Energy storage battery health state evaluation and thermal runaway risk prediction method and system

Abstract

The invention provides an energy storage battery health state evaluation and thermal runaway risk prediction method and system, and belongs to the technical field of energy storage batteries. The method comprises the following steps: establishing an electrochemical fusion model by fusing an electrochemical model, a thermal model and an aging model; training the electrochemical fusion model, and inputting physical parameter data of the energy storage battery into the trained electrochemical fusion model to obtain key performance parameter data of the energy storage battery; obtaining an energy storage battery health state evaluation result based on the key performance parameter data of the energy storage battery; and comparing the key performance parameter data of the energy storage battery with data detected by an actual sensor to obtain a thermal runaway risk prediction result. Accurate evaluation and fault prediction of the energy storage battery health state are achieved, the precision and safety of battery management in an energy storage system can be remarkably improved, and the method is suitable for a large-scale energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage batteries, and in particular to a method and system for evaluating the health status of energy storage batteries and predicting the risk of thermal runaway. Background Art

[0002] Energy storage batteries are increasingly used in renewable energy, electric vehicles, and grid regulation. Battery health monitoring and thermal runaway risk management have gradually become the focus of industry attention. The original battery management system (BMS) was mainly based on basic parameters such as voltage, current, and temperature for monitoring. Although this method can reflect the working status of the battery to a certain extent, it cannot accurately capture the complex chemical reactions and degradation processes inside the battery.

[0003] Existing battery health monitoring technologies mostly rely on simple physical parameters such as voltage, temperature and current. These methods have the following significant drawbacks:

[0004] 1. Health status monitoring is not accurate enough: Traditional BMS can only estimate the health status (SOH) of the battery through external parameters. It is difficult to reflect the chemical reaction and decay process inside the battery in real time, so the monitoring accuracy is low.

[0005] 2. Delayed warning of thermal runaway risk: Existing technologies mainly focus on external temperature changes, and cannot detect early signs of thermal runaway inside the battery, such as local overheating inside the battery or temperature rise caused by side reactions. This may lead to delayed warning of thermal runaway risk, which in turn may cause safety accidents.

[0006] 3. Poor performance under complex working conditions: Under complex working conditions such as high-rate discharge, high temperature or low temperature environment, it is difficult for traditional BMS to accurately capture the battery degradation process and potential failures, especially in the long-term use, there are errors in the assessment of battery health status.

[0007] The above problems need to be solved urgently. Summary of the invention

[0008] The purpose of the present invention is to overcome at least one technical problem existing in the prior art and to provide a method and system for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway.

[0009] On the one hand, an embodiment of the present invention provides a method for evaluating the health status of an energy storage battery and predicting the thermal runaway risk, the method comprising: establishing an electrochemical fusion model by fusing an electrochemical model, a thermal model and an aging model; training the electrochemical fusion model, comprising: acquiring physical parameter data of the energy storage battery in real time based on an energy storage battery management system; inputting the physical parameter data into the electrochemical fusion model, and obtaining a voltage response curve of the battery by applying multi-stage pulse current excitation; performing an aging parameter sensitivity analysis based on the voltage response curve of the battery to determine key aging parameters of the energy storage battery; completing the training process of the electrochemical fusion model by iteratively optimizing the key aging parameters; obtaining key performance parameter data of the energy storage battery by inputting the physical parameter data of the energy storage battery into the trained electrochemical fusion model; obtaining a health status evaluation result of the energy storage battery based on the key performance parameter data of the energy storage battery; and obtaining a thermal runaway risk prediction result by comparing the key performance parameter data of the energy storage battery with data detected by actual sensors.

