Temperature control method of power battery, electronic equipment and storage medium

Abstract

The invention provides a temperature control method of a power battery, electronic equipment and a storage medium, and the method comprises the steps: determining a temperature error value and an error change rate value of the power battery based on a temperature value of the power battery and a set temperature value; based on the temperature error value and the error change rate value of the power battery, determining an error expansion and contraction factor and a PID expansion and contraction factor; inputting the error scaling factor and the PID scaling factor into a fuzzy controller to determine a PID target correction coefficient; and inputting the PID target correction coefficient into a PID controller to obtain a target temperature value output by the PID controller, so that the power battery temperature control execution system adjusts the temperature of the power battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automation control, in particular to a temperature control method of a power battery, an electronic device and a storage medium. BACKGROUND

[0002] With the rapid development of the new energy automobile industry, the performance of the thermal management system of the power battery as a core component is directly related to the battery life, safety and vehicle energy efficiency. The traditional fuzzy PID (Proportional-Integral-Derivative) control method is widely used in the power battery thermal management system due to its simple structure, no need for accurate mathematical model and other characteristics.

[0003] In the prior art, the traditional PID control realizes the adjustment of the battery temperature by combining the advantages of fuzzy logic and the PID controller. Specifically, the system collects the battery temperature information through a sensor, compares it with the target temperature and inputs it into the fuzzy controller. The fuzzy controller reasons according to the preset fuzzy rule base, dynamically adjusts the PID parameters and finally outputs a control signal to drive the cooling or heating device. However, this control method exposes significant limitations in the actual operation of the battery: the fixed universe design cannot adapt to the dynamic characteristic changes of the battery from low-temperature start-up to high-temperature heat dissipation and other different working conditions, and in complex driving environments, the uncertainty of the heat dissipation conditions further amplifies the control deviation of the traditional method, resulting in intensified temperature fluctuations. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a temperature control method of a power battery, an electronic device and a storage medium, to solve the technical problems that the existing PID control cannot be applied to the temperature regulation of the power battery in complex driving environments, and the accuracy is low and the stability is poor.

[0005] In a first aspect, the present application provides a temperature control method of a power battery, which comprises determining a power battery temperature error value and an error change rate value based on a power battery temperature value and a set temperature value; determining an error scaling factor and a PID scaling factor based on the power battery temperature error value and the error change rate value; inputting the error scaling factor and the PID scaling factor into a fuzzy controller to determine a PID target correction coefficient; inputting the PID target correction coefficient into a PID controller to obtain a target temperature value output by the PID controller, so that a power battery temperature control execution system adjusts the temperature of the power battery.

[0006] In an optional implementation, the error scaling factor comprises a temperature error value scaling factor and an error change rate value scaling factor, and the temperature error value scaling factor is determined in the following manner: ; wherein, Terror is a temperature error value, Terror is a first constant value, Terror is a temperature error value Terror is an initial argument boundary value of the temperature error value, Terror is a first preset value; and the error change rate value scaling factor is determined by :

[0007] wherein, Terror is an error change rate value, Terror is an initial argument boundary value of the error change rate value, Terror is a second preset value.

[0008] In an optional embodiment, the PID scaling factor is determined by : .

[0009] In an optional embodiment, the first preset value is determined by : ; wherein, Terror is a second constant value, , Terror is a third constant value, .

[0010] In an optional embodiment, the second preset value is determined by : ; wherein, Terror is a second constant value, .

[0011] In an optional embodiment, the fuzzy controller determines the PID target correction coefficient by The temperature error initial argument is adjusted by a temperature error value scaling factor, the error change rate initial argument is adjusted by an error change rate value scaling factor, and the PID correction coefficient initial argument is adjusted by a PID scaling factor; a first quantization level corresponding to the temperature error value in the temperature error fuzzy argument is determined, and a second quantization level corresponding to the error change rate value in the error change rate fuzzy argument is determined; based on the first quantization level, the second quantization level, and a preset fuzzy control rule, a third quantization level corresponding to the PID correction coefficient in the PID correction coefficient fuzzy argument is determined; a corresponding PID standard correction coefficient is determined based on the third quantization level; and a PID target correction coefficient is determined based on the PID standard correction coefficient and a PID proportional factor.

