Method and device for evaluating and predicting health condition of energy storage battery

Abstract

The invention discloses an energy storage battery health condition evaluation and prediction method, and belongs to the field of battery management. The method comprises the following steps: acquiring operation data of each single battery in a target battery system in real time, and inputting the operation data into a battery system model created by using a Modelica language in advance so as to correspondingly update set parameter values of each single battery model in the battery system model; acquiring current scene parameters of the target battery system, performing simulation operation on the battery system model under the current scene parameters, and evaluating and predicting the health condition of the target battery system; and in each optimization time period, target parameters in the battery system model are optimized by using the real-time operation data of each single battery in the target battery system in the current optimization time period and the prediction result of the battery system model at the historical moment on the current optimization time period. According to the scheme, the model building process can be simplified, the simulation efficiency can be improved, and the comprehensiveness and accuracy of management prediction can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method and device for evaluating and predicting the health status of an energy storage battery. Background Art

[0002] Usually, single cells are expanded into energy storage battery packs by connecting them in series and parallel. However, there are several single cells in the battery pack, and the single cells will decay inconsistently during long-term use, which will lead to the capacity decay of the entire battery pack. Capacity decay not only shortens the energy storage capacity and service life of the energy storage battery, but also increases the user's cost and safety risks.

[0003] In the existing battery management methods, most of the focus is on thermal runaway monitoring of the battery system, and the inconsistency of each single cell in the battery system is rarely monitored and predicted, resulting in the life and safety of the battery system not being effectively guaranteed. In addition, the simulation models used to evaluate and predict the health of the battery system are mainly established using traditional modeling and simulation platforms such as Matlab and Simulink. Simulink and other simulation platforms are based on mathematical modeling to establish various equivalent models to perform parameter identification and high-precision calculations on the model. In some simulations with higher precision requirements, it is also necessary to design and calculate high-order equivalent circuit models. The entire modeling and simulation process requires the construction of mathematical models for different single cells. The modeling and simulation process is relatively cumbersome and difficult for newcomers in the research field to learn. It places high requirements on knowledge and technical capabilities for learners and researchers.

[0004] Therefore, a new method for evaluating and predicting the health status of energy storage batteries is urgently needed. Summary of the invention

[0005] The present invention provides a method and device for evaluating and predicting the health status of energy storage batteries, which can solve the problems that the existing battery management methods rarely monitor and predict the inconsistency of each single battery, resulting in incomplete and detailed management prediction, as well as complex battery system simulation model construction, low simulation efficiency, and high learning threshold. The technical solution is as follows:

[0006] On the one hand, a method for evaluating and predicting the health status of an energy storage battery is provided, the method comprising:

[0007] Acquire the operating data of each single battery in the target battery system in real time, and input the operating data into a battery system model of the target battery system created in advance using the Modelica language, so as to correspondingly update the setting parameter values ​​of each single battery model in the battery system model;

[0008] Acquire current scenario parameters of the target battery system to simulate and run the battery system model under the current scenario parameters, and evaluate and predict the health status of the target battery system; the health status includes inconsistent capacity decay and thermal runaway;

[0009] In each optimization time period, the target parameters in the battery system model are optimized by using the real-time operating data of each single battery in the target battery system at each moment in the current optimization time period and the prediction results of the battery system model at each moment in the current optimization time period.

[0010] On the other hand, a device for evaluating and predicting the health status of an energy storage battery is provided, which is used to implement the method described in any embodiment of the specification, and the device includes:

[0011] An input unit, used for acquiring the operating data of each single battery in the target battery system in real time, and inputting the operating data into a battery system model of the target battery system created in advance using the Modelica language, so as to correspondingly update the setting parameter values ​​of each single battery model in the battery system model;

[0012] A prediction unit, used to obtain current scenario parameters of the target battery system, so as to simulate and run the battery system model under the current scenario parameters, and evaluate and predict the health status of the target battery system; the health status includes inconsistent capacity decay and thermal runaway;

[0013] The optimization unit is used to optimize the target parameters in the battery system model in each optimization time period by using the real-time operation data of each single battery in the target battery system at each moment in the current optimization time period and the prediction results of the battery system model at each moment in the current optimization time period at the historical moment.

[0014] On the other hand, a computer device is provided, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of the above-mentioned method.