[0010] Furthermore, the electrochemical model includes:

[0011] V(t)=V ocv -I(t)×RV c ;

[0012] The thermal model includes:

[0013]

[0014] Q gen =I(t) 2 R+I(t)(V ocv -V(t));

[0015] The aging model includes:

[0016] Capacity decay model: Q(t) = Q0-k Q *t;

[0017] Internal resistance growth model: R(t)=R0+k R *t;

[0018] Where V(t) represents the terminal voltage, V ocv represents the open circuit voltage, R represents the internal resistance, V c represents the voltage on capacitor C, I(t) represents the current at time t, m represents the battery mass, c p represents specific heat, Q gen represents the heat generation rate, Q loss represents the rate of heat loss relative to the environment, I(t) 2 R represents Joule heat, I(t)(Vocv -V(t) represents polarization heat, Q0 represents initial capacity, k Q represents the capacity decay rate constant, t represents time, R0 represents the initial internal resistance, k R represents the internal resistance growth rate constant.

[0019] Furthermore, the fused model includes: coupling the electrochemical model with the thermal model to predict the temperature changes of the energy storage battery under different working conditions; coupling the electrochemical model with the aging model to predict the health status and life of the energy storage battery in real time, and adjusting the internal resistance and capacity of the energy storage battery so that the electrochemical model reflects the battery status after aging; coupling the thermal model with the aging model to dynamically adjust the aging rate of the aging model through the temperature information predicted by the thermal model.

[0020] Furthermore, the physical parameter data of the energy storage battery includes one or a combination of the current, voltage and temperature data of the energy storage battery; the key performance parameters of the energy storage battery include one or a combination of the capacity, internal resistance, temperature and state of charge of the energy storage battery.

[0021] Furthermore, the training process of the electrochemical fusion model by iteratively optimizing the key aging parameters includes: importing the collected physical parameter data of the energy storage battery into the MATLAB environment, preprocessing the data, and generating a data set for parameter identification; and iteratively optimizing the key aging parameters of the battery using the Levenberg-Marquardt algorithm.

[0022] Furthermore, the battery health status assessment result includes: one or a combination of charge state, health state, temperature monitoring, voltage abnormality and internal resistance change.

[0023] Furthermore, the battery health status assessment result includes: calculating the maximum capacity of the positive and negative electrodes of the energy storage battery respectively through the electrochemical reaction calculation formula in the electrochemical fusion model; determining the current state of charge of the energy storage battery based on the smaller value of the maximum capacity of the positive and negative electrodes of the energy storage battery; obtaining the health status based on the aging model; predicting the temperature of the energy storage battery based on the thermal model to obtain the temperature monitoring status; obtaining the voltage abnormality by comparing the actual voltage of the energy storage battery with the predicted voltage output by the electrochemical fusion model; and obtaining the internal resistance change by monitoring the internal resistance of the energy storage battery through the electrochemical impedance spectroscopy.

[0024] Furthermore, the electrochemical reaction calculation formula includes:

[0025]

[0026] In the formula, SOC(t) represents the remaining capacity at time t, SOC(0) represents the initial state of charge of the energy storage battery, and Q0 represents the initial capacity.

[0027] Furthermore, obtaining a thermal runaway risk prediction result by comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor includes: comparing the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model with the data detected by the actual sensor; in response to the deviation between the actual voltage and temperature data of the energy storage battery and the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model exceeding a preset range, judging whether an internal short circuit occurs in the energy storage battery based on the internal state of charge and internal resistance change of the battery; in response to the deviation between the actual voltage and temperature data of the energy storage battery and the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model not exceeding a preset range, judging that no internal short circuit occurs in the energy storage battery; and giving an alarm prompt when it is judged that an internal short circuit occurs.

[0028] On the other hand, an embodiment of the present invention provides a system for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway, the system comprising: a model building unit, adapted to establish an electrochemical fusion model by fusing an electrochemical model, a thermal model and an aging model; a model training unit, adapted to train the electrochemical fusion model, including: a physical parameter data acquisition subunit, adapted to acquire the physical parameter data of the energy storage battery in real time based on an energy storage battery management system; a voltage response curve acquisition subunit, adapted to input the physical parameter data into the electrochemical fusion model, and obtain the voltage response curve of the battery by applying multi-stage pulse current excitation; a key aging parameter determination subunit, adapted to determine the key aging parameter based on the voltage of the battery A response curve is used to perform aging parameter sensitivity analysis to determine the key aging parameters of the energy storage battery; a key aging parameter optimization subunit is used to complete the training process of the electrochemical fusion model by iteratively optimizing the key aging parameters; a key performance parameter data acquisition unit is used to obtain the key performance parameter data of the energy storage battery by inputting the physical parameter data of the energy storage battery into the trained electrochemical fusion model; a health status assessment result generation unit is used to obtain the health status assessment result of the energy storage battery based on the key performance parameter data of the energy storage battery; a thermal runaway risk prediction unit is used to obtain the thermal runaway risk prediction result by comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor.

