WO2022188760A1

WO2022188760A1 - Method and device for estimating heat of power battery pack

Info

Publication number: WO2022188760A1
Application number: PCT/CN2022/079655
Authority: WO
Inventors: 胡明睿; 程康
Original assignee: 华为技术有限公司
Priority date: 2021-03-09
Filing date: 2022-03-08
Publication date: 2022-09-15
Also published as: CN115047344A

Abstract

Provided is a method for estimating heat of a power battery pack, and may be applied in scenarios of including, but not limited to, a cloud device or an intelligent terminal. The method comprises: according to first state data of a power battery pack, determining second state data of the power battery pack, wherein the first state data comprises terminal current, terminal voltage and temperature, and the second state data comprises open-circuit voltage and a partial derivative of the open circuit voltage to the temperature (110); inputting the first state data and the second state data into a heat estimation model to obtain an internal heat of the power battery pack (120). The present method may improve the accuracy and efficiency of power battery pack heat estimation.

Description

Method and device for estimating heat of power battery pack

This application claims the priority of the Chinese patent application with the application number 202110255360.6 and the application title "Method and Device for Estimating Heat of a Power Battery Pack" filed with the China Patent Office on March 09, 2021, the entire contents of which are incorporated herein by reference middle.

technical field

The present application relates to the field of power battery packs, and more particularly, to a method and apparatus for estimating heat of a power battery pack.

Background technique

The power battery pack is the power source of an electric vehicle (EV), and the performance of the power battery pack directly affects the power performance and driving range of the EV. The complex electrochemical reaction inside the power battery pack is easily affected by the external ambient temperature. For example, high current charging and discharging in a low temperature environment will cause irreversible losses to the battery, and in a high temperature environment, the aging of the battery is accelerated, and the battery life is shortened. In order to ensure the safety and performance of the power battery pack, it is necessary to estimate and monitor the internal heat of the power battery pack in real time.

At present, the estimation of the power battery pack mainly relies on the temperature sensor installed on the power battery pack. Due to the large temperature difference between the inside of the power battery pack and the surface of the power battery pack, the heat estimation result is inaccurate. In the related art, the internal heat of the power battery pack is also estimated based on the electrochemical model of the power battery pack, but the process of establishing the electrochemical model of the power battery pack is very complicated.

SUMMARY OF THE INVENTION

The present application provides a method and device for estimating heat of a power battery pack, which can improve the accuracy and efficiency of heat estimation of a power battery pack.

In a first aspect, a method for estimating heat of a power battery pack is provided, the method comprising:

Determine the second state data of the power battery pack according to the first state data of the power battery pack, wherein the first state data includes terminal current, terminal voltage and temperature, and the second state data includes the open circuit voltage and the open circuit voltage the partial derivative with respect to the temperature;

The first state data and the second state data are input into a heat estimation model to obtain the internal heat of the power battery pack.

The above-mentioned first state data of the power battery pack can be understood as the first state data of one or more power battery packs, which is not specifically limited. Wherein, the first state data of the power battery pack can be obtained by measuring a sensor installed on the power battery pack, and the first state data obtained by measurement has high precision.

In the above technical solution, the parameters for determining the internal heat of the power battery pack only include the first state data and the second state data of the power battery pack, and the first state data and the second state data do not depend on the complex mechanical structure of the power battery pack, In this way, modeling the complex mechanical structure of the power battery pack is avoided, and the estimation efficiency can be improved. Among them, the first state data and the second state data include not only the temperature of the power battery pack, but also the terminal current and terminal voltage of the power battery pack, that is to say, the battery management system strategy is also considered when estimating the internal heat of the power battery pack (for example, equalization strategy, etc.), in this way, the calculation accuracy of heat estimation can be improved.

It should be noted that the above technical solution is not limited to estimating the internal heat of the power battery pack, and the above technical solution can also be applied to estimating the heat of a circuit structure with terminal current, terminal voltage and temperature characteristics. For example, using the above technical solution, the heat of the single cells in the circuit structure including the single cells can also be estimated.

With reference to the first aspect, in some implementations of the first aspect, the second state data of the power battery pack is determined according to the first state data of the power battery pack, including:

establishing an open circuit voltage model of the power battery pack according to the first state data and the equivalent circuit model of the power battery pack;

inputting the first state data into the open circuit voltage model to obtain the open circuit voltage;

Input the first state data into a first-order partial derivative model to obtain a partial derivative value of the open-circuit voltage with respect to the temperature, wherein the first-order partial derivative model is obtained by calculating the first-order partial derivative of the open-circuit voltage model with respect to the temperature .

Wherein, the method for determining the equivalent circuit model of the power battery pack and the specific structure of the equivalent circuit model of the power battery pack are not limited. The equivalent circuit model of the power battery pack includes, but is not limited to: an internal resistance model, a resistance-capacitance (RC) model, a PNGV model or a general non-linear (GNL) model.

In the above technical solution, when the open circuit voltage model is determined based on the first state data, the first state data includes not only the temperature of the power battery pack, but also the terminal current and terminal voltage of the power battery pack, so that the determined open circuit voltage model is more accurate. When the open-circuit voltage of the power battery pack is estimated based on the open-circuit voltage model, the obtained open-circuit voltage has high accuracy, which can further improve the accuracy of the heat estimation of the power battery pack.

With reference to the first aspect, in some implementations of the first aspect, the open circuit voltage model of the power battery pack is established according to the first state data and the equivalent circuit model of the power battery pack, including:

According to the terminal current, the terminal voltage and the temperature, establish a charging model or a discharging model of the power battery pack;

establishing the open circuit voltage model according to the equivalent circuit model of the power battery pack and the charging model or the discharging model;

Wherein, when the first state data is the data that the power battery pack is in a discharging state, the open-circuit voltage model is established according to the equivalent circuit model and the charging model;

When the first state data is data in which the power battery pack is in a charged state, the open circuit voltage model is established according to the equivalent circuit model and the discharge model.

Wherein, according to the terminal current, the terminal voltage and the temperature, a charging model or a discharging model of the power battery pack is established, which can be understood as performing data fitting on the terminal current, the terminal voltage and the temperature to obtain a terminal voltage Regarding the model of terminal current and temperature, the model is the charging model or discharging model of the power battery pack.

In the above technical solution, the terminal current, the terminal voltage and the temperature of one or more power battery packs are modeled based on the method of data fitting to obtain a charging model or a discharging model of the power battery pack, and the implementation process is relatively simple. .

With reference to the first aspect, in some implementations of the first aspect, the first state data and the second state data are the state data of the power battery pack at the first moment,

The method also includes:

According to the first state data of the power battery pack within the first preset time after the first moment, and the second state data of the power battery pack within the first preset time, one or more of the following model to modify:

The charging model or the discharging model, the open circuit voltage model, the first-order partial derivative model of the open circuit voltage, and the heat-temperature model, wherein the heat-temperature model is a function of the internal heat of the power battery pack on the temperature.

Optionally, the above-mentioned heat-temperature model may be obtained by fitting the internal heat of one or more power battery packs within a certain preset time and the temperature of the corresponding power battery pack.

In the above technical solution, using the first state data of the one power battery pack within the first preset time after the first moment, and the second state data of the one power battery pack within the first preset time, the One or more of the above are modified to obtain one or more modified models. The internal heat of the one power battery pack is determined based on one or more of the corrected models with high calculation accuracy.

In conjunction with the first aspect, in some implementations of the first aspect, the method further includes:

In response to the internal heat of the power battery pack being greater than or equal to the first threshold, determine that the internal heat of the power battery pack at the target moment is in an abnormal state and issue an alarm; or,

In response to the internal heat of the power battery pack being less than the first threshold, it is determined that the power battery pack is in a normal state at the target time.

Optionally, the first threshold may be a value preset according to experience, and the specific value of the first threshold is not limited.

Optionally, the first threshold may be determined according to the internal heat of one or more power battery packs within a certain preset time and the temperature of the corresponding power battery pack, wherein one or more power battery packs within a certain preset time The internal heat inside includes some abnormal heat. In one example, the first threshold is determined according to the temperature of the power battery pack and a first-order partial derivative model of a heat-temperature model with respect to temperature, where the heat-temperature model is a function of the internal heat of the power battery pack with respect to the temperature, The heat-temperature model is obtained by fitting the internal heat of one or more power battery packs within a certain preset time and the temperature of the corresponding power battery pack.

Optionally, according to business requirements, in some scenarios, an alarm is only issued in response to the internal heat of the power battery pack being greater than or equal to the first threshold, and no processing is performed in response to the internal heat of the power battery pack being less than the first threshold. .

Optionally, according to business requirements, in some scenarios (for example, a scenario including only one power battery pack), in response to the internal heat of the power battery pack being greater than or equal to the first threshold, it may be determined that the internal heat of the power battery pack is in the Abnormal state and a thermal runaway warning is issued.

In the above technical solution, the first threshold represents the upper threshold of the internal heat of the power battery pack corresponding to different temperatures of the power battery pack, and it can be determined whether the power battery pack is suitable for the power battery pack according to the temperature of the power battery pack and the corresponding internal heat of the power battery pack. It can improve the accuracy of thermal runaway warning and further reduce the harm caused by thermal runaway to a large extent.

