Battery cell temperature prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a battery cell temperature prediction method and device, electronic equipment and a storage medium, and relates to the technical field of battery thermal management, and the method comprises the steps: obtaining the average current, the current terminal voltage and the current battery cell temperature of a battery cell every preset period, determining the current heat production power in the current period according to the average current, the current terminal voltage and the current cell temperature; according to the historical battery cell temperature, the current battery cell temperature and the current heat generation power at the previous moment, a heat generation coefficient and a heat dissipation time constant in the current period are calculated through a recursive least square method, and the previous moment represents the moment before the current moment and away from the current moment by the preset period; and according to the current heat generation power, the heat generation coefficient and the heat dissipation time constant, predicting the future battery cell temperature when the battery cell is in the same discharge depth interval, and obtaining a prediction result so as to accurately estimate the temperature change of the single battery cell.

Description

Technical Field

[0001] The present invention relates to the technical field of battery thermal management, and in particular, to a method, device, electronic device and storage medium for predicting the temperature of a battery cell. Background Art

[0002] With the rapid development of new energy technologies, electric vehicles have become the main development direction in the future vehicle field. Among them, as the core component of electric vehicles, the performance and lifespan of power batteries directly determine the driving range, driving experience and service life of electric vehicles.

[0003] The power, energy and service life of power batteries are significantly affected by temperature. Generally, low temperature will cause an increase in the internal resistance of the battery and a decrease in the available capacity, resulting in a decline in the power performance and driving range of electric vehicles; while within the appropriate operating temperature range, the internal resistance of the battery decreases, the available capacity increases, and its power performance and energy performance also improve. Therefore, accurately predicting the temperature change of the battery during charge and discharge is crucial for the battery management system, which can improve the calculation accuracy of the state of charge (SOC) of the battery pack, optimize the discharge power and energy management, and thus more accurately estimate the driving range.

[0004] In related technologies, generally, the temperature rise rate of the battery pack is calibrated through a large number of experiments during the product design stage, and the data calibrated offline is used for calculation during real-time charge and discharge, thereby obtaining an approximate temperature rise data. However, this method is both time-consuming and lacks an estimation of the temperature change of individual battery cells, resulting in poor prediction effects. Summary of the Invention

[0005] The problem solved by the present invention is how to accurately estimate the temperature change of an individual battery cell.

[0006] To solve the above problems, the present invention provides a method, device, electronic device and storage medium for predicting the temperature of a battery cell.

[0007] In a first aspect, the present invention provides a method for predicting the temperature of a battery cell, including:

[0008] Obtaining the average current, the current terminal voltage and the current battery cell temperature of the battery cell at every preset period, and determining the current heat generation power within the current period according to the average current, the current terminal voltage and the current battery cell temperature;

[0009] Calculating and obtaining the heat generation coefficient and the heat dissipation time constant within the current period by using the recursive least squares method according to the historical battery cell temperature at the previous moment, the current battery cell temperature and the current heat generation power, where the previous moment represents the moment before the current moment and is one preset period away from the current moment;

[0010] Predict the future cell temperature when the cell is in the same discharge depth range according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtain a prediction result.

[0011] Optionally, the cell temperature prediction method further includes:

[0012] Determine the initial discharge depth according to the cell temperature and the open circuit voltage of the cell before charge and discharge that have been obtained;

[0013] Obtain the average current, the current terminal voltage, and the current cell temperature of the cell every preset period to determine the current internal resistance of the cell;

[0014] Determine the interval width of the discharge depth range according to the initial discharge depth and the current internal resistance of the cell.

[0015] Optionally, obtaining the average current, the current terminal voltage, and the current cell temperature of the cell every preset period, and determining the current heat generation power within the current period according to the average current, the current terminal voltage, and the current cell temperature includes:

[0016] Obtain the real-time discharge depth of the cell;

[0017] Obtain the average current, the current terminal voltage, and the current cell temperature of the cell every preset period;

[0018] Obtain the current open circuit voltage of the cell according to a preset first mapping relationship, the current cell temperature, and the real-time discharge depth, where the first mapping relationship is used to represent the mapping relationship among the cell temperature, the discharge depth, and the open circuit voltage;

[0019] Determine the current heat generation power according to the current open circuit voltage, the current terminal voltage, and the average current.

[0020] Optionally, calculating the heat generation coefficient and the heat dissipation time constant within the current period by recursive least squares according to the historical cell temperature at the previous moment, the current cell temperature, and the current heat generation power includes:

[0021] Construct a system equation based on a parameter matrix and an observation matrix, where the observation matrix is constructed by the historical cell temperature, the current cell temperature, and the current heat generation power, and the parameter matrix is constructed by the heat generation coefficient and the heat dissipation time constant;

[0022] Based on the recursive least squares method, obtain the heat generation coefficient and the heat dissipation time constant within the current period through the system equation.

[0023] Optionally, before predicting the future cell temperature when the cell is in the same discharge depth range according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant to obtain a prediction result, it further includes:

[0024] Establish a temperature change model according to the temperature change of the cell during charge and discharge;

[0025] Obtain a temperature prediction model based on the heat generation coefficient and the heat dissipation time constant according to the temperature change model.

