Battery risk analysis method and device, electronic equipment and storage medium

Abstract

The invention discloses a battery risk analysis method, and belongs to the technical field of battery safety. The method comprises the following steps: acquiring operation parameter data of a battery at the current moment, and determining operation parameter data of the battery at the next moment based on an unscented Kalman filtering algorithm; the operation parameter data are input into a risk analysis model, the fault danger degree and the life decline degree of the battery are obtained, and the operation parameter data comprise the operation parameter data of the battery at the current moment and the operation parameter data of the battery at the next moment; the risk analysis model comprises a fault risk analysis sub-model, a life decline analysis sub-model and a potential field analysis sub-model; based on the fault danger degree and the life decline degree of the battery, determining the equivalent field intensity of the battery; the risk of the battery is evaluated based on the equivalent field intensity of the battery, and the method carries out risk analysis based on the fault danger degree and the life decline degree of the battery, thereby improving the safety and reliability of the battery.

Description

Technical Field

[0001] The present application belongs to the field of battery safety technology, and in particular, relates to a battery risk analysis method. Background Art

[0002] A variety of new energy storage technologies, mainly based on batteries, are developing rapidly, especially in the field of new energy vehicles. Batteries, as a power source, play an important role in promoting the development of electrified transportation. However, with the surge in global battery usage, battery safety accidents have occurred frequently, causing serious loss of life and property. Battery safety has become a key challenge in the development of global electric vehicles and energy storage technologies.

[0003] Traditional battery safety testing methods mainly focus on thermal runaway mechanism analysis, risk assessment and fault diagnosis, but they still face some problems. First, the operating environment of power batteries is complex and changeable, and external factors such as temperature and current fluctuate greatly, resulting in diverse and complex failure modes. Existing risk assessment methods based on experimental data and models cannot fully cover all potential risks. Secondly, the long-term monitoring and evaluation technology of battery aging and degradation processes is still imperfect, especially under high-rate charging and discharging and extreme environmental conditions, the accuracy of existing prediction methods is insufficient.

[0004] The application of related technologies in the field of battery safety still has certain limitations. It is difficult to comprehensively assess the impact of multiple complex factors on battery safety, and existing risk assessment methods generally have problems such as data redundancy, inaccurate predictions, and poor adaptability. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a battery risk analysis method to improve the safety and reliability of the battery.

[0006] In a first aspect, the present application provides a battery risk analysis method, the method comprising: Acquire the operating parameter data of the battery at the current moment, and determine the operating parameter data of the battery at the next moment based on the unscented Kalman filter algorithm; Inputting the operating parameter data into a risk analysis model to obtain the failure risk degree and life decay degree of the battery, wherein the operating parameter data includes the operating parameter data of the battery at a current moment and the operating parameter data of the battery at a next moment, and the risk analysis model includes a failure risk analysis sub-model, a life decay analysis sub-model and a potential field analysis sub-model; Determining the equivalent field strength of the battery based on the failure risk level and life degradation level of the battery; Based on the equivalent field strength of the battery, the risk of the battery is evaluated.

[0007] According to one embodiment of the present application, the determining the operating parameter data of the battery at the next moment based on the unscented Kalman filter algorithm includes: Constructing a state space equation of the battery based on the operating parameter data of the battery at the current moment; Generate a sigma point at the current moment based on the operating parameter data and covariance matrix of the battery at the current moment; Based on the state space equation of the battery, the sigma point at the current moment is propagated to the next moment to obtain the estimated state value of the battery at the next moment; The state estimation value of the battery at the next moment is adjusted based on the Kalman gain to obtain the operating parameter data of the battery at the next moment.

[0008] According to an embodiment of the present application, the step of inputting the operating parameter data into a risk analysis model to obtain the failure risk level and life degradation level of the battery includes: Inputting the operating parameter data into a fault risk analysis sub-model to obtain a fault risk degree of the battery; The operating parameter data is input into a life decay analysis sub-model to obtain the life decay degree of the battery, wherein the life decay analysis sub-model is constructed based on a long short-term memory recurrent neural network.