[0015] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0016] On the other hand, a computer program product is provided, comprising a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0017] The technical solution provided by the present invention can at least bring the following beneficial effects:

[0018] Since the Modelica language environment has the characteristics of non-causal modeling, the battery system model established using the Modelica language is a simulation model based on physical modeling. It is more intuitive and easy to understand in form, more in line with the researchers' intuitive understanding of the model, and more convenient for the modification and adjustment of various parameters of the model. Compared with the traditional mathematical modeling method, it simplifies the complex adjustment of model parameters that may occur during the research process, and the simulation calculation efficiency is higher. Its simulation results are more consistent with the experimental results of the actual physical circuit. By inputting the real-time operation data of each single battery in the target battery system into the battery system model of the target battery system created in advance using the Modelica language, the simulation battery system model is run according to the current actual scenario parameters to evaluate and predict the inconsistent capacity decay and thermal runaway of the target battery system. Since the target battery system will encounter many unknown and complex situations during the actual operation, in order to improve the simulation accuracy, an optimization cycle is also set to periodically optimize the target parameters in the battery system model according to the real-time operation data of each single battery and the prediction results of the battery system model. Therefore, this solution uses the Modelica language to establish a battery system model, which can simplify the model building process, lower the learning threshold, improve simulation efficiency, and monitor and predict the inconsistency of each single battery, which can improve the comprehensiveness and accuracy of management predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is a flow chart of a method for evaluating and predicting the health status of an energy storage battery provided by an embodiment of the present invention;

[0021] Figure 2 It is a schematic diagram of the composition and data flow of a single cell battery model provided by an embodiment of the present invention;

[0022] Figure 3 It is a composition and data flow diagram of a battery system model provided by an embodiment of the present invention;

[0023] Figure 4 This is a structural diagram of a device for evaluating and predicting the health status of an energy storage battery provided by an embodiment of the present invention;

[0024] Figure 5 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] The specific implementation of the above concept is described below.

[0027] Please refer to Figure 1 , an embodiment of the present invention provides a method for evaluating and predicting the health status of an energy storage battery, the method comprising:

[0028] Step 100, acquiring the operating data of each single battery in the target battery system in real time, and inputting the operating data into a battery system model of the target battery system created in advance using the Modelica language, so as to correspondingly update the setting parameter values ​​of each single battery model in the battery system model;

[0029] Step 102, obtaining current scenario parameters of the target battery system, so as to simulate and run the battery system model under the current scenario parameters, and evaluate and predict the health status of the target battery system; the health status includes inconsistent capacity decay and thermal runaway;

[0030] Step 104, in each optimization time period, the target parameters in the battery system model are optimized by using the real-time operation data of each single battery in the target battery system at each moment in the current optimization time period and the battery system model at the historical moment to predict the results at each moment in the current optimization time period.

[0031] In the embodiment of the present invention, since the Modelica language environment has the non-causal modeling characteristics, the battery system model established by using the Modelica language is a simulation model based on physical modeling, which is more intuitive and easy to understand in form, more in line with the researchers' intuitive understanding of the model, and more convenient for the modification and adjustment of each parameter of the model. Compared with the traditional mathematical modeling method, it simplifies the complex adjustment of model parameters that may occur in the research process, and the simulation calculation efficiency is higher. The simulation results are more consistent with the experimental results of the actual physical circuit. By inputting the real-time operation data of each single battery in the target battery system into the battery system model of the target battery system created in advance using the Modelica language, the simulation battery system model is run according to the current actual scene parameters, so as to evaluate and predict the inconsistent capacity decay and thermal runaway of the target battery system. Since the target battery system will encounter many unknown and complex situations during the actual operation, in order to improve the simulation accuracy, an optimization cycle is also set, and the target parameters in the battery system model are periodically optimized according to the real-time operation data of each single battery and the prediction results of the battery system model. Therefore, this solution uses the Modelica language to establish a battery system model, which can simplify the model building process, lower the learning threshold, improve simulation efficiency, and monitor and predict the inconsistency of each single battery, which can improve the comprehensiveness and accuracy of management predictions.

[0032] Described below Figure 1 How the various steps are performed.

[0033] For step 100:

[0034] Next, the construction process of the battery system model is described in detail.