[0029] On the other hand, the present invention also provides a computer-readable storage medium, in which one or more instructions are stored, and the computer instructions are used to enable the computer to execute the above-mentioned energy storage battery health status assessment and thermal runaway risk prediction method.

[0030] On the other hand, the present invention provides an electronic device, comprising: a memory and a processor; the memory stores at least one program instruction; the processor implements the above-mentioned energy storage battery health status assessment and thermal runaway risk prediction method by loading and executing the at least one program instruction.

[0031] The beneficial effects of the present invention are:

[0032] 1. This application uses a parameter identification method based on an electrochemical model to update the electrochemical fusion model of the energy storage battery in real time, thereby improving the accuracy of energy storage battery capacity estimation, and is particularly suitable for the high-precision requirements of energy storage systems for battery capacity management.

[0033] 2. This application can compare the voltage and temperature prediction values ​​of the electrochemical fusion model with the actual sensor data to timely detect faults such as internal short circuits. Combined with the analysis of the internal parameters of the battery (such as SOC and internal resistance), it can provide early warning of potential risks, greatly improving the safety of the energy storage system.

[0034] 3. This application can accurately identify the aging parameters of the battery, and then monitor and evaluate the health status of the battery in real time, thereby effectively extending the battery life and improving the stability and efficiency of the battery.

[0035] 4. This application is particularly suitable for large-scale energy storage systems, grid frequency regulation and distributed energy storage systems. It can cope with the complex and changeable operating environment in these systems and improve the overall management capabilities of the system.

[0036] 5. This application can timely diagnose potential battery failures through precise parameter identification and internal short-circuit fault detection, thereby avoiding possible thermal runaway and other safety hazards and ensuring the safety and operational reliability of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0038] Figure 1 This is a flow chart of a method for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway provided in Example 1 of the present invention.

[0039] Figure 2 It is a schematic diagram of the sensitivity analysis results of the electrochemical aging parameters of the energy storage battery provided in Example 1 of the present invention.

[0040] Figure 3 It is a schematic diagram of the structure of a system for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway provided in Example 2 of the present invention.

[0041] Figure 4 This is a partial block diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0042] It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations as sequential processes, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0043] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0044] The present invention will now be described in detail with reference to the accompanying drawings. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore only shows the components related to the present invention.

[0045] In order to facilitate subsequent understanding, the following professional terms are explained here:

[0046] BMS: (Building Management System), battery management system.

[0047] SOC: (State of charge), that is, the state of charge, is used to reflect the remaining capacity of the battery. Its value is defined as the ratio of the remaining capacity to the battery capacity, usually expressed as a percentage.

[0048] SOH: (state of health), battery health, which is the percentage of the battery's current capacity to its factory capacity.

[0049] Example 1

[0050] like Figure 1 As shown, it is a flow chart of a method for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway provided by the present invention.

[0051] As an example, the method includes:

[0052] S110: Establishing an electrochemical fusion model by fusing the electrochemical model, the thermal model and the aging model.

[0053] Preferably, the electrochemical model includes: V(t)=V ocv -I(t)×RV c ;

[0054] The thermal model includes: Q gen =I(t) 2 R+I(t)(V ocv -V(t));

[0055] The aging model includes: Capacity decay model: Q(t) = Q0-k Q *t; internal resistance growth model: R(t)=R0+k R *t;

[0056] Where V(t) represents the terminal voltage, V ocv represents the open circuit voltage, R represents the internal resistance, V c represents the voltage on capacitor C, I(t) represents the current at time t, m represents the battery mass, c p represents specific heat, Q gen represents the heat generation rate, Q loss represents the rate of heat loss relative to the environment, I(t) 2 R represents Joule heat, I(t)(V ocv -V(t) represents polarization heat, Q0 represents initial capacity, k Q represents the capacity decay rate constant, t represents time, R0 represents the initial internal resistance, k R represents the internal resistance growth rate constant.

