Battery charging temperature rise prediction method and device, controller and storage medium

Abstract

The invention provides a battery charging temperature rise prediction method and device, a controller and a storage medium, and the method comprises the steps: dividing the state of charge of a battery into a plurality of intervals according to a preset proportion, so as to divide the charging process of the battery into a plurality of prediction intervals; based on the temperature estimation value of the battery cell, respectively calculating a temperature variation first prediction value corresponding to each prediction interval of the battery; based on the heat production power and the heat exchange power of the battery cell, respectively calculating a second predicted value of the temperature variation corresponding to each predicted interval of the battery; and according to the first predicted value of the temperature variation and the second predicted value of the temperature variation, determining a final predicted value of the temperature variation corresponding to each prediction interval of the battery. According to the method, the charging process is divided into a plurality of intervals, and the temperature rise prediction in each interval is corrected, so that the accuracy of temperature rise estimation in the battery charging process is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery management, and in particular relates to a battery charging temperature rise prediction method, a battery charging temperature rise prediction device and a controller, and a computer-readable storage medium. Background Art

[0002] The precise thermal management control requirements during fast charging of power battery systems, safe charging power output, and the user's need for accuracy of the remaining charging time have put forward higher requirements for temperature estimation and temperature rise prediction during the entire charging process. Effective real-time temperature estimation during the charging process can better serve the precise control of the thermal management system and reduce thermal management energy consumption. At the same time, accurate temperature rise estimation during the charging process can improve the accuracy of the remaining charging time estimation and ensure a good user experience. Based on this, how to improve the accuracy of temperature rise estimation of the battery during charging has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a battery charging temperature rise prediction method, a battery charging temperature rise prediction device and a controller and a computer-readable storage medium to improve the accuracy of temperature rise estimation during battery charging.

[0004] In order to solve or improve the above technical problems to a certain extent, according to one aspect of the present invention, a method for predicting battery charging temperature rise is provided, comprising:

[0005] Dividing the state of charge of the battery into a plurality of intervals according to a preset ratio, so as to divide the charging process of the battery into a plurality of prediction intervals;

[0006] Calculate first predicted values ​​of temperature variation corresponding to each predicted interval of the battery based on the estimated temperature value of the battery cell;

[0007] Calculating the second predicted value of the temperature change corresponding to each of the predicted intervals of the battery based on the heat generation power and the heat exchange power of the battery cell;

[0008] A final predicted value of the temperature change corresponding to each of the predicted intervals of the battery is determined according to the first predicted value of the temperature change and the second predicted value of the temperature change.

[0009] In some embodiments, the calculation formula of the first predicted value of the temperature change is as follows:

[0010]

[0011] In the formula, is the first predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the estimated temperature of the cell at the kth moment, is the estimated temperature of the cell at the k-1th moment, is the estimated charging time for the prediction interval.

[0012] In some embodiments, the steps of obtaining the estimated temperature value of the battery cell are as follows:

[0013] Based on a pre-established second-order equivalent circuit model of the battery, a voltage estimation value of the terminal voltage of the battery is obtained through Kalman filtering;

[0014] The voltage estimation value is input into a pre-established battery heat generation model, and the temperature estimation value of the battery cell is obtained through Kalman filtering.

[0015] In some embodiments, the state space expression of the second-order equivalent circuit model of the battery is as follows:

[0016]

[0017]

[0018] Where, T s represents the detection period after discretization, U 0,k-1 , U 0,k They represent the terminal voltage corresponding to the ohmic internal resistance at the k-1th moment and the kth moment, respectively, U p1,k-1 , U p1,k Indicates the ohmic internal resistance R at the k-1 and k moments p1 With ohmic capacitance C p1 The terminal voltage of the parallel circuit, U p2,k-1 , U p2,k Indicates the polarization internal resistance R at the k-1th and kth moments p2 With polarization capacitance C p2 The terminal voltage of the parallel circuit, SOC k-1 , SOC k They represent the SOC of the battery at the k-1th and kth moments, SOH is the SOH of the battery, and C N represents the battery capacity, η represents the calibration factor, I k-1 represents the current at the kth moment, OVC represents the open circuit voltage of the battery, and w 1,k-1 ~w 4,k-1 represents the system noise, U k represents the terminal voltage of the battery at the kth moment, v k represents the observation noise.

[0019] In some embodiments, the state space expression of the battery heat generation model is as follows:

[0020]

[0021]

[0022] Where, T k-1 ,T k They represent the actual temperature of the battery cell at the k-1th and kth moments respectively, A represents the contact area between the battery cell and the cooling system, h represents the convection heat transfer coefficient, η1 represents the calibration coefficient considering the difference between the reversible heat, the contact surface temperature of the battery cell and the cooling system and the actual temperature of the battery cell, and U k-1 represents the terminal voltage of the battery at the k-1th moment, I k-1 represents the charging current at the k-1th moment, η2 represents the calibration coefficient of heat exchange between different cells and the cooling system, represents the temperature of the coolant at the k-1th moment, represents the cell temperature sampled by NTC, and C represents the transfer function matrix of the mapping relationship between the sampled temperature and the actual cell temperature.