[0009] According to one embodiment of the present application, the life decay degree of the battery can be calculated by the following formula:

[0010] in, is the life decay rate, for The remaining useful life of the moment, for The remaining useful life of the moment, For the time interval.

[0011] According to an embodiment of the present application, the potential field analysis sub-model is a physical model constructed based on the fault entity characteristics and the state following characteristics. The expression corresponding to the potential field analysis sub-model is as follows:

[0012] in, is the equivalent field strength, is the fault body characteristic, is the state following characteristic, is the Euclidean distance between the next state point and the fault state point in the state space, is the Euclidean distance between the state point at the next moment and the state point at the current moment in the state space, is the fault correction factor, is the state correction factor.

[0013] According to an embodiment of the present application, determining the equivalent field strength of the battery based on the failure risk level and life degradation level of the battery includes: Constructing a mapping relationship between the fault risk level of the battery and the fault entity characteristics; Constructing a mapping relationship between the battery life decay degree and the state following characteristic; The failure risk degree and life decay degree of the battery are input into the potential field analysis sub-model to obtain the equivalent field strength of the battery.

[0014] According to an embodiment of the present application, the risk assessment of the battery based on the equivalent field strength of the battery includes: Determining whether the equivalent field strength is greater than zero; When the equivalent field strength is greater than zero, it is determined that the failure risk level of the battery at the current moment is greater than the life decay level, and there is a risk, and the operating state of the battery is adjusted to a state predicted at the next moment; When the equivalent field strength is less than or equal to zero, it is determined that the fault risk level of the battery at the current moment is smaller than the life decay level, and there is no risk, and the current operating state of the battery is maintained unchanged.

[0015] In a second aspect, the present application provides a battery risk analysis device, the device comprising: An acquisition module, used to acquire multimodal data, wherein the data type of the multimodal data includes at least one of video image data, audio data, and text data; a processing module, configured to input the operating parameter data into a risk analysis model to obtain a failure risk degree and a life decay degree of the battery, wherein the operating parameter data includes operating parameter data of the battery at a current moment and operating parameter data of the battery at a next moment, and the risk analysis model includes a failure risk analysis sub-model, a life decay analysis sub-model and a potential field analysis sub-model; A determination module, configured to determine an equivalent field strength of the battery based on a failure risk level and a life degradation level of the battery; An evaluation module is used to evaluate the risk of the battery based on the equivalent field strength of the battery.

[0016] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the battery risk analysis method as described in the first aspect above is implemented.

[0017] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the battery risk analysis method as described in the first aspect above is implemented.

[0018] In a fifth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the battery risk analysis method as described in the first aspect.

[0019] In a sixth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the battery risk analysis method as described in the first aspect above.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application.

[0021] The battery risk analysis method provided by the present invention has the following beneficial effects compared with the prior art: (1) The present invention obtains the operating parameter data of the battery at the current moment, and predicts the operating parameters at the next moment based on the unscented Kalman filter algorithm. In combination with the fault risk analysis, life decay analysis and potential field analysis sub-models, the present invention can calculate the fault risk degree and life decay degree of the battery. Based on the fault risk degree and life decay degree of the battery, the equivalent field strength of the battery is further determined and the risk of the battery is evaluated, thereby improving the accuracy and effectiveness of the battery risk analysis, reducing human intervention and prediction errors, and optimizing the battery service life and safety.

[0022] (2) The present invention constructs a potential field analysis sub-model based on the fault entity characteristics and state following characteristics, combines the joint effects of battery failure risk and life degradation, and equates their impact on the battery to the electrostatic potential field formed by charges of heterogeneous field sources in the state space. This can effectively predict the state changes of the battery, reduce the dependence on complex algorithm models, reduce the computational cost of battery fault prediction, and improve the accuracy and reliability of battery risk analysis.