[0026] Optionally, the establishing a temperature change model according to the temperature change of the cell during charge and discharge includes:

[0027] Construct the temperature change model of the cell according to the obtained cell parameters, where the cell parameters include at least one of cell mass, cell specific heat capacity, cell thermal resistance, cell temperature change rate, ambient temperature, and cell temperature.

[0028] Optionally, the obtaining a temperature prediction model based on the heat generation coefficient and the heat dissipation time constant according to the temperature change model includes:

[0029] Perform a discretization transformation on the temperature change model, and convert the cell parameters in the temperature change model into cell operating parameters that change with time, and the heat generation coefficient and the heat dissipation time constant that change with the heat state;

[0030] Construct the temperature prediction model according to the cell operating parameters, the heat generation coefficient, and the heat dissipation time constant.

[0031] Optionally, after predicting the future cell temperature when the cell is in the same discharge depth range according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant to obtain a prediction result, it further includes:

[0032] Determine the future discharge depth in a future preset time period according to the obtained current filtered current;

[0033] Determine the future open-circuit voltage according to the future discharge depth, and determine the future internal resistance of the cell according to the future open-circuit voltage;

[0034] Determine the future discharge depth in a future cycle and the future heat generation power corresponding to the future discharge depth according to the future internal resistance, the heat generation coefficient, and the heat dissipation time constant;

[0035] Process the future heat generation power, the heat generation coefficient, and the heat dissipation coefficient through the temperature prediction model to obtain the future cell temperature in the future cycle.

[0036] Optionally, determining the future depth of discharge in a future period and the future heat generation power corresponding to the future depth of discharge according to the future internal resistance, the heat generation coefficient, and the heat dissipation time constant includes:

[0037] Obtaining the heat dissipation state of the battery cell, and determining the heat dissipation power and the heat generation weight according to the heat dissipation state;

[0038] Optimizing the future heat generation power according to the heat dissipation power and the heat generation weight to obtain the weighted heat generation power.

[0039] Optionally, obtaining the average current, the current terminal voltage, and the current battery cell temperature of the battery cell every preset period includes:

[0040] Determining the current filtered current according to the current filtered value of the current of the battery cell at the previous moment and the current current of the battery cell, and obtaining the average current according to the current filtered current;

[0041] Determining the front-end voltage according to the filtered value of the terminal voltage of the battery cell at the previous moment and the current collected value of the terminal voltage of the battery cell;

[0042] Determining the current battery cell temperature according to the filtered value of the battery cell temperature of the battery cell at the previous moment and the current collected value of the battery cell temperature of the battery cell.

[0043] In a second aspect, the present invention further provides a battery cell temperature prediction device, including:

[0044] A first module, configured to obtain the average current, the current terminal voltage, and the current battery cell temperature of the battery cell every preset period, and determine the current heat generation power within the current period according to the average current, the current terminal voltage, and the current battery cell temperature;

[0045] A second module, configured to calculate and obtain the heat generation coefficient and the heat dissipation time constant within the current period by using the recursive least squares method according to the historical battery cell temperature at the previous moment, the current battery cell temperature, and the current heat generation power, where the previous moment represents a moment that is one preset period before the current moment;

[0046] A third module, configured to predict the future battery cell temperature when the battery cell is in the same depth-of-discharge interval according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtain a prediction result.

[0047] In a third aspect, the present invention further provides an electronic device, including a memory and a processor;

[0048] The memory is used for storing a computer program;

[0049] The processor is used to implement the cell temperature prediction method as described above when executing the computer program.

[0050] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the cell temperature prediction method as described above is implemented.

[0051] The beneficial effects of the cell temperature prediction method of the present invention are as follows:

[0052] By periodically collecting the operating parameters of the cell, the working state of the cell can be monitored in real time. According to the operating parameters, the heat generation power within each period can be accurately calculated, ensuring that the calculation of the heat generation power is based on the latest actual operating data, improving the accuracy and real-time performance of the heat generation power estimation, and enabling a more accurate understanding of the heat generation situation of the cell under different working conditions, thereby providing a data basis for subsequent temperature prediction. Using the recursive least squares method to iteratively calculate the heat generation coefficient and the heat dissipation time constant online can quickly adapt to changes in the internal and external conditions of the cell, improving the accuracy and robustness during prediction. For parameters that change over time (such as the heat generation coefficient and the heat dissipation time constant), the recursive least squares method can continuously optimize, enabling the prediction method to always maintain the best prediction effect and enhancing the adaptive ability of the prediction method. By applying the heat generation power, the heat generation coefficient, and the heat dissipation time constant for temperature prediction, the cell temperature can be accurately estimated within different discharge depth intervals, achieving an accurate estimation of the temperature change of a single cell. Description of the Drawings

[0053] Figure 1 It is a schematic flowchart of the cell temperature prediction method according to an embodiment of the present invention;

[0054] Figure 2 It is a schematic flowchart of the cell temperature prediction method according to another embodiment of the present invention;

[0055] Figure 3 It is a schematic flowchart after refining step S100 of the cell temperature prediction method according to an embodiment of the present invention;

[0056] Figure 4 It is a schematic flowchart after refining step S200 of the cell temperature prediction method according to an embodiment of the present invention;

[0057] Figure 5 It is an example diagram of the cell temperature prediction device according to an embodiment of the present invention;

[0058] Figure 6 It is an example diagram of the electronic device according to an embodiment of the present invention. Detailed Embodiments

[0059] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of specific embodiments of the present invention with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Instead, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0060] It should be understood that the various steps recited in the method embodiments of the present invention can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0061] As used herein, the term "comprising" and its variations are open-ended, i.e., "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules, or units, and are not used to limit the order of functions performed by these devices, modules, or units or their interdependent relationships.