The internal heat of the power battery pack at the second preset time is used as the input characteristic value, and the internal heat of the power battery pack at the third preset time is used as the output characteristic value for model training, and a heat trend prediction model is established, wherein the The third preset time is the preset time after the second preset time;

Input the internal heat of the power battery pack at a fourth preset time to the heat trend prediction model, and obtain the internal heat of the power battery pack at a fifth preset time, where the fifth preset time is the fourth preset time. The preset time after the set time.

In the above technical solution, the internal heat of the power battery pack for a predetermined period of time in the future can be predicted according to the determined heat trend prediction model, which can be used for thermal runaway warning.

The method also includes:

Inputting the first state data of the power battery pack at the second moment into the open-circuit voltage model and the first-order partial derivative model to obtain the second state data of the power battery pack at the second moment, wherein the second time is the time after the first time;

Inputting the first state data of the power battery pack at the second moment and the second status data of the power battery pack at the second moment into the heat estimation model to obtain the power battery pack at the second moment of internal heat.

In the above technical solution, the second state of the power battery pack at the second moment after the first moment can be determined according to the open circuit voltage model and the first-order partial derivative model determined by the state data of one or more power battery packs at the first moment. Therefore, the internal heat of the power battery pack at the second moment can be estimated according to the first state data and the second status data of the power battery pack at the second moment.

With reference to the first aspect, in some implementations of the first aspect, the power battery pack is a lithium-ion power battery pack.

With reference to the first aspect, in some implementations of the first aspect, the power battery pack includes one battery cell module or multiple battery cell modules.

With reference to the first aspect, in some implementations of the first aspect, the power battery pack is used in an electric vehicle EV.

It can be understood that the following models obtained in the first aspect above can also be called regression models: a charging model, a discharging model, a heat-temperature model, and a heat trend prediction model.

In a second aspect, a power battery pack heat estimation device is provided, the device comprising:

a determining unit, configured to determine second state data of the power battery pack according to the first state data of the power battery pack, wherein the first state data includes terminal current, terminal voltage and temperature, and the second state data includes open circuit voltage and the partial derivative of the open circuit voltage with respect to the temperature;

The estimation unit is used for inputting the first state data and the second state data into a heat estimation model to obtain the internal heat of the power battery pack.

In conjunction with the second aspect, in some implementations of the second aspect, the determining unit is further configured to:

With reference to the second aspect, in some implementations of the second aspect, the first state data and the second state data are the state data of the power battery pack at the first moment,

The determination unit is also used to:

It should be understood that, regarding the technical effects brought by the second aspect or various implementations of the second aspect, reference may be made to the introduction of the technical effects of the first aspect or various implementations of the first aspect, and details are not repeated.

In a third aspect, a method for establishing a power battery pack charging model is provided, the method comprising:

Obtain N pieces of first request information from N vehicles, wherein each first request information is used to request to obtain a charging model of a power battery pack, and the charging model is based on a first state in which the M power battery packs are in a charging state Determined by the data, the first state data includes terminal current, terminal voltage and temperature, the first state data of the M power battery packs in the charging state and the first state data of the power battery packs in the N vehicles in the charging state. The state data are not the same, N and M are integers, and N≥1, M≥1;

Send the charging model to the N vehicles.

Wherein, the first state data of the M power battery packs in the charging state is different from the first state data of the power battery packs in the N vehicles in the charging state, which can be understood as the power battery pack for which the charging model is established. The data of are not the data of the power battery packs in the N vehicles. For example, the first state data of the M power battery packs in the charging state may be desensitized data provided by an automobile manufacturer, or data obtained according to an experimental simulation model, or the like.

The execution body of the above method may be a cloud device, for example, the cloud device is a cloud server.

In the above technical solution, after acquiring the request of the vehicle, the cloud device can send the charging model in the cloud device to the vehicle to satisfy the request of the vehicle.

In conjunction with the third aspect, in some implementations of the third aspect,

Receive N parameter sets from the N vehicles, wherein the N parameter sets are determined according to the N first charging models and the charging model, the N first charging models and the power batteries in the N vehicles Corresponding to the pack, each first charging model is established according to the first state data of the power battery pack in the corresponding vehicle in the charging state;

The charging model is updated by using the N parameter sets to obtain an updated charging model.

In the above technical solution, the charging model can be updated by using the parameter set provided by the N vehicles, so that the result of estimating the terminal voltage of the power battery packs in the N vehicles in the charging state using the updated charging model is more accurate.

receiving N pieces of second request information from the N vehicles, wherein each second request information is used to request to acquire an updated charging model;

The updated charging model is sent to the N vehicles.

Optionally, in some implementations, it is also possible to only receive the second request information from at least one vehicle among the N vehicles, and send the updated charging model to the at least one vehicle.

In the above technical solution, the cloud device can send the updated charging model to each vehicle according to the request of each vehicle, so that each vehicle uses the updated charging model to calculate that the power battery pack in the vehicle is in a charging state. The terminal voltage has a high calculation accuracy, so as to provide the accuracy of the heat estimation of the power battery pack. In the above process of updating the charging model, the cloud device only obtains N parameter sets from the N vehicles, and does not involve the local data of the N vehicles (that is, the first state data of the power battery pack in each vehicle) ), which can ensure the privacy and security of the data of the N vehicles.

Optionally, in some implementation manners, the discharge model of the first aspect may also be established based on the foregoing method for establishing a charging model of a power battery pack. At this time, the first state data of the M power battery packs in the charged state should be replaced with the first state data of the M power battery packs in the discharge state.

Optionally, in some implementation manners, the heat-temperature model of the first aspect may also be established based on the foregoing method for establishing a power battery pack charging model. At this time, the first state data of the M power battery packs in the charging state should be replaced with the heat of the M power battery packs and the temperature of the M power battery packs.

Optionally, in some implementation manners, the heat trend prediction model of the first aspect may also be established based on the foregoing method for establishing a power battery pack charging model. At this time, the first state data of the M power battery packs in the charging state should be replaced with the internal heat of the M power battery packs within the first preset time and the M power battery packs within the second preset time. of internal heat.

It should be understood that the method for determining the charging model, the discharging model, the heat-temperature model and the heat trend prediction model provided in the third aspect is the same as the method in the first aspect. For the content not introduced in detail here, please refer to The first aspect above.

In a fourth aspect, a method for establishing a power battery pack charging model is provided, the method comprising:

The first vehicle sends first request information to the cloud device, where the first request information is used to request to obtain a charging model of the power battery pack, and the charging model is determined according to the first state data of the M power battery packs in a charging state Yes, the first state data includes terminal current, terminal voltage and temperature, the first state data of the M power battery packs in the charging state and the first state data of the power battery pack in the first vehicle in the charging state are not the same, M is an integer, and M≥1;

The first vehicle receives the charging model sent from the cloud device.

Wherein, the first state data of the M power battery packs in the charging state is different from the first state data of the power battery packs in the first vehicle in the charging state, and it can be understood that the power battery pack for which the charging model is established The data of is not the data of the power battery pack in the first vehicle. For example, the first state data of the M power battery packs in the charging state may be desensitized data provided by an automobile manufacturer, or data obtained according to an experimental simulation model.

The execution subject of the above method is the first vehicle, and the first vehicle may be any one of the N vehicles in the above third aspect.

In the above technical solution, the first vehicle can send the first request message to the cloud device according to its own requirements, so as to obtain the charging model provided by the cloud device, so as to meet the requirements of the first vehicle.

In conjunction with the fourth aspect, in some implementations of the fourth aspect,

The first vehicle sends a first parameter set to the cloud device, wherein the first parameter set is determined according to a first charging model and the charging model, the first charging model is the first vehicle according to the first vehicle The first state data of the power battery pack in the charging state is established.

In the above technical solution, the first vehicle determines the difference between the two models based on the first charging model determined by the first vehicle itself and the charging model sent by the cloud device to the first vehicle, and uses the difference result (ie the first parameter) to determine the difference between the two models. set) to the cloud device, so that the cloud device updates the charging model sent by the cloud device to the first vehicle based on the difference result.

The first vehicle sends second request information to the cloud device, where the second request information is used for requesting to acquire the updated charging model;

The first vehicle receives the updated charging model sent from the cloud device.

In the above technical solution, the updated charging model is determined based on the first parameter set of the first vehicle, and the first vehicle estimates the terminal voltage of the power battery pack in the first vehicle in the charging state based on the updated charging model, make the estimation result more accurate.

It should be understood that the method for determining the charging model, the discharging model, the heat-temperature model and the heat trend prediction model provided in the fourth aspect is the same as the method in the first aspect. The first aspect above.

It should also be understood that, in the above third aspect and the above fourth aspect, exemplary, the execution subject for determining the charging model and the updated charging model is a cloud device, and the execution subject for determining the first charging model is a vehicle (for example, a vehicle). , the first) in the above fourth aspect is introduced as an example, but this application does not make any specific limitation. For example, in some implementations, the cloud device may perform the following operations: determine a charging model, determine a first charging model, and determine an updated charging model. For example, in some implementations, the vehicle may perform operations to determine a charging model, determine a first charging model, and determine an updated charging model.

