Lithium ion battery core temperature estimation method and device, computer program product, storage medium and electronic equipment

Abstract

The invention discloses a lithium ion battery core temperature estimation method and device, a computer program product, a storage medium and electronic equipment. According to the method, the core temperature of a to-be-measured battery is predicted through a real-time core temperature prediction model to obtain a battery core temperature prediction value, and then the battery core temperature prediction value is input into a real-time surface temperature prediction model to predict the surface temperature of the to-be-measured battery to obtain a battery surface temperature prediction value; and finally, in combination with the battery surface temperature predicted value and the actually measured surface temperature value, performing feedback correction on the battery core temperature predicted value through extended Kalman filtering to obtain a core temperature estimated value of the battery to be measured. The core temperature estimation value output by the method basically conforms to the actual value, the error is small, the core temperature of the lithium ion battery can be well estimated online in real time, and a basis is provided for the thermal management technology of the battery. The method is beneficial to maintaining the optimal performance of the battery, can prevent thermal runaway, and improves the safety of the battery.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery management, and in particular relates to a method, device, computer program product, storage medium and electronic equipment for estimating the core temperature of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries have become the first choice for energy storage systems such as electric vehicles and energy storage power stations due to their high energy density, long cycle life, and low self-discharge rate. During the use of lithium-ion batteries, controlling the temperature of lithium-ion batteries so that they work at the most suitable operating temperature can give full play to their optimal performance and help avoid potential thermal runaway risks. Existing large-scale energy storage systems usually only install temperature sensors on the surface of lithium-ion batteries to obtain surface temperature. As the size of lithium-ion batteries increases, for larger battery models such as 21700 and 26650, the difference between the surface temperature and the core temperature of the battery is large. Only monitoring the surface temperature of the battery will cause thermal delay and underestimate the actual temperature of the lithium-ion battery. In some fast-charging application scenarios of electric vehicles, the core temperature of the lithium-ion battery will rise sharply in a short period of time. If the battery management system only monitors the surface temperature of the lithium-ion battery, it may not be possible to take external cooling measures in time, which will cause thermal runaway or heat spread of the lithium-ion battery. Therefore, estimating the core temperature based on the surface temperature measurement of lithium-ion batteries is an urgent problem to be solved in the application of lithium-ion batteries, which is conducive to the safe application of energy storage systems. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a lithium-ion battery core temperature estimation method, which takes into account the nonlinearity and time-varying characteristics of thermodynamic parameters in a lithium-ion battery temperature model and estimates the core temperature of a lithium-ion battery in real time.

[0004] A method for estimating the core temperature of a lithium-ion battery disclosed in the present invention comprises the following steps:

[0005] Performing a working condition experiment on the battery to be tested to obtain a measured data set, wherein the measured data set at least includes current measured data, surface temperature measured data, and ambient temperature measured data;

[0006] The core temperature of the battery to be tested is predicted by a real-time core temperature prediction model to obtain a predicted value of the battery core temperature;

[0007] The surface temperature of the battery to be tested is predicted by a real-time surface temperature prediction model to obtain a predicted value of the battery surface temperature;

[0008] Combining the battery surface temperature prediction and the battery surface temperature actual value, feedback correction is performed on the battery core temperature prediction value through an extended Kalman filter to obtain an estimated value of the battery core temperature to be measured;

[0009] The real-time core temperature prediction model is constructed based on a state-dependent model;

[0010] The real-time surface temperature prediction model is constructed based on a state-dependent model and a coupling relationship between the core temperature and the surface temperature of a lithium-ion battery.

[0011] Furthermore, the real-time core temperature prediction model and the real-time surface temperature prediction model are obtained by the following steps:

[0012] S100, constructing the state model of lithium-ion batteries based on the autoregressive form of the state-dependent model:

[0013]

[0014] Where, T c is the core temperature of the lithium-ion battery, T a is the ambient temperature, I is the current of the lithium-ion battery, φ 0,c , It's about T c Functional coefficients of ;

[0015] S200, determining φ in the state model by using a radial basis function neural network 0,c ,

[0016] S300, construct an observation model based on the general form of the state-dependent model and the coupling relationship between the core temperature and the surface temperature of the lithium-ion battery:

[0017]

[0018] Where, T s is the surface temperature of the lithium-ion battery, T c is the core temperature of the lithium-ion battery, T a is the ambient temperature, φ 0,s , It's about T s Functional coefficients of ;

[0019] S400, determining φ in the observation model by using a radial basis function neural network 0,s ,

[0020] S500, conducting a lithium-ion battery dynamic operating condition experiment to obtain a data set; the data set at least includes current data, core temperature data, surface temperature data, and ambient temperature data;

[0021] S600: Using the data in the data set, determine all parameters in the state model and the observation model through a gradient descent method to obtain a real-time core temperature prediction model for lithium-ion battery core temperature prediction and a real-time surface temperature prediction model for lithium-ion battery surface temperature prediction.