[0023] In some embodiments, the calculation formula of the second predicted value of the temperature change is as follows:

[0024]

[0025] In the formula, is the second predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the heat generation power of the battery cell corresponding to the kth moment in the Nth prediction interval, is the heat exchange power of the battery corresponding to the kth moment in the Nth prediction interval, c is the specific heat of the battery cell, m is the mass of the battery cell, is the duration of the kth moment of the Nth prediction interval.

[0026] In some embodiments, the step of determining the final predicted value of the temperature variation corresponding to each predicted interval of the battery according to the first predicted value of the temperature variation and the second predicted value of the temperature variation comprises:

[0027] Calculate an average value of the first predicted value of the temperature change and the second predicted value of the temperature change, and use the average value as the final predicted value of the temperature change; or

[0028] The first predicted value of the temperature change and the second predicted value of the temperature change are respectively multiplied by corresponding weight coefficients and then added together to obtain the final predicted value of the temperature change.

[0029] According to another aspect of the present invention, a battery charging temperature rise prediction device is provided, comprising:

[0030] An interval division module, configured to divide the state of charge of the battery into a plurality of intervals according to a preset ratio, so as to divide the charging process of the battery into a plurality of prediction intervals;

[0031] A prediction value calculation module, configured to calculate first prediction values ​​of temperature variation corresponding to each prediction interval of the battery based on the temperature estimation value of the battery cell, and to calculate second prediction values ​​of temperature variation corresponding to each prediction interval of the battery based on the heat generation power and heat exchange power of the battery cell;

[0032] The prediction value correction module is configured to determine a final prediction value of the temperature change corresponding to each prediction interval of the battery according to the first prediction value of the temperature change and the second prediction value of the temperature change.

[0033] In some embodiments, the calculation formula of the first predicted value of the temperature change is as follows: ·

[0034]

[0035] In the formula, is the first predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the estimated temperature of the cell at the kth moment, is the estimated temperature of the cell at the k-1th moment, is the estimated charging time for the prediction interval.

[0036] In some implementations, the prediction value calculation module is further configured to:

[0037] Based on a pre-established second-order equivalent circuit model of the battery, a voltage estimation value of the terminal voltage of the battery is obtained through Kalman filtering;

[0038] The voltage estimation value is input into a pre-established battery heat generation model, and the temperature estimation value of the battery cell is obtained through Kalman filtering.

[0039] In some embodiments, the state space expression of the second-order equivalent circuit model of the battery is as follows:

[0040]

[0041]

[0042] Where, T s represents the detection period after discretization, U 0,k-1 , U 0,k They represent the terminal voltage corresponding to the ohmic internal resistance at the k-1th moment and the kth moment, respectively, U p1,k-1 , U p1,kIndicates the ohmic internal resistance R at the k-1 and k moments p1 With ohmic capacitance C p1 The terminal voltage of the parallel circuit, U p2,k-1 , U p2,k Indicates the polarization internal resistance R at the k-1th and kth moments p2 With polarization capacitance C p2 The terminal voltage of the parallel circuit, SOC k-1 , SOC k They represent the SOC of the battery at the k-1th and kth moments, SOH is the SOH of the battery, and C N represents the battery capacity, η represents the calibration factor, I k-1 represents the current at the kth moment, OVC represents the open circuit voltage of the battery, and w 1,k-1 ~w 4,k-1 represents the system noise, U k represents the terminal voltage of the battery at the kth moment, v k represents the observation noise.

[0043] In some embodiments, the state space expression of the battery heat generation model is as follows:

[0044]

[0045] T NTCk =CT k

[0046] Where, T k-1 ,T k They represent the actual temperature of the battery cell at the k-1th and kth moments respectively, A represents the contact area between the battery cell and the cooling system, h represents the convection heat transfer coefficient, η1 represents the calibration coefficient considering the difference between the reversible heat, the contact surface temperature of the battery cell and the cooling system and the actual temperature of the battery cell, and U k-1 represents the terminal voltage of the battery at the k-1th moment, I k-1 represents the charging current at the k-1th moment, η2 represents the calibration coefficient of heat exchange between different cells and the cooling system, Represents the temperature of the coolant at the k-1th moment. represents the cell temperature sampled by NTC, and C represents the transfer function matrix of the mapping relationship between the sampled temperature and the actual cell temperature.

[0047] In some embodiments, the calculation formula of the second predicted value of the temperature change is as follows:

[0048]

[0049] Where, ΔT2 Nk is the second predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the heat generation power of the battery cell corresponding to the kth moment in the Nth prediction interval,

[0050] is the heat exchange power of the battery corresponding to the kth moment in the Nth prediction interval, c is the specific heat of the battery cell, m is the mass of the battery cell, is the duration of the kth moment of the Nth prediction interval.

[0051] In some implementations, the predicted value correction module is specifically configured as follows:

[0052] Calculate an average value of the first predicted value of the temperature change and the second predicted value of the temperature change, and use the average value as the final predicted value of the temperature change; or

[0053] The first predicted value of the temperature change and the second predicted value of the temperature change are respectively multiplied by corresponding weight coefficients and then added together to obtain the final predicted value of the temperature change.