[0035] In some embodiments, the battery system model of the target battery system is created by:

[0036] Use the model components in the Modelica model library to build a single cell battery model, and set default parameter values ​​in the single cell battery model; the parameters include at least cell electrical parameters and thermal parameters, and the single cell battery model contains at least a battery pack, a signal transmission module, and a thermal model module. The thermal model module is used to predict the temperature change of the single cell battery under different scenario parameters;

[0037] Use the Model class to arrange the positions of multiple single-cell battery models and connect them in series and parallel to build a battery system model;

[0038] Based on the preset parameters of each single battery model in the inconsistency simulation experiment, the parameter values ​​of each single battery model in the battery system model are adjusted to simulate the battery system model under different scenario parameters to determine the inconsistency simulation results of the battery system;

[0039] The inconsistency simulation results are compared with the actual experimental results of the battery system, and the battery system model and parameters are modified and iterated based on the comparison results until a battery system model that meets expectations is obtained.

[0040] In this embodiment, the Modelica language is used to first establish a single battery pack model and set the single battery parameters for packaging, and then the packaged single battery pack model is combined into a complete battery system model. The parameters of each single battery pack model are modified based on the initial parameter difference of each single battery and the difference in thermal related parameters caused by different locations and environments, so as to introduce inconsistency related factors. Since the Modelica language environment has the characteristics of non-causal modeling, it is a simulation model based on physical modeling, which is more intuitive and easy to understand in form, more in line with the researchers' intuitive understanding of the model, and more convenient for the modification and adjustment of each parameter of the model. Compared with the traditional mathematical modeling method, the physical modeling method based on the Modelica language in this embodiment simplifies the complex adjustment of model parameters that may occur during the research process, and the simulation calculation efficiency is higher, and its simulation results are more in line with the experimental results of the actual physical circuit. And the C code generated after the simulation compilation is completed on the target platform can be executed on different platforms, which improves the compatibility and universal applicability of the model and code. Therefore, this simulation method can not only simplify the model building process and reduce the threshold for learning and use, but also reduce the difficulty of simulation and improve the efficiency of simulation.

[0041] In some embodiments, the step of “using model components in the Modelica model library to build a single-cell battery model and setting default parameter values ​​in the single-cell battery model” may include:

[0042] Call the cell model in the Modelica model library, and set the cell electrical parameters and thermal parameters in the electrical submodule and thermal submodule of the cell model respectively;

[0043] Call the battery module model in the Modelica model library to generate a battery pack using multiple battery cell models;

[0044] A signal transmission module is formed by using a signal receiver and a ground pin, and a signal interface is set for the signal transmission module;

[0045] Establish a thermal model module and set a thermal conversion interface for connecting to the battery pack;

[0046] Connect the battery pack, signal transmission module and thermal model module, and set the positive and negative electrode interfaces;

[0047] Use the Block class to encapsulate the battery pack, signal transmission module, and thermal model module to obtain a single battery model.

[0048] In an embodiment of the present invention, the battery cell electrical parameters include at least: SOC starting value, maximum capacity, OCV coefficient and power supply internal resistance coefficient; the thermal related parameters include at least: battery cell starting temperature, thermal conductivity between the battery cell and the shell, and module shell temperature.

[0049] In this embodiment, reference can be made to Figure 2 Schematic diagram of the composition and data flow of the single-cell battery model. The model library includes battery cell models, battery module models, battery pack models, etc. First, call the battery cell model in the Modelica model library, and set the battery cell electrical parameters and thermal-related parameters in the electrical sub-module and thermal sub-module of the battery cell model respectively. Then, call the battery module model in the Modelica model library to generate a battery pack using multiple battery cell models; use the signal receiver and the ground pin to form a signal transmission module, and set the signal interface for the signal transmission module; finally, establish a thermal model module, and set the thermal conversion interface for connecting to the battery pack, then connect the battery pack, signal transmission module and thermal model module, and set the positive and negative electrode interfaces, then use the Block class to encapsulate the lithium battery pack, signal transmission module and thermal model module, you can get the following Figure 2 The single cell battery model is shown.

[0050] In some embodiments, the thermal model module predicts the temperature change of the single battery under different scenario parameters in the following manner:

[0051] Get the ambient temperature in the current scene parameters to calculate the conduction heat loss and convection heat loss, and get the total heat loss equation;

[0052] Get the current voltage and current of the battery pack and calculate the heat generation;

[0053] A heat balance equation is constructed based on the total heat loss equation and heat generation to calculate the current temperature of the single cell.

