Multi-model interactive battery SOC estimation method and related equipment

Abstract

This application provides a multi-model interactive battery SOC estimation method and related equipment. The method includes: obtaining the corresponding model temperature based on the preset temperature difference of each model and the real-time acquired battery temperature, and obtaining the corresponding model parameters; inputting the real-time acquired battery voltage, battery current, and model parameters into the corresponding model for interactive mixing; estimating the model state based on the result of the interactive mixing to obtain the model probability corresponding to each model, and using the model probability as the feedback value for the next interactive mixing; obtaining the battery SOC based on the model state and model probability, and using the battery SOC as the feedback value for the next acquisition of model parameters; repeating the above steps cyclically, and before executing the last iteration, updating the temperature difference corresponding to each model based on the average model probability of each model in the previous iterations, and then executing the last iteration to obtain the final battery SOC. The technical solution of this application can improve the calculation accuracy of battery SOC.

Description

Technical Field

[0001] This application relates to the technical field of battery SOC management, and more specifically, to a multi-model interactive battery SOC estimation method and related equipment. Background Technology

[0002] The battery SOC (State of Charge) is defined as the ratio of the battery's remaining usable capacity to its total usable capacity. Accurately estimating the battery SOC value is key to using the battery rationally and preventing overcharging and over-discharging.

[0003] Methods for estimating battery SOC in related technologies include the ampere-hour integration method, the open-circuit voltage lookup method, and model-based methods. Among them, the model-based SOC estimation method generates battery model parameters at the corresponding battery temperature based on the battery model, and uses the battery current and voltage, along with the corresponding battery model parameters, as input. It then uses a state estimation algorithm to fuse the measured current and voltage data to estimate the corresponding battery SOC.

[0004] Since the computational accuracy of model-based SOC estimation methods is positively correlated with the accuracy of the model, and the chemical characteristics of the battery mean that the battery parameters are easily affected by temperature, when the battery temperature is inaccurate, the battery parameters obtained will also have errors, resulting in lower accuracy of the battery model, which in turn reduces the accuracy of the battery SOC obtained by model-based SOC estimation methods. Summary of the Invention

[0005] To address the aforementioned technical problems, embodiments of this application provide a multi-model interactive battery SOC estimation method and related equipment.

[0006] According to one aspect of the embodiments of this application, a battery SOC estimation method with multi-model interaction is provided, the method comprising:

[0007] The model temperature of each model is obtained based on the preset temperature difference of each model and the real-time battery temperature, and the model parameters of each model are obtained based on the model temperature.

[0008] Input the model parameters of each model into the corresponding model, and input the real-time acquired battery voltage and battery current into each model, and interactively mix the output results of each model;

[0009] Based on the results of the interactive blending, the model state of each model is estimated to obtain the model probability corresponding to each model, and the model probability is used as the feedback value for the next interactive blending.

[0010] The model state and the model probability are fused, and the battery SOC is output based on the fusion result. The battery SOC is then used as the feedback value for the next acquisition of the model parameters.

[0011] The above steps are executed repeatedly for a preset number of times, and before the last iteration, the average model probability of each model is calculated based on the model probabilities obtained from the number of executions.

[0012] The temperature difference corresponding to each model is updated based on the average probability of each model, and the final loop is executed based on the updated temperature difference to obtain the final battery SOC.

[0013] In an exemplary embodiment, multiple models include a left-end model, a middle model, and a right-end model, wherein the temperature difference of the left-end model is the left-end temperature difference, the temperature difference of the middle model is the middle temperature difference, and the temperature difference of the right-end model is the right-end temperature difference; the step of obtaining the model temperature of each model based on the preset temperature difference of each model and the real-time acquired battery temperature includes:

[0014] The absolute value of the difference between the battery temperature and the intermediate temperature difference is taken as the model temperature of the intermediate model;

[0015] The absolute value of the difference between the model temperature of the intermediate model and the temperature difference at the left end is taken as the model temperature of the left end model;

[0016] The sum of the model temperature of the intermediate model and the temperature difference at the right end is taken as the model temperature of the right end model.

[0017] In an exemplary embodiment, updating the temperature difference corresponding to each model based on the average probability of each model includes:

[0018] Calculate the product of the average model probability of each model and the corresponding temperature difference, and use the sum of the products as the updated intermediate temperature difference.

[0019] The absolute value of the difference between the updated intermediate temperature difference and the left-end temperature difference is compared with the preset minimum offset value, and the maximum value is taken as the updated left-end temperature difference.

[0020] The absolute value of the difference between the updated intermediate temperature difference and the right-end temperature difference is compared with the minimum offset value, and the maximum value is taken as the updated right-end temperature difference.

[0021] In an exemplary embodiment, when the average model probability of the intermediate model is greater than the average model probability of the left-end model and the average model probability of the right-end model, the method further includes:

[0022] Determine whether the average model probability of the left-hand model is less than a preset lower threshold for model probability;

[0023] If so, then based on the preset first offset, reduce the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly; and reduce the absolute value of the difference between the updated right end temperature difference and the updated middle temperature difference, and adjust the updated right end temperature difference accordingly.