[0022] Furthermore, the φ in step S200 0,c , is a functional coefficient; 0,c , After being determined by the radial basis function neural network:

[0023]

[0024] In the formula,

[0025]

[0026] is the weight of the corresponding radial basis function neural network;

[0027] is the scaling factor of the corresponding radial basis function neural network;

[0028] is the center of the corresponding radial basis function neural network.

[0029] Furthermore, the φ in step S400 0,s , is a functional coefficient; 0,s , After being determined by the radial basis function neural network:

[0030]

[0031] In the formula,

[0032] is the weight of the corresponding radial basis function neural network;

[0033] is the scaling factor of the corresponding radial basis function neural network;

[0034] is the center of the corresponding radial basis function neural network.

[0035] Furthermore, the step S600 includes the following steps:

[0036] S610, dividing the data set into a training set and a test set;

[0037] S620, using the data in the training set to determine all parameters in the state model and the observation model by a gradient descent method;

[0038] S630, using the data in the test set to verify the determined state model and the determined observation model, if the verification result deviation value is less than the set value, proceed to step S640; if the verification result deviation value is greater than the set value, return to step S620;

[0039] S640: The determined state model is a real-time core temperature prediction model; and the determined observation model is a real-time surface temperature prediction model.

[0040] Another aspect of the present invention also provides a lithium-ion battery core temperature estimation device, the device comprising:

[0041] A first processing module is used to obtain a measured data set of a dynamic working condition experiment of a battery to be tested, wherein the measured data set at least includes current measured data, surface temperature measured data and ambient temperature measured data;

[0042] The second processing module is used to input the current measured data and the ambient temperature measured data into the real-time core temperature prediction model to predict the core temperature of the battery to be tested, and obtain the battery core temperature prediction value; and then input it into the real-time surface temperature prediction model to predict the surface temperature of the battery to be tested, and obtain the battery surface temperature prediction value;

[0043] The third processing module is used to combine the actual value of the battery surface temperature and the predicted value of the battery surface temperature; perform feedback correction on the predicted value of the battery core temperature through an extended Kalman filter to obtain an estimated value of the core temperature of the battery to be measured.

[0044] Furthermore, the real-time core temperature estimation model and the real-time surface temperature prediction model are based on the current data, core temperature data, surface temperature data, and ambient temperature data of the dynamic operating conditions of the lithium-ion battery, and are obtained through radial basis function neural network and gradient descent method training and optimization.

[0045] Another aspect of the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the above-mentioned lithium-ion battery core temperature estimation method is implemented.

[0046] Another aspect of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the above-mentioned lithium-ion battery core temperature estimation method when executed by a processor.

[0047] Another aspect of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned lithium-ion battery core temperature estimation method when executing the program.

[0048] The present invention has the following beneficial effects:

[0049] The lithium-ion battery core temperature estimation method provided by the present invention predicts the core temperature of the battery to be tested by a real-time core temperature prediction model obtained by constructing a state-dependent model, and obtains a battery core temperature prediction value, which is then input into a real-time surface temperature prediction model obtained by constructing a state-dependent model to predict the surface temperature of the battery to be tested, and obtains a battery surface temperature prediction value; finally, the battery surface temperature prediction value and the measured surface temperature value are combined, and the battery core temperature prediction value is feedback-corrected by an extended Kalman filter to obtain a core temperature estimation value. The core temperature value estimated by the method is basically consistent with the actual value, with a small error, and can well estimate the core temperature of the lithium-ion battery in real time, with good robustness. The lithium-ion battery core temperature estimation method provided by the present invention can enable the battery management system to perceive the core temperature of the lithium-ion battery in real time, which is helpful for the thermal management of the battery management system, and is conducive to the battery to exert its optimal performance, while preventing thermal runaway and improving the safety of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is one of the flow charts of a method for estimating the core temperature of a lithium-ion battery provided in some embodiments of the present invention,