[0023] (3) The present invention constructs a mapping relationship between the fault risk level of the battery and the fault entity characteristics, as well as a mapping relationship between the battery life decay level and the state following characteristics. The fault risk level is equivalent to the repulsive force on the state point at the next moment, and the life decay level is equivalent to the attractive force on the state point at the next moment. Based on these mapping relationships, the battery's fault risk level and life decay level are input into the potential field analysis sub-model for processing to obtain the equivalent field strength of the battery, which can reduce the complexity of the battery state assessment process and improve the accuracy and reliability of battery risk analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 This is one of the flow charts of the battery risk analysis method provided in the embodiment of the present application; Figure 2 is a schematic diagram of a flow chart for calculating the remaining service life of a battery provided in an embodiment of the present application; Figure 3 This is the process of establishing the potential field analysis sub-model provided in the embodiment of the present application; Figure 4 This is the second flow chart of the battery risk analysis method provided in the embodiment of the present application; Figure 5 is a schematic diagram of the structure of a battery risk analysis device provided in an embodiment of the present application; Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0026] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0027] The battery risk analysis method, battery risk analysis device, electronic device and readable storage medium provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0028] The battery risk analysis method may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0029] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or a tablet computer having a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer having a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0030] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0031] The battery risk analysis method provided in the embodiment of the present application may be executed by an electronic device or a functional module or functional entity in the electronic device that can implement the battery risk analysis method. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras, and wearable devices. The battery risk analysis method provided in the embodiment of the present application is described below using an electronic device as an example of an execution subject.

[0032] Figure 1 is one of the flow charts of the battery risk analysis method provided in the embodiment of the present application, such as Figure 1 As shown, the battery risk analysis method includes: step 110, step 120, step 130 and step 140.

[0033] Step 110, obtaining the operating parameter data of the battery at the current moment, and determining the operating parameter data of the battery at the next moment based on the unscented Kalman filter algorithm; Optionally, the battery can be a lithium battery, a lead-acid battery, a nickel-metal hydride battery, or the like.

[0034] The current operating parameter data of the battery include battery voltage, current, capacity, output power, state of charge, remaining service life, temperature and other data. The electronic device obtains the current operating parameter data of the battery and constructs the battery state space equation according to the current operating parameter data. The expression of the battery state space equation is as follows:

[0035] in, For batteries State variables at the moment; is the state transfer function; for The process noise at each moment; For batteries State variables at the moment; for Input variables at time; is the observed variable; is the observation function; for Input variables at time; For batteries The observation noise at that moment.

[0036] The unscented Kalman filter method is used to perform state estimation based on the above battery state space equation to determine the operating parameter data at the next moment.

[0037] Step 120: input the operating parameter data into a risk analysis model to obtain the failure risk degree and life decay degree of the battery, wherein the operating parameter data includes the operating parameter data of the battery at the current moment and the operating parameter data of the battery at the next moment, and the risk analysis model includes a failure risk analysis sub-model, a life decay analysis sub-model and a potential field analysis sub-model; It is easy to understand that the risk analysis model includes a failure risk analysis sub-model, a life degradation analysis sub-model and a potential field analysis sub-model. The failure risk analysis sub-model is used to obtain the failure risk degree of the battery, the life degradation sub-model is used to obtain the life degradation degree of the battery, and the potential field analysis sub-model is used to obtain the equivalent field strength of the battery.

[0038] The electronic device inputs the operating parameter data of the battery at the current moment and the operating parameter data of the next moment into the fault risk analysis sub-model and the life decline analysis sub-model to obtain the fault risk degree and life decline degree of the battery.

[0039] It should be noted that the life decay analysis sub-model determines the degree of life decay by calculating the life decay rate of the battery. Among them, the life decay rate can use a long short-term memory recurrent neural network to predict the remaining service life of the battery based on the operating parameter data of the battery at the current moment and the operating parameter data at the next moment, and then determine the life decay rate of the battery.

[0040] Step 130: determining the equivalent field strength of the battery based on the failure risk level and life degradation level of the battery; Furthermore, by comparing the relationship between the degree of failure risk and the degree of life degradation of the battery, the equivalent field strength of the battery can be determined.

