermally condition the drive energy storage 10 of the hybrid or electric vehicle 1; a prediction module 120, which is configured to predict a temperature of the drive energy storage 10 based on a thermal model TM of the drive energy storage 10; and a control module 130, which is configured to control the temperature control module 120 such that the thermal conditioning of the drive energy storage 10 takes place based on the predicted temperature of the drive energy storage.

[0074] In some embodiments, the temperature control module 110 comprises at least one heating device configured to heat the drive energy storage device 10 for thermal conditioning, for example, to the target temperature for charging the drive energy storage device 10. The at least one heating device may comprise electric heating elements, a liquid-based heater, a heat pump, an internal resistance heater, and / or an induction heater; however, the present disclosure is not limited to these.

[0075] The thermal model TM enables a prediction of the temperature of the drive energy storage unit 10. The temperature of the drive energy storage unit 10 is determined by one or more of the following aspects:

[0076] 1. Passive cooling by convection (modeled by Newton's law of cooling)

[0077] 2. Active cooling through heat extraction, e.g. by a heat pump (illustration e.g. by supplied electrical power and efficiency)

[0078] 3. Active heating, e.g., by trimming, heating, etc. (Figure, e.g., by supplied electrical power and efficiency)

[0079] 4. Internal heating due to power input and output (illustration e.g. by current and resistance of the drive energy storage 10)

[0080] The contribution of passive cooling by convection can be described by Newton's law of cooling with a one-dimensional heat conduction term in its exponent.

[0081] 26.08.2024 24-0128 will be taken into account. Active cooling, active heating and / or internal heating can be taken into account – if necessary – by introducing an additional heat energy term described later.

[0082] Newton's general law of cooling is given by:

[0083] Integration results in:

[0084] Here, t denotes time, T(t) the predicted temperature of the drive energy storage at time t, T v the ambient temperature, T o the temperature of the

[0085] drive energy storage at time Z=0, A a surface of the

[0086] drive energy storage, c p the specific heat capacity of the drive energy storage system, m the mass of the drive energy storage system, and k the one-dimensional heat conduction term.

[0087] The one-dimensional heat conduction term X can be given by:

[0088] Here, X denotes the (temperature-dependent) thermal conductivity of the drive energy storage system, v the velocity of one-dimensional, free convection, and c x and c2 coefficients of one-dimensional free convection.

[0089] The one-dimensional heat conduction term k can be determined based on heat transfer and free convection.

[0090] 26.08.2024 24-0128 For this purpose, the heat transfer coefficient k can first be used. w the heat transfer is considered:

[0091] Here, X denotes the (temperature-dependent) thermal conductivity of the drive energy storage system, s the length of the drive energy storage system, and a a and c heat transfer coefficients from the heat transfer model (a a c and are the heat transfer coefficients of free convection to the inside and outside of the drive energy storage system; the inside heat transfer coefficient is not taken into account, and the outside heat transfer coefficient is approximated using the following formula. + c2 imaged).

[0092] For one-dimensional heat transfer, the following results:

[0093] 1 k = -j -

[0094] — + - a a X

[0095] For free convection, a a expressed as:

[0096] This results in the equation mentioned above for the one-dimensional heat conduction term k:

[0097] In some embodiments, a heat energy term dT is added to Newton's law of cooling, where dT is given by:

[0098] 26.08.2024 24-0128 24-0128

[0099] Here, / (t) denotes the current through the drive energy storage system, 7?(t) the internal resistance of the drive energy storage system (e.g., SoC, C-rate, etc.), c p the specific heat capacity of the drive energy storage and m the mass of the drive energy storage.

[0100] The thermal energy term dT can be used to represent or take into account the active cooling, active heating and / or internal heating of the drive energy storage 10.

[0101] Based on the above derivation, the temperature of the drive energy storage device can be predicted continuously or discretely over time. Within the scope of this disclosure, discrete-time prediction is chosen.

[0102] Here, t denotes time.

[0103] T n+1 the predicted temperature of the drive energy storage at time t( n +i)

[0104] T n the predicted temperature of the drive energy storage at time t( n )

[0105] The ambient temperature

[0106] / the current through the drive energy storage a a the heat transfer coefficient of free convection to the outside

[0107] X the (temperature-dependent) thermal conductivity of the drive energy storage

[0108] 26.08.2024 24-0128 24-0128 s the length of the drive energy storage

[0109] - A a surface of the drive energy storage c p the specific heat capacity of the drive energy storage m the mass of the drive energy storage the internal resistance of the drive energy storage (e.g. SoC, C-rate etc.) a and b represent model parameters of the thermal model, which include geometric and time-dependent influencing factors and are adapted by means of an adaptation algorithm using historical data relating to the drive energy storage.

[0110] The following is a detailed explanation of an exemplary procedure.

[0111] The discrete-time thermal model can be implemented in an output error (OE) model form. The term "output error" refers to the difference (error) between the predicted output of the model and the actually measured output. The goal is to minimize this error by appropriately selecting the model parameters.

[0112] Preferably, the OE model shape is defined as follows: with and

[0113] 26.08.2024 24-0128 24-0128

[0114] Y represents the temperature of the drive energy storage system. <t> T The historical data (e.g., measured values), 0 the model parameters, and £ the error. 0 contains so-called adaptation variables and f2, which modify the original model parameters a and b. In particular, the error E can be minimized using the adaptation variables and f2.