[0062] It should be noted that the modifications of "one" and "plural" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless clearly stated otherwise in the context, it should be understood as "one or more".

[0063] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0064] In the related art, electric vehicles have become the main development direction in the future vehicle field. As the core component of electric vehicles, the performance and lifespan of power batteries directly determine the vehicle's driving range, driving experience, and service life. The power, energy, and service life of power batteries are significantly affected by temperature: under low-temperature conditions, the chemical reaction rate slows down, the battery internal resistance increases, and the available capacity decreases, resulting in a decline in the power performance and driving range of electric vehicles; while within the appropriate operating temperature range, the lithium-ion diffusion rate accelerates, the battery internal resistance decreases, and the available capacity increases, thereby improving the battery's power performance and energy performance.

[0065] For a Battery Management System (BMS), accurately predicting the temperature change of the battery during charging and discharging is crucial. By precisely estimating future temperature changes, the calculation accuracy of the State of Charge (SOC) of the battery pack can be improved, the discharge power and energy management can be optimized, and thus the driving range can be estimated more accurately, the driving experience can be improved, and the service life of electric vehicles can be extended. During the product design phase, the temperature rise rate of the battery pack under different working conditions is usually calibrated through a large number of experiments. Subsequently, during the real-time charging and discharging process, the data calibrated offline is used for calculation to obtain an approximate temperature rise data. This method is time-consuming and cannot obtain the temperature change of each single cell.

[0066] During the charging and discharging process of the battery, a part of the chemical energy inside the battery pack will be converted into heat, resulting in heat accumulation between the heat generation and heat dissipation of the battery cells. The heat that fails to be dissipated in time will cause the temperature of the battery pack to rise. The increase in temperature will accelerate the chemical reaction rate inside the battery pack, thereby improving the power performance and the energy that can be released of the battery pack. Therefore, to accurately estimate the capacity and energy of the battery pack, it is necessary to accurately predict its future temperature change, especially the temperature value at the discharge cut-off state is particularly important. Accurate temperature prediction helps to optimize the performance of the battery management system, ensure that the battery operates in the best state, extend its service life, and improve the overall efficiency and reliability of electric vehicles.

[0067] In view of the problems existing in the above related technologies, this embodiment provides a method for predicting the temperature of battery cells.

[0068] As Figure 1 shown, a method for predicting the temperature of battery cells provided by an embodiment of the present invention includes:

[0069] Step S100, obtain the average current, the current terminal voltage, and the current battery cell temperature of the battery cell at each preset period, and determine the current heat generation power within the current period according to the average current, the current terminal voltage, and the current battery cell temperature.

[0070] Specifically, set a fixed sampling period, and obtain the current of the battery cell every i seconds to obtain the average current within the period, the terminal voltage of the battery cell at the end of the period, and the temperature of the battery cell at the end of the period, that is, the average current, the current terminal voltage, and the current battery cell temperature.

[0071] The heat generation power within the current period can be determined according to the average current, the current terminal voltage, and the current battery cell temperature. When predicting the temperature of the battery cell, the heat generation power within the current period is used as a reference value to predict the temperature change of the battery cell during future charging and discharging according to future working condition changes (charging and discharging power changes, heat generation and heat dissipation changes).

[0072] Step S200, according to the historical cell temperature at the previous moment, the current cell temperature, and the current heat generation power, calculate and obtain the heat generation coefficient and the heat dissipation time constant within the current cycle by using the recursive least squares method, where the previous moment refers to the moment before the current moment and is one preset cycle away from the current moment.

[0073] The recursive least squares method is an algorithm for online parameter estimation, which is particularly suitable for real-time data processing and dynamic system modeling. By recursively updating the parameter estimates, each time new data arrives, the model parameters can be quickly adjusted to minimize the prediction error. In the embodiments of the present invention, the temperature rise coefficient and the heat dissipation coefficient are estimated online by using the recursive least squares method to quickly iterate the temperature prediction model to meet the prediction accuracy requirements.

[0074] Specifically, the current moment represents the k-th moment, and the previous moment can be represented as the (k - 1)-th moment.

[0075] In one embodiment, the heat generation coefficient and the heat dissipation time constant can be iterated online according to the historical cell temperature, the current cell temperature, and the current heat generation power at the (k - 1)-th moment, where the heat generation coefficient is used to characterize the heat generation situation of the cell, the heat dissipation time constant is used to characterize the heat dissipation situation of the cell, and the heat generation coefficient and the heat dissipation time constant are related to the heat dissipation path and heat dissipation material from the cell to the outside.

[0076] Step S300, according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, predict the future cell temperature when the cell is in the same discharge depth interval to obtain a prediction result.

[0077] In one embodiment, the heat generation and heat dissipation situations of the cell can be quickly updated by using the quickly iterated heat generation coefficient and heat dissipation time constant obtained based on the recursive least squares method, and the temperature change trend of the cell in the current cycle can be obtained by combining the current heat generation power; on the other hand, when the discharge depth is different, the internal resistance of the cell will also change, and as the cell reaches the end of discharge, the change trend of the internal resistance of the cell will also change. Then, according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, predict the cell temperature when the cell is in the same discharge depth interval at a future moment. When the discharge depth changes from one interval to another interval, it is necessary to re-sample the cell parameters and working parameters of the cell and re-predict the cell temperature to obtain a prediction result.