[0012] In an optional implementation, the PID controller determines and outputs the target temperature value in the following manner: a corrected PID coefficient is determined based on the PID target correction coefficient and the current PID coefficient; and the target temperature value is determined based on the corrected PID coefficient and the temperature error value.

[0013] In a second aspect, the present application provides a temperature control device for a power battery, which comprises: a collection module configured to determine a power battery temperature error value and an error change rate value based on a power battery temperature value and a set temperature value; a calculation module configured to determine an error scaling factor and a PID scaling factor based on the power battery temperature error value and the error change rate value, so as to calculate a power battery temperature scaling value and an error change rate scaling value; a correction module configured to input the power battery temperature scaling value and the error change rate scaling value into a fuzzy controller, so as to determine a PID target correction coefficient; an execution module configured to input the PID correction coefficient revised by the PID scaling factor into a PID controller, so as to obtain a target temperature value output by the PID controller, and to enable a power battery temperature control execution system to adjust the temperature of the power battery.

[0014] In a third aspect, the present application provides an electronic device, which comprises a processor, a memory, and a bus, the memory stores machine readable instructions executable by the processor, the processor and the memory communicate with each other through the bus when the electronic device is running, and the processor executes the machine readable instructions to perform the steps of the temperature control method for a power battery according to any one of the preceding embodiments.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program performs the steps of the temperature control method for a power battery according to any one of the preceding embodiments when executed by a processor.

[0016] The application provides a temperature control method of a power battery, an electronic device and a storage medium. The method comprises the following steps: determining a power battery temperature error value and an error change rate value based on a power battery temperature value and a set temperature value; determining an error scaling factor and a PID scaling factor based on the power battery temperature error value and the error change rate value; inputting the error scaling factor and the PID scaling factor into a fuzzy controller to determine a PID target correction coefficient; and inputting the PID target correction coefficient into a PID controller to obtain a target temperature value output by the PID controller, so that a power battery temperature control execution system adjusts the temperature of the power battery. The variable domain fuzzy PID controller dynamically adjusts the domain range of input / output variables and the weight of fuzzy rules according to working conditions, realizes rapid heating response at low temperature and accurate temperature control at high temperature, and realizes efficient, safe and stable temperature regulation of the power battery under complex working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0018] Figure 1 A flowchart of a temperature control method of a power battery provided by the embodiments of the application. Figure 2 A structural schematic diagram of a power battery thermal management system provided by the embodiments of the application. Figure 3 A flowchart of a calculation step of a PID correction coefficient provided by the embodiments of the application. Figure 4 A structural schematic diagram of a temperature control device of a power battery provided by the embodiments of the application. Figure 5 A structural schematic diagram of an electronic device provided by the embodiments of the application. DETAILED DESCRIPTION

[0019] Firstly, the application scenarios of the application are described. The technical solutions of the application can be applied to power battery thermal management.

[0020] The technical solutions in the embodiments of the application will be described in detail below with reference to the drawings in the embodiments of the application.

[0021] Embodiment one Figure 1 A flowchart of a temperature control method of a power battery provided by the embodiments of the application. Figure 2A structural schematic diagram of a power battery thermal management system provided by an embodiment of the present application.

[0022] As shown in Figure 1 and Figure 2 , a temperature control method of a power battery provided by an embodiment of the present application comprises: S1, determining a power battery temperature error value and an error change rate value based on a power battery temperature value and a set temperature value.

[0023] The power battery temperature value herein can be collected by a temperature sensor. The set temperature value is set in advance based on the power battery.

[0024] The power battery temperature error value is the difference between the power battery temperature value and the set temperature value. The error change rate value is the change rate of the power battery temperature error value relative to time.

[0025] S2, determining an error scaling factor and a PID scaling factor based on the power battery temperature error value and the error change rate value.

[0026] In step S2, the error scaling factor comprises a temperature error value scaling factor and an error change rate value scaling factor, and the temperature error value scaling factor is determined in the following manner: ; wherein, the temperature error value is, a first constant value, an initial argument boundary value of the temperature error value , and a first preset value.