[0051] Figure 2 Schematic diagram of data sets collected by some embodiments of the present invention, (a) is a core temperature, surface temperature and ambient temperature diagram of working condition 1, (b) is a current diagram of working condition 1; (c) is a core temperature, surface temperature and ambient temperature diagram of working condition 2, (d) is a current diagram of working condition 2,

[0052] Figure 3 is a comparison chart of the predicted core temperature value, the predicted surface temperature value and the actual value provided by some embodiments of the present invention,

[0053] Figure 4 is a schematic diagram of the error of the core temperature prediction value and the surface temperature prediction value provided by some embodiments of the present invention,

[0054] Figure 5is a second flowchart of a method for estimating the core temperature of a lithium-ion battery provided in some embodiments of the present invention.

[0055] Figure 6 is a comparison chart of the estimated result obtained by the lithium-ion battery core temperature estimation method provided in Example 1 of the present invention and the true value,

[0056] Figure 7 is a comparison chart of the estimated result and the true value obtained by the lithium-ion battery core temperature estimation method provided in Example 2 of the present invention,

[0057] Figure 8 is a schematic diagram of the error of the estimation result obtained by the lithium-ion battery core temperature estimation method provided in Example 2 of the present invention,

[0058] Fig. 9 is a comparison chart of the estimated result obtained by the lithium-ion battery core temperature estimation method provided in Example 3 of the present invention and the true value,

[0059] Fig.10 Schematic diagram of the error of the estimation result obtained by the lithium-ion battery core temperature estimation method provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0060] In order to more clearly and completely describe the technical solution of the present invention, the present invention is further described in detail through specific embodiments below. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention. Various changes can be made within the scope of the rights of the present invention.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0062] See also Figure 1 The present invention provides a method for estimating the core temperature of a lithium-ion battery, the method comprising the following steps:

[0063] S010, performing a working condition experiment on the battery to be tested to obtain a measured data set, where the measured data set at least includes measured current data, measured surface temperature data, and measured ambient temperature data;

[0064] S011. Predicting the core temperature of the battery to be tested by using a real-time core temperature prediction model to obtain a predicted value of the battery core temperature;

[0065] S012. Predicting the surface temperature of the battery to be tested by using a real-time surface temperature prediction model to obtain a predicted value of the battery surface temperature;

[0066] S013. Combining the predicted value of the battery surface temperature with the measured surface temperature value, feedback correction is performed on the predicted value of the battery core temperature through an extended Kalman filter to obtain an estimated value of the core temperature of the battery to be measured.

[0067] The real-time core temperature prediction model and the real-time surface temperature prediction model in the above lithium-ion battery core temperature estimation method are obtained by the following steps:

[0068] S100, construct the following lithium-ion battery state model based on the autoregressive form of the state-dependent model:

[0069]

[0070] Where, T c is the core temperature of the lithium-ion battery, T a is the ambient temperature, I is the current of the lithium-ion battery, φ 0,c , It's about T c Functional coefficients of ;

[0071] S200, determine φ in the state model through radial basis function neural network 0,c ,

[0072] φ 0,c , is a functional coefficient; optionally, φ in step S200 0,c , After being determined by the radial basis function neural network:

[0073]

[0074] In the formula,

[0075]

[0076] is the weight of the corresponding radial basis function neural network;

[0077] is the scaling factor of the corresponding radial basis function neural network;

[0078] is the center of the corresponding radial basis function neural network.

[0079] S300, constructing an observation model of lithium-ion batteries based on the general form of the state-dependent model and the coupling relationship between the core temperature and the surface temperature of the lithium-ion battery:

[0080]

[0081] Where, T s is the surface temperature of the lithium-ion battery, T c is the core temperature of the lithium-ion battery, T a is the ambient temperature, φ 0,s , It's about T s Functional coefficients of ;

[0082] S400, determine φ in the state model through radial basis function neural network 0,s ,

[0083] φ 0,s , is a functional coefficient; optionally, φ in step S400 0,s , After being determined by the radial basis function neural network:

[0084]

[0085] In the formula,

[0086] is the weight of the corresponding radial basis function neural network;

[0087] is the scaling factor of the corresponding radial basis function neural network;

[0088] is the center of the corresponding radial basis function neural network.