[0054] According to another aspect of the present invention, a controller is provided, which includes a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the battery charging temperature rise prediction method described in any of the above embodiments can be implemented.

[0055] According to another aspect of the present invention, a computer-readable storage medium is provided for storing a computer program, wherein when the program is executed by a computer or a processor, the program implements the steps of the battery charging temperature rise prediction method described in any of the above embodiments.

[0056] Compared with the prior art, the present invention has obvious advantages and beneficial effects. By means of the above technical scheme, the battery charging temperature rise prediction method, battery charging temperature rise prediction device, controller and computer-readable storage medium of the present invention can achieve considerable technical advancement and practicality, and have wide industrial utilization value, and have at least the following advantages:

[0057] The present invention divides the entire charging process into multiple prediction intervals according to the charge amount of the battery's state of charge, and calculates the temperature change in each prediction interval respectively, and then corrects the temperature change in each interval, thereby avoiding the problem of poor accuracy in temperature rise prediction caused by continuous accumulation of temperature change deviations during the battery's temperature change prediction process, and improving the accuracy of temperature rise prediction during the battery charging process.

[0058] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following specifically cites a preferred embodiment and describes it in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of a flow chart of a method for predicting battery charging temperature rise according to an embodiment of the present invention;

[0060] Figure 2 It is a schematic block diagram of a battery charging temperature rise prediction device according to another embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods and effects of the battery charging temperature rise prediction method, battery charging temperature rise prediction device and controller and computer-readable storage medium proposed in the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0062] See also Figure 1 As shown, the battery charging temperature rise prediction method provided by the embodiment of the present invention includes:

[0063] Step S10, dividing the state of charge of the battery into a plurality of intervals according to a preset value, so as to divide the charging process into a plurality of prediction intervals.

[0064] In one embodiment, the state of charge of the battery is divided into a preset number of equal parts according to the capacity of the battery. For example, based on 10% of the battery capacity, the SOC (State of Charge) of the battery is divided into 10 equal parts, that is, SOC is 0% to 10% as one interval, SOC is 11% to 20% as one interval, and so on.

[0065] Of course, the battery capacity can also be divided into 20 equal parts based on 5% of the battery capacity, or divided into 5 equal parts based on 20% of the battery capacity.

[0066] In this embodiment, the interval in which the real-time SOC of the battery during the charging process is located is recorded as the first prediction interval, and the subsequent intervals are recorded as the 2nd, ..., N-1, and N prediction intervals in sequence, where N≤10.

[0067] In another embodiment, the current amount of electricity to be charged of the battery is obtained, and the SOC to be charged is divided into a plurality of equal parts. For example, based on 10% of the amount of electricity to be charged, the SOC to be charged of the battery is divided into 10 equal parts, that is, the SOC to be charged is 0% to 10% as one interval, the SOC to be charged is 11% to 20% as one interval, and so on.

[0068] Of course, the battery SOC to be charged can be divided into 20 equal parts based on 5% of the amount of electricity to be charged, or the battery SOC to be charged can be divided into 5 equal parts based on 20% of the battery capacity.

[0069] In this embodiment, the first interval is recorded as the first prediction interval, and the subsequent intervals are recorded as the 2nd, ..., N-1, and N prediction intervals, respectively, where N≤10.

[0070] Step S20 , calculating first predicted values ​​of temperature variation corresponding to each predicted interval of the battery based on the estimated temperature value of the battery cell.

[0071] In this step, the temperature estimation value of the battery cell is obtained through the pre-established battery second-order equivalent circuit model and battery heat generation model.

[0072] Specifically, the pre-established second-order equivalent circuit model of the battery is discretized, and the terminal voltage of the second-order equivalent circuit model of the battery is obtained through Kalman filtering.

[0073] The state space expression of the battery second-order equivalent circuit model is as follows:

[0074]

[0075]

[0076] Where, T s represents the detection period (step length) after discretization, U 0,k-1 , U 0,k They represent the terminal voltage corresponding to the ohmic internal resistance at the k-1th moment and the kth moment, respectively, U p1,k-1 , U p1,k Represents the polarization internal resistance R at the kth moment p1 With polarization capacitance C p1 The terminal voltage of the parallel circuit, U p2,k-1 , U p2,k Indicates the polarization internal resistance R at the k-1, kth moment p2 With polarization capacitance C p2 The terminal voltage of the parallel circuit, SOC k-1 , SOC k They represent the SOC of the battery at the k-1th and kth moments, SOH is the SOH of the battery, and C N represents the battery capacity, η represents the calibration factor, I k-1 represents the current at the kth moment, OVC represents the open circuit voltage of the battery, and w 1,k-1 ~w 4,k-1 represents the system noise, U k represents the terminal voltage of the battery at the kth moment, v k represents the observation noise.

[0077] By identifying the parameters of given operating conditions, the R and C parameter tables that change in real time with SOC and cell temperature are obtained, the state transfer matrix and input matrix of the second-order equivalent circuit model of the battery are calculated, and the optimal estimate of the terminal voltage of the second-order equivalent circuit model of the battery is obtained through Kalman filtering.