[0078] In this embodiment, by periodically collecting the operating parameters of the battery cell, the working state of the battery cell can be monitored in real time. According to the operating parameters, the heat generation power within each period can be accurately calculated, ensuring that the calculation of the heat generation power is based on the latest actual operating data, improving the accuracy and real-time performance of the heat generation power estimation, and enabling a more accurate understanding of the heat generation situation of the battery cell under different working conditions, thereby providing a data basis for subsequent temperature prediction. Using the recursive least squares method to iteratively calculate the heat generation coefficient and the heat dissipation time constant online can quickly adapt to changes in the internal and external conditions of the battery cell, improving the accuracy and robustness during prediction. For parameters that change over time (such as the heat generation coefficient and the heat dissipation time constant), the recursive least squares method can be continuously optimized to keep the prediction method always in the best prediction effect, enhancing the adaptive ability of the prediction method. By applying the heat generation power, the heat generation coefficient, and the heat dissipation time constant for temperature prediction, the temperature of the battery cell can be accurately estimated within different discharge depth intervals, achieving an accurate estimation of the temperature change of a single battery cell.

[0079] Optionally, as Figure 2 shown, the battery cell temperature prediction method further includes:

[0080] S10. Determine the initial discharge depth according to the battery cell temperature and the open circuit voltage of the battery cell before charge and discharge that have been obtained.

[0081] S11. Obtain the average current, the current terminal voltage, and the current battery cell temperature of the battery cell every preset period to determine the current internal resistance of the battery cell.

[0082] S12. Determine the interval width of the discharge depth interval according to the initial discharge depth and the current internal resistance of the battery cell.

[0083] In one embodiment, during the discharge process, calculate the discharge depth DOD of the battery cell. Based on the known internal resistance R new [DOD,T] of the new battery cell, update the internal resistance R old [DOD,T] of the battery cell in different SOH states. Record the battery cell temperature value T cell,0 and the voltage value OCV cell,0 of the battery cell at the starting state. According to the look-up table DOD[OCV,T cell,0 , obtain the current discharge depth DOD0.

[0084] In one embodiment, since the internal resistance change trend of the battery cell corresponding to different discharge depths is different, in the initial stage and the middle stage of discharge, the internal resistance of the battery cell changes smoothly. When reaching the end of discharge, that is, when discharging in the low SOC interval of the battery cell, the internal resistance of the battery cell under the same conditions increases rapidly as the discharge progresses. Therefore, when predicting the battery cell temperature, it is also necessary to determine the discharge depth interval according to the initial discharge depth. In different discharge depth intervals, determine the internal resistance of the battery cell corresponding to the discharge depth interval, and then predict the battery cell temperature according to the internal resistance of the battery cell through steps S100 - S300. When the discharge depth interval changes, determine the internal resistance of the battery cell corresponding to the new discharge depth interval, and predict the battery cell temperature according to the newly determined internal resistance of the battery cell. For example, assume that the discharge depth intervals are [0% - 10%], [10% - 20%], which means that when the battery power is 90% - 100%, it is in the first discharge depth interval, and when the battery power is 80% - 90%, it is in the second discharge depth interval. Then when the battery power is in the range of 90% - 100%, calculate the corresponding internal resistance of the battery cell, and predict the battery cell temperature according to the internal resistance of the battery cell. When the battery power continues to consume and is in the range of 80% - 90%, calculate the internal resistance of the battery cell corresponding to the second discharge depth interval, and predict the battery cell temperature according to the new internal resistance of the battery cell, which helps to accurately predict the battery cell temperature even when the internal resistance of the battery cell changes non-linearly.

[0085] The determination method for determining the discharge depth interval according to the initial discharge depth includes:

[0086] During the charge and discharge process of the battery, according to the initial state of charge DOD0 of the battery cell and the changing discharge depth ΔDOD during the charge and discharge process, the real-time discharge depth DOD of the battery cell is calculated, where ΔDOD = ∫idt, and DOD = DOD0 + ΔDOD.

[0087] Optionally, in the initial stage and the middle stage of discharge, the value of the interval width of the discharge depth interval is 5% - 15%, preferably, the interval width is 10%; at the end of discharge, the value of the interval width of the discharge depth interval is 1% - 5%, preferably, the interval width is 3%.

[0088] Optionally, as Figure 3 shown, obtaining the average current, the current terminal voltage, and the current battery cell temperature of the battery cell at each preset period, and determining the current heat generation power within the current period according to the average current, the current terminal voltage, and the current battery cell temperature includes:

[0089] Step S101, obtaining the real-time discharge depth of the battery cell.

[0090] Step S102, obtaining the average current, the current terminal voltage, and the current battery cell temperature of the battery cell at each preset period.

[0091] Step S103: Obtain the current open-circuit voltage of the battery cell according to a preset first mapping relationship, the current battery cell temperature, and the real-time depth of discharge, where the first mapping relationship is used to characterize the mapping relationship among the battery cell temperature, the depth of discharge, and the open-circuit voltage.