[0054] In this embodiment, the Joule heat caused by the electrochemical reaction and the internal resistance of each single cell in the battery system during the charging and discharging process will cause the single cell to generate heat during operation, causing the operating temperature of the single cell to rise continuously. The temperature of the single cell will affect its electrochemical performance, and the operation of the single cell at high temperature will accelerate aging and shorten the service life. In addition, different cell electrical parameters and heat-related parameters will also cause different temperatures. Therefore, the thermal reaction of the single cell is closely related to the inconsistency of the battery. Therefore, in order to simulate the inconsistency of each single cell more accurately, when constructing a single cell model, a thermal model module needs to be established in the single cell model. By setting a thermal conversion interface for the battery pack and connecting it to the thermal model module, the thermal model part of the single cell is simulated and tested, so that the thermal model module can predict the temperature changes of each single cell model under different operating conditions based on the inconsistency parameters of each single cell model, thereby involving an effective thermal management system to prevent thermal runaway and accurate simulation of inconsistency.

[0055] In some embodiments, the real-time temperature of the single cell during operation is calculated by the following formula:

[0056]

[0057] In the formula,

[0058]

[0059] Where ρ is the density, C p is the specific heat capacity, V is the volume of the single cell, T is the current temperature of the single cell, Q gen is the heat generation, Q loss is the total amount of heat loss, I is the current, V is the current voltage, Q conv is the convective heat loss, Q cond is the conduction heat loss, h is the convective heat transfer coefficient, A is the surface area of ​​the single cell, T amb is the ambient temperature and k is the thermal conductivity.

[0060] In this embodiment, the thermal model module takes into account the heat generated by the battery during the charging and discharging process, as well as the heat dissipated through different pathways, including convection and conduction. The thermal model of the battery includes a variety of heat transfer mechanisms, taking into full account the electrochemical reactions, material properties, geometric structures, and external environmental conditions inside the battery. The above formula can be used to predict the temperature distribution of each single cell, thereby optimizing the thermal management system of the battery to ensure that the battery operates within a safe operating temperature range. Therefore, in the process of modeling the power battery, this embodiment models the heat conversion part of the model according to the principles of the thermal model to ensure accurate simulation of the operating environment of the battery.

[0061] After the single battery model is established, a new Model class is created in Modelica. According to the structure and organizational connection method of the battery pack required for the study, the single battery models are reasonably arranged and connected in series and parallel. The signal transmission module and thermal model module in each single battery model are connected through each defined interface to form a complete data flow and input and output signals. The complete battery system model is as follows: Figure 3 shown.

[0062] In some embodiments, the step of “adjusting the parameter value of each single cell model in the battery system model based on the preset parameters of each single cell pack in the inconsistency simulation experiment” includes:

[0063] Based on the preset parameters of each single cell in the inconsistency simulation experiment, adjust the SOC starting value, OCV coefficient, power supply internal resistance coefficient of each single cell model in the battery system model, the difference in the starting temperature of the battery cell caused by the different location and environment of the single cell pack, and the difference in the thermal conductivity coefficient between the battery cell and the shell caused by the different location of the single cell pack.

[0064] In this embodiment, in order to study the inconsistency problem of the battery system, on the basis of using the same single cell model, inconsistency related factors are introduced, based on the differences in the initial parameters of different single cells (SOC starting value and OCV coefficient, power supply internal resistance coefficient), and the differences in the starting temperature of the battery cell caused by the different locations and environments, as well as the differences in the heat dissipation conditions caused by the different locations of the single cells (heat transfer coefficient between the battery cell and the casing), the battery cell electrical parameters and thermal related parameters in each single cell model are modified, and the model parameters of each single cell are modified through related factors to establish a battery system model that can reflect the inconsistency of the battery system.

[0065] After the battery system parameters are set, set the simulation scenario parameters, and set the external environment parameters according to the scenarios required for the simulation, such as the charge and discharge rate, operating environment temperature, etc., to simulate the battery system model under different scenario parameters. When simulating the battery system model under different scenario parameters, observe and analyze the changes in the model parameters of each single battery pack in the battery model, and compare the results to observe the inconsistency of the battery system when each battery cell in the battery system model is running under different scenarios. For example, whether there are differences in the rate of change of the SOC of different single battery packs during operation, and the impact of these parameter differences on the performance of the entire battery pack, determine the inconsistency simulation results of the battery system.