[0041] Step 140: Evaluate the risk of the battery based on the equivalent field strength of the battery.

[0042] Finally, the risk of the battery is evaluated based on the size of the battery's equivalent field strength. If the battery's equivalent field strength is greater than zero, it means that the battery's failure risk is greater than its life decay, and the battery is at risk. The battery's operating parameters are controlled to change to the next state. If the battery's equivalent field strength is less than or equal to zero, the battery is not at risk, and the battery's operating parameters are controlled to remain unchanged at the current state.

[0043] According to the battery risk analysis method provided in the embodiment of the present application, by acquiring the operating parameter data of the battery at the current moment, and predicting the operating parameters at the next moment based on the unscented Kalman filter algorithm, combined with the fault risk analysis, life degradation analysis and potential field analysis sub-models, the fault risk degree and life degradation degree of the battery can be calculated, and based on the fault risk degree and life degradation degree of the battery, the equivalent field strength of the battery is further determined and the risk of the battery is evaluated, thereby improving the accuracy and effectiveness of the battery risk analysis, reducing human intervention and prediction errors, and optimizing the battery service life and safety.

[0044] In some embodiments, the determining the operating parameter data of the battery at the next moment based on the unscented Kalman filter algorithm includes: Constructing a state space equation of the battery based on the operating parameter data of the battery at the current moment; Generate a sigma point at the current moment based on the operating parameter data and covariance matrix of the battery at the current moment; Based on the state space equation of the battery, the sigma point at the current moment is propagated to the next moment to obtain the estimated state value of the battery at the next moment; The state estimation value of the battery at the next moment is adjusted based on the Kalman gain to obtain the operating parameter data of the battery at the next moment.

[0045] The process of determining the operating parameter data of the battery at the next moment based on the unscented Kalman filter algorithm is as follows: (1) Based on the current operating parameter data of the battery, the state space equation of the battery is constructed. The expression of the battery state space equation is:

[0046] in, For batteries The state variables at time, is the state transfer function, for The process noise at each moment; For batteries The state variables at time, for The input variables at time, is the observed variable is the observation function, for The input variables at time, For batteries The observation noise at that moment.

[0047] (2) Based on the battery state space equation, the battery parameters are initialized. The calculation formula of the initial parameters is as follows:

[0048] in, is the battery initial state variable, is the initial value of the state covariance matrix, is the mathematical expectation of the initial state variables, is the initial state variable The covariance of .

[0049] (3) Predict the battery’s operating parameter data at the next moment. The optimal value of the system state variable at time is obtained by unscented transformation sigma points, substitute the sigma points into the state equation, obtain the one-step prediction of the state quantity, construct a set of sigma points, and update the state variables in time according to the mean and weight. The calculation formula is as follows:

[0050] in, Based on The optimal value of the state variable at the moment is obtained The predicted value of the state variable at time , is the first covariance matrix of the state variables List, is the first covariance matrix of the state variables The predicted value of the state variable at time k is obtained based on the optimal value of the state variable at time k-1 on the column. is the first covariance matrix of the next state variable at time k-1 The status value on the column, for The input variables at time, is the mean weight.

[0051] The mean weight calculation formula is:

[0052] in, is the dimension of the state variables, is the spread factor, is the distribution factor before calibration, is the scaling parameter, is the initial value of the mean weight, is the initial value of the variance weight, is the covariance matrix List.

[0053] It should be noted that The selection of determines the closeness between the sampling point and the mean, usually a positive number between 10-6 and 1. When the Gaussian distribution is =2 is optimal, , To satisfy Auxiliary scaling factor, combined with the mean weight and variance weights For example, to obtain the parameter , , .

[0054] (4) State variable covariance update. The calculation process of state variable covariance update is:

[0055] in, is the first covariance matrix of the observed variables The predicted value of the observed variable at time k is obtained based on the optimal value of the observed variable at time k-1. is a nonlinear function expression, Based on The optimal value of the variable is obtained by observing the The predicted value of the observed variable at time .