[0057] Specifically, the electrochemical model describes the charge transfer and electrochemical reactions inside the battery; the thermal model is used to describe the thermal behavior of the battery, taking into account the heat generation and heat dissipation process; the aging model describes the process of battery capacity decay and internal resistance increase. By integrating the above models, a comprehensive assessment of the battery status can be achieved.

[0058] Preferably, the fused model includes: coupling the electrochemical model with the thermal model to predict the temperature change of the energy storage battery under different working conditions. Specifically, the electrochemical reaction will generate heat, so the thermal model needs to be coupled with the electrochemical model to predict the temperature change of the battery under different working conditions. For example, the current, voltage and other parameters calculated by the electrochemical model can be input into the thermal model to calculate the Joule heat and polarization heat inside the battery: this heat is used as the input of the thermal model to predict the temperature rise of the battery. At the same time, temperature changes will also affect the rate of electrochemical reactions and the performance of the battery, forming a two-way coupling.

[0059] Preferably, the fused model includes: coupling the electrochemical model with the aging model to predict the health status and life of the energy storage battery in real time, and adjusting the internal resistance and capacity of the energy storage battery so that the electrochemical model reflects the battery state after aging. Specifically, the current and voltage changes in the electrochemical model will affect the aging rate of the battery. For example, information such as the number of battery cycles, charge / discharge rate, etc. can be used to adjust the parameters in the aging model to predict the battery's state of health (SOH) and life in real time. In addition, the aging model can also be fed back to the electrochemical model to adjust the internal resistance and capacity of the battery so that the electrochemical model reflects the battery state after aging.

[0060] Preferably, the fused model includes: coupling the thermal model with the aging model to dynamically adjust the aging rate of the aging model through the temperature information predicted by the thermal model. Specifically, temperature has a significant impact on battery aging, especially in a high temperature environment, the aging rate of the battery will be accelerated. Therefore, the thermal model and the aging model can be coupled to dynamically adjust the aging rate of the aging model through the temperature information predicted by the thermal model. For example, when the battery temperature rises, the capacity decay rate k Q and the internal resistance growth rate k R will also increase accordingly. That is, by integrating electrochemical, thermal and aging models, the following practical applications can be achieved: Real-time status monitoring: Combining electrochemical and thermal models, it is possible to monitor the SOC, temperature and SOH of the battery in real time and predict potential safety risks; Life prediction: By integrating the aging model with the electrochemical and thermal models, the remaining life of the battery can be predicted under different usage conditions; Thermal management optimization: By combining electrochemical and thermal models, the thermal management strategy of the battery can be optimized to prevent overheating and extend the battery life; Safety diagnosis: If the model predicts abnormal battery temperature or abnormal voltage fluctuation, the fault can be discovered in advance through the results of model fusion to ensure the safety of the battery.

[0061] Preferably, the method further comprises: S120: training the electrochemical fusion model, comprising:

[0062] S1201: Based on the energy storage battery management system, the energy storage battery is collected in real time to obtain the physical parameter data of the energy storage battery; S1202: The physical parameter data is input into the electrochemical fusion model, and the voltage response curve of the battery is obtained by applying multi-stage pulse current excitation; S1203: Based on the voltage response curve of the battery, an aging parameter sensitivity analysis is performed to determine the key aging parameters of the energy storage battery; S1204: The training process of the electrochemical fusion model is completed by iteratively optimizing the key aging parameters. Specifically, combined with Figure 2As shown, based on the current, voltage, temperature and other data collected in real time by the energy storage battery management system (BMS), combined with the electrochemical model, the voltage response curve of the battery is obtained by applying multi-stage pulse current excitation or monitoring the random operation of the battery. On this basis, parameter sensitivity analysis is performed to determine the key aging parameters of the energy storage battery under specific working conditions, such as the maximum lithium ion concentration of the negative electrode, and other sensitive parameters are identified in turn, such as the maximum lithium ion concentration of the positive electrode, the percentage of negative electrode active material and the percentage of positive electrode active material. Among them, the physical parameter data of the energy storage battery includes one or a combination of the current, voltage and temperature data of the energy storage battery.