[0078] Among them, the R and C parameter tables are parameters of resistance and capacitance in the second-order equivalent circuit model of the battery obtained through experiments in advance, which change with SOC and cell temperature. The cell temperature is the optimal estimate of the cell temperature obtained by the battery heat generation model. Of course, when the optimal estimate of the terminal voltage of the second-order equivalent circuit model of the battery is first obtained, the optimal estimate of the cell temperature cannot be determined by the battery heat generation model. Therefore, the historical value of the optimal estimate of the cell temperature can be used, or the currently collected cell temperature can be used.

[0079] The R, C parameter table can be pre-stored in the battery management system, or pre-stored in the server, and then the resistance and capacitance parameters of the battery second-order equivalent circuit model can be obtained through offline identification or online identification.

[0080] The expression of the state transfer matrix is ​​as follows:

[0081]

[0082] The expression of the input matrix is ​​as follows:

[0083]

[0084] Specifically, a battery heat generation model is pre-selected and discretized, and the temperature estimation value of the battery cell is obtained through Kalman filtering. After obtaining the temperature estimation value of the battery cell, the temperature estimation value is provided to the battery second-order equivalent circuit model to obtain the R and C parameters required for the above-mentioned calculation of the state transfer matrix and the input matrix.

[0085] The filling space expression of the battery heat generation model is as follows:

[0086]

[0087]

[0088] Where, T k-1 ,T k They represent the actual temperature of the battery cell at the k-1th and kth moments respectively, A represents the contact area between the battery cell and the cooling system, h represents the convection heat transfer coefficient, η1 represents the calibration coefficient considering the difference between the reversible heat, the contact surface temperature of the battery cell and the cooling system and the actual temperature of the battery cell, and U k-1 represents the terminal voltage of the battery at the k-1th moment, I k-1represents the charging current at the k-1th moment, η2 represents the calibration coefficient of heat exchange between different cells and the cooling system, Represents the temperature of the coolant at the k-1th moment. represents the cell temperature sampled by NTC, and C represents the transfer function matrix of the mapping relationship between the sampled temperature and the actual cell temperature.

[0089] Based on the above-mentioned battery heat generation model, the optimal estimate of the terminal voltage of the battery second-order equivalent circuit model, the open circuit voltage and the given current, the optimal estimate of the battery cell temperature (i.e., the estimated temperature value of the battery cell) is obtained by Kalman filtering. The estimated temperature value of the battery cell at the Kth moment is recorded as The double Kalman filter under the battery heat generation model (hot spot coupling model) performs prediction correction to obtain the real-time detection step length T s Estimated temperature of the lower cell

[0090] In one embodiment, the prediction formula of Kalman filtering is as follows:

[0091]

[0092]

[0093] The correction formula of Kalman filter is as follows:

[0094]

[0095]

[0096] P′ k =(IK′C k ) k

[0097] In the above formula, A k , which can be understood as a rule matrix, predicts the state at time k from the state at time k-1;

[0098] u k , input, can be considered as external influence;

[0099] B k , the input coefficient matrix;

[0100] The covariance of k-1 , according to the properties of the covariance matrix, The covariance matrix of

[0101] The uncertainty of the external environment, which is regarded as covariance Q kThe new Gaussian distribution has the same mean as the original distribution, but a different covariance

[0102]

[0103] The new best estimate is a forecast based on the original best estimate and corrected for known external influences.

[0104] The new uncertainty is a prediction based on the original uncertainty and the uncertainty of the external environment.

[0105] Introducing the concept of sensor, the sensor reading matrix C k , the Gaussian distribution is centered around the predicted mean

[0106]

[0107]

[0108] The covariance of the sensor noise is set to R k , the mean is set to y k ,This block of Gaussian distribution surrounds the sensor readings. After multiplying the two Gaussian distributions, we get the overlapping part, which is the area where the best estimate appears.

[0109] The product of two Gaussian distributions

[0110]

[0111] make Kalman gain, i.e.

[0112] μ=μ1+K(μ2-μ1)

[0113]

[0114]

[0115]

[0116]

[0117] Considering that the formulas all contain Ck, they can be further simplified to:

[0118]

[0119]

[0120]

[0121] Prediction and correction formulas for the final form Kalman filter.

[0122] In one embodiment, the first predicted value of the temperature change is obtained according to the product of the change of the temperature estimation value within the prediction period and the estimated charging time of the prediction interval.

[0123] Specifically, the calculation formula of the first predicted value of the temperature change is as follows:

[0124]

[0125] In the formula, is the first predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the estimated temperature of the cell at the kth moment, is the estimated temperature of the cell at the k-1th moment, is the estimated charging time for the prediction interval.

[0126] Step S30 , calculating second predicted values ​​of temperature variation corresponding to each predicted interval of the battery based on the heat generation power and heat exchange power of the battery cell.

[0127] In this step, a second predicted value of the temperature change in the corresponding prediction interval is determined by the heat generation power of the battery cell and the heat exchange power of the battery.