In a fifth aspect, a power battery pack heat estimation device is provided, the device includes a memory and a processor, the memory is used for storing instructions, and the processor is used for reading the instructions stored in the memory, so that the device executes the above-mentioned first Aspects, any one of the third aspect or the fourth aspect, and a method in any possible implementation manner of the above-mentioned first aspect, the third aspect or the fourth aspect.

In a sixth aspect, a processor is provided, including: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive a signal through the input circuit and output a signal through the output circuit, so that any one of the first aspect, the third aspect or the fourth aspect, and the first aspect and the third aspect described above A method in any of the possible implementations of the aspect or the fourth aspect is implemented.

In a specific implementation process, the above-mentioned processor may be a chip, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a flip-flop, and various logic circuits. The input circuit and the output circuit may be the same circuit, which is used as the input circuit and the output circuit, respectively, at different times. The embodiments of the present application do not limit the specific implementation manners of the processor and various circuits.

In a seventh aspect, a processing apparatus is provided, including a processor and a memory. The processor is used to read the instructions stored in the memory, and can receive signals through the receiver and output signals through the output device, so as to perform any one of the first aspect, the third aspect or the fourth aspect, and the first aspect mentioned above. Aspects, the method in any of the possible implementations of the third aspect or the fourth aspect.

Optionally, there are one or more processors and one or more memories.

Optionally, the memory may be integrated with the processor, or the memory may be provided separately from the processor.

In the specific implementation process, the memory can be a non-transitory memory, such as a read only memory (ROM), which can be integrated with the processor on the same chip, or can be separately set in different On the chip, the embodiment of the present application does not limit the type of the memory and the setting manner of the memory and the processor.

It should be understood that the relevant data interaction process, such as sending indication information, may be a process of outputting indication information from the processor, and receiving capability information may be a process of receiving input capability information by the processor. Specifically, the data output by the processing can be output to the exporter, and the input data received by the processor can be from the receiver.

In an eighth aspect, a computer-readable storage medium is provided, a computer program is stored in the computer-readable storage medium, and when the computer program is executed on one or more processors, the first aspect, the third A method described in any one of the aspects or the fourth aspect (or implementing any of the possible implementations thereof).

A ninth aspect provides a computer program product comprising instructions that, when run on a computer, cause the computer to perform any one of the first, third or fourth aspects above, as well as the first aspect above, The method in any possible implementation manner of the third aspect or the fourth aspect.

A tenth aspect provides a chip system, the chip system includes at least one processor for supporting the implementation of the functions involved in any one of the first aspect, the third aspect or the fourth aspect, for example, according to The first state data of the power battery pack determines the second state data of the power battery pack.

In a possible design, the chip system further includes a memory for storing program instructions and data, and the memory is located inside the processor or outside the processor. The chip system may be composed of chips, or may include chips and other discrete devices.

In an eleventh aspect, a power battery pack heat estimation system is provided, the system comprising the power battery pack heat estimation apparatus described in the second aspect or the fifth aspect.

A twelfth aspect provides a vehicle, which includes the power battery pack heat estimation system described in the eleventh aspect.

A thirteenth aspect provides a system, a vehicle and a cloud device, where the vehicle is used to obtain the first information of the power battery pack in the method described in any one of the first aspect, the third aspect or the fourth aspect. Status data, the vehicle is further configured to send the first status data of the power battery pack to the cloud device, and the cloud device is configured to execute the method described in any one of the first aspect or the third aspect.

Description of drawings

FIG. 1 is a schematic diagram of an application scenario 100 suitable for the method for estimating heat of a power battery pack provided by an embodiment of the present application.

FIG. 2 is a schematic flowchart of a method 100 for estimating heat of a power battery pack provided by an embodiment of the present application.

FIG. 3 is a schematic flowchart of a method 200 for estimating heat of a power battery pack provided by an embodiment of the present application.

FIG. 4 is a schematic diagram of a method 300 for establishing a power battery pack charging model provided by an embodiment of the present application.

FIG. 5 is a schematic structural diagram of a power battery pack heat estimation apparatus 1000 provided by an embodiment of the present application.

FIG. 6 is a schematic structural diagram of a power battery pack heat estimation device 2000 provided by an embodiment of the present application.

FIG. 7 is a schematic structural diagram of a system 3000 provided by an embodiment of the present application.

FIG. 8 is a schematic structural diagram of a system 4000 provided by an embodiment of the present application.

Detailed ways

The technical solutions in the present application will be described below with reference to the accompanying drawings. It should be understood that the terms used in the embodiments of the present application are only used to explain specific embodiments of the present application, and are not intended to limit the present application.

It should be noted that ordinal numbers such as "first" and "second" are used in the embodiments of the present application to distinguish multiple objects, and are not used to limit the order, sequence, priority or importance of multiple objects. For example, the first state data, the second state data, etc., are only for distinguishing different data types, but do not indicate that the two types of data are different in structure and importance.

For ease of understanding, related terms involved in the embodiments of the present application are first introduced.

1. Electric vehicle (EV)

EV refers to a vehicle that uses the electrical energy stored in the battery as power and uses the motor to drive the wheels on the road. For example, but not limited to, according to the principle of vehicle power driving, EVs can be divided into three categories: pure electric vehicle (PEV), hybrid electric vehicle (HEV) and fuel cell electric vehicle (FCEV). types. PEV is a car powered entirely by rechargeable batteries; HEV is a car that can obtain power from more than two energy sources; FCEV is a car that can convert chemical energy in fuel into electrical energy through chemical reactions.

2. Driving mileage

Driving mileage, also known as cruising capacity, refers to the total mileage that vehicles, ships and other vehicles can travel continuously with the largest fuel reserve. The cruising mileage of an EV refers to the mileage that the power battery on the EV travels from the start of the fully charged state to the end of the test specified in the standard. It is an important economic indicator of the EV. It can be understood that the above-mentioned driving range may also have different names in different scenarios, which is not limited in this embodiment of the present application.

3. Power battery pack

The power battery pack is also called the power battery pack. A power battery pack can be obtained by connecting a plurality of single power battery modules in series, wherein each single power battery module is obtained by connecting a plurality of single cells in parallel. Among them, a single cell is the smallest unit of a power battery pack and an electric energy storage unit.

4. Equivalent circuit model (ECM)

ECM is a circuit model established by common electronic components (such as resistors, inductors, capacitors, voltage sources, etc.) to describe the variation law of voltage and current during battery operation. The advantage of this equivalent circuit model is that different orders can be selected according to different battery characteristics and it is easy to implement in engineering. Commonly used equivalent circuit models include internal resistance model, RC model, PNGV model and GNL model.

5. Open circuit voltage (OCV)

OCV refers to the voltage across the battery when the battery is in an open circuit (ie, open circuit) and the potential difference between the positive and negative electrodes of the battery does not change.

6. Terminal voltage

The terminal voltage, also known as the working voltage, refers to the potential difference between the positive and negative electrodes of the battery when the battery is in working state, that is, when there is current flowing through the circuit. In the working state of battery discharge, when the current flows through the battery, it needs to overcome the resistance caused by the internal resistance of the battery, which will cause ohmic voltage drop and electrode polarization, so the working voltage is always lower than the open circuit voltage, and it is the same when charging. Instead, the terminal voltage is always higher than the open circuit voltage. That is, as a result of polarization, the terminal voltage of the battery is lower than the electromotive force of the battery when the battery is discharged, and the terminal voltage of the battery is higher than the electromotive force of the battery when the battery is charged.

7. Battery power indicator (state of charge, SOC)

SOC, also known as the state of charge of the battery, is the remaining power of the battery. SOC determines the remaining driving range and energy management strategy of EV, and its importance is the same as the engine management of traditional vehicles. Therefore, the accurate estimation of the state of charge of EV power battery pack is of great significance to the running state of pure EV.

8. Battery health indicator (state of health, SOH)

SOH, refers to how much charge a battery can store.

9. Battery management system (BMS)

BMS refers to a system that manages batteries (for example, battery terminal current, battery terminal voltage, or battery temperature, etc.), and usually includes a monitoring module and an arithmetic control module. BMS mainly includes two parts: battery monitor unit (BMU) and battery control unit (BCU).

In the related art, a temperature sensor outside the power battery pack is usually used to monitor and warn the internal heat of the power battery pack, or, based on electrochemical modeling, to detect and warn the internal heat of the power battery pack. In the technology of estimating the internal heat of the power battery pack based on the temperature sensor, the surface temperature of the power battery pack measured by the temperature sensor is usually used as the internal heat of the power battery pack. There is a big difference, so based on this method, it cannot truly reflect the internal heat of the power battery pack. In the technology of estimating the internal heat of the power battery pack based on the electrochemical model, the voltage and current sampling with high sampling frequency is required to conduct electrochemical modeling and estimation of the internal resistance of the power battery pack, but the current EV does not have high frequency sampling conditions. In addition, a power battery pack is usually formed by a larger number of single cells in series and parallel in an EV to provide power for the EV. Due to the inconsistency of the multiple single cells included in the power battery pack, the use of this modeling method requires electrochemical modeling of each single cell, resulting in a very complicated modeling process.

The present application provides a method for estimating the heat of a power battery pack, which can improve the accuracy and efficiency of the heat estimation of a power battery pack.