[0089] S500, conduct dynamic working condition experiments on lithium-ion batteries and obtain data sets; please refer to Figure 2 ,The data set includes current data, core temperature data, surface temperature data, and ambient temperature data.

[0090] S600, using the data in the data set, respectively determine all parameters in the state model and the observation model through the gradient descent method, and obtain a real-time core temperature prediction model for lithium-ion battery core temperature prediction and a real-time surface temperature prediction model for lithium-ion battery surface temperature prediction.

[0091] In some implementation modes provided by the present invention, step S600 includes the following steps:

[0092] S610, dividing the data set into a training set and a validation set;

[0093] S620, using the data in the training set, respectively determine all parameters of the state model and the observation model by the gradient descent method; specifically, respectively optimize all weights, scaling factors, and centers in the state model and the observation model by the gradient descent method;

[0094] S630, using the data in the test set to verify the optimized state model and observation model, if the verification result deviation value is less than the set value, proceed to step S640; if the verification result deviation value is greater than the set value, return to step S620;

[0095] S640: Determine the optimized state model as the real-time core temperature prediction model; and determine the optimized observation model as the real-time surface temperature prediction model.

[0096] The real-time core temperature prediction model and the real-time surface temperature prediction model obtained in this embodiment are combined to form a system equation for online estimation of lithium ion core temperature:

[0097]

[0098] See also Figure 2 , Figure 2 a is the core temperature, surface temperature and ambient temperature diagram of working condition 1, Figure 2 b is the current diagram of working condition 1; Figure 2 c is the core temperature, surface temperature and ambient temperature diagram of working condition 2, Figure 2 d is the current diagram of working condition 2. In this embodiment, the data set of working condition 1 is selected as the training set to optimize the state model and the observation model. The data set of working condition 2 is used as the test set to verify the accuracy of the optimized state model and prediction model. The current data, surface temperature data, and ambient temperature data in working condition 2 are input into the above system equation to obtain the corresponding core temperature prediction value and surface temperature prediction value.

[0099] Please refer to the comparison chart of the above core temperature prediction value and surface temperature prediction value with the actual value. Figure 3 ;Depend on Figure 3 It can be seen that the core temperature and surface temperature predicted by the state model and prediction model optimized by the training set in this embodiment are basically consistent with the true values.

[0100] The error between the core temperature prediction value and the surface temperature prediction value and the actual value can be found in Figure 4 ,Depend on Figure 4 It can be seen that the positive and negative errors between the core temperature prediction value and the true value in this implementation are no more than 0.1°C, and the positive and negative errors between the surface temperature prediction value and the true value are no more than 0.3°C, indicating that the state model and prediction model obtained in this implementation have good accuracy.

[0101] Figure 5 This is a flowchart of a method for estimating the core temperature of a lithium-ion battery provided by some embodiments of the present invention. Figure 5 The lithium-ion battery core temperature estimation method provided by the present invention inputs the collected measured ambient temperature data and the measured current data of the battery to be tested into the determined state model to predict the real-time core temperature of the battery to be tested to obtain the core temperature prediction data. Then the obtained core temperature prediction data is input into the determined observation model to obtain the real-time surface temperature prediction value of the battery; finally, the obtained core temperature prediction data is updated by the extended Kalman filter in combination with the battery surface temperature prediction value and the measured surface temperature value of the battery to be tested to obtain the estimated value of the core temperature of the battery to be tested, and finally the estimated value is output, and the real-time core temperature estimation of the next moment is progressively entered.

[0102] The lithium-ion battery core temperature estimation method provided by the present invention is described in detail below with reference to the accompanying drawings by way of specific embodiments.

[0103] Example 1

[0104] In this embodiment, the measured surface temperature, ambient temperature and current in a set of measured data sets are arbitrarily selected and input into the real-time core temperature prediction model and the real-time surface temperature prediction model to predict the core temperature and surface temperature of the battery to be tested, and the core temperature and surface temperature prediction results are feedback-corrected by the extended Kalman filter to obtain the estimated core temperature value of the battery to be tested. Figure 6 The estimated value obtained by the lithium-ion battery core temperature estimation method provided by the present invention for estimating the core temperature of the battery to be tested is basically consistent with the true value, indicating that the estimation method provided by the present invention can accurately estimate the core temperature of the lithium-ion battery.