[0024] If not, then maintain the temperature difference after each model update.

[0025] In an exemplary embodiment, when the average model probability of the left-end model is greater than the average model probability of the middle model and the average model probability of the right-end model, the method further includes:

[0026] Determine whether the average model probability of the left-end model is greater than a preset upper threshold for model probability, wherein the upper threshold for model probability is greater than the lower threshold for model probability;

[0027] If so, then according to the preset second offset, increase the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly, while maintaining the updated right end temperature difference;

[0028] If not, then maintain the temperature difference after each model update.

[0029] In an exemplary embodiment, when the average model probability of the right-end model is greater than the average model probability of the middle model and the average model probability of the left-end model, the method further includes:

[0030] Determine whether the average model probability of the right-hand model is greater than the upper threshold of the model probability;

[0031] If so, then according to the preset third offset, increase the absolute value of the difference between the updated right-end temperature difference and the updated middle temperature difference, and adjust the updated right-end temperature difference accordingly, while maintaining the updated left-end temperature difference.

[0032] If not, then maintain the temperature difference after each model update.

[0033] In one exemplary embodiment, after obtaining the final battery SOC, the method further includes:

[0034] The process of repeatedly executing multiple rounds to obtain the final battery SOC is performed.

[0035] During the loop, the final battery SOC of the next round is calculated based on the final battery SOC obtained in the previous round.

[0036] According to one aspect of the embodiments of this application, a battery SOC estimation apparatus with multi-model interaction is provided, comprising:

[0037] The acquisition module is used to obtain the model temperature of each model based on the preset temperature difference of each model and the real-time acquired battery temperature, and to obtain the model parameters of each model based on the model temperature.

[0038] The interaction module is used to perform interactive mixing after inputting the corresponding model based on the model parameters and the real-time acquired battery voltage and battery current.

[0039] The probability calculation module is used to estimate the model state of each model based on the result of interactive mixing, obtain the model probability corresponding to each model, and use the model probability as the feedback value for the next interactive mixing.

[0040] The fusion estimation module is used to fuse the model state and the model probability, output the battery SOC based on the fusion result, and use the battery SOC as the feedback value for the next acquisition of the model parameters;

[0041] The loop module is used to execute the above steps repeatedly for a preset number of times, and before executing the last loop, calculates the average model probability of each model in the number of executions.

[0042] The output module is used to update the temperature difference corresponding to each model based on the average probability of each model, and execute the last loop to obtain the final battery SOC.

[0043] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which computer-readable instructions are stored, which, when executed by a computer's processor, cause the computer to perform the multi-model interactive battery SOC estimation method as described in the above embodiments.

[0044] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the multi-model interaction battery SOC estimation method as described in the above embodiments.

[0045] In the technical solution of this application embodiment, the battery SOC is calculated iteratively according to a preset number of times, and the cycle is divided into two parts. The first part consists of all cycles except the last one, and the second part is the last cycle. In the first part, the model temperature corresponding to each model is obtained based on the preset initial temperature difference and the acquired battery temperature. The model parameters of each model are obtained based on the model temperature. The model parameters are then interactively mixed with the acquired battery voltage and battery current into each model to estimate the model state and obtain the corresponding model probability. The battery SOC calculation result for one cycle is obtained based on the model state and model probability. The initial temperature difference is updated based on the average model probability obtained from all cycles in the first part. The model temperature of each model is updated based on the updated temperature difference, and the corresponding model parameters are determined based on the model temperature. In the second part, the determined model parameters, battery voltage, and battery current are used as the input values ​​for model state estimation in the last cycle. Based on the obtained model state estimation result and model probability, the final state estimation vector is obtained through weighted calculation. Finally, a target battery SOC is obtained based on the final state estimation vector. By updating the temperature difference of each model, the model temperature is adaptively adjusted, making the adjusted model temperature closer to the real temperature used for battery state description, thereby improving the calculation accuracy of battery SOC.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0048] Figure 1 This is a structural diagram of a battery SOC estimation method based on a single model, as illustrated in an exemplary embodiment of this application.

[0049] Figure 2 This is a structural diagram of a battery SOC estimation method based on multiple models, as illustrated in an exemplary embodiment of this application.

[0050] Figure 3 yes Figure 2 The flowchart of a multi-model interaction battery SOC estimation method is shown in the structural diagram below as an exemplary embodiment.

[0051] Figure 4 yes Figure 3 The flowchart of step S110 in the illustrated embodiment is a process for obtaining the model temperature of each model in an example embodiment;

[0052] Figure 5 yes Figure 3 The flowchart of step S160 in the illustrated embodiment is a temperature difference update flowchart for each model in an example embodiment;

[0053] Figure 6 This is a block diagram of a multi-model interactive battery SOC estimation device 600 according to an embodiment of this application;

[0054] Figure 7 This is a schematic diagram of the structure of an electronic device shown in an exemplary embodiment of this application. Detailed Implementation

[0055] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0056] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0057] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0058] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0059] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0060] like Figure 1 As shown, Figure 1 This is a structural diagram of a battery SOC estimation method based on a single model. For battery SOC estimation based on a single model, the current battery temperature, battery voltage, and battery current are first obtained. Based on the battery temperature, battery model parameters are obtained. These battery model parameters are determined based on the pre-defined mapping relationship between battery temperature and battery model parameters. Then, the determined battery model parameters, battery voltage, and battery current are used as inputs, and iterative calculations are performed using an extended Kalman filter to obtain the estimated battery SOC. The calculated results are then fed back to the next calculation to achieve battery SOC updates for multiple usage nodes during battery use.