The method for estimating the heat of a power battery pack provided by the present application can be applied to, but not limited to, the following application scenarios: a scenario including only a vehicle, a scenario including a cloud device (for example, a cloud server) and a vehicle at the same time, or only a cloud device in the scene. For example, when only a vehicle is included in the application scenario, only the vehicle (for example, an on-board module, on-board module, on-board component, on-board chip or on-board unit in the vehicle) can execute the method for estimating the heat of the power battery pack provided in this application . For example, when the application scenario includes both a cloud device and a vehicle, the cloud device and the vehicle can jointly execute the method for estimating the heat of a power battery pack provided by the present application.

Exemplarily, FIG. 1 is a schematic diagram of an application scenario 100 suitable for the method for estimating heat of a power battery pack provided by the embodiment of the present application. As shown in FIG. 1 , the application scenario 100 includes one cloud device 110 and two vehicles 120 . In FIG. 1, the cloud device 110 can communicate with each vehicle 120, and the two vehicles 120 can also communicate with each other. For example, the frequency spectrum of the cellular link may be used for communication between the cloud device 110 and each vehicle 120 . Alternatively, the intelligent transportation spectrum near 5.9GHz can also be used for communication.

It should be understood that FIG. 1 is only for illustration and does not constitute any limitation to the application scenarios to which the method for estimating the heat of a power battery pack provided in the present application is applicable. For example, a larger number of vehicles 120 may also be included in the application scenario 100 . For example, in some scenarios, only the vehicle 120 in the application scenario 100 may be included, or only the cloud device 110 in the application scenario 100 may be included, which is not limited in this application.

Below, with reference to FIG. 2 and FIG. 3 , the method for estimating the heat of the power battery pack provided by the present application will be described in detail.

FIG. 2 is a schematic flowchart of a method 100 for estimating heat of a power battery pack provided by an embodiment of the present application. As shown in FIG. 2 , the method 100 includes

steps

110 and 120 , and the

steps

110 and 120 will be described in detail below. The execution subject of the method 100 includes, but is not limited to, the cloud device 110 in FIG. 1 and the vehicle 120 in FIG. 1 . It should be understood that the example in FIG. 2 is only for helping those skilled in the art to understand the embodiments of the present application, and is not intended to limit the application embodiments to specific numerical values or specific scenarios in FIG. 2 . Those skilled in the art can obviously make various equivalent modifications or changes based on the given examples, and such modifications and changes also fall within the scope of the embodiments of the present application.

Step 110: Determine the second state data of the power battery pack according to the first state data of the power battery pack, wherein the first state data includes terminal current, terminal voltage and temperature, and the second state data includes open circuit voltage and open circuit voltage with respect to temperature. partial derivative.

The above-mentioned first state data includes terminal current, terminal voltage and temperature. Among them, the terminal current is also called the working current, and the terminal voltage is also called the working voltage. The temperature can be understood as the temperature of the outer surface of the power battery pack, and/or the temperature of the inner surface of the power battery pack, and/or the ambient temperature where the power battery pack is located, etc., which are not specifically limited.

Optionally, before step 110, the following operation may be performed: acquiring the first state data of the power battery pack. Exemplarily, when the execution subject of the method 100 is the cloud device 110, obtaining the first state data of the power battery pack may include the following steps: the vehicle 120 obtains the first state data of the power battery pack in the vehicle 120; the vehicle 120 obtains the first state data of the power battery pack in the vehicle 120; The first state data of the power battery pack in the vehicle 120 is sent to the cloud device 110 , so that the cloud device 110 obtains the first state data of the power battery pack in the vehicle 120 . The manner in which the vehicle 120 obtains the first state data of the power battery pack in the vehicle 120 is not limited, for example, the first state data can be obtained through a sensor installed on the power battery pack in the vehicle 120 .

The above-mentioned first state data of the power battery pack can be understood as the first state data of one or more power battery packs, which is not specifically limited. Specifically, when the first state data of the power battery pack is the first state data of a power battery pack, the above step 110 can be understood as determining according to the first state data of the one power battery pack, the one power battery pack of the second state data. When the first state data of the power battery pack is the first state data of multiple power battery packs, the above step 110 can be understood as determining the second state data of one power battery pack according to the first state data of the multiple power battery packs Status data, at this time, the one power battery pack may be one power battery pack among multiple power battery packs, and the one power battery pack may also be other power battery packs other than the multiple power battery packs.

In the embodiment of the present application, determining the second state data of the power battery pack according to the first state data of the power battery pack may include the following steps:

establishing an open-circuit voltage model of the power battery pack according to the first state data and the equivalent circuit model of the power battery pack;

Input the first state data into the open circuit voltage model to obtain the open circuit voltage;

Input the first state data into the first-order partial derivative model to obtain the partial derivative value of the open-circuit voltage with respect to temperature, wherein the first-order partial derivative model is obtained by calculating the first-order partial derivative of the open-circuit voltage model with respect to temperature.

It can be understood that, when the first state data of the power battery pack is the first state data of multiple power battery packs, the equivalent circuit model of the power battery pack matches the multiple power battery packs. That is to say, the equivalent circuit model of the power battery pack has the same or similar effect as the circuit structures of the plurality of power battery packs. Wherein, the method for obtaining the equivalent circuit model of the power battery pack and the specific form of the equivalent circuit model are not limited. For example, the equivalent circuit model of the power battery pack includes but is not limited to: an internal resistance model, an RC model, a PNGV model or a GNL model.

In some implementations, establishing an open-circuit voltage model of the power battery pack according to the first state data and the equivalent circuit model of the power battery pack may include the following steps:

According to the terminal current, terminal voltage and temperature, establish the charging model or discharging model of the power battery pack;

According to the equivalent circuit model of the power battery pack, as well as the charging model or the discharging model, an open circuit voltage model is established;

Wherein, when the first state data is the data of the power battery pack in the discharge state, the open circuit voltage model is established according to the equivalent circuit model and the charging model;

When the first state data is the data that the power battery pack is in a charged state, the open circuit voltage model is established according to the equivalent circuit model and the discharge model.

The first state data and the second state data are the state data of the power battery pack at the first moment.

It can be understood that when the terminal current, terminal voltage and temperature are the data of the power battery pack in the charging state, the established model is the charging model. When the terminal current, terminal voltage and temperature are the data of the power battery pack in the discharge state, the established model is the discharge model.

In some implementations, a charging model or a discharging model of the power battery pack is established according to the terminal current, terminal voltage and temperature, which may include:

Determine the state of charge SOC of the power battery pack according to the terminal voltage;

Regression fitting is performed on SOC, terminal voltage and temperature to obtain a charging model or a discharging model.

In the embodiment of the present application, the above obtained charge model and discharge model are not specifically limited, and only the expressions of the obtained charge model and discharge model are required to have a first-order partial derivative with respect to the temperature of the power battery pack.

In one example, the above charging model can be expressed by the following formula:

U _charge = f ₁ (SOC, T)

Among them, U _charge represents the terminal voltage of the power battery pack in the charging state, f ₁ represents the charging model, SOC represents the state of charge of one or more power battery packs, and T represents the temperature of one or more power battery packs.

In one example, the above discharge model can be expressed by the following formula:

U _discharge =f ₂ (SOC,T)

Among them, U _discharge represents the terminal voltage of the power battery pack in the discharged state, f ₂ represents the discharge model, SOC represents the state of charge of one or more power battery packs, and T represents the state of charge of one or more power battery packs temperature.

In the embodiment of the present application, the specific expressions of the charging model and discharging model obtained above are not limited, and it is only required that the expressions of the charging model and discharging model obtained by fitting have a first-order partial derivative with respect to the temperature T of the power battery pack That's it.

In the embodiments of the present application, the open-circuit voltage model obtained above is not specifically limited.

In an example, the open-circuit voltage model of the power battery pack in the charging state at the target time can be expressed by the following formula:

U _ocv =f ₃ (U _charge ,U _discharge )

Among them, U _ocv represents the open circuit voltage of the power battery pack at the target time, the function f ₃ represents the model established based on the equivalent circuit model and the discharge model of the power battery pack, and U _charge represents the end of the power battery pack actually measured at the target time Voltage, U _discharge represents the terminal voltage determined based on the discharge model of the power battery pack according to the SOC of the power battery pack at the target time and the T of the power battery pack at the target time.

In another example, the open-circuit voltage model of the power battery pack in the discharge state at the target time can be expressed by the following formula:

U _ocv =f ₃ (U _charge ,U _discharge )

Among them, U _ocv represents the open circuit voltage of the power battery pack at the target time, the function f ₃ represents the model established based on the equivalent circuit model and the discharge model of the power battery pack, and U _charge represents the SOC of the power battery pack at the target time and the power at the target time The T of the battery pack is based on the terminal voltage estimated by the charging model, and the U _discharge represents the terminal voltage of the power battery pack actually measured at the target time.

In the embodiments of the present application, the first-order partial derivative model obtained above is not specifically limited.

In an example, when the power battery pack is in a charged state, the first-order partial derivative function of the open-circuit voltage model of the power battery pack with respect to temperature can be expressed by the following formula:

in,

Represents the partial derivative of the open circuit voltage with respect to temperature, the function _f3 represents the equivalent circuit model + discharge model of the power battery pack, SOC represents the state of charge of the power battery pack, T represents the temperature of the power battery pack, and Θ represents the power established above. The parameter value of the discharge model (for example, f ₂ ) of the battery pack, the function f ₄ represents the first-order partial derivative function of the open-circuit voltage model with respect to temperature, U _measured represents the actually measured terminal voltage of the power battery pack, and m represents the power battery pack equivalent circuit model.