[0128] In one embodiment, the calculation formula of the second predicted value of the temperature change is as follows:

[0129]

[0130] In the formula, is the second predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the heat generation power of the battery cell corresponding to the kth moment in the Nth prediction interval, is the heat exchange power of the battery corresponding to the kth moment in the Nth prediction interval, c is the specific heat of the battery cell, m is the mass of the battery cell, is the duration of the kth moment of the Nth prediction interval.

[0131] In this calculation formula, in, is the calibration factor considering the reversible heat part obtained from the thermal insulation experiment, is the charging current at the Kth moment in the corresponding prediction interval, is the internal resistance of the battery at the Kth moment in the corresponding prediction interval.

[0132] Battery heat exchange power It can be obtained from the thermal management power table. The thermal management power table can be pre-selected and set according to specific circumstances. The setting of the thermal management power table needs to consider the environmental exchange heat, including: the battery cooling system, the surrounding environment, the surrounding materials, etc.

[0133] Step S40, determining a final predicted value of the temperature variation corresponding to each predicted interval of the battery according to the first predicted value of the temperature variation and the second predicted value of the temperature variation.

[0134] In one embodiment, an average value of the first predicted value of the temperature change and the second predicted value of the temperature change is calculated, and the average value is used as the final predicted value of the temperature change.

[0135] In this embodiment, the temperature change prediction values ​​(the first temperature change prediction value and the second temperature change prediction value) obtained in two different ways are averaged to obtain the final temperature change prediction value. The final temperature change prediction value can be made closer to the actual temperature change, thereby improving the accuracy of the temperature change prediction.

[0136] In another embodiment, the first predicted value of the temperature change and the second predicted value of the temperature change are respectively multiplied by corresponding weight coefficients and then added together to obtain a final predicted value of the temperature change.

[0137] In this embodiment, the weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change are assigned in advance through experimental data to ensure that the final predicted value of the temperature change is closer to the actual temperature change, thereby improving the accuracy of the temperature change prediction.

[0138] The weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change may be the same coefficients, for example, the weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change are both 50%; of course, the weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change may be different weight coefficients, for example, the weight coefficient corresponding to the first predicted value of the temperature change is 30%, and the weight coefficient corresponding to the second predicted value of the temperature change is 70%, or the weight coefficient corresponding to the first predicted value of the temperature change is 40%, and the weight coefficient corresponding to the second predicted value of the temperature change is 60%, etc.

[0139] In one embodiment, when the battery is fully charged, the predicted value of the temperature rise of the battery can be calculated by the following formula:

[0140]

[0141] Where, T Init is the second predicted value of the temperature change when fully charged, which can be calculated by the above formula and will not be described here. ΔTn Final predicted value of temperature change in each prediction interval when fully charged.

[0142] According to another embodiment of the present invention, a battery charging temperature rise prediction device is provided. Figure 2 As shown, it includes: an interval division module 10, a prediction value calculation module 20 and a prediction value correction module 30.

[0143] The interval division module 10 is configured to divide the state of charge of the battery into a plurality of intervals according to a preset ratio, so as to divide the charging process of the battery into a plurality of prediction intervals.

[0144] In one embodiment, the state of charge of the battery is divided into a preset number of equal parts according to the capacity of the battery. For example, based on 10% of the battery capacity, the SOC (State of Charge) of the battery is divided into 10 equal parts, that is, SOC is 0% to 10% as one interval, SOC is 11% to 20% as one interval, and so on.

[0145] Of course, the battery capacity can also be divided into 20 equal parts based on 5% of the battery capacity, or divided into 5 equal parts based on 20% of the battery capacity.

[0146] In this embodiment, the interval in which the real-time SOC of the battery during the charging process is located is recorded as the first prediction interval, and the subsequent intervals are recorded as the 2nd, ..., N-1, and N prediction intervals in sequence, where N≤10.

[0147] In another embodiment, the current amount of electricity to be charged of the battery is obtained, and the SOC to be charged is divided into a plurality of equal parts. For example, based on 10% of the amount of electricity to be charged, the SOC to be charged of the battery is divided into 10 equal parts, that is, the SOC to be charged is 0% to 10% as one interval, the SOC to be charged is 11% to 20% as one interval, and so on.

[0148] Of course, the battery SOC to be charged can be divided into 20 equal parts based on 5% of the amount of electricity to be charged, or the battery SOC to be charged can be divided into 5 equal parts based on 20% of the battery capacity.

[0149] In this embodiment, the first interval is recorded as the first prediction interval, and the subsequent intervals are recorded as the 2nd, ..., N-1, and N prediction intervals, respectively, where N≤10.

[0150] The prediction value calculation module 20 is configured to calculate the first prediction value of the temperature change corresponding to each prediction interval of the battery based on the temperature estimation value of the battery cell, and to calculate the second prediction value of the temperature change corresponding to each prediction interval of the battery based on the heat generation power and heat exchange power of the battery cell.

[0151] The prediction value calculation module 20 is further configured to obtain the temperature estimation value of the battery cell through a pre-established second-order equivalent circuit model of the battery and a battery heat generation model.

[0152] Specifically, the pre-established second-order equivalent circuit model of the battery is discretized, and the terminal voltage of the second-order equivalent circuit model of the battery is obtained through Kalman filtering.