[0061] In this application, as Figure 2 As shown, Figure 2 This is a structural diagram of a battery SOC estimation method based on multiple models. In order to improve the accuracy of battery SOC acquisition, the battery SOC estimation method based on the above single model is extended by interactively mixing multiple of the above models.

[0062] Specifically, the technical solution of this application proposes a multi-model interactive battery SOC estimation method, see details below. Figure 3 , Figure 3 This is a flowchart illustrating a multi-model interaction battery SOC estimation method according to an exemplary embodiment. The method can be executed by a battery management system and includes at least steps S110 to S160, detailed below:

[0063] In step S110, the model temperature of each model is obtained based on the preset temperature difference of each model and the real-time battery temperature, and the model parameters of each model are obtained based on the model temperature.

[0064] For example, multiple models include a left-end model, a middle model, and a right-end model. The temperature difference of the left-end model is the left-end temperature difference, the temperature difference of the middle model is the middle temperature difference, and the temperature difference of the right-end model is the right-end temperature difference. Of course, the number of models in this application is not limited to three: a left-end model, a middle model, and a right-end model. There can be more, and the selection of the left-end model, the middle model, and the right-end model is not limited here.

[0065] For ease of description and understanding of the solution, in the following other embodiments, this application only uses the left-end model, the middle model and the right-end model for illustrative purposes.

[0066] Among them, see Figure 4 , Figure 4 yes Figure 3 The flowchart of step S110 in the illustrated embodiment is shown in an example embodiment, illustrating the model temperature acquisition process for each model. For example... Figure 4 As shown, in step S110, the model temperature of each model is obtained based on the preset temperature difference of each model and the real-time battery temperature, including at least steps S210 to S230, which are described in detail below:

[0067] In step S210, the absolute value of the difference between the battery temperature and the intermediate temperature difference is taken as the model temperature of the intermediate model, and expressed by the following expression:

[0068] T m2 =T measurea -W2

[0069] In the formula, T m2 The model temperature, T, is represented as the intermediate model temperature. measurea W1 represents the real-time battery temperature, and W2 represents the intermediate temperature difference.

[0070] In step S220, the absolute value of the difference between the model temperature of the intermediate model and the temperature difference of the left end is taken as the model temperature of the left end model, and expressed by the following expression:

[0071] T m1 =T m2 -W1

[0072] In the formula, T m1 The model temperature, T, is represented by the model temperature of the left-hand model. m2 The temperature is represented by the intermediate model temperature, and W1 represents the temperature difference at the left end.

[0073] In step S230, the sum of the model temperature of the intermediate model and the temperature difference at the right end is taken as the model temperature of the right end model, and expressed by the following expression:

[0074] T m3 =T m2 +W3

[0075] In the formula, T m3 The model temperature, T, is represented by the model temperature on the right side of the model. m2 W3 represents the temperature of the intermediate model, and W3 represents the temperature difference at the right end.

[0076] Based on the model temperature calculation methods described above, it should be noted that the actual battery temperature T...measurea It is not equal to the actual temperature used to reflect the state of the battery, precisely because T measurea In fact, it is inaccurate, so if T is taken as... measurea Using this directly as the model temperature will inevitably lead to calculation errors. This is because the battery generates heat during use, causing the actual battery temperature T to be inaccurate. measurea It is higher than the actual temperature used for battery state calculation.

[0077] Therefore, in this embodiment, the temperature of each model is processed by shifting the intermediate model temperature downward, that is, subtracting an initial temperature deviation value. Furthermore, this is done to ensure that the actual temperature of the battery state falls as close as possible to T. m1 and T m3 Between these two models, the model temperature of the left model is shifted downward, which means that the model temperature after processing the middle model is reduced by an initial temperature deviation value, and the model temperature of the right model is shifted upward, which means that the model temperature after processing the middle model is increased by an initial temperature deviation value.

[0078] In addition, this application also pre-defines a mapping table between battery parameters and model temperatures of each model. Once the model temperature of each model is determined, the model parameters of the corresponding model can be obtained according to the mapping table.

[0079] In step S120, the model parameters of each model are input into the corresponding model, and the real-time acquired battery voltage and battery current are input into each model, and the output results of each model are interactively mixed.

[0080] The estimated values ​​and covariances of each model are interactively mixed according to the rules of the IMM (Interacting Multiple Model) algorithm.