[0092] Step S104: Determine the current heat generation power according to the current open-circuit voltage, the current terminal voltage, and the average current.

[0093] Obtain the real-time depth of discharge DOD at time k according to the formula ΔDOD = ∫idt and DOD = DOD0 + ΔDOD k During the discharge process, read the current terminal voltage V at fixed intervals of a period cell,k and the current battery cell temperature T cell,k , calculate the average current I within a preset period avg,k . According to the first mapping relationship OCV[DOD, T], obtain the current open-circuit voltage OCV corresponding to the current battery cell temperature T cell,k and the real-time depth of discharge DOD k . cellk .

[0094] According to the formula Q DOD =(OCV cell,k -V cell,k )*I avg,t , obtain the heat generation power Q within the current period k-1 , where the current direction is positive when it is the discharge direction.

[0095] Optionally, as Figure 4 shown, the calculating the heat generation coefficient and the heat dissipation time constant within the current period according to the historical battery cell temperature at the previous moment, the current battery cell temperature, and the current heat generation power by using the recursive least squares method includes:

[0096] Step S201: Construct a system equation based on a parameter matrix and an observation matrix, where the observation matrix is constructed by the historical battery cell temperature, the current battery cell temperature, and the current heat generation power, and the parameter matrix is constructed by the heat generation coefficient and the heat dissipation time constant.

[0097] Step S202: Based on the recursive least squares method, obtain the heat generation coefficient and the heat dissipation time constant within the current period through the system equation.

[0098] In an embodiment, describe the heat dissipation system in the form of a difference equation, expressed as:

[0099] y(t)+a1y(t - 1)+…+a ny(t - n) = b1u(t - 1) + … + b m u(t - m),

[0100] Rewrite the difference equation into the form of sampling and observation, expressed as:

[0101] y(t) = -a1y(t - 1) - … - a n y(t - n) + b1u(t - 1) + … + b m u(t - m),

[0102] where y represents the output, a represents the autoregressive coefficient, b represents the moving average coefficient, t represents the time, y(t - 1), y(t - 2), …, y(t - n) represent the outputs at the past n time instants respectively, and u(t - 1), u(t - 2), …, u(t - m) represent the inputs at the past m time instants respectively.

[0103] Furthermore, introduce vectors and stipulate that:

[0104]

[0105] Then the system equation can be expressed as y(t) = φ T (t) * θ,

[0106] where θ represents the parameter matrix, which contains all the parameters to be estimated, namely the heat generation coefficient R and the heat dissipation time constant Tau; represents the observation matrix, which contains all the relevant historical data at the current time instant t, namely the heat generation power Q, the ambient temperature T amb and the historical temperature T of the battery cell cell .

[0107] In an embodiment, when there is no charge - discharge current in the battery pack, record the current battery cell temperature T cell,0 ambient temperature T amb,0 , and give reasonable initial values to the variables that need to be calculated iteratively online, namely the heat generation coefficient and the heat dissipation time constant of each battery cell. The selection of the initial values should be within a reasonable range. For example, the initial value range of the heat generation coefficient is 5 - 15, and the initial value range of the heat dissipation time constant is 800 - 1200.

[0108] The specific calculation method based on the recursive least squares is expressed as:

[0109]

[0110] Θ k = Θ k-1 + K k * Inn,

[0111] where, represents the observation vector The transpose of, P k-1 represents the covariance matrix at time k-1, γ represents the forgetting factor, represents the cell temperature at time k, Inn represents the residual between the actual temperature and the predicted temperature, K k represents the gain matrix at time k, P k represents the covariance matrix at time k, Θ k represents the parameter matrix at time k, and the value range of the parameter matrix is 0.95-1. S, K, and P represent intermediate variables for iterative calculation.

[0112] Optionally, before predicting the future cell temperature when the cell is in the same discharge depth range according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant to obtain a prediction result, it further includes:

[0113] Establish a temperature change model based on the temperature change of the cell during charge and discharge.

[0114] Construct the temperature change model of the cell according to the obtained cell parameters, where the cell parameters include at least one of cell mass, cell specific heat capacity, cell thermal resistance, cell temperature change rate, ambient temperature, and cell temperature.

[0115] The temperature change model of the cell is expressed as:

[0116]

[0117] where, m cell represents the mass of the cell, C cell represents the specific heat capacity of the cell, represents the rate of change of temperature with respect to time, Q represents the heat generation power, T amb represents the ambient temperature, T cell represents the cell temperature, and Res represents the thermal resistance.

[0118] Obtain a temperature prediction model based on the heat generation coefficient and the heat dissipation time constant according to the temperature change model.

[0119] Discretize the temperature change model, and convert the cell parameters in the temperature change model into cell working parameters that change with time, and the heat generation coefficient and the heat dissipation time constant that change with the heat state;

[0120] Construct the temperature prediction model according to the cell working parameters, the heat generation coefficient, and the heat dissipation time constant.

[0121] After converting the temperature change model into a discrete form, the temperature prediction model is expressed as:

[0122]

[0123] Among them, T cell,k represents the cell temperature at time k, and T cell,k-1 represents the cell temperature at time k - 1. Q k-1 represents the heat generation power between time k - 1 and time k. In each preset cycle, the heat generation power is calculated periodically. Δt represents the time interval from time k - 1 to time k. R k-1 represents the heat generation coefficient from time k - 1 to time k. Tau represents the heat dissipation time constant from time k - 1 to time k. R k-1 and Tau are related to the heat dissipation path and heat dissipation material from the cell to the outside. T amb,k-1 represents the ambient temperature at time k - 1.