[0027] And the error change rate value scaling factor is determined in the following manner :

[0028] wherein, the error change rate value is, an initial argument boundary value of the error change rate value, a second preset value.

[0029] In an optional implementation, the PID scaling factor is determined in the following manner : .

[0030] In this implementation, the first preset value and the second preset value are both fixed values. They can be obtained by pre-simulation calibration.

[0031] S3, input the error scaling factor and the PID scaling factor into the fuzzy controller to determine the PID target correction coefficient.

[0032] As shown in FIG. 3, in step S3, the fuzzy controller can determine the PID target correction coefficient by the following way: Figure 3 S300, adjust the temperature error initial domain by the temperature error value scaling factor, adjust the error change rate initial domain by the error change rate value scaling factor, and adjust the PID correction coefficient initial domain by the PID scaling factor.

[0033] Specifically, the upper and lower boundary values of the temperature error value initial domain can be multiplied by the temperature error value scaling factor to obtain the corresponding temperature error fuzzy domain. The error change rate initial domain and the PID correction coefficient initial domain adjustment principle are similar.

[0034] For example, the initial domain of the temperature error value e can be [-5, 5], the initial domain of the error change rate ec can be [-0.5, 0.5], and the initial domain of the PID correction coefficient can be [-0.08, 0.08], [-0.02, 0.02], and [-0.5, 0.5] respectively.

[0035] The fuzzy domains of e and ec after adjustment can be [-1, 1] and [-0.25, 0.25] respectively, and the fuzzy domains of the PID correction coefficient can be [-0.05, 0.05], [-0.02, 0.02], and [-0.2, 0.2] respectively.

[0036] In step S300, based on the calculated scaling factor, the domain is dynamically adjusted, and the temperature of the power battery can be more finely controlled, so that the battery pack temperature can be maintained in a narrow interval suitable for working, effectively reducing the battery performance degradation and life shortening problem caused by temperature fluctuation, and greatly improving the stability and reliability of the battery working.

[0037] S301, determine the first quantization level corresponding to the temperature error value in the temperature error fuzzy domain, and the second quantization level corresponding to the error change rate value in the error change rate fuzzy domain.

[0038] In an embodiment, the quantization levels of the fuzzy domain can be negative large, negative medium, negative small, zero, positive small, positive medium, and positive large respectively. The corresponding fuzzy set can be {NB, NM, NS, ZO, PS, PM, PB}.

[0039] The membership function of each domain subset can be a triangular function.

[0040] ​Exemplarily, the membership degree corresponding to the temperature error value can be calculated by a membership function, and the first quantization level is determined based on the size of the membership degree.

[0041] S302, based on the first quantization level, the second quantization level and the pre-set fuzzy control rule, a third quantization level corresponding to a PID correction coefficient is determined.

[0042] Specifically, the fuzzy control rule herein can refer to Table 1.

[0043] Table 1 Fuzzy control rule

[0044] Exemplarily, the first quantization level can be PB, and the second quantization level can be NM, and then The respective third quantization levels are ZO / ZO / PM.

[0045] S303, based on the third quantization level, a corresponding PID standard correction coefficient is determined in the fuzzy domain of the PID correction coefficient.

[0046] In step S303, when Taking the quantization level ZO as an example, the corresponding fuzzy domain is [-0.05, 0.05], and the standard correction coefficient value of is 0.

[0047] S304, based on the PID standard correction coefficient and the PID proportional factor, a PID target correction coefficient is determined.

[0048] The PID target correction coefficient can be obtained by multiplying the PID standard correction coefficient and the PID proportional factor.

[0049] S4, the PID target correction coefficient is input into the PID controller to obtain a target temperature value output by the PID controller, so that the power battery temperature control execution system adjusts the temperature of the power battery.

[0050] In step S4, the PID controller can determine the target temperature value and output by the following way: Based on the PID target correction coefficient and the current PID coefficient, a corrected PID coefficient is determined. Based on the corrected PID coefficient and the temperature error value, a target temperature value is determined.