[0081] Specifically, we define k as a discrete time sequence. First, we assume that at time k-1, each model has a model estimate. And estimating covariance Based on the IMM filtering algorithm, the correctness probability is calculated. The estimated states and covariance matrices of each model are then weighted and fused to obtain the optimal model estimate of the target at time k-1. and covariance matrix Among them, by and Calculated and The process is represented by expressions as shown in equations (1) to (4):

[0082]

[0083]

[0084]

[0085]

[0086] Where, ρ ji Let represent the probability of jumping from model j to model i. Equation (1) calculates the correlation coefficient from model j to i. Equation (2) calculates the correlation coefficient from model j to i after fusion. Equation (3) calculates the optimal model estimate after fusion. Equation (4) calculates the covariance matrix after fusion.

[0087] In step S130, the model state of each model is estimated based on the result of the interactive mixing, the model probability corresponding to each model is obtained, and the model probability is used as the feedback value for the next interactive mixing.

[0088] For example, the model states of each model can be estimated based on the results of interactive mixing. This can be done using an extended Kalman filter as the state observer, and the calculation process can be represented by expressions such as (5) to (11):

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Equation (5) calculates the predicted state value; Equation (6) calculates the predicted covariance; Equation (7) calculates the measurement residual; Equation (8) calculates the residual covariance; Equation (9) calculates the filter increment; Equation (10) calculates the updated state value; and Equation (11) calculates the updated covariance.

[0097] It should be noted that the battery SOC changes continuously with the use of the battery. Therefore, the calculation of battery SOC is a continuous process, rather than a single calculation. When necessary, the result of the previous calculation can be used as a feedback condition for the next calculation. For example, the model probability calculated in this application can be used as the feedback value for the next interactive mixing. This can make the subsequent model state calculation results closer and closer to the true value, thereby improving the accuracy of battery SOC calculation.

[0098] Furthermore, the calculation process for the model probabilities corresponding to each model is expressed using the expressions in equations (12) and (13):

[0099]

[0100]

[0101] Equation (12) calculates the model probability; Equation (13) calculates the model probability.

[0102] In step S140, the model state and model probability are fused, the battery SOC is output based on the fusion result, and the battery SOC is used as the feedback value for the next acquisition of model parameters.

[0103] Specifically, the model probabilities obtained from the above calculations and the updated state values ​​are weighted to obtain the final state estimation vector used to estimate the battery SOC, and expressed as in equation (14):

[0104]

[0105] Equation (14) calculates the model state estimation vector.

[0106] It should be noted that the battery SOC is one of the elements of the model state estimation vector, thus the corresponding battery SOC estimate can be obtained from the model state estimation vector. Furthermore, using the battery SOC as feedback for the next model parameter acquisition ensures that subsequent battery SOC calculations increasingly approximate the true value, thereby improving the accuracy of battery SOC calculations.

[0107] To reduce the random error in a single battery SOC calculation, in step S150, the above steps are executed repeatedly for a preset number of times, and before the last iteration, the average model probability of each model is calculated based on the model probabilities obtained from the number of executions.

[0108] Specifically, the above steps are executed repeatedly for a preset number of times as a round of calculation to improve the accuracy of battery SOC. In each round of calculation, the model probabilities obtained in each calculation of battery SOC before the last round are retained, and the average value of the retained model probabilities is calculated.

[0109] Based on the average model probability in one cycle, in step S160, the temperature difference corresponding to each model is updated according to the average model probability, and the last cycle is executed based on the updated temperature difference to obtain the final battery SOC.

[0110] See details Figure 5 ,yes Figure 3 The illustrated embodiment shows a flowchart of step S160, which is a temperature difference update method for each model in an example embodiment. For example... Figure 5 As shown, in step S160, the temperature difference corresponding to each model is updated based on the average probability of each model, which includes at least steps S310 to S330, detailed as follows:

[0111] In step S310, the product of the model probability average value and the corresponding temperature difference for each model is calculated. The sum of the product values ​​is used as the updated intermediate temperature difference and expressed as in equation (15):

[0112] D M =μ M1 W1+μ M2 W2+μ M3 W3 (15)

[0113] In the formula, D M W1 represents the left-hand temperature difference before the update; W2 represents the middle-hand temperature difference before the update; W3 represents the right-hand temperature difference before the update; μ M1 μ represents the average model probability of the left-hand model over one cycle. M2 μ represents the average model probability of the intermediate model over one cycle. M3 It is represented as the average model probability of the right-hand side model in one cycle.

[0114] In step S320, the absolute value of the difference between the updated intermediate temperature difference and the left-end temperature difference is compared with a preset minimum offset value, and the maximum value is taken as the updated left-end temperature difference, and expressed by the expression as shown in equation (16):

[0115] D L =max(D M -W1,D min (16)

[0116] In the formula, DL Let represent the updated left-hand temperature difference; max represents the function that takes the maximum value; D M The updated intermediate temperature difference is represented by W1; the temperature difference at the left end is represented by D. min This is represented as the preset minimum offset value.