In the embodiments of the present application, the power battery pack is not specifically limited. For example, the power battery pack can be a cylindrical lithium battery, a square lithium battery, a soft package battery, an aluminum shell battery, and the like. For example, a power battery pack may include one battery cell module or multiple battery cell modules.

Step 120: Input the first state data and the second state data into the heat estimation model to obtain the internal heat of the power battery pack.

The input parameters of the above heat estimation model only include the first state data and the second state data.

In the embodiments of the present application, the method for determining the heat estimation model and the heat estimation model are not limited. It is only necessary to ensure that the input of the heat estimation model includes the first state data and the second state data, and the output of the heat estimation model includes heat.

In one example, a heat estimation model may be determined from a single cell heat model, and the heat estimation model may be expressed by the following formula:

Among them, the function f represents the heat estimation model, I represents the terminal current in the power battery pack, T represents the temperature of the power battery pack, U _ocv represents the open circuit voltage of the power battery pack, U _t represents the terminal voltage of the power battery pack,

Indicates the partial derivative of the open circuit voltage of the power battery pack with respect to temperature.

Optionally, after step 120, the following steps may also be performed:

In response to the internal heat of the power battery pack being greater than or equal to the first threshold, determine that the internal heat of the power battery pack at the target time is in an abnormal state and issue an alarm; or,

In this embodiment of the present application, the method for determining the first threshold is not specifically limited.

As an example and not limitation, the method for determining a heat-temperature model and a first threshold provided by the present application may include the following steps:

Obtain the internal heat {Q ₁ ,Q ₂ ,...,Q _s } of one or more power battery packs under different operating conditions and the corresponding temperature of one or more power battery packs {T ₁ ,T ₂ ,. ..,T _s }, where s is an integer greater than or equal to 2, where {Q ₁ ,Q ₂ ,...,Q _s } includes some abnormal heat;

Fit {Q ₁ ,Q ₂ ,...,Q _s } and {T ₁ ,T ₂ ,...,T _s } to establish a heat-temperature model. The heat-temperature model can be expressed by the following formula:

Q=f ₅ (T)

where f5 represents the heat _- temperature model.

Calculate the first-order partial derivative of the heat _- temperature model f5 with respect to the temperature of the power battery pack, and obtain the heat generation exponential function

Further, according to the temperature of the power battery pack and the heat generation exponential function

A first threshold corresponding to the power battery pack at the temperature can be determined.

Optionally, in some scenarios according to business requirements, an alarm is issued only in response to the internal heat of the power battery pack being greater than or equal to the first threshold, and no processing is performed in response to the internal heat of the power battery pack being less than the first threshold. .

Optionally, after step 120, the following steps may also be performed:

Correct one or more of the following models according to the first state data of the power battery pack within the first preset time after the first moment, and the second state data of the power battery pack within the first preset time:

Charge model or discharge model, open circuit voltage model, first-order partial derivative model of open circuit voltage, heat-temperature model, wherein the heat-temperature model is a function of the internal heat of the power battery pack on the temperature.

The above can be the first state data of one or more power battery packs within the first preset time after the first moment, and the first state data of one or more power battery packs within the first preset time after the first moment. Two-state data, which is not specifically limited.

By way of example and not limitation, one or more models described above are determined according to the first state data of 50 power battery packs #1 before time #1. It is necessary to estimate the heat inside the power battery pack #2 at time #1. You can first use the first state data of the power battery pack #2 at time #1 to compare the charge model or discharge model, the open circuit voltage model, and the first order of the open circuit voltage. The partial derivative model is corrected, and then the second state data of the power battery pack #2 at time #2 can be estimated according to the revised charging model or discharge model, the open circuit voltage model, and the first-order partial derivative model of the open circuit voltage, so that the power can be estimated and obtained. Internal heat of battery pack #2 at time #2.

Optionally, after step 120, the following steps may also be performed:

The internal heat of the power battery pack at the second preset time is used as the input characteristic value, and the internal heat of the power battery pack at the third preset time is used as the output characteristic value for model training, and a heat trend prediction model is established. Set the time as the preset time after the second preset time;

Input the internal heat of the power battery pack at the fourth preset time into the heat trend prediction model, and obtain the internal heat of the power battery pack at the fifth preset time, where the fifth preset time is the prediction after the fourth preset time. set time.

The above may be to use the internal heat of one or more power battery packs at the second preset time as the input feature value, and the internal heat of the one or more power battery packs at the third preset time as the feature output to perform model training, This is not specifically limited.

In the embodiment of the present application, the execution subject of the execution method 100 is not specifically limited. For example, the method 100 may be performed by a server, or may be performed by a vehicle including a power battery pack.

In the embodiment of the present application, the following models obtained above may also be called regression models: a charging model, a discharging model, a heat-temperature model, and a heat trend prediction model.

It should be understood that FIG. 2 is for illustration only, and does not constitute any limitation to the method 100 for estimating heat of a power battery pack provided by the embodiment of the present application. The method 100 provided in this embodiment of the present application is not limited to estimating the internal heat of a power battery pack, and the method 100 can also be applied to estimating the heat of a circuit structure having terminal current, terminal voltage and temperature. For example, before step 110, the step of acquiring the first state data of the power battery pack may also be included. For example, after step 120, it may further include steps such as estimating the internal heat of the power battery pack in a future period of time according to the acquired internal heat of one or more power battery packs.

In the embodiment of the present application, the parameters for determining the internal heat of the power battery pack only include the first state data and the second state data of the power battery pack, and the first state data and the second state data do not depend on the complex mechanical properties of the power battery pack. In this way, the modeling of the complex mechanical structure of the power battery pack is avoided, and the estimation efficiency can be improved. Among them, the first state data and the second state data include not only the temperature of the power battery pack, but also the terminal current and terminal voltage of the power battery pack, that is to say, the battery management system BMS is also considered when estimating the internal heat of the power battery pack. strategies, in this way, can improve the computational accuracy of heat estimation.

Below, with reference to FIG. 3 , the above-mentioned method 100 for estimating heat of a power battery pack will be introduced by taking the first state data of N (N is an integer greater than or equal to 1) power battery pack #1 as an example.

FIG. 3 is a schematic flowchart of a method 200 for estimating heat of a power battery pack provided by an embodiment of the present application. As shown in FIG. 3 , the method 200 includes steps 210 to 271 , and the steps 210 to 271 will be described in detail below.

Step 210: Obtain first state data of N power battery packs #1, where the first state data includes terminal current, terminal voltage and temperature, and N is an integer greater than or equal to 1.

Exemplarily, the first state data of the N power battery packs #1 may be acquired through sensors installed on the N power battery packs #1.

The specific value of N is not limited. For example, N can take 50, 200 or 1000 and so on.

Step 220: Determine an open-circuit voltage model according to the first state data of the N power battery packs #1 and the equivalent circuit model of the power battery pack, wherein the open-circuit voltage model is used to determine the second state data according to the first state data, and the second state data. The state data includes the open circuit voltage and the partial derivative of the open circuit voltage of the power battery pack with respect to temperature.

The equivalent circuit model of the above-mentioned power battery pack matches the above-mentioned N power battery packs #1. That is to say, the equivalent circuit model of the power battery pack has the same or similar effect as the circuit structure of each power battery pack #1.

The method for establishing the open-circuit voltage model described in step 220 is the same as the method for establishing the open-circuit voltage model described in the foregoing step 110, and details are not described herein again.

Step 230: Obtain the second state data of the N power battery packs #1 according to the first state data of the N power battery packs #1 and the open-circuit voltage model.

The method for determining the second state data in step 230 is the same as the method for determining the second state data in step 110 above, and details are not described herein again.

Step 240: Input the first state data of the N power battery packs #1 and the second state data of the N power battery packs #1 into the heat estimation model to obtain the internal heat of the N power battery packs #1, of which N Part of the internal heat of the power battery pack #1 is abnormal.

Steps 250 to 271 may also be performed after the above-mentioned steps 210 to 240 .

Step 250 , establish a heat-temperature model according to the internal heat of the N power battery packs #1 at the first moment and the temperatures of the N power battery packs #1 at the first moment.

Step 251: At the second moment, it is determined that the internal heat of the power battery pack #2 is greater than the first threshold value, and the thermal runaway phenomenon of the power battery pack #2 is determined and an alarm is issued, wherein the first threshold value is based on the power battery pack #2 at the second The temperature at the moment, and the first-order partial derivative model of the heat-temperature model is determined.

Step 260: Acquire the first state data of the power battery pack #2, and correct the open circuit voltage model according to the first state number of the power battery pack #2.

The correction method in step 260 is the same as the correction method in step 110 above, and details are not repeated here.

Step 261: Obtain second state data of the power battery pack #2 according to the revised open circuit voltage model and the first state data of the power battery pack #2.

Step 262: Input the first state data of the power battery pack #2 and the second state data of the power battery pack #2 into the heat estimation model to obtain the internal heat of the power battery pack #2.