[0056] (5) Error covariance update. The error covariance update calculation process is:

[0057] in, The observation noise The expected value of is the error covariance of the observation at time k, is the joint error covariance of the state value and the observation value at time k, is the matrix transpose symbol, is the first covariance matrix of the observed variables The predicted value of the observed variable at time k is obtained based on the optimal value of the observed variable at time k-1. Based on The optimal value of the variable is obtained by observing the The predicted value of the observed variable at time is the first covariance matrix of the state variables The predicted value of the state variable at time k is obtained based on the optimal value of the state variable at time k-1 on the column. Based on The optimal value of the state variable at the moment is obtained The predicted value of the state variable at time.

[0058] (6) Kalman gain update. The calculation process of Kalman gain update is:

[0059] in, Expressed as the Kalman gain, is the error covariance of the observation at time k, is the joint error covariance of the state value and the observation value at time k.

[0060] (7) State update and optimal covariance matrix. The state update and optimal covariance matrix are:

[0061] in, for The optimal estimate of the state variables at time t, is the optimal covariance of the state variables at time k, is the transpose of the Kalman gain; is the covariance prediction value at time k obtained based on the covariance of the state variables at time k-1, It is denoted as Kalman gain.

[0062] In this embodiment, by performing state space equation modeling, sigma point generation, state propagation and Kalman gain adjustment on the operating parameter data of the battery at the current moment based on the unscented Kalman filter algorithm, the operating parameter data of the battery at the next moment can be predicted, thereby reducing the prediction error and improving the accuracy and reliability of the battery state prediction.

[0063] In some embodiments, inputting the operating parameter data into a risk analysis model to obtain the failure risk level and life degradation level of the battery includes: Inputting the operating parameter data into a fault risk analysis sub-model to obtain a fault risk degree of the battery; The operating parameter data is input into a life decay analysis sub-model to obtain the life decay degree of the battery, wherein the life decay analysis sub-model is constructed based on a long short-term memory recurrent neural network.

[0064] It is easy to understand that the operating parameter data of the battery at the current moment is input into the fault risk analysis sub-model to obtain the fault risk degree of the battery. The calculation formula of the fault risk degree is as follows:

[0065] in, is the degree of failure risk, is the operating parameter data measured at the current time t, is the threshold value corresponding to the parameter data, is the weight corresponding to each parameter, and e is the base of the natural logarithm.

[0066] It should be noted that the thresholds corresponding to the parameter data are determined based on the abuse test data of the battery before leaving the factory or after adjustment based on engineering experience, and the weights corresponding to each parameter can be adjusted based on the actual data.

[0067] The battery's current operating parameter data and the next operating parameter data are input into the life decay analysis sub-model to obtain the battery's life decay degree. The calculation formula for the life decay degree is as follows:

[0068] in, is the life decay rate, for The remaining useful life of the moment, for The remaining useful life of the moment, For the time interval.

[0069] In this embodiment, by inputting the operating parameter data into the fault risk analysis sub-model and the life decline analysis sub-model, the fault risk degree and life decline degree of the battery are obtained respectively. The life decline analysis sub-model constructed by the long short-term memory recurrent neural network can realize the prediction of the battery life decline degree, obtain the battery decline trend, and improve the prediction accuracy of the battery life decline.

[0070] In some embodiments, the battery life degradation degree can be calculated by the following formula:

[0071] in, is the life decay rate, for The remaining useful life of the moment, for The remaining useful life of the moment, For the time interval.