[0117] In step S330, the absolute value of the difference between the updated intermediate temperature difference and the right-end temperature difference is compared with the minimum offset value, and the maximum value is taken as the updated right-end temperature difference, and expressed by the expression as shown in equation (17):

[0118] D R =max(W3-D M D min (17)

[0119] In the formula, D R Let represent the updated left-hand temperature difference; max represents the function that takes the maximum value; D M The updated intermediate temperature difference is represented by W3; the right-hand temperature difference is represented by D. min This is represented as the preset minimum offset value.

[0120] It should be noted that the minimum offset value in the left-end temperature difference update can be the same as or different from the minimum offset value in the right-end temperature difference update.

[0121] Through the above implementation method, based on the average model probability in one loop, the initial left-end temperature difference, middle temperature difference, and right-end temperature difference are updated. Based on the updated left-end, middle, and right-end temperature differences, and the calculation method of the corresponding model temperature for each model, the model temperature of each model is updated. After updating the model temperature for each model, the corresponding model parameters are determined. Finally, the determined model parameters, battery voltage, and battery current are used as input values ​​for model state estimation in the last loop. Based on the obtained model state estimation results and model probabilities, a final state estimation vector is obtained through weighted calculation. A target battery SOC is then obtained based on the final state estimation vector. By weighting the average model probability of each model with each temperature difference, the difference between the obtained battery temperature and the updated middle temperature difference is made closer to the true temperature used for battery state description. Furthermore, when describing the true temperature range used for battery state description based on the left-end and right-end temperature differences obtained from the middle temperature difference, the true temperature used for battery state description can be made to fall as close as possible to the model temperature values ​​of the left-end and right-end models, thereby improving the calculation accuracy of battery SOC.

[0122] In some embodiments, to improve the stability of model parameter updates, after obtaining the final battery SOC, the method further includes:

[0123] The process of repeatedly executing multiple rounds to obtain the final battery SOC is performed.

[0124] During the loop, the final battery SOC of the next round is calculated based on the final battery SOC obtained in the previous round.

[0125] Specifically, in the actual use of the battery corresponding to this application, a multi-round calculation process for the final battery SOC is designed for one usage cycle of the battery, and the specific value of the battery SOC is updated at the end of each round of calculation.

[0126] Through the above implementation methods, a relatively accurate battery SOC value can be obtained during a complete battery usage process. At the same time, by iterating the calculation of the final battery SOC based on the previous round of final battery SOC calculation, the obtained battery SOC can become more and more accurate, thereby improving the reliability of the calculation results.

[0127] In some embodiments, to further improve the reliability of the left-end temperature difference and the right-end temperature difference, when the average model probability of the intermediate model is greater than the average model probability of the left-end model and the average model probability of the right-end model, respectively, the method further includes:

[0128] Determine whether the average model probability of the left-hand model is less than a preset lower threshold for model probability;

[0129] If so, then based on the preset first offset, reduce the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly; and reduce the absolute value of the difference between the updated right end temperature difference and the updated middle temperature difference, and adjust the updated right end temperature difference accordingly.

[0130] If not, then maintain the temperature difference after each model update.

[0131] Specifically, based on the average probability of each model, the temperature difference at the left and right ends are adaptively adjusted, and expressed using the expression in equation (18):

[0132]

[0133] In the formula, D on the left side of the equation L This is expressed as the adjusted temperature difference on the left side; D on the right side of the equation... L This is expressed as the updated temperature difference on the left side of the equation; D on the left side of the equation R This is expressed as the adjusted temperature difference on the right side of the equation; D on the right side of the equation R Represented as the updated right-hand temperature difference; D M Represented as the updated intermediate temperature difference; μ M1 Represented as the average model probability of the left-hand side model; μ THLThis is represented as the threshold under the model probability.

[0134] It should be noted that in the above expression, the adjustment base for the first offset of the left-end temperature difference and the right-end temperature difference is half of the updated left-end temperature difference and half of the updated right-end temperature difference. Of course, this adjustment base can also be other values, and the adjustment bases for the left-end temperature difference and the right-end temperature difference can be the same or different.

[0135] In addition, since the intermediate temperature difference is the intermediate concept between the left-end temperature difference and the right-end temperature difference, when judging whether the average model probability of the left-end model is less than the preset lower threshold of model probability, it is also possible to choose whether the average model probability of the right-end model is less than the preset lower threshold of model probability.

[0136] Through the above implementation method, if the average model probability of the intermediate model is greater than the average model probability of the other two models, it is necessary to judge the average model probability of the left-end model. If the average model probability of the left-end model is less than a preset lower threshold, it indicates that the offset between the temperature difference of the other two models and the intermediate temperature difference is too large, and the offset needs to be reduced so that the average model probability of the corresponding model can at least meet the lower threshold. Conversely, it indicates that the offset from the intermediate temperature difference is sufficient to make the average model probability of the corresponding model meet the lower threshold. This improves the reliability of the average model probability of the left-end and right-end models, thereby improving the reliability of the battery SOC calculation results.

[0137] In some embodiments, to further improve the reliability of the left-end temperature difference and the right-end temperature difference, when the average model probability of the left-end model is greater than the average model probability of the middle model and the average model probability of the right-end model, the method further includes:

[0138] Determine whether the average model probability of the left-hand model is greater than the preset upper threshold of model probability; if the upper threshold of model probability is greater than the lower threshold of model probability.