[0153] The state space expression of the battery second-order equivalent circuit model is as follows:

[0154]

[0155]

[0156] Where, T s represents the detection period (step length) after discretization, U 0,k-1 , U 0,k They represent the terminal voltage corresponding to the ohmic internal resistance at the k-1th moment and the kth moment, respectively, U p1,k-1 , U p1,k Represents the polarization internal resistance R at the kth moment p1 With polarization capacitance C p1 The terminal voltage of the parallel circuit, U p2,k-1 , U p2,k Indicates the polarization internal resistance R at the k-1, kth moment p2 With polarization capacitance C p2 The terminal voltage of the parallel circuit, SOC k-1 , SOC k They represent the SOC of the battery at the k-1th and kth moments, SOH is the SOH of the battery, and C N represents the battery capacity, η represents the calibration factor, I k-1 represents the current at the kth moment, OVC represents the open circuit voltage of the battery, and w 1,k-1 ~w 4,k-1 represents the system noise, U k represents the terminal voltage of the battery at the kth moment, v k represents the observation noise.

[0157] By identifying the parameters of given operating conditions, the R and C parameter tables that change in real time with SOC and cell temperature are obtained, the state transfer matrix and input matrix of the second-order equivalent circuit model of the battery are calculated, and the optimal estimate of the terminal voltage of the second-order equivalent circuit model of the battery is obtained through Kalman filtering.

[0158] Among them, the R and C parameter tables are parameters of resistance and capacitance in the second-order equivalent circuit model of the battery obtained through experiments in advance, which change with SOC and cell temperature. The cell temperature is the optimal estimate of the cell temperature obtained by the battery heat generation model. Of course, when the optimal estimate of the terminal voltage of the second-order equivalent circuit model of the battery is first obtained, the optimal estimate of the cell temperature cannot be determined by the battery heat generation model. Therefore, the historical value of the optimal estimate of the cell temperature can be used, or the currently collected cell temperature can be used.

[0159] The R, C parameter table can be pre-stored in the battery management system, or pre-stored in the server, and then the resistance and capacitance parameters of the battery second-order equivalent circuit model can be obtained through offline identification or online identification.

[0160] The expression of the state transfer matrix is ​​as follows:

[0161]

[0162] The expression of the input matrix is ​​as follows:

[0163]

[0164] Specifically, a battery heat generation model is pre-selected and discretized, and the temperature estimation value of the battery cell is obtained through Kalman filtering. After obtaining the temperature estimation value of the battery cell, the temperature estimation value is provided to the battery second-order equivalent circuit model to obtain the R and C parameters required for the above-mentioned calculation of the state transfer matrix and the input matrix.

[0165] The filling space expression of the battery heat generation model is as follows:

[0166]

[0167]

[0168] Where, T k-1 ,T k They represent the actual temperature of the battery cell at the k-1th and kth moments respectively, A represents the contact area between the battery cell and the cooling system, h represents the convection heat transfer coefficient, η1 represents the calibration coefficient considering the difference between the reversible heat, the contact surface temperature of the battery cell and the cooling system and the actual temperature of the battery cell, and U k-1 represents the terminal voltage of the battery at the k-1th moment, I k-1 represents the charging current at the k-1th moment, η2 represents the calibration coefficient of heat exchange between different cells and the cooling system, Represents the temperature of the coolant at the k-1th moment. represents the cell temperature sampled by NTC, and C represents the transfer function matrix of the mapping relationship between the sampled temperature and the actual cell temperature.

[0169] Based on the above-mentioned battery heat generation model, the optimal estimate of the terminal voltage of the battery second-order equivalent circuit model, the open circuit voltage and the given current, the optimal estimate of the battery cell temperature (i.e., the estimated temperature value of the battery cell) is obtained by Kalman filtering. The estimated temperature value of the battery cell at the Kth moment is recorded as The double Kalman filter under the battery heat generation model (hot spot coupling model) performs prediction correction to obtain the real-time detection step length T s Estimated temperature of the lower cell

[0170] In one embodiment, the prediction formula of Kalman filtering is as follows:

[0171]

[0172]

[0173] The correction formula of Kalman filter is as follows:

[0174]

[0175]

[0176]

[0177] In the above formula, A k , which can be understood as a rule matrix, predicts the state at time k from the state at time k-1;

[0178] u k , input, can be considered as external influence;

[0179] B k , the input coefficient matrix;

[0180] The covariance of k-1 , according to the properties of the covariance matrix, The covariance matrix of

[0181] The uncertainty of the external environment, which is regarded as covariance Q k The new Gaussian distribution has the same mean as the original distribution, but a different covariance

[0182]

[0183] The new best estimate is a forecast based on the original best estimate and corrected for known external influences.

[0184] The new uncertainty is a prediction based on the original uncertainty and the uncertainty of the external environment.

[0185] Introducing the concept of sensor, the sensor reading matrix C k , the Gaussian distribution is centered around the predicted mean

[0186]

[0187]

[0188] The covariance of the sensor noise is set to R k , the mean is set to y k ,This block of Gaussian distribution surrounds the sensor readings. After multiplying the two Gaussian distributions, we get the overlapping part, which is the area where the best estimate appears.