Step 270: Model training is performed using the internal heat of the N power battery packs #1 at the second preset time as the input characteristic value, and the internal heat of the N power battery packs #1 at the third predetermined time as the output characteristic value, A heat trend prediction model is established, wherein the third preset time is a preset time after the second preset time.

Step 271: Input the internal heat of the power battery pack at the fourth preset time into the heat trend prediction model to obtain the internal heat of the power battery pack at the fifth preset time, where the fifth preset time is after the fourth preset time preset time.

It should be understood that FIG. 3 is for illustration only, and does not constitute any limitation to the method 200 for estimating the heat of a power battery pack provided by the embodiment of the present application. For example, in some implementations, steps 250 to 271 may not be performed after step 240 . For example, in some implementations, only steps 260 , 261 and 262 are performed after step 240 .

In the embodiment of the present application, on the basis of estimating the internal heat of the power battery pack in real time, the heat generation trend of the internal heat of the power battery pack can be predicted in advance, so that the power battery pack that may be thermally runaway can be identified in advance, and the power Damage to the battery pack. When the power battery pack is used in a vehicle, a thermal runaway warning can be given in advance for the personnel using the vehicle, so as to reserve time for escape.

Above, with reference to FIG. 2 and FIG. 3 , the method for estimating the heat of the power battery pack provided by the embodiment of the present application is described in detail. Next, the method for establishing a charging model involved in the above method 100 will be specifically introduced. Wherein, the execution subject of the method for establishing the power battery pack charging model is not specifically limited. In one example, only the cloud device may be used as the execution subject of the method for establishing the charging model of the power battery pack. In another example, only the vehicle may be used as the executing subject of the method for establishing the charging model of the power battery pack. In yet another example, a power battery pack charging model can also be jointly established by the vehicle and the cloud device. It can be understood that the charging model in the above method 100 is a regression model, and the regression model in the above method 100 further includes a discharge model, a heat-temperature model and a heat trend prediction model.

In the following, with reference to FIG. 4 , the method for establishing a power battery pack charging model provided by the embodiment of the present application is introduced by taking the vehicle and the cloud device jointly establishing a power battery pack charging model as an example.

FIG. 4 is an interactive schematic diagram of a method 300 for establishing a power battery pack charging model provided by an embodiment of the present application. As shown in FIG. 4 , the method 300 includes steps 310 to 393 , and the steps 310 to 393 will be described in detail below. The execution subjects of the method 300 include cloud device #1, vehicle #1 and vehicle #2, and the cloud device #1 may be cloud device 110, vehicle #1 and vehicle in the application scenario 100 shown in FIG. 1 above. #2 may be the vehicle 120 in the application scenario 100 . The cloud device #1 is not specifically limited, for example, the cloud device #1 may be a cloud server or the like. The vehicle #1 and the vehicle #2 are not specifically limited. Taking vehicle #1 as an example, the vehicle #1 may be an on-board module, on-board chip, or on-board unit in vehicle #1. It should be noted that, in FIG. 4 , there may also be a greater number (eg, 20) of vehicles and a greater number (eg, 2) of cloud devices, and the principle is the same, which is omitted. The charging model in the method 300 can also be replaced with any one of the following models: a discharge model, a heat-temperature model, and a heat-trend prediction model. The principles are the same, which is omitted.

Step 310: Vehicle #1 sends first request information #1 to cloud device #1.

The first request information #1 is used to request the cloud device #1 to issue the charging model to the vehicle #1. The charging model may be determined by the cloud device #1 according to the first state data of the power battery pack in the charging state obtained by the experimental simulation model, and the first state data includes terminal current, terminal voltage and temperature. The charging model can also be determined by the cloud device #1 according to the first state data of the power battery pack in the charging state provided by the car manufacturer, and the data provided by the car manufacturer are desensitized data. In other words, the data provided by the car manufacturer is not the data of the power battery pack in vehicle #1, nor the data of the power battery pack in vehicle #2.

In this embodiment of the present application, the vehicle #1 may determine to send the first request information #1 to the cloud device #1 according to the actual situation of the vehicle #1 itself (for example, the safety of vehicle operation).

Optionally, before step 310, the method further includes: establishing a charging model for cloud device #1. In one example, cloud device #1 may perform regression fitting on the first state data of one or more power battery packs provided by the car manufacturer in a charging state to obtain a charging model, where the input of the charging model includes terminal current and temperature , the output of the charging model includes the terminal voltage.

Step 320: Vehicle #2 sends first request information #2 to cloud device #1.

The first request information #2 is used to request the cloud device #1 to issue the charging model to the vehicle #2.

Step 330, the cloud device #1 sends the charging model to the vehicle #1.

Step 340, cloud device #1 sends the charging model to vehicle #2.

Step 350, vehicle #1 uses the first state data of the power battery pack #1 of vehicle #1 in the charging state to train the charging model to obtain parameter set #1.

The above vehicle #1 uses the first state data of the power battery pack #1 of the vehicle #1 in the charging state to train the charging model, and obtains the parameter set #1, including:

Vehicle #1 uses the first state data of vehicle #1's power battery pack #1 in the charging state to train the charging model to obtain charging model #1;

The vehicle #1 compares the parameters of the charging model with the parameters of the charging model #1, and determines the parameters that are different in the two models as the parameters included in the parameter set #1. Before step 350, the method may further include: vehicle #1 acquiring first state data of power battery pack #1 in a charging state of vehicle #1.

Step 360, the vehicle #1 sends the parameter set #1 to the cloud device #1.

Step 370: Use the first state data of the power battery pack #2 of the vehicle #2 in the charging state to train the charging model to obtain a parameter set #2.

The above vehicle #2 uses the first state data of the power battery pack #1 of the vehicle #2 in the charging state to train the charging model, and obtains the parameter set #2, including:

Vehicle #2 uses the first state data of vehicle #2's power battery pack #1 in the charging state to train the charging model to obtain charging model #2;

The vehicle #2 compares the parameters of the charging model with the parameters of the charging model #2, and determines the parameters that are different in the two models as the parameters included in the parameter set #2.

Before step 370, the method may further include: vehicle #2 acquiring first state data of the power battery pack #2 of vehicle #2 in a charging state.

Optionally, in the scenario where only the cloud device #1 performs the establishment of the charging model, the method for determining the parameter set #1 in the above step 350 and the method for determining the parameter set #2 in the above step 370 can be performed by the cloud device #1. implement. For example, after the above step 320, the cloud device #1 does not perform the above steps 330 and 340, and the cloud device #1 specifically performs the following operations: receiving the first state data from the vehicle #1 that the power battery pack #1 is in the charging state , and receive the first state data from the vehicle #2 that the power battery pack #2 is in the charging state; according to the first state data that the power battery pack #1 is in the charging state, and the power battery pack #2 is in the charging state The first state data trains the charging model to obtain a parameter set #1; the charging model is updated according to the parameter set #1 and the parameter set #2 to obtain an updated charging model.

Step 380: Vehicle #2 sends parameter set #2 to cloud device #1.

It can be understood that, in this embodiment of the present application, the execution order of the above steps 310 to 380 is not specifically limited, and it is only necessary to ensure that after step 310, steps 330 and 340 are executed, and after step 320, step 340 is executed. and step 370. For example, in some implementations, steps 310 to 380 described above may be performed in the following order: step 310 , step 320 , step 330 , step 350 , step 340 , step 360 , step 370 , and step 380 . The sequence of steps may be adjusted according to actual usage requirements, which is not limited in this embodiment of the present application.

Step 390, the cloud device #1 uses the parameter set #1 and the parameter set #2 to update the charging model to obtain an updated charging model.

In this embodiment of the present application, the updating method of the foregoing step 390 is not limited.

Step 391, the vehicle #1 sends the second request information #1 to the cloud device #1.

Wherein, the second request information #1 is used to request to acquire the updated charging model from the cloud device #1.

The vehicle #1 may determine to send the second request information #1 to the cloud device #1 according to the situation of the vehicle #1.

Step 392, the cloud device #1 sends the updated charging model to the vehicle #1.

Step 393, vehicle #1 obtains the terminal voltage of power battery pack #1 based on the updated charging model.

Among them, vehicle #1 obtains the terminal voltage of power battery pack #1 based on the updated charging model, including:

Vehicle #1 inputs the terminal current and the corresponding temperature of the power battery pack #1 of the vehicle #1 in the charging state into the updated charging model, and obtains the terminal voltage of the power battery pack #1 in the charging state.

It should be understood that FIG. 4 is for illustration only, and does not constitute any limitation to the method for establishing a charging model provided by the embodiments of the present application. For example, the method 300 may also be applicable to an application scenario including a larger number of vehicles, which is not limited in this embodiment of the present application. For example, in some implementations, the discharge model, the heat-temperature model, or the heat trend prediction model in the above-described method 100 may be established based on the above-described method for establishing a charging model. For example, in some other implementations, after step 390, the following operations may be included: the vehicle #2 sends the second request information #2 to the cloud device #1, and the second request information #2 is used to request the slave cloud device #1 Obtain the updated charging model from the cloud; cloud device #1 sends the updated charging model to vehicle #2; vehicle #2 obtains the terminal voltage of power battery pack #2 based on the updated charging model.