[0139] If so, then according to the preset second offset, increase the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly, while maintaining the updated right end temperature difference;

[0140] If not, then maintain the temperature difference after each model update.

[0141] Specifically, based on the average probability of each model, the temperature difference at the left and right ends are adaptively adjusted, and expressed using the expression in equation (19):

[0142]

[0143] In the formula, D on the left side of the equation L This is expressed as the adjusted temperature difference on the left side; D on the right side of the equation...L This is expressed as the updated temperature difference on the left side of the equation; D on the left side of the equation R This is expressed as the adjusted temperature difference on the right side of the equation; D on the right side of the equation R Represented as the updated right-hand temperature difference; D M Represented as the updated intermediate temperature difference; μ M1 Represented as the average model probability of the left-hand side model; μ THU This is represented as the threshold for model probability.

[0144] It should be noted that the interpretation of the adjustment base of the second offset in the above expression is similar to the interpretation of the first offset in the above embodiment, and will not be repeated here.

[0145] Through the above implementation method, if the average model probability of the left-end model is greater than the average model probability of the other two models, it is necessary to judge the average model probability of the left-end model. If the average model probability of the left-end model is greater than a preset upper threshold, it indicates that the offset between the intermediate temperature difference and the left-end temperature difference is too large, and its offset needs to be reduced so that the average model probability of the left-end model can at least meet the upper threshold. Conversely, it indicates that the offset from the intermediate temperature difference is sufficient for the average model probability of the corresponding model to meet the upper threshold. This improves the reliability of the average model probability of the left-end model, thereby improving the reliability of the battery SOC calculation results.

[0146] In some embodiments, to further improve the reliability of the left-end temperature difference and the right-end temperature difference, when the average model probability of the right-end model is greater than the average model probability of the middle model and the average model probability of the left-end model, the method further includes:

[0147] Determine whether the average model probability of the right-hand model is greater than the upper threshold of the model probability;

[0148] If so, then according to the preset third offset, increase the absolute value of the difference between the updated right-end temperature difference and the updated middle temperature difference, and adjust the updated right-end temperature difference accordingly, while maintaining the updated left-end temperature difference.

[0149] If not, then maintain the temperature difference after each model update.

[0150] Specifically, based on the average probability of each model, the temperature difference at the left and right ends are adaptively adjusted, and expressed using the expression shown in equation (20):

[0151]

[0152] In the formula, D on the left side of the equation L This is expressed as the adjusted temperature difference on the left side; D on the right side of the equation... L This is expressed as the updated temperature difference on the left side of the equation; D on the left side of the equationR The adjusted temperature difference on the right side is represented by D on the left side of the equation. R Represented as the updated right-hand temperature difference; D M Represented as the updated intermediate temperature difference; μ M1 Represented as the average model probability of the left-hand side model; μ THU This is represented as the threshold for model probability.

[0153] It should be noted that the interpretation of the adjustment base of the third offset in the above expression is similar to the interpretation of the first offset in the above embodiment, and will not be repeated here.

[0154] Through the above implementation method, if the average model probability of the right-hand model is greater than the average model probability of the other two models, it is necessary to judge the average model probability of the right-hand model. If the average model probability of the right-hand model is greater than a preset upper threshold, it indicates that the offset between the middle temperature difference and the right-hand temperature difference is too large, and its offset needs to be reduced so that the average model probability of the left-hand model can at least meet the upper threshold. Conversely, it indicates that the offset from the middle temperature difference is sufficient for the average model probability of the corresponding model to meet the upper threshold. This improves the reliability of the average model probability of the right-hand model, thereby improving the reliability of the battery SOC calculation results.

[0155] It should also be noted that in the above embodiments, the upper threshold for adjusting only the temperature difference at the left end or the temperature difference at the right end can be the same or different. However, if the upper threshold is different, it is still necessary to satisfy that the upper threshold is greater than the lower threshold.

[0156] The following describes an apparatus embodiment of this application, which can be used to execute the multi-model interaction battery SOC estimation method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the adaptive multi-model interaction battery SOC estimation method described above.

[0157] Figure 6 A block diagram of a multi-model interactive battery SOC estimation device 600 according to an embodiment of this application is shown.

[0158] Reference Figure 6 As shown, a multi-model interactive battery SOC estimation device 600 according to an embodiment of this application includes:

[0159] The acquisition module 610 is used to obtain the model temperature of each model based on the preset temperature difference of each model and the real-time acquired battery temperature, and to obtain the model parameters of each model based on the model temperature.

[0160] The interactive module 620 is used to perform interactive mixing after inputting the corresponding model based on the model parameters and the real-time acquired battery voltage and battery current.

[0161] The probability calculation module 630 is used to estimate the model state of each model based on the result of interactive mixing, obtain the model probability corresponding to each model, and use the model probability as the feedback value for the next interactive mixing.

[0162] The fusion estimation module 640 is used to fuse the model state and model probability, output the battery SOC based on the fusion result, and use the battery SOC as the feedback value for the next acquisition of model parameters.