[0066] Afterwards, the inconsistency simulation results are compared with the actual experimental results of the battery system, so that the battery system model and parameters are modified and iterated based on the comparison results until a battery system model that meets expectations is obtained.

[0067] In this embodiment, after the simulation is completed, the final analysis results are compared with the results of the actual battery system experiment to verify the accuracy and effectiveness of the established model and the set inconsistency conditions. After the verification results, the model and parameters are modified and iterated so that the established model can more effectively reflect the actual operating conditions of the battery system.

[0068] The above is the process of establishing the battery system model. Since the target battery system is always affected by harsh operating conditions, harsh seasonal environment and accidental manufacturing defects during actual use, the performance of each single battery will be accelerated to degrade or even thermal runaway. The complexity and suddenness of the use environment will cause deviations in the simulation effect of the battery system model. Therefore, it is necessary to determine the setting parameters in the battery system model. These setting parameters can be determined based on the operating data of each single battery in the target battery system, such as the voltage, current, SOC, module shell temperature, etc. of each single battery. The setting parameter values ​​of each single battery model in the battery system model can be updated according to the real-time acquired operating data of each single battery in the target battery system to improve the simulation prediction accuracy.

[0069] Regarding step 102:

[0070] After updating the set parameter values ​​of each single cell model, the scenario parameters of the battery system model simulation operation are set according to the current scenario parameters of the target battery system. The battery system model is used to evaluate and predict the health status of the target battery system in the future, including whether the capacity decay of each single cell is consistent and whether thermal runaway occurs, so as to monitor the health status of each single cell and the entire target battery system.

[0071] Regarding step 104:

[0072] In some implementations, step 104 may include:

[0073] Based on the real-time operating data of each single battery in the target battery system at each moment in the current optimization time period and the battery system model at the historical moment, the prediction results of the corresponding moment in the current optimization time period are scored for the accuracy of the prediction results at each moment in the current optimization time period;

[0074] Determine the fitness function based on the accuracy score results at each moment in the current optimization time period;

[0075] The target parameters to be optimized in the battery system model are used to construct the positions and velocities of particles in the particle swarm-simulated annealing algorithm, and the initial annealing temperature and the final annealing temperature are determined;

[0076] In each optimization cycle when the annealing temperature is less than the final annealing temperature, the following are performed: the speed and position of each particle in the particle group are updated, and the fitness function value of each particle after the update is calculated based on the fitness function, so as to determine whether the new position is acceptable based on the fitness function value of each particle after the update and the fitness function value of each particle before the update, and then the annealing temperature is updated;

[0077] When the annealing temperature is greater than or equal to the final annealing temperature, the optimal solution is determined and the optimization of the target parameters in the battery system model is completed.

[0078] It should be noted that due to the long service life of the battery system, some fixed parameters in the battery system model may become invalid, and the target parameters need to be optimized at regular intervals to ensure that the battery system model is effective and accurate throughout the life cycle of the target battery system. This optimization period can be three months, six months or one year.

[0079] In this embodiment, since the battery system model at a historical moment will predict the capacity decay, temperature, etc. (used to assess the health status) of each single battery in the target battery system in a period of time in the future, and within each optimization time period, the real-time operating data of each single battery in the target battery system at each moment can be obtained in real time, the target parameters in the battery system model can be optimized based on the difference between the real-time operating data and the prediction results at each moment in the current optimization time period.

[0080] Specifically, the accuracy scoring method of the prediction results at each moment in the current optimization time period is:

[0081] For each moment in the current optimization period, execute:

[0082] Calculate the difference between the actual SOC change rate and the predicted SOC change rate of each single battery at the current moment, determine the number of single batteries whose difference is greater than a first preset threshold, and assign scores based on the number of single batteries whose difference is greater than the first preset threshold;

[0083] Calculate the difference between the actual temperature and the predicted temperature of each single cell at the current moment, determine the number of single cells whose difference is greater than the second preset threshold, and give corresponding scores based on the number of single cells whose difference is greater than the second preset threshold to obtain the accuracy score result at the current moment within the current optimization time period.

[0084] In this embodiment, assuming that the number of single cells whose difference is greater than the first preset threshold is 2, 2 points can be given, and the number of single cells whose difference is greater than the second preset threshold is 3, 3 points can also be given directly. The scoring strategy can be set, which is not described in detail here.