In the embodiment of the present application, cloud device #1 delivers the charging model in cloud device #1 to each vehicle according to the request of each vehicle (ie, vehicle #1 and vehicle #2). Among them, the charging model is that the cloud device #1 uses the first state data (desensitization data provided by the automobile manufacturer, or data obtained from the experimental simulation model) of one or more power battery packs in the charging state to establish a charging model. The process is simpler. After acquiring the charging model issued by the cloud device #1, each vehicle uses the charging model to local data for each vehicle (that is, the first state data of the power battery pack #1 in the vehicle #1 in the charging state) , or the first state data of the power battery pack #2 in vehicle #2 being in a charging state) training to obtain a parameter set (ie, parameter set #1 or parameter set #2) for each vehicle. After that, each vehicle only sends the parameter set to the cloud device #1. This process does not involve leaking the data of vehicle #1 and vehicle #2, which ensures the security of the data of vehicle #1 and vehicle #2. And reduce the communication overhead.

Based on the technical solutions provided in the embodiments of the present application, each vehicle (ie, vehicle #1 or vehicle #2) has a higher calculation rate when determining the terminal voltage of the power battery pack in each vehicle by using the updated charging model Therefore, the accuracy of the estimation of the internal heat of the power battery pack in each vehicle can be improved.

Above, with reference to FIGS. 1 to 4 , the application scenarios applicable to the method for estimating the heat of the power battery pack provided by the embodiments of the present application, the method for estimating the heat of the power battery pack provided by the embodiments of the present application, and determining the charging of the power battery pack are described in detail with reference to FIGS. 1 to 4 . method of the model. Hereinafter, the related devices and systems provided by the present application will be described in detail with reference to FIG. 5 to FIG. 8 . It should be understood that the descriptions of the method embodiments correspond to the descriptions of the detection device embodiments. Therefore, for the parts not described in detail, reference may be made to the foregoing method embodiments.

FIG. 5 is a schematic structural diagram of a power battery pack heat estimation apparatus 1000 provided by an embodiment of the present application. As shown in Figure 5, the device 1000 includes:

A determination unit 1001, configured to determine second state data of the power battery pack according to the first state data of the power battery pack, wherein the first state data includes terminal current, terminal voltage and temperature, and the second state data includes open circuit voltage and the partial derivative of the open circuit voltage with respect to the temperature;

The estimation unit 1002 is configured to input the first state data and the second state data into a heat estimation model to obtain the internal heat of the power battery pack.

Optionally, in some implementations, the determining unit 1001 is further configured to:

Optionally, in some implementations, the first state data and the second state data are the state data of the power battery pack at the first moment,

The determining unit 1001 is also used for:

It should be understood that the above FIG. 5 is only for illustration, and does not constitute any limitation to the apparatus 1000 provided in the embodiment of the present application. For example, in some scenarios, the apparatus 1000 may further include a storage module, and the storage module may be used to store the processing results of the determination unit 1001 and/or the estimation unit 1002 and corresponding computer programs and the like.

In this embodiment of the present application, the device for estimating the heat of the power battery pack should include a processor. Optionally, in some implementations, the power battery pack heat estimation device may further include a memory.

In the following, with reference to FIG. 6 , the description will be given by taking as an example that a power battery pack heat estimation device includes a processor and a memory.

As shown in FIG. 6 , the device 2000 includes: a processor 2010 and a memory 2020 . The processor 2010 and the memory 2020 communicate with each other through an internal connection path to transmit control and/or data signals, the memory 2020 is used to store computer programs, and the processor 2010 is used to call and run the computer from the memory 2020 A program to execute any possible method of the above-mentioned method 100 , method 200 , and method 300 .

Specifically, the functions of the processor 2010 correspond to the specific functions of the determining unit 4001 and the estimating unit 4002 shown in FIG. 6 , and will not be repeated here.

Optionally, in some embodiments, the device 2000 may further include a receiver and/or an output device. The receiver may be configured to receive the first state data of the power battery pack in the above method 100 and/or the method 200 and/or the method 300, etc., which will not be repeated here.

FIG. 7 is a schematic structural diagram of a system 3000 provided by an embodiment of the present application. As shown in FIG. 7 , the system 3000 includes: a power battery pack heat estimation apparatus 1000 and/or a power battery pack heat estimation device 2000 .

FIG. 8 is a schematic structural diagram of a system 4000 provided by an embodiment of the present application. As shown in FIG. 8 , the system 4000 includes: a vehicle 4001 and a cloud device 4002 .

In some implementations, vehicle 4001 is used to obtain step 210 in method 100 above, method 200 above.

Optionally, in other implementation manners, the vehicle 4001 is further configured to perform steps 310 , 310 , 350 , 360 , 370 , 380 , 391 , and 393 in the above method 300 .

In some implementations, the cloud device 4002 is used to perform steps 220 to 271 in the above method 100 and 200 above.

Optionally, in other implementation manners, the cloud device 4002 is further configured to perform steps 330 , 340 , 390 , and 392 in the foregoing method 300 .

Embodiments of the present application further provide a computer-readable storage medium, on which a program is stored, and when it runs on a computer, enables the computer to implement any possible method among the foregoing method 100 , method 200 , and method 300 .

The embodiment of the present application provides a computer program product, when the computer program product runs on the power battery pack heat estimation apparatus 1000 and/or the power battery pack heat estimation apparatus 2000, the power battery pack heat estimation apparatus 1000 and/or the power battery pack heat estimation apparatus 2000 is provided. The power battery pack heat estimation apparatus 2000 executes the method 100 and/or 200 and/or the method 300 in the above method embodiments.

Embodiments of the present application further provide a vehicle, such as a smart car, where the smart car includes at least one device for estimating heat of a power battery pack mentioned in the above embodiments of the present application or any of the above systems.

Those of ordinary skill in the art can realize that, in combination with the method steps and units described in the embodiments disclosed herein, they can be implemented in electronic hardware, computer software, or a combination of the two. Interchangeability, the steps and components of the various embodiments have been generally described in terms of functions in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Persons of ordinary skill in the art may use different methods of implementing the described functionality for each particular application, but such implementations should not be considered beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, for the specific working process of the above-described systems, devices and units, reference may be made to the corresponding processes in the foregoing method embodiments, which are not repeated here.

In the several embodiments provided in this application, the disclosed systems, devices and methods may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the unit is only a logical function division. In actual implementation, there may be other division methods, for example, multiple units or components may be combined or Integration into another system, or some features can be ignored, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may also be electrical, mechanical or other forms of connection.

The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solutions of the embodiments of the present application.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application are essentially or part of contributions to the prior art, or all or part of the technical solutions can be embodied in the form of software products, and the computer software products are stored in a storage medium , including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

The above descriptions are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications within the technical scope disclosed in the present application. or replacement, these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer program instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions according to the embodiments of the present application are generated in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program instructions may be transmitted from a website site, computer, server or data center via Wired or wireless transmission to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, digital video discs (DVDs), or semiconductor media (eg, solid state drives), and the like.

Those of ordinary skill in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium can be read-only memory, magnetic disk or optical disk, etc.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

A method for estimating heat of a power battery pack, characterized in that the method comprises:

Determine the second state data of the power battery pack according to the first state data of the power battery pack, wherein the first state data includes terminal current, terminal voltage and temperature, and the second state data includes open circuit voltage and the partial derivative of the open circuit voltage with respect to the temperature;

The first state data and the second state data are input into a heat estimation model to obtain the internal heat of the power battery pack.
The method according to claim 1, wherein the determining the second state data of the power battery pack according to the first state data of the power battery pack comprises:

establishing an open circuit voltage model of the power battery pack according to the first state data and the equivalent circuit model of the power battery pack;

inputting the first state data into the open circuit voltage model to obtain the open circuit voltage;

Inputting the first state data into a first order partial derivative model to obtain a partial derivative value of the open circuit voltage with respect to the temperature, wherein the first order partial derivative model is to calculate the open circuit voltage model with respect to the temperature The first partial derivative is obtained.
The method according to claim 2, wherein the establishing an open circuit voltage model of the power battery pack according to the first state data and the equivalent circuit model of the power battery pack comprises:

establishing a charging model or a discharging model of the power battery pack according to the terminal current, the terminal voltage and the temperature;

establishing the open circuit voltage model according to the equivalent circuit model of the power battery pack and the charging model or the discharging model;

Wherein, when the first state data is the data that the power battery pack is in a discharge state, the open circuit voltage model is established according to the equivalent circuit model and the charging model;

When the first state data is data in which the power battery pack is in a charged state, the open circuit voltage model is established according to the equivalent circuit model and the discharge model.
The method according to claim 3, wherein the first state data and the second state data are the state data of the power battery pack at a first moment,

The method also includes:

According to the first state data of the power battery pack within a first preset time after the first moment, and the second state data of the power battery pack within the first preset time Make corrections to one or more of the following models:

The charging model or the discharging model, the open-circuit voltage model, the first-order partial derivative model of the open-circuit voltage, and the heat-temperature model, wherein the heat-temperature model is the relationship between the internal heat of the power battery pack and all function of temperature.
The method according to any one of claims 1-4, wherein the method further comprises:

In response to the internal heat of the power battery pack being greater than or equal to the first threshold, determine that the internal heat of the power battery pack is in an abnormal state at the target moment and issue an alarm; or,