[0163] The loop module 650 is used to execute the above steps repeatedly for a preset number of times, and before executing the last loop, calculates the average model probability of each model in the number of executions.

[0164] The output module 660 is used to update the temperature difference corresponding to each model based on the average probability of each model, and execute the last loop to obtain the final battery SOC.

[0165] In some embodiments of this application, based on the foregoing scheme, the acquisition module 610 is further configured as follows: multiple models include a left-end model, a middle model, and a right-end model, wherein the temperature difference of the left-end model is the left-end temperature difference, the temperature difference of the middle model is the middle temperature difference, and the temperature difference of the right-end model is the right-end temperature difference; the model temperature of each model is obtained according to the preset temperature difference of each model and the real-time acquired battery temperature, including:

[0166] The absolute value of the difference between the battery temperature and the intermediate temperature difference is used as the model temperature of the intermediate model;

[0167] The absolute value of the difference between the model temperature of the middle model and the temperature difference of the left end is taken as the model temperature of the left end model;

[0168] The sum of the model temperature of the intermediate model and the temperature difference at the right end is taken as the model temperature of the right end model.

[0169] In some embodiments of this application, based on the foregoing scheme, the acquisition module 610 is further configured to: update the temperature difference corresponding to each model according to the average probability of each model, including:

[0170] Calculate the product of the average model probability of each model and the corresponding temperature difference, and use the sum of the products as the updated intermediate temperature difference.

[0171] The absolute value of the difference between the updated middle temperature difference and the left temperature difference is compared with the preset minimum offset value, and the maximum value is taken as the updated left temperature difference.

[0172] The absolute value of the difference between the updated intermediate temperature difference and the right-end temperature difference is compared with the minimum offset value, and the maximum value is taken as the updated right-end temperature difference.

[0173] In some embodiments of this application, based on the foregoing scheme, the output module 660 is further configured to: when the average model probability of the intermediate model is greater than the average model probability of the left-end model and the average model probability of the right-end model, respectively, it further includes:

[0174] Determine whether the average model probability of the left-hand model is less than a preset lower threshold for model probability;

[0175] If so, then based on the preset first offset, reduce the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly; and reduce the absolute value of the difference between the updated right end temperature difference and the updated middle temperature difference, and adjust the updated right end temperature difference accordingly.

[0176] If not, then maintain the temperature difference after each model update.

[0177] In some embodiments of this application, based on the foregoing scheme, the output module 660 is further configured to: when the average model probability of the left-end model is greater than the average model probability of the middle model and the average model probability of the right-end model, respectively, it further includes:

[0178] Determine whether the average model probability of the left-hand model is greater than the preset upper threshold of model probability; if the upper threshold of model probability is greater than the lower threshold of model probability.

[0179] If so, then according to the preset second offset, increase the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly, while maintaining the updated right end temperature difference;

[0180] If not, then maintain the temperature difference after each model update.

[0181] In some embodiments of this application, based on the foregoing scheme, the output module 660 is further configured to: when the average model probability of the right-end model is greater than the average model probability of the middle model and the average model probability of the left-end model, the method further includes:

[0182] Determine whether the average model probability of the right-hand model is greater than the upper threshold of the model probability;

[0183] If so, then according to the preset third offset, increase the absolute value of the difference between the updated right-end temperature difference and the updated middle temperature difference, and adjust the updated right-end temperature difference accordingly, while maintaining the updated left-end temperature difference.

[0184] If not, then maintain the temperature difference after each model update.

[0185] In some embodiments of this application, based on the foregoing scheme, the output module 660 is further configured as follows:

[0186] The process of repeatedly executing multiple rounds to obtain the final battery SOC is performed.

[0187] During the loop, the final battery SOC of the next round is calculated based on the final battery SOC obtained in the previous round.

[0188] It should be noted that the multi-model interactive battery SOC estimation device 800 provided in the above embodiments and the multi-model interactive battery SOC estimation method provided in the above embodiments belong to the same concept. The specific way in which each module and unit performs operations has been described in detail in the method embodiments, and will not be repeated here.

[0189] Embodiments of this application also provide an electronic device including a processor and a memory, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the battery SOC estimation method with multi-model interaction as described above.

[0190] Figure 7 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0191] It should be noted that, Figure 7 The computer system 700 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0192] like Figure 7 As shown, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 702 or programs loaded from storage portion 708 into Random Access Memory (RAM) 703, such as performing the methods described in the above embodiments. The RAM 703 also stores various programs and data required for system operation. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An Input / Output (I / O) interface 705 is also connected to the bus 704.

[0193] The following components are connected to I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 710 as needed so that computer programs read from it can be installed into storage section 708 as needed.

[0194] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs various functions defined in the system of this application.