[0085] Based on the accuracy score results at each moment in the current optimization time period, determine the fitness function:

[0086]

[0087] In the formula, N represents the total number of single cells in the target battery system, score t,i Represents the accuracy score result of the i-th single cell at the t-th time in the current optimization time period, score avg Represents the comprehensive accuracy score. The smaller the value, the better the accuracy of the algorithm.

[0088] In this step, the initial annealing temperature is determined by the following formula:

[0089]

[0090] The final annealing temperature is determined by the following formula:

[0091]

[0092] Where χ1 and χ2 are the factors that determine the initial annealing temperature and the final annealing temperature, respectively, and π0 is {f(x i )-f(x i-1 )}, π n is {f(x i )-f(x i-1 )}, where f is the cooling rate function, x i-1 is the position of the particle in the previous iteration, x i The position of the particle in this iteration.

[0093] The speed and position of each particle in the particle swarm are updated by the following formula:

[0094]

[0095] In the formula, is the velocity of particle i in dimension d at time t; is the position of particle i in dimension d at time t, w is the inertia weight, which is used to control the continuity of particle velocity, c1 and c2 are learning factors, which are used to adjust the particle to the optimal position of the individual and the global optimal position Move, r1, r2 are random numbers between 0 and 1, used to increase the randomness of the search.

[0096] The acceptance of the new position is determined as follows:

[0097] P(ΔE,T (t) )=exp(-ΔE / (k·T (t) ))

[0098] Where, T (t) is the temperature at time t, ΔE is the difference in fitness between the new position and the position before the update, k is the Boltzmann constant, P(ΔE,T (t) ) is the probability of accepting a solution with poor fitness at temperature T.

[0099] Update annealing temperature by:

[0100] T (t+1) =α·T (t)

[0101] Where, T (t+1) is the temperature at time t+1, T (t) is the temperature at time t, and α is the cooling rate, which takes values ​​between 0 and 1.

[0102] Please refer to Figure 4 The embodiment of the present invention provides a device for evaluating and predicting the health status of an energy storage battery, which is used to implement the method of any embodiment of the specification, and the device includes:

[0103] The input unit 401 is used to obtain the operating data of each single battery in the target battery system in real time, and input the operating data into the battery system model of the target battery system created in advance using the Modelica language, so as to update the setting parameter values ​​of each single battery model in the battery system model accordingly;

[0104] The prediction unit 402 is used to obtain the current scenario parameters of the target battery system, so as to simulate and run the battery system model under the current scenario parameters, and evaluate and predict the health status of the target battery system; the health status includes capacity inconsistency decay and thermal runaway;

[0105] The optimization unit 403 is used to optimize the target parameters in the battery system model in each optimization time period by using the real-time operation data of each single battery in the target battery system at each moment in the current optimization time period and the battery system model at the historical moment to predict the results at each moment in the current optimization time period.

[0106] In one embodiment of the present invention, the battery system model of the target battery system in the input unit 401 is created in the following manner:

[0107] Use the model components in the Modelica model library to build a single cell battery model, and set default parameter values ​​in the single cell battery model; the parameters include at least cell electrical parameters and thermal parameters, and the single cell battery model contains at least a battery pack, a signal transmission module, and a thermal model module. The thermal model module is used to predict the temperature change of the single cell battery under different scenario parameters;

[0108] Use the Model class to arrange the positions of multiple single-cell battery models and connect them in series and parallel to build a battery system model;

[0109] Based on the preset parameters of each single battery model in the inconsistency simulation experiment, the parameter values ​​of each single battery model in the battery system model are adjusted to simulate the battery system model under different scenario parameters to determine the inconsistency simulation results of the battery system;

[0110] The inconsistency simulation results are compared with the actual experimental results of the battery system, and the battery system model and parameters are modified and iterated based on the comparison results until a battery system model that meets expectations is obtained.

[0111] In one embodiment of the present invention, when the input unit 401 executes the construction of a single-cell battery model using the model components in the Modelica model library and sets default parameter values ​​in the single-cell battery model, it is used to:

[0112] Call the cell model in the Modelica model library, and set the cell electrical parameters and thermal parameters in the electrical submodule and thermal submodule of the cell model respectively;

[0113] Call the battery module model in the Modelica model library to generate a battery pack using multiple battery cell models;

[0114] A signal transmission module is formed by using a signal receiver and a ground pin, and a signal interface is set for the signal transmission module;

[0115] Establish a thermal model module and set a thermal conversion interface for connecting to the battery pack;

[0116] Connect the battery pack, signal transmission module and thermal model module, and set the positive and negative electrode interfaces;

[0117] Use the Block class to encapsulate the battery pack, signal transmission module, and thermal model module to obtain a single battery model.