[0063] Preferably, the step S1204: completing the training process of the electrochemical fusion model by iteratively optimizing the key aging parameters includes: importing the collected physical parameter data of the energy storage battery into the MATLAB environment, preprocessing the data, and generating a data set for parameter identification; and iteratively optimizing the key aging parameters of the battery using the Levenberg-Marquardt algorithm.

[0064] Preferably, the method further includes: S130: obtaining key performance parameter data of the energy storage battery by inputting the physical parameter data of the energy storage battery into the trained electrochemical fusion model. Specifically, the key performance parameter of the energy storage battery includes one or a combination of the capacity, internal resistance, temperature and state of charge of the energy storage battery.

[0065] Preferably, the method further includes: S140: obtaining a health status assessment result of the energy storage battery based on the key performance parameter data of the energy storage battery. Specifically, the battery health status assessment result includes: one or a combination of charge state, health status, temperature monitoring, voltage abnormality and internal resistance change. The battery health status assessment result includes: respectively calculating the maximum capacity of the positive and negative electrodes of the energy storage battery through the electrochemical reaction calculation formula in the electrochemical fusion model; determining the current charge state of the energy storage battery based on the smaller value of the maximum capacity of the positive and negative electrodes of the energy storage battery; obtaining the health status based on the aging model; predicting the temperature of the energy storage battery based on the thermal model to obtain the temperature monitoring status; obtaining the voltage abnormality by comparing the actual voltage of the energy storage battery with the predicted voltage output by the electrochemical fusion model; and obtaining the internal resistance change by monitoring the internal resistance of the energy storage battery through the electrochemical impedance spectrum. The electrochemical reaction calculation formula includes:

[0066]

[0067] In the formula, SOC(t) represents the remaining capacity at time t, SOC(0) represents the initial state of charge of the energy storage battery, and Q0 represents the initial capacity.

[0068] Preferably, the method further includes: S150: obtaining a thermal runaway risk prediction result by comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor. Specifically, the thermal runaway risk prediction result obtained by comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor includes: comparing the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model with the data detected by the actual sensor; in response to the deviation of the actual voltage and temperature data of the energy storage battery from the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model exceeding the preset range, judging whether the energy storage battery has an internal short circuit based on the change of the internal state of charge and internal resistance of the battery; in response to the deviation of the actual voltage and temperature data of the energy storage battery from the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model not exceeding the preset range, judging that the energy storage battery has not an internal short circuit; and giving an alarm when it is judged that an internal short circuit has occurred. That is, if the deviation between the detected voltage or temperature and the predicted value of the electrochemical fusion model exceeds the set range, it is judged that the battery may have an internal short circuit, and the fault is further confirmed by combining the changes of SOC and internal resistance. Since the method of confirming a fault based on the change of SOC and internal resistance is already very mature in the prior art, it will not be described in detail here.

[0069] In the above embodiments, accurate monitoring of the health status of the energy storage battery and fault warning can be achieved, especially timely diagnosis and treatment of internal short circuit risks, thereby significantly improving the safety and operational stability of the energy storage system. The present application is particularly suitable for use in large-scale energy storage power stations, grid frequency modulation and distributed energy storage systems.

[0070] Example 2

[0071] See also Figure 3 , this embodiment provides a schematic diagram of the structure of a storage battery health status assessment and thermal runaway risk prediction system.

[0072] As an example, the system includes:

[0073] The model building unit 310 is adapted to build an electrochemical fusion model by fusing an electrochemical model, a thermal model and an aging model.