[0195] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0196] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0197] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0198] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0199] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0200] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0201] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0202] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A battery SOC estimation method with multi-model interaction, characterized in that, The method includes: The model temperature of each model is obtained based on the preset temperature difference of each model and the real-time battery temperature, and the model parameters of each model are obtained based on the model temperature. Input the model parameters of each model into the corresponding model, and input the real-time acquired battery voltage and battery current into each model, and interactively mix the output results of each model; Based on the results of the interactive blending, the model state of each model is estimated to obtain the model probability corresponding to each model, and the model probability is used as the feedback value for the next interactive blending. The model state and the model probability are fused, and the battery SOC is output based on the fusion result. The battery SOC is then used as the feedback value for the next acquisition of the model parameters. The above steps are executed repeatedly for a preset number of times, and before the last iteration, the average model probability of each model is calculated based on the model probabilities obtained from the number of executions. The temperature difference corresponding to each model is updated based on the average probability of each model, and the final loop is executed based on the updated temperature difference to obtain the final battery SOC.

2. The method according to claim 1, characterized in that, Multiple models include a left-end model, a middle model, and a right-end model. The temperature difference of the left-end model is the left-end temperature difference, the temperature difference of the middle model is the middle temperature difference, and the temperature difference of the right-end model is the right-end temperature difference. The process of obtaining the model temperature of each model based on the preset temperature difference of each model and the real-time acquired battery temperature includes: The absolute value of the difference between the battery temperature and the intermediate temperature difference is taken as the model temperature of the intermediate model; The absolute value of the difference between the model temperature of the intermediate model and the temperature difference at the left end is taken as the model temperature of the left end model; The sum of the model temperature of the intermediate model and the temperature difference at the right end is taken as the model temperature of the right end model.

3. The method according to claim 2, characterized in that, The step of updating the temperature difference corresponding to each model based on the average probability of each model includes: Calculate the product of the average model probability of each model and the corresponding temperature difference, and use the sum of the products as the updated intermediate temperature difference. The absolute value of the difference between the updated intermediate temperature difference and the left-end temperature difference is compared with the preset minimum offset value, and the maximum value is taken as the updated left-end temperature difference. The absolute value of the difference between the updated intermediate temperature difference and the right-end temperature difference is compared with the minimum offset value, and the maximum value is taken as the updated right-end temperature difference.

4. The method according to claim 3, characterized in that, When the average model probability of the intermediate model is greater than the average model probability of the left-end model and the average model probability of the right-end model, the method further includes: Determine whether the average model probability of the left-hand model is less than a preset lower threshold for model probability; If so, then based on the preset first offset, reduce the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly; and reduce the absolute value of the difference between the updated right end temperature difference and the updated middle temperature difference, and adjust the updated right end temperature difference accordingly. If not, then maintain the temperature difference after each model update.

5. The method according to claim 4, characterized in that, When the average model probability of the left-end model is greater than the average model probability of the middle model and the average model probability of the right-end model, the method further includes: Determine whether the average model probability of the left-end model is greater than a preset upper threshold for model probability, wherein the upper threshold for model probability is greater than the lower threshold for model probability; If so, then according to the preset second offset, increase the absolute value of the difference between the updated left end temperature difference and the updated middle temperature difference, and adjust the updated left end temperature difference accordingly, while maintaining the updated right end temperature difference. If not, then maintain the temperature difference after each model update.

6. The method according to claim 5, characterized in that, When the average model probability of the right-end model is greater than the average model probability of the middle model and the average model probability of the left-end model, the method further includes: Determine whether the average model probability of the right-hand model is greater than the upper threshold of the model probability; If so, then according to the preset third offset, increase the absolute value of the difference between the updated right-end temperature difference and the updated middle temperature difference, and adjust the updated right-end temperature difference accordingly, while maintaining the updated left-end temperature difference. If not, then maintain the temperature difference after each model update.

7. The method according to any one of claims 1-6, characterized in that, After obtaining the final battery SOC, the process also includes: The process of repeatedly executing multiple rounds to obtain the final battery SOC is performed. During the loop, the final battery SOC of the next round is calculated based on the final battery SOC obtained in the previous round.

8. A battery SOC estimation device with multi-model interaction, characterized in that, include: The acquisition module is used to obtain the model temperature of each model based on the preset temperature difference of each model and the real-time acquired battery temperature, and to obtain the model parameters of each model based on the model temperature. The interaction module is used to perform interactive mixing after inputting the corresponding model based on the model parameters and the real-time acquired battery voltage and battery current. The probability calculation module is used to estimate the model state of each model based on the result of interactive mixing, obtain the model probability corresponding to each model, and use the model probability as the feedback value for the next interactive mixing. The fusion estimation module is used to fuse the model state and the model probability, output the battery SOC based on the fusion result, and use the battery SOC as the feedback value for the next acquisition of the model parameters; The loop module is used to execute the functions of the above modules repeatedly for a preset number of times, and before executing the last loop, calculates the average model probability of each model in the number of executions. The output module is used to update the temperature difference corresponding to each model based on the average probability of each model, and execute the last loop to obtain the final battery SOC.

9. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that, when executed by a computer's processor, cause the computer to perform the multi-model interactive battery SOC estimation method according to any one of claims 1-7.

10. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the electronic device to implement the multi-model interaction battery SOC estimation method as described in any one of claims 1 to 7.

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