[0105] Example 2

[0106] In this embodiment, when the real-time core temperature prediction model and the real-time surface temperature prediction model are used to predict the core temperature and the surface temperature of the battery to be tested, an incorrect initial value of the core temperature of 17°C is used; see Figure 7 When the wrong core temperature initial value of 17°C is used, the wrong surface temperature estimate will be calculated, which will be significantly different from the actual measured surface temperature value. However, through the real-time core temperature prediction model and real-time surface temperature prediction model established based on the state-dependent model in this method, and feedback correction through the extended Kalman filter, the wrong core temperature estimate can be quickly corrected to the vicinity of the actual measured value, indicating that the estimation method provided by the present invention has good robustness. Please refer to Figure 7When an incorrect initial value that is higher than the correct initial value is used to estimate the battery to be tested, the positive and negative errors between the estimated result obtained by the lithium-ion battery core temperature estimation method and the true value do not exceed 0.2°C, indicating that even if an incorrect initial value is used, the estimation method provided by the present invention can still estimate the core temperature of the lithium-ion battery well.

[0107] Example 3

[0108] In this embodiment, when the real-time core temperature prediction model and the real-time surface temperature prediction model are used to predict the core temperature and the surface temperature of the battery to be tested, an incorrect initial value of the core temperature of 0°C is used; see Fig. 9 Even if the wrong initial value of 0℃ is used to estimate the battery under test, the real-time core temperature prediction model and real-time surface temperature prediction model established based on the state-dependent model in this method, and feedback correction through the extended Kalman filter, can also quickly correct the wrong core temperature estimation value to near the actual measurement value. Fig.10 When using an incorrect initial value that is lower than the correct initial value to estimate the battery under test, the positive and negative errors of the estimated results obtained by the lithium-ion battery core temperature estimation method and the true value do not exceed 0.2℃.

[0109] Embodiments 2 and 3 illustrate that no matter whether a high or low erroneous initial value is used, the estimation method provided by the present invention can still accurately estimate the core temperature of the lithium-ion battery, and the estimation method has good robustness.

[0110] Another aspect of the present invention also provides a lithium-ion battery core temperature estimation device, the device comprising:

[0111] A first processing module is used to obtain a measured data set of a dynamic working condition experiment of a battery to be tested, where the measured data set at least includes current measured data, surface temperature measured data and ambient temperature measured data;

[0112] The second processing module is used to input the current measured data and the ambient temperature measured data into the real-time core temperature prediction model to predict the core temperature of the battery to be tested, and obtain the battery core temperature prediction value; and then input it into the real-time surface temperature prediction model to predict the surface temperature of the battery to be tested, and obtain the battery surface temperature prediction value;

[0113] The third processing module is used to combine the actual value of the battery surface temperature and the predicted value of the battery surface temperature; feedback correction is performed on the predicted value of the battery core temperature through the extended Kalman filter to obtain an estimated value of the core temperature of the battery to be measured.

[0114] In some embodiments provided by the present invention, the real-time core temperature prediction model and the real-time surface temperature prediction model are based on the current data, core temperature data, surface temperature data, and ambient temperature data of the dynamic operating conditions of the lithium-ion battery, and are obtained through radial basis function neural network and gradient descent method training and optimization.

[0115] Another aspect of the present invention further provides a computer program product, including a computer program, which implements the above-mentioned lithium-ion battery core temperature estimation method when executed by a processor.

[0116] Another aspect of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned lithium-ion battery core temperature estimation method is implemented.

[0117] Another aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned lithium-ion battery core temperature estimation method when executing the program.

[0118] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for estimating the core temperature of a lithium-ion battery, characterized in that: The following steps are involved: Performing a working condition experiment on the battery to be tested to obtain a measured data set, wherein the measured data set at least includes current measured data, surface temperature measured data, and ambient temperature measured data; The core temperature of the battery to be tested is predicted by a real-time core temperature prediction model to obtain a predicted value of the battery core temperature; The surface temperature of the battery to be tested is predicted by a real-time surface temperature prediction model to obtain a predicted value of the battery surface temperature; Combining the predicted value of the battery surface temperature with the measured value of the battery surface temperature, performing feedback correction on the predicted value of the battery core temperature through an extended Kalman filter to obtain an estimated value of the core temperature of the battery to be measured; The real-time core temperature prediction model is constructed based on a state-dependent model; The real-time surface temperature prediction model is constructed based on a state-dependent model and a coupling relationship between the core temperature and the surface temperature of a lithium-ion battery.