[0051] The power battery temperature control method provided by the embodiment of the application can realize fast heating response at low temperature and accurate temperature control at high temperature by dynamically adjusting the domain range of input / output variables and the weight of fuzzy rules of the variable domain fuzzy PID controller according to the working condition, so that the efficient, safe and stable temperature regulation of the power battery is realized under complex working conditions. ​

[0052] Embodiment Two In one embodiment of the present application, in order to further improve the accuracy of the temperature control of the power battery, the calculation parameters of the scaling factor can be further dynamically adjusted on the basis of the dynamic adjustment of the argument domain.

[0053] Specifically, in one embodiment, the first preset value can be determined by the following method : ; wherein, is a second constant value, , is a third constant value, .

[0054] and the second preset value can be determined by the following method : ; wherein, is a second constant value, .

[0055] In this embodiment, by changing the preset value in the proportional exponential function type scaling factor in the variable argument domain fuzzy PID controller from a constant fixed value determined by human experience to a real-time adjustment based on the power battery temperature difference and the rate of change of the temperature difference, the real-time adjustment capability of the input and output argument domain of the fuzzy controller can be improved, which is equivalent to increasing the number of fuzzy rules of the fuzzy controller in the local range, and the control effect of the temperature in the power battery thermal management system can be improved.

[0056] Embodiment Three In one embodiment of the present application, a thermal management system is provided. The thermal management system collects battery state information in real time through temperature, current, voltage, etc. sensors, dynamically adjusts the argument domain range and fuzzy rule weight of the input / output variable according to the working condition by a variable argument domain fuzzy PID controller, realizes fast heating response at low temperature and accurate temperature control at high temperature, and then adjusts the temperature through PTC heater, liquid cooling pump, etc. execution mechanism, and optimizes the thermal management strategy by combining battery SOC, environmental temperature, etc. parameters, so as to realize efficient, safe and stable temperature regulation of the power battery under complex working conditions.

[0057] The adaptive fuzzy PID power battery thermal management control method based on the thermal management system has the following implementation steps: Step one, determine the fuzzy input variable e, ec and output variable ΔK p , ΔK i , ΔK d; wherein the input variable e is the error between the current temperature value and the set temperature value; the input variable ec is the rate of change of the error e relative to time; the output variable ΔK p is the correction amount of the PID controller parameter P in the temperature control system; the output variable ΔK i is the correction amount of the PID controller parameter I in the temperature control system.

[0058] Step two, determine the input variable and the scaling factor of the output variable, wherein the scaling factor of the input variable e and ec is α(e), α(ec), the scaling factor of the output variable ΔK p , ΔK i , ΔK d is β(p), β(i), β(d), and the most commonly used scaling factor is generally the scaling factor of the double-input and multiple-output adaptive fuzzy controller based on the proportional exponential function model, the scaling factor of the input variable e and ec is selected according to the function form-based mode, and the function model is as follows:

[0059] wherein ε is a sufficiently small positive number, is the initial domain boundary of the input variable e and ec.

[0060] Step three, determine the parameters of the scaling factor Since is a constant fixed value determined artificially, the determination of the optimal value mainly depends on experience, so that the scaling factor does not have flexibility. In view of the defects of the scaling factor for fixed parameters, the present application proposes a parameter : ; wherein 0 <1, is a sufficiently small positive number, and the new scaling factor of the present application can be obtained after the proportional exponential function is taken with , and the form is as follows:

[0061] Step four, according to the calculation formula of the quantization factor: ; wherein the basic domain of the input variable is , and the fuzzy domain of the input variable is .

[0062] The calculation formula of the proportional factor is: ; wherein the basic domain of the output variable is , the output variable fuzzy domain is The quantization factor p of the input variable e and ec can be determined e = 0.2, p ec = 0.5; the scaling factor of the output variable ΔK p , ΔK i , ΔK d is p p = 1.5, p i = 0.8, p d = 2.5.

[0063] Step five, the quantization level of the fuzzy domain is determined as: negative large, negative medium, negative small, zero, positive small, positive medium, positive large, that is, the fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}; then, the membership function is determined, and the membership function of each domain subset is a triangular function.