[0124] Written in the form of matrix multiplication, it is expressed as:

[0125]

[0126] Among them,

[0127] Optionally, after predicting the future cell temperature when the cell is in the same discharge depth interval according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtaining the prediction result, it further includes:

[0128] Determine the future discharge depth in a future preset time period according to the obtained current filtered current;

[0129] Determine the future open - circuit voltage according to the future discharge depth, and determine the future internal resistance of the cell according to the future open - circuit voltage;

[0130] Determine the future discharge depth in the future cycle and the future heat generation power corresponding to the future discharge depth according to the future internal resistance, the heat generation coefficient, and the heat dissipation time constant;

[0131] Process the future heat generation power, the heat generation coefficient, and the heat dissipation coefficient through the temperature prediction model to obtain the future cell temperature in the future cycle.

[0132] In one embodiment, the filtered current at the current moment is obtained and averaged to obtain the average current. At the current depth of discharge, the change trend of the depth of discharge at the current current is calculated using ampere-hour integration to obtain the future depth of discharge. The open-circuit voltage is obtained according to the mapping relationship between the current and the depth of discharge and the open-circuit voltage. The internal resistance value of the future battery cell is obtained according to the relationship R[DOD, T] between the temperature and the depth of discharge and the internal resistance of the battery cell, and then the heat generation power at different future depths of discharge under the current specified current (average current) is calculated. The heat generation power is substituted into the temperature prediction model to obtain the battery cell temperature under future working conditions, that is, based on the past accumulated filtered average current I cell,filled , combined with the internal resistance R old [DOD, T] at different aging stages updated and stored during the discharge process. Here, the internal resistance data for different DOD and battery cell temperature T can be obtained by looking up the table through the mapping relationship. Furthermore, the future heat generation power can be obtained, expressed as:

[0133] Q DOD =(OCV cell,k -I cell,filled *R old [DOD, T])*I cell,filled ,

[0134] where Q DOD represents the heat generation power at this depth of discharge, OCV cell,k represents the open-circuit voltage at time k, and I cell,filled represents the filtered current.

[0135] Optionally, the determining the future depth of discharge in the future period and the future heat generation power corresponding to the future depth of discharge according to the future internal resistance, the heat generation coefficient, and the heat dissipation time constant includes:

[0136] Obtaining the heat dissipation state of the battery cell, and determining the heat dissipation power and the heat generation weight according to the heat dissipation state;

[0137] Optimizing the future heat generation power according to the heat dissipation power and the heat generation weight to obtain the weighted heat generation power.

[0138] The iteration within a single preset cycle needs to consider the heating power of the battery cell. For a single battery cell, the total heating power is equal to the total heat generation power minus the total heat dissipation power. If the total heat generation power of the battery cell is Q1 and the total heat dissipation power is Q2, then the total heating power is Q = Q1 - Q2; where Q1 can be further decomposed into reversible heat (entropy heat) and irreversible heat (Ohmic heat); the heat dissipation power Q2 can be decomposed into the heat dissipated through different heat dissipation paths, including thermal radiation, heat conduction, and heat convection. After only considering the main heat dissipation path of conduction, the heat dissipation power Q2 from the battery cell to the external environment is related to the heat dissipation configuration of the battery cell / battery pack, and different heat dissipation powers correspond to when the water cooling is turned on and off.

[0139] In one embodiment, the same vehicle may have different heat dissipation states. For example, under the conditions of water cooling on and water cooling off, the heat dissipation states are different, and the resulting temperature rise changes are also different. The battery management system generates different discharge currents under different heat dissipation states. Therefore, by determining the heat dissipation state of the battery cell, obtaining the heat dissipation power and heat generation weight, optimizing the heat generation power, and obtaining the weighted heat generation power, the accuracy and robustness of the prediction can be further increased.

[0140] For example, when the predicted temperature rise reaches the temperature threshold for turning on the water cooling heat dissipation system, the heat generation power should be the weighted value considering the water cooling heat dissipation power. Here, the average current should also be the weighted value corresponding to the higher discharge current after considering the water cooling system is turned on. Substitute the heat generation power, heat dissipation power, and average current after adding the heat generation weight and heat dissipation power weight back into the temperature prediction model, and combine the heat generation coefficient and heat dissipation time constant deduced in step S200 to predict the temperature rise changes of the battery cell at different discharge depths DOD and at the end of discharge in the future for a period of time.

[0141] Optionally, obtaining the average current, the current terminal voltage, and the current battery cell temperature of the battery cell every preset cycle includes:

[0142] Determine the current filtered current according to the current filtered value of the battery cell at the previous moment and the current current of the battery cell, and obtain the average current according to the current filtered current;

[0143] Determine the front-end voltage according to the terminal voltage filtered value of the battery cell at the previous moment and the current terminal voltage acquisition value of the battery cell;

[0144] Determine the current battery cell temperature according to the battery cell temperature filtered value of the battery cell at the previous moment and the current battery cell temperature acquisition value of the battery cell.