2. A lithium-ion battery core temperature estimation method as claimed in claim 1, characterized in that: The real-time core temperature prediction model and the real-time surface temperature prediction model are obtained by the following steps: S100, constructing the state model of lithium-ion batteries based on the autoregressive form of the state-dependent model: Where, T c is the core temperature of the lithium-ion battery, T a is the ambient temperature, I is the current of the lithium-ion battery, φ 0,c , It's about T c Functional coefficients of ; S200, determining φ in the state model by using a radial basis function neural network 0,c , S300, constructing an observation model of lithium-ion batteries based on the general form of the state-dependent model and the coupling relationship between the core temperature and the surface temperature of the lithium-ion battery: Where, T s is the surface temperature of the lithium-ion battery, T c is the core temperature of the lithium-ion battery, T a is the ambient temperature, φ 0,s , It's about T s Functional coefficients of ; S400, determining φ in the observation model by using a radial basis function neural network 0,s , S500, conducting a lithium-ion battery dynamic operating condition experiment to obtain a data set; the data set at least includes current data, core temperature data, surface temperature data, and ambient temperature data; S600: Using the data in the data set, all parameters in the state model and the observation model are determined by gradient descent method to obtain a real-time core temperature prediction model for lithium-ion battery core temperature prediction and a real-time surface temperature prediction model for lithium-ion battery surface temperature prediction.

3. A lithium-ion battery core temperature estimation method as claimed in claim 2, characterized in that: In step S200, φ 0,c , is a functional coefficient; 0,c , After being determined by the radial basis function neural network: In the formula, is the weight of the corresponding radial basis function neural network; is the scaling factor of the corresponding radial basis function neural network; is the center of the corresponding radial basis function neural network.

4. A lithium-ion battery core temperature estimation method as claimed in claim 2, characterized in that: In step S400, φ 0,s , is a functional coefficient; 0,s , After being determined by the radial basis function neural network: In the formula, is the weight of the corresponding radial basis function neural network; is the scaling factor of the corresponding radial basis function neural network; is the center of the corresponding radial basis function neural network.

5. A lithium-ion battery core temperature estimation method as claimed in claim 2, characterized in that: The steps S600 includes the following steps: S610, dividing the data set into a training set and a test set; S620, using the data in the training set to determine all parameters in the state model and the observation model respectively by a gradient descent method; S630, using the data in the test set to verify the determined state model and the determined observation model, if the verification result deviation value is less than the set value, proceed to step S640; if the verification result deviation value is greater than the set value, return to step S620; S640: The determined state model is a real-time core temperature prediction model; and the determined observation model is a real-time surface temperature prediction model.

6. A lithium-ion battery core temperature estimation device, characterized in that: The device comprises: A first processing module is used to obtain a measured data set of a dynamic working condition experiment of a battery to be tested, wherein the measured data set at least includes current measured data, surface temperature measured data and ambient temperature measured data; The second processing module is used to input the current measured data and the ambient temperature measured data into the real-time core temperature prediction model to predict the core temperature of the battery to be tested, and obtain the battery core temperature prediction value; and then input it into the real-time surface temperature prediction model to predict the surface temperature of the battery to be tested, and obtain the battery surface temperature prediction value; The third processing module is used to combine the actual value of the battery surface temperature and the predicted value of the battery surface temperature; perform feedback correction on the predicted value of the battery core temperature through an extended Kalman filter to obtain an estimated value of the core temperature of the battery to be measured.

7. A lithium-ion battery core temperature estimation device as claimed in claim 6, characterized in that: The real-time core temperature prediction model and the real-time surface temperature prediction model are based on the current data, core temperature data, surface temperature data, and ambient temperature data of the dynamic working condition of the lithium-ion battery, and are obtained through radial basis function neural network and gradient descent method training and optimization.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for estimating the core temperature of a lithium-ion battery as claimed in any one of claims 1 to 5 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for estimating the core temperature of a lithium-ion battery as claimed in any one of claims 1 to 5 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for estimating the core temperature of a lithium-ion battery as described in any one of claims 1 to 5 is implemented.

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