[0064] Step six, the fuzzy rule is designed according to the fuzzy set and the membership function, and the fuzzy control rule of the output variable ΔK p , ΔK i , ΔK d can be determined according to the parameter adjustment law and combined with the experience of the invention.

[0065] Step seven, the variable domain self-adaptive fuzzy PID controller is established.

[0066] The power battery thermal management system adopted in the application can ensure that the battery always operates in the best working temperature range, and avoid the influence of high and low temperature on the overall performance, service life and safety of the battery. Based on the variable domain self-adaptive fuzzy algorithm, the energy supply is accurately matched according to the real-time temperature demand of the battery, which reduces the energy consumption of the thermal management system itself while ensuring the stability of the battery temperature, and further improves the energy utilization efficiency of the entire electric vehicle or energy storage system, prolongs the cruising range or energy storage time. Through more accurate temperature control, the abnormal internal chemical reaction of the battery caused by high temperature or low temperature can be reduced, the capacity attenuation and internal resistance increase of the battery can be effectively inhibited, and thus the cycle life of the battery can be significantly prolonged, the battery replacement cost can be reduced, and the economy of the entire system can be improved. Compared with the traditional fuzzy PID thermal management system, the regulation and control of the battery temperature in the application is more precise, the battery pack temperature can be maintained in a narrow range suitable for working, the problems of battery performance attenuation and service life shortening caused by temperature fluctuation can be effectively reduced, and the stability and reliability of the battery working can be greatly improved.

[0067] Embodiment four As shown in Figure 4 , based on the same inventive concept, the application embodiment further provides a temperature control device 40 of a power battery, the device comprises: The acquisition module 410 is used to determine the power battery temperature error value and the error change rate value based on the power battery temperature value and the set temperature value; A calculation module 420 is configured to determine an error scaling factor and a PID scaling factor based on the power battery temperature error value and the error change rate value, so as to calculate a power battery temperature scaling value and an error change rate scaling value; Correction module 430, for inputting the power battery temperature expansion value and the error change rate expansion value into the fuzzy controller to determine a PID target correction coefficient; The execution module 440 is configured to input the PID correction coefficient after the PID scaling factor is revised into the PID controller to obtain a target temperature value output by the PID controller, so that the power battery temperature control execution system can adjust the temperature of the power battery.

[0068] In a preferred embodiment, the error scaling factor includes a temperature error value scaling factor and an error change rate value scaling factor. The temperature error value scaling factor is determined by: ; in, is the temperature error value, is the first constant value, is the temperature error value The initial domain boundary value of is the first preset value; And determine the error change rate value scaling factor by the following method :

[0069] in, is the error change rate value, is the initial domain boundary value of the error change rate value, is the second preset value.

[0070] In a preferred embodiment, the PID scaling factor is determined by: : .

[0071] In a preferred embodiment, the first preset value is determined by: : ; in, is the second constant value, , is the third constant value, ; In a preferred embodiment, the second preset value is determined by: : ; wherein, is a second constant value, .

[0072] In a preferred embodiment, the fuzzy controller determines the target PID correction coefficient by the following way: adjusting the temperature error initial domain by the temperature error value scaling factor, adjusting the error change rate initial domain by the error change rate value scaling factor, and adjusting the PID correction coefficient initial domain by the PID scaling factor; determining a first quantization level corresponding to the temperature error value in the temperature error fuzzy domain, and a second quantization level corresponding to the error change rate value in the error change rate fuzzy domain; determining a third quantization level corresponding to the PID correction coefficient in the PID correction coefficient fuzzy domain based on the first quantization level, the second quantization level and the preset fuzzy control rule; determining the corresponding PID standard correction coefficient based on the third quantization level; and determining the target PID correction coefficient based on the PID standard correction coefficient and the PID proportional factor.

[0073] In a preferred embodiment, the PID controller determines the target temperature value and outputs by the following way: determining the corrected PID coefficient based on the target PID correction coefficient and the current PID coefficient; and determining the target temperature value based on the corrected PID coefficient and the temperature error value.

[0074] Please refer to Figure 5 , Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 5 , the electronic device 500 includes a processor 510, a memory 520 and a bus 530.