[0145] In one embodiment, the collected voltage, current, and temperature signals need to be filtered first. Among them, the voltage, current, and temperature filtering calculations are expressed as:

[0146] Vcell,filled = V cell,k *α1 + V cell,k-1 *(1 - α1),

[0147] I cell,filled = I cell,k *α2 + I cell,k-1 *(1 - α2),

[0148] T cell,filled = T cell,k *α3 + T cell,k-1 *(1 - α3),

[0149] Wherein, V cell,filled represents the current terminal voltage, V cell,k-1 represents the voltage filtering value at the (k - 1)th moment, V cell,k represents the voltage acquisition value at the kth moment, α1 represents the voltage filtering coefficient; I cell,filled represents the average current value, I cell,k-1 represents the filtering value at the (k - 1)th moment, I cell,k represents the current acquisition value at the kth moment, α2 represents the current filtering coefficient; T cell,filled represents the current cell temperature, T cell,k-1 represents the temperature filtering value at the previous (k - 1)th moment, T cell,k represents the temperature acquisition value at the kth moment, α3 represents the temperature filtering coefficient, where the value ranges of α1, α2, and α3 are 0 - 1.

[0150] In a second aspect, as Figure 5 shown, a cell temperature prediction device 500 provided by an embodiment of the present invention includes:

[0151] A first module 510, configured to obtain the average current, the current terminal voltage, and the current cell temperature of the cell at each preset period, and determine the current heat generation power within the current period according to the average current, the current terminal voltage, and the current cell temperature.

[0152] A second module 520, configured to calculate and obtain the heat generation coefficient and the heat dissipation time constant within the current period by using the recursive least squares method according to the historical cell temperature at the previous moment, the current cell temperature, and the current heat generation power, wherein the previous moment represents the moment that is one preset period before the current moment.

[0153] A third module 530, configured to predict the future cell temperature when the cell is in the same discharge depth range according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtain a prediction result.

[0154] In a third aspect, as Figure 6As shown in the figure, an electronic device 600 provided by an embodiment of the present invention includes a memory 610 and a processor 620; the memory 610 is used to store a computer program; the processor 620 is used to implement the above-mentioned battery cell temperature prediction method when executing the computer program.

[0155] Or, an electronic device 600 includes a memory 610 and a processor 620 coupled to the memory 610; the memory 610 is configured to store a computer program; the processor 620 is configured to perform the following operations when executing the computer program:

[0156] Obtain the average current, the current terminal voltage, and the current battery cell temperature of the battery cell at each preset period, and determine the current heat generation power within the current period according to the average current, the current terminal voltage, and the current battery cell temperature;

[0157] Calculate and obtain the heat generation coefficient and the heat dissipation time constant within the current period by using the recursive least squares method according to the historical battery cell temperature at the previous moment, the current battery cell temperature, and the current heat generation power, where the previous moment represents the moment before the current moment and is one preset period away from the current moment;

[0158] Predict the future battery cell temperature when the battery cell is in the same discharge depth range according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtain a prediction result.

[0159] In a fourth aspect, a computer-readable storage medium provided by an embodiment of the present invention has a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned battery cell temperature prediction method is implemented.

[0160] Or, a non-volatile computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the processor is caused to perform the following operations:

[0161] Obtain the average current, the current terminal voltage, and the current battery cell temperature of the battery cell at each preset period, and determine the current heat generation power within the current period according to the average current, the current terminal voltage, and the current battery cell temperature;

[0162] Calculate and obtain the heat generation coefficient and the heat dissipation time constant within the current period by using the recursive least squares method according to the historical battery cell temperature at the previous moment, the current battery cell temperature, and the current heat generation power, where the previous moment represents the moment before the current moment and is one preset period away from the current moment;

[0163] Predict the future temperature of the battery cell at the same depth of discharge interval according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtain a prediction result.

[0164] An electronic device 600 that can be a server or a client of the present invention will now be described. It is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 600 is intended to represent various forms of digital electronic computer devices, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 600 can also represent various forms of mobile devices, such as, personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0165] The electronic device 600 includes a computing unit that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0166] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention. In addition, the functional units in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0167] Although the present invention is disclosed as above, the scope of protection of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the scope of protection of the present invention.

Claims

1. A method for predicting the temperature of an electric cell, characterized in that, Including: Obtaining the average current, the current terminal voltage, and the current cell temperature of the cell at every preset period, and determining the current heat generation power within the current period according to the average current, the current terminal voltage, and the current cell temperature; Calculating and obtaining the heat generation coefficient and the heat dissipation time constant within the current period by using the recursive least squares method according to the historical cell temperature at the previous moment, the current cell temperature, and the current heat generation power, where the previous moment represents the moment before the current moment and is one preset period away from the current moment; Predicting the future cell temperature when the cell is in the same discharge depth interval according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtaining a prediction result.

2. The method for predicting the temperature of an electric cell according to claim 1, wherein The cell temperature prediction method further includes: Determining the initial discharge depth according to the cell temperature and the open circuit voltage of the cell before charge and discharge that have been obtained; Obtaining the average current, the current terminal voltage, and the current cell temperature of the cell at every preset period to determine the current internal resistance of the cell; Determining the interval width of the discharge depth interval according to the initial discharge depth and the current internal resistance of the cell.