In response to the internal heat of the power battery pack being less than the first threshold, it is determined that the power battery pack is in a normal state at the target time.
The method according to any one of claims 1-5, wherein the method further comprises:

Using the internal heat of the power battery pack at the second preset time as the input feature value, and using the internal heat of the power battery pack at the third preset time as the output feature value, model training is performed, and a heat trend prediction model is established, wherein , the third preset time is the preset time after the second preset time;

Input the internal heat of the power battery pack at a fourth preset time into the heat trend prediction model, and obtain the internal heat of the power battery pack at a fifth preset time, where the fifth preset time is The preset time after the fourth preset time.
A power battery pack heat estimation device, characterized in that the device comprises:

a determining unit, configured to determine the second state data of the power battery pack according to the first state data of the power battery pack, wherein the first state data includes terminal current, terminal voltage and temperature, the second state data the data includes an open circuit voltage and a partial derivative of the open circuit voltage with respect to the temperature;

an estimation unit, configured to input the first state data and the second state data into a heat estimation model to obtain the internal heat of the power battery pack.
The device according to claim 7, wherein the determining unit is further configured to:

establishing an open circuit voltage model of the power battery pack according to the first state data and the equivalent circuit model of the power battery pack;

inputting the first state data into the open circuit voltage model to obtain the open circuit voltage;

Inputting the first state data into a first order partial derivative model to obtain a partial derivative value of the open circuit voltage with respect to the temperature, wherein the first order partial derivative model is to calculate the open circuit voltage model with respect to the temperature The first partial derivative is obtained.
The device according to claim 8, wherein the determining unit is further configured to:

establishing a charging model or a discharging model of the power battery pack according to the terminal current, the terminal voltage and the temperature;

establishing the open circuit voltage model according to the equivalent circuit model of the power battery pack and the charging model or the discharging model;

Wherein, when the first state data is the data that the power battery pack is in a discharge state, the open circuit voltage model is established according to the equivalent circuit model and the charging model;

When the first state data is data in which the power battery pack is in a charged state, the open circuit voltage model is established according to the equivalent circuit model and the discharge model.
The device according to claims 7-9, wherein the first state data and the second state data are state data of the power battery pack at a first moment,

The determining unit is also used for:

According to the first state data of the power battery pack within a first preset time after the first moment, and the second state data of the power battery pack within the first preset time Make corrections to one or more of the following models:

The charging model or the discharging model, the open-circuit voltage model, the first-order partial derivative model of the open-circuit voltage, and the heat-temperature model, wherein the heat-temperature model is the relationship between the internal heat of the power battery pack and all function of temperature.
The device according to any one of claims 7-10, wherein the determining unit is further configured to:

In response to the internal heat of the power battery pack being greater than or equal to the first threshold, determine that the internal heat of the power battery pack is in an abnormal state at the target moment and issue an alarm; or,

In response to the internal heat of the power battery pack being less than the first threshold, it is determined that the power battery pack is in a normal state at the target time.
The device according to any one of claims 7-10, wherein the determining unit is further configured to:

Using the internal heat of the power battery pack at the second preset time as the input feature value, and using the internal heat of the power battery pack at the third preset time as the output feature value, model training is performed, and a heat trend prediction model is established, wherein , the third preset time is the preset time after the second preset time;

Input the internal heat of the power battery pack at a fourth preset time into the heat trend prediction model, and obtain the internal heat of the power battery pack at a fifth preset time, where the fifth preset time is The preset time after the fourth preset time.
A method for establishing a power battery pack charging model, characterized in that the method comprises:

The cloud device obtains N pieces of first request information from N vehicles, wherein each first request information is used to request to obtain a charging model of the power battery pack, and the charging model is based on the charging state of the M power battery packs Determined by the first state data, the first state data includes terminal current, terminal voltage and temperature, and the first state data of the M power battery packs in the charging state and the power battery packs in the N vehicles are in the same state. The first state data in the charging state are different, N and M are integers, and N≥1, M≥1;

The cloud device sends the charging model to the N vehicles.
The method of claim 13, wherein the method further comprises:

The cloud device receives N parameter sets from the N vehicles, wherein the N parameter sets are determined according to the N first charging models and the charging models, and the N first charging models are the same as the N first charging models. The power battery packs in the N vehicles correspond to each other, and each first charging model is established according to the first state data of the power battery pack in the corresponding vehicle being in a charging state;

The cloud device uses the N parameter sets to update the charging model to obtain an updated charging model.
The method according to claim 13 or 14, wherein the method further comprises:

The cloud device receives N pieces of second request information from the N vehicles, wherein each second request information is used for requesting to acquire an updated charging model;

The cloud device sends the updated charging model to the N vehicles.
A method for establishing a power battery pack charging model, characterized in that the method comprises:

The first vehicle sends first request information to the cloud device, wherein the first request information is used to request to obtain a charging model of the power battery pack, and the charging model is based on a first state in which the M power battery packs are in a charging state As determined by the data, the first state data includes terminal current, terminal voltage and temperature, and the first state data of the M power battery packs in the charging state and the power battery pack in the first vehicle are in the charging state The first state data of are not the same, M is an integer, and M≥1;

The first vehicle receives the charging model sent from the cloud device.
The method of claim 16, wherein the method further comprises:

The first vehicle sends a first parameter set to the cloud device, wherein the first parameter set is determined according to a first charging model and the charging model, and the first charging model is the first vehicle It is established according to the first state data that the power battery pack in the first vehicle is in a charging state.
The method according to claim 16 or 17, wherein the method further comprises:

sending, by the first vehicle, second request information to the cloud device, where the second request information is used to request to acquire the updated charging model;

The first vehicle receives the updated charging model sent from the cloud device.
A device for establishing a power battery pack charging model, characterized in that the device comprises:

The transceiver unit is used to obtain N pieces of first request information from N vehicles, wherein each first request information is used to request to obtain a charging model of the power battery pack, and the charging model is based on the fact that the M power battery packs are under charging Determined by the first state data in the state, the first state data includes terminal current, terminal voltage and temperature, the first state data of the M power battery packs in the charging state and the power in the N vehicles The first state data of the battery pack in the charging state are different, N and M are integers, and N≥1, M≥1;

The transceiver unit is further configured to send the charging model to the N vehicles.
The apparatus according to claim 19, wherein the apparatus further comprises a processing unit,

The transceiver unit is further configured to: receive N parameter sets from the N vehicles, where the N parameter sets are determined according to the N first charging models and the charging models, and the N first charging models A charging model corresponds to the power battery packs in the N vehicles, and each first charging model is established according to first state data that the power battery packs in the corresponding vehicles are in a charging state;

The processing unit is configured to update the charging model by using the N parameter sets to obtain an updated charging model.
The device according to claim 19 or 20, wherein the transceiver unit is further configured to:

receiving N pieces of second request information from the N vehicles, wherein each second request information is used for requesting to acquire an updated charging model;

Sending the updated charging model to the N vehicles.
A device for establishing a power battery pack charging model, characterized in that the device comprises a transceiver unit,

The transceiver unit is configured to send first request information to the cloud device, wherein the first request information is used to request to obtain a charging model of the power battery pack, and the charging model is based on the fact that the M power battery packs are in a charging state Determined by the first state data of the first state data, the first state data includes terminal current, terminal voltage and temperature, the first state data of the M power battery packs in the charging state and the power battery pack in the first vehicle The first state data in the charging state are different, M is an integer, and M≥1;

The transceiver unit is further configured to receive the charging model sent from the cloud device.
The apparatus of claim 22, wherein:

The transceiver unit is further configured to: send a first parameter set to the cloud device, where the first parameter set is determined according to a first charging model and the charging model, and the first charging model is the The first vehicle is established according to the first state data that the power battery pack in the first vehicle is in a charging state.
The device according to claim 22 or 23, wherein the transceiver unit is further configured to:

sending second request information to the cloud device, wherein the second request information is used to request to obtain the updated charging model;

The updated charging model sent from the cloud device is received.
An apparatus, characterized by comprising a processor and a memory, wherein the memory is used to store computer-executed instructions, and the processor is used to read the computer-executed instructions stored in the memory, so as to realize the steps of claims 1 to 1 6. The method of any one of claims 13 to 15, or the method of any one of claims 16 to 18.
A computer-readable storage medium, characterized in that, a computer program is stored in the computer-readable storage medium, and when the computer program is executed on one or more processors, the computer is made to execute the method according to claim 1. The method of any one of claims 13 to 15, or the method of any one of claims 16 to 18.
A chip system, characterized by comprising at least one processor and an interface, wherein the at least one processor is used to call and run a computer program, so that the chip system executes any one of claims 1 to 6 Item 1, or any one of claims 13 to 15, or any one of claims 16 to 18.
A vehicle, characterized in that the vehicle comprises a device as claimed in any one of claims 7 to 12 , or a device as claimed in any one of claims 22 to 24 .
A system, characterized in that the system includes a vehicle and a cloud device, wherein the vehicle is used to obtain the first state data of the power battery pack in the method according to any one of claims 1 to 6, The vehicle is further configured to send the first state data to the cloud device, and the cloud device is configured to execute the method according to any one of claims 1 to 6, or as in claims 13 to 15 The method of any one.