[0075] The memory 520 stores machine readable instructions executable by the processor 510, when the electronic device 500 is running, the processor 510 and the memory 520 communicate through the bus 530, and the machine readable instructions are executed by the processor 510, which can execute the steps of the power battery temperature control method in the method embodiment as shown in Figure 1 The specific implementation can be referred to the method embodiment, which will not be repeated here.

[0076] The present embodiment also provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is run by the processor, which can execute the steps of the power battery temperature control method in the method embodiment as shown in Figure 1 The specific implementation can be referred to the method embodiment, which will not be repeated here.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0078] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented by other manners. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and another division manner can be used in actual implementation, and for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0079] In addition, the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0080] Further, the function modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0081] It should be noted that if the functions are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0082] In this document, relational terms such as first and second and the like can only be used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations exist in any such actual relationship or order.

[0083] The above embodiments are only used to illustrate the present application, but not to limit the present application. The present application can be modified and changed by those skilled in the art without departing from the spirit and principle of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A temperature control method for a power battery, characterized in that: The method comprises: Determining a power battery temperature error value and an error change rate value based on the power battery temperature value and the set temperature value; Based on the power battery temperature error value and the error change rate value, an error scaling factor and a PID scaling factor are determined; The error scaling factor and the PID scaling factor are input into the fuzzy controller to determine the PID target correction factor; The PID target correction coefficient is input into the PID controller to obtain the target temperature value output by the PID controller, so that the power battery temperature control execution system can adjust the temperature of the power battery.

2. The method according to claim 1, characterized in that The error scaling factor includes the temperature error scaling factor and the error change rate scaling factor. The temperature error scaling factor is determined by the following method: ； in, is the temperature error value, is the first constant value, is the temperature error value The initial domain boundary value of is the first preset value; And determine the error change rate value scaling factor by the following method : in, is the error change rate value, is the initial domain boundary value of the error change rate value, is the second preset value.

3. The method according to claim 2, characterized in that The PID scaling factor is determined by : 。 4. The method according to claim 2, characterized in that The first preset value is determined by the following method : ； in, is the second constant value, , is the third constant value, .

5. The method according to claim 3, characterized in that The second preset value is determined by the following method : ； in, is the second constant value, .

6. The method according to claim 2, characterized in that The fuzzy controller determines the PID target correction coefficient in the following way: Adjusting the temperature error initial domain by the temperature error value scaling factor, adjusting the error change rate initial domain by the error change rate value scaling factor, and adjusting the PID correction coefficient initial domain by the PID scaling factor; Determine a first quantization level corresponding to the temperature error value in the temperature error fuzzy domain, and a second quantization level corresponding to the error change rate value in the error change rate fuzzy domain; Determining a third quantization level corresponding to the PID correction coefficient in the PID correction coefficient fuzzy domain based on the first quantization level, the second quantization level, and a preset fuzzy control rule; Determining a corresponding PID standard correction coefficient based on the third quantization level; Based on the PID standard correction coefficient and the PID proportional factor, the PID target correction coefficient is determined.

7. The method according to claim 1, characterized in that The PID controller determines and outputs the target temperature value in the following way: Determine a corrected PID coefficient based on the PID target correction coefficient and the current PID coefficient; A target temperature value is determined based on the modified PID coefficient and the temperature error value.

8. A temperature control device for a power battery, characterized in that: The device comprises: An acquisition module is used to determine a power battery temperature error value and an error change rate value based on a power battery temperature value and a set temperature value; A calculation module, configured to determine an error scaling factor and a PID scaling factor based on a power battery temperature error value and an error change rate value; A correction module is used to input the error scaling factor and the PID scaling factor into the fuzzy controller to determine the PID target correction coefficient; The execution module is used to input the PID target correction coefficient into the PID controller to obtain the target temperature value output by the PID controller, so that the power battery temperature control execution system can adjust the temperature of the power battery.

9. An electronic device, characterized in that: include: A processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the temperature control method for a power battery as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the temperature control method for a power battery according to any one of claims 1 to 7 are executed.

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