[0118] In one embodiment of the present invention, the thermal model module in the input unit 401 predicts the temperature change of the single battery under different scenario parameters in the following manner:

[0119] Get the ambient temperature in the current scene parameters to calculate the conduction heat loss and convection heat loss, and get the total heat loss equation;

[0120] Get the current voltage and current of the battery pack and calculate the heat generation;

[0121] A heat balance equation is constructed based on the total heat loss equation and heat generation to calculate the current temperature of the single cell.

[0122] In one embodiment of the present invention, the real-time temperature of the single battery in the input unit 401 during operation is calculated by the following formula:

[0123]

[0124] In the formula,

[0125]

[0126] Where ρ is the density, C p is the specific heat capacity, V is the volume of the single cell, T is the current temperature of the single cell, Q gen is the heat generation, Q loss is the total amount of heat loss, I is the current, V is the current voltage, Q conv is the convective heat loss, Q cond is the conduction heat loss, h is the convective heat transfer coefficient, A is the surface area of ​​the single battery pack, T amb is the ambient temperature and k is the thermal conductivity.

[0127] In one embodiment of the present invention, the optimization unit 403 is used to perform:

[0128] Based on the real-time operating data of each single battery in the target battery system at each moment in the current optimization time period and the battery system model at the historical moment, the prediction results of the corresponding moment in the current optimization time period are scored for the accuracy of the prediction results at each moment in the current optimization time period;

[0129] Determine the fitness function based on the accuracy score results at each moment in the current optimization time period;

[0130] The target parameters to be optimized in the battery system model are used to construct the positions and velocities of particles in the particle swarm-simulated annealing algorithm, and the initial annealing temperature and the final annealing temperature are determined;

[0131] In each optimization cycle when the annealing temperature is less than the final annealing temperature, the following are performed: the speed and position of each particle in the particle group are updated, and the fitness function value of each particle after the update is calculated based on the fitness function, so as to determine whether the new position is acceptable based on the fitness function value of each particle after the update and the fitness function value of each particle before the update, and then the annealing temperature is updated;

[0132] When the annealing temperature is greater than or equal to the final annealing temperature, the optimal solution is determined and the optimization of the target parameters in the battery system model is completed.

[0133] It should be noted that the energy storage battery health status assessment and prediction device provided in the above embodiment is only illustrated by the division of the above functional units. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device is divided into different functional units to complete all or part of the functions described above. In addition, the above device embodiment and the method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0134] The embodiment of the present application also provides a computer device, please refer to Figure 5 The computer device includes a processor and a memory, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the energy storage battery health status assessment and prediction method provided by the above-mentioned method embodiments.

[0135] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the energy storage battery health status assessment and prediction method provided by the above-mentioned method embodiments.

[0136] An embodiment of the present application also provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the energy storage battery health status assessment and prediction method described in any of the above embodiments.

[0137] For the convenience of description, the above system or device is described by dividing it into various modules or units according to its functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0138] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0139] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0140] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for evaluating and predicting the health status of an energy storage battery, characterized in that: include: Acquire the operating data of each single battery in the target battery system in real time, and input the operating data into a battery system model of the target battery system created in advance using the Modelica language, so as to correspondingly update the setting parameter values ​​of each single battery model in the battery system model; Acquire current scenario parameters of the target battery system to simulate and run the battery system model under the current scenario parameters, and evaluate and predict the health status of the target battery system; the health status includes inconsistent capacity decay and thermal runaway; In each optimization time period, the target parameters in the battery system model are optimized by using the real-time operating data of each single battery in the target battery system at each moment in the current optimization time period and the prediction results of the battery system model at each moment in the current optimization time period.