[0189] The product of two Gaussian distributions

[0190]

[0191] make Kalman gain, i.e.

[0192] μ=μ1+K(μ2-μ1)

[0193]

[0194]

[0195]

[0196]

[0197] Considering that the formulas all contain Ck, they can be further simplified to:

[0198]

[0199] P k ′=P k -K′C k P k =(IK′C k ) k

[0200]

[0201] Prediction and correction formulas for the final form Kalman filter.

[0202] In one embodiment, the first predicted value of the temperature change is obtained according to the product of the change of the temperature estimation value within the prediction period and the estimated charging time of the prediction interval.

[0203] Specifically, the calculation formula of the first predicted value of the temperature change is as follows:

[0204]

[0205] In the formula, is the first predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the estimated temperature of the cell at the kth moment, is the estimated temperature of the cell at the k-1th moment, is the estimated charging time for the prediction interval.

[0206] The prediction value calculation module 20 determines a second prediction value of the temperature change in the corresponding prediction interval according to the heat generation power of the battery cell and the heat exchange power of the battery.

[0207] In one embodiment, the calculation formula of the second predicted value of the temperature change is as follows:

[0208]

[0209] In the formula, is the second predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the heat generation power of the battery cell corresponding to the kth moment in the Nth prediction interval, is the heat exchange power of the battery corresponding to the kth moment in the Nth prediction interval, c is the specific heat of the battery cell, m is the mass of the battery cell, is the duration of the kth moment of the Nth prediction interval.

[0210] In this calculation formula, in, is the calibration factor considering the reversible heat part obtained from the thermal insulation experiment, is the charging current at the Kth moment in the corresponding prediction interval, is the internal resistance of the battery at the Kth moment in the corresponding prediction interval.

[0211] Battery heat exchange power It can be obtained from the thermal management power table. The thermal management power table can be pre-selected and set according to specific circumstances. The setting of the thermal management power table needs to consider the environmental exchange heat, including: the battery cooling system, the surrounding environment, the surrounding materials, etc.

[0212] The correction module is configured to determine a final predicted value of the temperature change corresponding to each predicted interval of the battery according to the first predicted value of the temperature change and the second predicted value of the temperature change.

[0213] In one embodiment, an average value of the first predicted value of the temperature change and the second predicted value of the temperature change is calculated, and the average value is used as the final predicted value of the temperature change.

[0214] In this embodiment, the temperature change prediction values ​​(the first temperature change prediction value and the second temperature change prediction value) obtained in two different ways are averaged to obtain the final temperature change prediction value. The final temperature change prediction value can be made closer to the actual temperature change, thereby improving the accuracy of the temperature change prediction.

[0215] In another embodiment, the first predicted value of the temperature change and the second predicted value of the temperature change are respectively multiplied by corresponding weight coefficients and then added together to obtain a final predicted value of the temperature change.

[0216] In this embodiment, the weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change are assigned in advance through experimental data to ensure that the final predicted value of the temperature change is closer to the actual temperature change, thereby improving the accuracy of the temperature change prediction.

[0217] The weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change may be the same coefficients, for example, the weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change are both 50%; of course, the weight coefficients corresponding to the first predicted value of the temperature change and the second predicted value of the temperature change may be different weight coefficients, for example, the weight coefficient corresponding to the first predicted value of the temperature change is 30%, and the weight coefficient corresponding to the second predicted value of the temperature change is 70%, or the weight coefficient corresponding to the first predicted value of the temperature change is 40%, and the weight coefficient corresponding to the second predicted value of the temperature change is 60%, etc.

[0218] In one embodiment, when the battery is fully charged, the predicted value of the temperature rise of the battery can be calculated by the following formula:

[0219]

[0220] Where, T Init is the second predicted value of the temperature change when fully charged, which can be calculated by the above formula and will not be described here. ΔT n Final predicted value of temperature change in each prediction interval when fully charged.

[0221] According to another embodiment of the present invention, a controller is provided, which includes a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the battery charging temperature rise prediction method in any of the above-mentioned embodiments can be implemented.

[0222] According to another embodiment of the present invention, a readable storage medium is provided for storing a computer program, wherein when the program is executed by a computer or a processor, the program implements the steps of the battery charging temperature rise prediction method in any of the above embodiments.

[0223] The present invention adopts a combined estimation method of the second-order equivalent circuit model of the power battery using Kalman filtering and the heat generation model to obtain the optimal estimate of the real-time power battery temperature. The real-time optimal temperature estimate is used as the initial condition for the temperature estimation between the power battery partitions, which reduces the initial temperature deviation of the real-time temperature prediction process. The temperature prediction method between partitions can calibrate the temperature deviation within the interval, ensuring the accuracy of the temperature prediction during the charging process. By combining cloud big data, the parameters in the charging process are further effectively calibrated.