[0115] In particular, the adaptation variables must be chosen such that the expected error E is minimized or ideally reduced to zero. By transforming the model into the OE form, the Recursive Least Squares (RLS) algorithm or its special form, "RLS with continuous forgetting," can be applied. Alternatively, a Kalman filter could also be used. The choice between these methods depends on the available computing power of the target hardware and on whether not only a parameter update (RLS) but also a parameter prediction (Kalman) is required.

[0116] Thus, the adaptation values ​​for f and f2 at any time n can be determined from the adaptation parameter vector for all past and weighted values ​​in k. These adapted values ​​can then be used again for the prediction of the temperature of the drive energy storage system:

[0117] Figure 3 schematically shows a flowchart of a method 200 for operating a hybrid or electric vehicle according to embodiments of the present disclosure.

[0118] 26.08.2024 24-0128 24-0128

[0119] Method 200 can be implemented by appropriate software that can be executed by one or more processors (e.g., a CPU).

[0120] The method 200 comprises in block 210 a prediction, by a prediction module, of a temperature of a drive energy storage device based on a thermal model of the drive energy storage device, wherein the prediction module is configured to adapt at least one model parameter of the thermal model by means of an adaptation algorithm, wherein the adaptation algorithm uses historical data relating to the drive energy storage device; and in block 220 a control, by a control module, of a temperature control module such that thermal conditioning of the drive energy storage device is carried out based on the predicted temperature of the drive energy storage device.

[0121] According to the invention, geometric and time-dependent thermal parameters of a thermal model are adapted using an adaptation algorithm. Such model adaptation enables the reduction of model errors and a more precise adaptation to the specific conditions of the drive energy storage system, which may not have been fully considered in the original model. This allows for the use of a more accurate model for prediction, improving the efficiency and accuracy of forecasting the drive energy storage system's temperature. As a result, improved thermal conditioning of the drive energy storage system, e.g., before a charging process, is possible.

[0122] Although the invention has been further illustrated and explained in detail by means of preferred embodiments, the invention is not limited by the disclosed examples, and other variations can be derived from them by a person skilled in the art without departing from the scope of protection of the invention. It is therefore clear that a multitude of possible variations exist. It is also clear that the embodiments mentioned as examples are truly only examples and are not to be understood in any way as limiting, for example, the scope of protection, the possible applications, or the configuration of the invention. Rather, the preceding

[0123] 26.08.2024 24-0128 24-0128

[0124] The description and the description of the figures enable the person skilled in the art to implement the exemplary embodiments in concrete terms, whereby the person skilled in the art, with knowledge of the disclosed inventive concept, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without leaving the scope of protection defined by the claims and their legal equivalents, such as further explanations in the description.

[0125] 26.08.2024 24-0128< / t> < / t>

Claims

24-0128 Patent claims 1. System (100) for operating a hybrid or electric vehicle (1), comprising: a temperature control module (HO) configured to thermally condition a drive energy storage device (10) of the hybrid or electric vehicle (1); a prediction module (120) configured to predict a temperature of the drive energy storage device (10) based on a thermal model (TM) of the drive energy storage device (10), wherein the prediction module (120) is configured to adapt at least one model parameter of the thermal model (TM) by means of an adaptation algorithm, the adaptation algorithm using historical data relating to the drive energy storage device (10); and a control module (130) configured to control the temperature control module (110) such that the thermal conditioning of the drive energy storage device (10) is carried out based on the predicted temperature of the drive energy storage device (10).

2. System (100) according to claim 1, wherein the temperature control module (110) is configured to thermally precondition the drive energy storage device (10) before a charging process.

3. System (100) according to claim 1 or 2, wherein the control module (130) is configured to control the temperature control module (110) such that the drive energy storage device (10) has a substantially target temperature at a specific time and / or at a specific geoposition.

4. System (100) according to any one of claims 1 to 3, wherein the thermal model (TM) is a discrete-time thermal model.

5. System (100) according to any one of claims 1 to 4, wherein the thermal model (TM) is in an output error model form. 26.08.2024 24-0128 24-0128 6. System (100) according to claim 5, wherein the output error model form comprises at least one adaptation variable, and wherein the adaptation algorithm is configured to adapt the at least one adaptation variable such that an error of the output error model form is minimized.

7. System (100) according to claim 6, wherein the adaptation algorithm is configured to adjust the at least one adaptation variable by means of a recursive least squares algorithm, such that the error of the output error model form is minimized.

8. Hybrid or electric vehicle, in particular motor vehicle, comprising the system (100) according to any one of claims 1 to 7.

9. Computer-implemented method (200) for operating a hybrid or electric vehicle (1), comprising: Predict (210), by means of a prediction module (120), a temperature of a drive energy storage device (10) based on a thermal model (TM) of the drive energy storage device (10), wherein the prediction module (120) is configured to adapt at least one model parameter of the thermal model (TM) by means of an adaptation algorithm, wherein the adaptation algorithm uses historical data relating to the drive energy storage device (10); and Ansteuem (220), by a control module (130), a temperature control module (110) such that thermal conditioning of the drive energy storage (10) takes place based on the predicted temperature of the drive energy storage (10).

10. Storage medium comprising a software program configured to run on one or more processors and thereby to execute the method according to claim 9. 26.08.2024 24-0128

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