[0074] The model training unit 320 is adapted to train the electrochemical fusion model, including:

[0075] The physical parameter data collection subunit 3201 is adapted to collect the physical parameter data of the energy storage battery in real time based on the energy storage battery management system;

[0076] The voltage response curve obtaining subunit 3202 is adapted to input the physical parameter data into the electrochemical fusion model and obtain the voltage response curve of the battery by applying multi-stage pulse current excitation;

[0077] The key aging parameter determination subunit 3203 is adapted to perform aging parameter sensitivity analysis based on the voltage response curve of the battery to determine the key aging parameters of the energy storage battery;

[0078] The key aging parameter optimization subunit 3204 is adapted to complete the training process of the electrochemical fusion model by iteratively optimizing the key aging parameters.

[0079] The key performance parameter data acquisition unit 330 is adapted to obtain the key performance parameter data of the energy storage battery by inputting the physical parameter data of the energy storage battery into the trained electrochemical fusion model.

[0080] The health status assessment result generating unit 340 is adapted to obtain the health status assessment result of the energy storage battery based on the key performance parameter data of the energy storage battery.

[0081] The thermal runaway risk prediction unit 350 is adapted to obtain a thermal runaway risk prediction result by comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor.

[0082] It is not difficult to find that this embodiment is a square system embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the first embodiment.

[0083] It is worth mentioning that all units involved in this embodiment are logical units. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0084] Example 3

[0085] The embodiment of the present invention further proposes a storage medium, on which a method for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway is stored, and when the program for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway is executed by a processor, the steps of the method for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway as described above are implemented. Since the storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be repeated here one by one.

[0086] Example 4

[0087] See also Figure 4 An embodiment of the present invention further provides an electronic device, comprising: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the energy storage battery health status assessment and thermal runaway risk prediction method provided in Example 1.

[0088] The memory 502 and the processor 501 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 501 and the memory 502 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 501 is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor 501.

[0089] The processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 502 can be used to store data used by the processor 501 when performing operations.

[0090] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the scope of protection of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for evaluating the health status of energy storage batteries and predicting thermal runaway risks, characterized in that: The method comprises: An electrochemical fusion model is established by fusing the electrochemical model, thermal model and aging model; Training the electrochemical fusion model includes: Based on the energy storage battery management system, the energy storage battery is collected in real time to obtain the physical parameter data of the energy storage battery; Inputting the physical parameter data into an electrochemical fusion model, and obtaining a voltage response curve of the battery by applying multi-stage pulse current excitation; Performing aging parameter sensitivity analysis based on the voltage response curve of the battery to determine key aging parameters of the energy storage battery; The training process of the electrochemical fusion model is completed by iteratively optimizing the key aging parameters; By inputting the physical parameter data of the energy storage battery into the trained electrochemical fusion model, the key performance parameter data of the energy storage battery is obtained; Obtaining a health status assessment result of the energy storage battery based on the key performance parameter data of the energy storage battery; By comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor, a thermal runaway risk prediction result is obtained.

2. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 1, characterized in that: The electrochemical model includes: V(t)=V ocv -I(t)×R-V c ; The thermal model includes: Q gen =I(t) 2 R+I(t)(V ocv -V(t)); The aging model includes: Capacity decay model: Q(t) = Q0-k Q *t; Internal resistance growth model: R(t)=R0+k R *t; Where V(t) represents the terminal voltage, V ocv represents the open circuit voltage, R represents the internal resistance, V c represents the voltage on capacitor C, I(t) represents the current at time t, m represents the battery mass, c p represents specific heat, Q gen represents the heat generation rate, Q loss represents the rate of heat loss relative to the environment, I(t) 2 R represents Joule heat, I(t)(V ocv -V(t) represents polarization heat, Q0 represents initial capacity, k Q represents the capacity decay rate constant, t represents time, R0 represents the initial internal resistance, k R represents the internal resistance growth rate constant.

3. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 2, characterized in that: The fused model includes: The electrochemical model is coupled with the thermal model to predict the temperature change of the energy storage battery under different working conditions; The electrochemical model is coupled with the aging model to predict the health status and life of the energy storage battery in real time, and to adjust the internal resistance and capacity of the energy storage battery so that the electrochemical model reflects the battery status after aging; The thermal model is coupled with the aging model to dynamically adjust the aging rate of the aging model according to the temperature information predicted by the thermal model.

4. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 1, characterized in that: The physical parameter data of the energy storage battery includes one or a combination of current, voltage and temperature data of the energy storage battery; The key performance parameters of the energy storage battery include one or a combination of the capacity, internal resistance, temperature and state of charge of the energy storage battery.

5. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 1, characterized in that: The process of completing the training of the electrochemical fusion model by iteratively optimizing the key aging parameters includes: Import the collected physical parameter data of the energy storage battery into the MATLAB environment, preprocess the data, and generate a data set for parameter identification; The Levenberg-Marquardt algorithm is used to iteratively optimize the key aging parameters of the battery.

6. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 1, characterized in that: The battery health status assessment result includes: one or a combination of: charge state, health state, temperature monitoring, voltage abnormality and internal resistance change.

7. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 6, characterized in that: The battery health status assessment result includes: The maximum capacities of the positive and negative electrodes of the energy storage battery are calculated respectively by the electrochemical reaction calculation formula in the electrochemical fusion model; Determine the current state of charge of the energy storage battery based on the smaller value of the maximum capacity of the positive electrode and the negative electrode of the energy storage battery; Get the health status based on the aging model; Predict the temperature of the energy storage battery based on the thermal model and obtain the temperature monitoring situation; The voltage anomaly is obtained by comparing the actual voltage of the energy storage battery with the predicted voltage output by the electrochemical fusion model; The internal resistance of the energy storage battery is monitored by electrochemical impedance spectroscopy to obtain the change of internal resistance.

8. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 7, characterized in that: The electrochemical reaction calculation formula includes: In the formula, SOC(t) represents the remaining capacity at time t, SOC(0) represents the initial state of charge of the energy storage battery, and Q0 represents the initial capacity.

9. The method for evaluating the health status of energy storage batteries and predicting thermal runaway risks according to claim 1, characterized in that: The thermal runaway risk prediction result obtained by comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor includes: By comparing the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model with the data detected by the actual sensor; In response to the deviation between the actual voltage and temperature data of the energy storage battery and the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model exceeding a preset range, it is determined whether an internal short circuit occurs in the energy storage battery based on the internal state of charge and internal resistance change of the battery; In response to the fact that the deviation between the actual voltage and temperature data of the energy storage battery and the voltage and temperature data of the energy storage battery obtained by the electrochemical fusion model does not exceed a preset range, it is determined that the energy storage battery does not have an internal short circuit; When an internal short circuit is detected, an alarm will be given.

10. A system for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway, applied to the method for evaluating the health status of an energy storage battery and predicting the risk of thermal runaway according to any one of claims 1 to 9, characterized in that: The system comprises: A model building unit, adapted to build an electrochemical fusion model by fusing an electrochemical model, a thermal model and an aging model; A model training unit, adapted to train the electrochemical fusion model, comprising: The physical parameter data collection subunit is suitable for collecting the physical parameter data of the energy storage battery in real time based on the energy storage battery management system; A voltage response curve obtaining subunit is adapted to input the physical parameter data into an electrochemical fusion model and obtain a voltage response curve of a battery by applying a multi-stage pulse current excitation; A key aging parameter determination subunit is adapted to perform aging parameter sensitivity analysis based on a voltage response curve of the battery to determine key aging parameters of the energy storage battery; A key aging parameter optimization subunit, adapted to complete the training process of the electrochemical fusion model by iteratively optimizing the key aging parameters; A key performance parameter data acquisition unit is used to obtain the key performance parameter data of the energy storage battery by inputting the physical parameter data of the energy storage battery into the trained electrochemical fusion model; A health status assessment result generating unit is adapted to obtain a health status assessment result of an energy storage battery based on key performance parameter data of the energy storage battery; The thermal runaway risk prediction unit is adapted to obtain a thermal runaway risk prediction result by comparing the key performance parameter data of the energy storage battery with the data detected by the actual sensor.

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