[0224] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A method for predicting battery charging temperature rise, characterized in that: include: Dividing the state of charge of the battery into a plurality of intervals according to a preset ratio, so as to divide the charging process of the battery into a plurality of prediction intervals; Calculate first predicted values ​​of temperature variation corresponding to each predicted interval of the battery based on the estimated temperature value of the battery cell; Calculating the second predicted value of the temperature change corresponding to each of the predicted intervals of the battery based on the heat generation power and the heat exchange power of the battery cell; A final predicted value of the temperature change corresponding to each of the predicted intervals of the battery is determined according to the first predicted value of the temperature change and the second predicted value of the temperature change.

2. The battery charging temperature rise prediction method according to claim 1, characterized in that: The calculation formula of the first predicted value of the temperature change is as follows: In the formula, is the first predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the estimated temperature of the cell at the kth moment, is the estimated temperature of the cell at the k-1th moment, is the estimated charging time for the prediction interval.

3. The battery charging temperature rise prediction method according to claim 1 or 2, characterized in that: The steps of obtaining the estimated temperature value of the battery cell are as follows: Based on a pre-established second-order equivalent circuit model of the battery, a voltage estimation value of the terminal voltage of the battery is obtained through Kalman filtering; The voltage estimation value is input into a pre-established battery heat generation model, and the temperature estimation value of the battery cell is obtained through Kalman filtering.

4. The battery charging temperature rise prediction method according to claim 3, characterized in that: The state space expression of the battery second-order equivalent circuit model is as follows: Where, T s represents the detection period after discretization, U 0,k-1 , U 0,k They represent the terminal voltage corresponding to the ohmic internal resistance at the k-1th moment and the kth moment, respectively, U p1,k-1 , U p1,k Indicates the ohmic internal resistance R at the k-1 and k moments p1 With ohmic capacitance C p1 The terminal voltage of the parallel circuit, U p2,k-1 , U p2,k Indicates the polarization internal resistance R at the k-1th and kth moments p2 With polarization capacitance C p2 The terminal voltage of the parallel circuit, SOC k-1 , SOC k They represent the SOC of the battery at the k-1th and kth moments, SOH is the SOH of the battery, and C N represents the battery capacity, η represents the calibration factor, I k-1 represents the current at the kth moment, OVC represents the open circuit voltage of the battery, and w 1,k-1 ~w 4,k-1 represents the system noise, U k represents the terminal voltage of the battery at the kth moment, v k represents the observation noise.

5. The method for predicting battery charging temperature rise according to claim 3, characterized in that: The state space expression of the battery heat generation model is as follows: Where, T k-1 ,T k They represent the actual temperature of the battery cell at the k-1th and kth moments respectively, A represents the contact area between the battery cell and the cooling system, h represents the convection heat transfer coefficient, η1 represents the calibration coefficient considering the difference between the reversible heat, the contact surface temperature of the battery cell and the cooling system and the actual temperature of the battery cell, and U k-1 represents the terminal voltage of the battery at the k-1th moment, I k-1 represents the charging current at the k-1th moment, η2 represents the calibration coefficient of heat exchange between different cells and the cooling system, represents the temperature of the coolant at the k-1th moment, represents the cell temperature sampled by NTC, and C represents the transfer function matrix of the mapping relationship between the sampled temperature and the actual cell temperature.

6. The method for predicting battery charging temperature rise according to claim 1, characterized in that: The calculation formula of the second predicted value of the temperature change is as follows: In the formula, is the second predicted value of the temperature change corresponding to the kth moment in the Nth prediction interval, is the heat generation power of the battery cell corresponding to the kth moment in the Nth prediction interval, is the heat exchange power of the battery corresponding to the kth moment in the Nth prediction interval, c is the specific heat of the battery cell, m is the mass of the battery cell, is the duration of the kth moment of the Nth prediction interval.

7. The battery charging temperature rise prediction method according to claim 1, characterized in that: The step of determining the final predicted value of the temperature variation corresponding to each predicted interval of the battery according to the first predicted value of the temperature variation and the second predicted value of the temperature variation comprises: Calculate an average value of the first predicted value of the temperature change and the second predicted value of the temperature change, and use the average value as the final predicted value of the temperature change; or The first predicted value of the temperature change and the second predicted value of the temperature change are respectively multiplied by corresponding weight coefficients and then added together to obtain the final predicted value of the temperature change.

8. A battery charging temperature rise prediction device, characterized in that: include: An interval division module, configured to divide the state of charge of the battery into a plurality of intervals according to a preset ratio, so as to divide the charging process of the battery into a plurality of prediction intervals; A prediction value calculation module, configured to calculate first prediction values ​​of temperature variation corresponding to each prediction interval of the battery based on the temperature estimation value of the battery cell, and to calculate second prediction values ​​of temperature variation corresponding to each prediction interval of the battery based on the heat generation power and heat exchange power of the battery cell; The prediction value correction module is configured to determine a final prediction value of the temperature change corresponding to each prediction interval of the battery according to the first prediction value of the temperature change and the second prediction value of the temperature change.

9. A controller comprising a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the battery charging temperature rise prediction method according to any one of claims 1 to 7 can be implemented.

10. A computer-readable storage medium for storing a computer program, wherein the program, when executed by a computer or a processor, implements the steps of the battery charging temperature rise prediction method according to any one of claims 1 to 7.

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