2. The method according to claim 1, characterized in that The battery system model of the target battery system is created in the following way: A single-cell battery model is constructed using the model components in the Modelica model library, and default parameter values ​​are set in the single-cell battery model; wherein the parameters include at least cell electrical parameters and thermal-related parameters, and the single-cell battery model contains at least a battery pack, a signal transmission module, and a thermal model module, and the thermal model module is used to predict the temperature change of the single-cell battery under different scenario parameters; Use the Model class to arrange the positions of the plurality of single battery models and connect them in series and parallel to form a battery system model; Based on the preset parameters of each single battery model in the inconsistency simulation experiment, the parameter value of each single battery model in the battery system model is adjusted to simulate the battery system model under different scenario parameters to determine the inconsistency simulation result of the battery system; The inconsistency simulation results are compared with the actual experimental results of the battery system, and the battery system model and parameters are modified and iterated based on the comparison results until a battery system model that meets expectations is obtained.

3. The method according to claim 2, characterized in that The method of using the model components in the Modelica model library to build a single cell battery model and setting default parameter values ​​in the single cell battery model includes: Call the cell model in the Modelica model library, and set the cell electrical parameters and thermal parameters in the electrical submodule and thermal submodule of the cell model respectively; Calling a battery module model in a Modelica model library to generate a battery pack using a plurality of the battery cell models; A signal transmission module is formed by using a signal receiver and a ground pin, and a signal interface is provided for the signal transmission module; Establishing a thermal model module and providing a thermal conversion interface for connecting to the battery pack; Connecting the battery pack, the signal transmission module and the thermal model module, and setting positive and negative electrode interfaces; The battery pack, the signal transmission module and the thermal model module are encapsulated using the Block class to obtain a single cell battery model.

4. The method according to claim 2, characterized in that The thermal model module predicts the temperature change of the single battery under different scenario parameters in the following way: Get the ambient temperature in the current scene parameters to calculate the conduction heat loss and convection heat loss, and get the total heat loss equation; Obtaining the current voltage and current of the battery pack and calculating heat generation; A heat balance equation is constructed based on the total heat loss equation and the heat generation to calculate the current temperature of the single battery.

5. The method according to claim 4, characterized in that The real-time temperature of the single cell during operation is calculated by the following formula: In the formula, Where ρ is the density, C p is the specific heat capacity, V is the volume of the single cell, T is the current temperature of the single cell, Q gen is the heat generation, Q loss is the total amount of heat loss, I is the current, V is the current voltage, Q conv is the convective heat loss, Q cond is the conduction heat loss, h is the convective heat transfer coefficient, A is the surface area of ​​the single cell, T amb is the ambient temperature and k is the thermal conductivity.

6. The method according to any one of claims 1 to 5, characterized in that The method optimizes the target parameters in the battery system model by using the real-time operation data of each single battery in the target battery system at each moment in the current optimization time period and the prediction results of the battery system model at each moment in the current optimization time period, including: Based on the real-time operating data of each single battery in the target battery system at each moment in the current optimization time period and the prediction results of the battery system model at the historical moment for the corresponding moment in the current optimization time period, the accuracy score is given to the prediction results at each moment in the current optimization time period; Determine the fitness function based on the accuracy score results at each moment in the current optimization time period; The target parameters to be optimized in the battery system model are used to construct the positions and velocities of particles in the particle swarm-simulated annealing algorithm, and the initial annealing temperature and the final annealing temperature are determined; In each optimization cycle when the annealing temperature is less than the final annealing temperature, the following steps are performed: updating the speed and position of each particle in the particle group, and calculating the fitness function value of each particle after the update based on the fitness function, so as to determine whether the new position is acceptable based on the fitness function value of each particle after the update and the fitness function value of each particle before the update, and then updating the annealing temperature; When the annealing temperature is greater than or equal to the final annealing temperature, the optimal solution is determined to complete the optimization of the target parameters in the battery system model.

7. A device for evaluating and predicting the health status of an energy storage battery, used to implement the method according to any one of claims 1 to 6, characterized in that: The device comprises: An input unit, used for acquiring the operating data of each single battery in the target battery system in real time, and inputting the operating data into a battery system model of the target battery system created in advance using the Modelica language, so as to correspondingly update the setting parameter values ​​of each single battery model in the battery system model; A prediction unit, used to obtain current scenario parameters of the target battery system, so as to simulate and run the battery system model under the current scenario parameters, and evaluate and predict the health status of the target battery system; the health status includes inconsistent capacity decay and thermal runaway; The optimization unit is used to optimize the target parameters in the battery system model in each optimization time period by using the real-time operation data of each single battery in the target battery system at each moment in the current optimization time period and the prediction results of the battery system model at each moment in the current optimization time period at the historical moment.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of any one of the methods described in claims 1-6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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