3. The method for predicting the temperature of the battery cell according to claim 2, wherein The obtaining the average current, the current terminal voltage, and the current cell temperature of the cell at every preset period, and determining the current heat generation power within the current period according to the average current, the current terminal voltage, and the current cell temperature includes: Obtaining the real-time discharge depth of the cell; Obtaining the average current, the current terminal voltage, and the current cell temperature of the cell at every preset period; Obtaining the current open circuit voltage of the cell according to a preset first mapping relationship, the current cell temperature, and the real-time discharge depth, where the first mapping relationship is used to characterize the mapping relationship among the cell temperature, the discharge depth, and the open circuit voltage; Determining the current heat generation power according to the current open circuit voltage, the current terminal voltage, and the average current.

4. The method for predicting the temperature of an electric cell according to any one of claims 1 to 3, characterized in that, The calculating and obtaining the heat generation coefficient and the heat dissipation time constant within the current period by using the recursive least squares method according to the historical cell temperature at the previous moment, the current cell temperature, and the current heat generation power includes: Constructing a system equation based on a parameter matrix and an observation matrix, where the observation matrix is constructed by the historical cell temperature, the current cell temperature, and the current heat generation power, and the parameter matrix is constructed by the heat generation coefficient and the heat dissipation time constant; Obtaining the heat generation coefficient and the heat dissipation time constant within the current period through the system equation based on the recursive least squares method.

5. The method for predicting the temperature of an electric cell according to claim 4, wherein, Before the predicting the future cell temperature when the cell is in the same discharge depth interval according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtaining a prediction result, it further includes: Establishing a temperature change model according to the temperature change situation of the cell during charge and discharge; Obtaining a temperature prediction model based on the heat generation coefficient and the heat dissipation time constant according to the temperature change model.

6. The method for predicting the temperature of an electric cell according to claim 5, wherein The establishing a temperature change model according to the temperature change situation of the cell during charge and discharge includes: Construct the temperature change model of the battery cell according to the obtained battery cell parameters, where the battery cell parameters include at least one of battery cell mass, battery cell specific heat capacity, battery cell thermal resistance, battery cell temperature change rate, ambient temperature, and battery cell temperature.

7. The method for predicting the temperature of an electric cell according to claim 6, wherein The obtaining of the temperature prediction model based on the heat generation coefficient and the heat dissipation time constant according to the temperature change model includes: Performing discretization conversion on the temperature change model, and respectively converting the battery cell parameters in the temperature change model into battery cell working parameters that change with time, and the heat generation coefficient and the heat dissipation time constant that change with the heat state; Construct the temperature prediction model according to the battery cell working parameters, the heat generation coefficient, and the heat dissipation time constant.

8. The method for predicting the temperature of the battery cell according to claim 5, wherein, After predicting the future battery cell temperature when the battery cell is in the same discharge depth interval according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtaining the prediction result, it further includes: Determine the future discharge depth in a future preset time period according to the obtained current filtered current; Determine the future open circuit voltage according to the future discharge depth, and determine the future internal resistance of the battery cell according to the future open circuit voltage; Determine the future discharge depth in a future cycle and the future heat generation power corresponding to the future discharge depth according to the future internal resistance, the heat generation coefficient, and the heat dissipation time constant; Process the future heat generation power, the heat generation coefficient, and the heat dissipation coefficient through the temperature prediction model to obtain the future battery cell temperature in the future cycle.

9. The method for predicting the temperature of an electric cell according to claim 8, wherein, After determining the future discharge depth in a future cycle and the future heat generation power corresponding to the future discharge depth according to the future internal resistance, the heat generation coefficient, and the heat dissipation time constant, it further includes: Obtain the heat dissipation state of the battery cell, and determine the heat dissipation power and the heat generation weight according to the heat dissipation state; Optimize the future heat generation power according to the heat dissipation power and the heat generation weight to obtain the weighted heat generation power.

10. The method for predicting the temperature of the battery cell according to claim 5, wherein The obtaining of the average current, the current terminal voltage, and the current battery cell temperature of the battery cell every preset cycle includes: Determine the current filtered current according to the current filtered value of the current of the battery cell at the previous moment and the current current of the battery cell, and obtain the average current according to the current filtered current; Collect the front-end voltage according to the filtered value of the terminal voltage of the battery cell at the previous moment and the current terminal voltage acquisition value of the battery cell; Determine the current battery cell temperature according to the filtered value of the battery cell temperature of the battery cell at the previous moment and the current battery cell temperature acquisition value of the battery cell.

11. A battery cell temperature prediction device, characterized in that, It includes: A first module, configured to obtain the average current, the current terminal voltage, and the current battery cell temperature of the battery cell every preset cycle, and determine the current heat generation power in the current cycle according to the average current, the current terminal voltage, and the current battery cell temperature; A second module, configured to calculate and obtain the heat generation coefficient and the heat dissipation time constant in the current cycle by using the recursive least squares method according to the historical battery cell temperature at the previous moment, the current battery cell temperature, and the current heat generation power, where the previous moment represents the moment one preset cycle before the current moment; A third module, configured to predict a future cell temperature when the cell is in the same discharge depth range according to the current heat generation power, the heat generation coefficient, and the heat dissipation time constant, and obtain a prediction result.

12. An electronic device, characterized in that, It includes a memory and a processor; The memory is configured to store a computer program; The processor is configured to, when executing the computer program, implement the cell temperature prediction method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by the processor, the cell temperature prediction method according to any one of claims 1 to 10 is implemented.

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