[0072] It is easy to understand that the remaining service life of the battery can be calculated using a long short-term memory recurrent network. Figure 2 is a schematic diagram of a flow chart of calculating the remaining service life of a battery provided in an embodiment of the present application, such as Figure 2As shown, the method comprises the following steps: (1) Structural initialization of LSTM-RNN (Long Short-Term Memory Recurrent Neural Network), including parameter settings such as input, output, number of neurons, number of hidden layers, and activation function type of the deep network; (2) Extract the historical capacity data of the battery and construct training samples for the LSTM-RNN; (3) Start training LSTM-RNN; (4) Perform multi-step forward predictions; It is easy to understand that the acquired system parameter data is input into the long short-term memory recurrent neural network. After completing the LSTM-RNN network training, the historical capacity data needs to be input into the network for multi-step forward recursive prediction, guiding the prediction to be terminated when the predicted capacity value is lower than the failure threshold, and performing multi-step forward prediction. The number of recursive steps experienced during the recursive prediction is counted and used as the remaining service life of the battery.

[0073] Exemplarily, the capacity value corresponding to the measured n cycles is Input the LSTM-RNN network and predict the capacity value corresponding to n+1 cycles , and then take the predicted capacity value of n+1 cycles Input the LSTM-RNN network to obtain the capacity prediction value of n+2 cycles , and repeat the cycle until the prediction result is When it is lower than the specified capacity failure value, the iteration is terminated, and the x value is output as the RUL value to estimate the remaining service life of the battery at the current moment and the next moment respectively. Then, the number of recursive steps experienced during the recursive prediction period is counted and used as the remaining service life of the battery.

[0074] (5) Establishing probability density function based on Monte Carlo simulation It should be noted that in order to improve the prediction accuracy of the remaining service life, the probability distribution function of the battery remaining service life prediction is added to quantify the influence of the uncertainty of the established prediction method on the RUL prediction results.

[0075] Randomly generated based on the statistical characteristics of historical capacity data near the forecast starting point Based on the Monte Carlo method, each set of samples is input into the LSTM-RNN and a multi-step forward prediction simulation is carried out to obtain The simulated predicted value of RUL is used to calculate the probability density function of RUL prediction. The calculation formula is as follows:

[0076] in, is the probability density function of RUL prediction; Represented as a Gaussian kernel function; is bandwidth; and are the upper and lower bounds of the Monte Carlo simulation results respectively; For the RUL simulation prediction results. is the deviation between the simulated prediction result of the i-th RUL and the lower bound; is the deviation between the simulation prediction result of the i-th RUL and the upper bound; The simulation prediction results for RUL.

[0077] Calculate remaining useful life Based on the above process, it can be calculated that the battery Moment and The remaining service life at the time is calculated to calculate the battery life decay rate.

[0078] In this embodiment, by adopting a long short-term memory recurrent network to model the operating parameter data of the battery, the long-term dependency of the battery performance can be effectively captured, and the remaining service life of the battery can be obtained based on the prediction results of the model, thereby reducing the computational complexity in the battery life prediction process and improving the prediction accuracy of the remaining service life of the battery.

[0079] In some embodiments, the potential field analysis sub-model is a physical model constructed based on fault entity characteristics and state following characteristics, and the expression corresponding to the potential field analysis sub-model is as follows:

[0080] in, is the equivalent field strength, is the fault entity characteristic, is the state following characteristic, is the Euclidean distance between the next state point and the fault state point in the state space, is the Euclidean distance between the state point at the next moment and the state point at the current moment in the state space, is the fault correction factor, is the state correction factor.

[0081] It is easy to understand that the potential field analysis sub-model is a physical simulation model constructed based on the electrostatic theory, which equates the following effect between the battery's state at the next moment and the battery's state at the current moment to the attraction between opposite charges, and equates the distance effect between the battery's state at the next moment and the fault state to the repulsion between like charges.

[0082] Figure 3 This is the process of establishing the potential field analysis sub-model provided in the embodiment of the present application, such as Figure 3 As shown in the figure, a battery potential field analysis sub-model is established, and the point set of the entire state space is divided into two types of state points. The first type is the safe working state point, which includes the new state point and the original state point. The new state point is the state point at the next moment, and the original state point is the state point at the current moment. The frequent fluctuations of the state of the safe working state point will cause the performance of each component to decline, thereby affecting the overall life of the system. The change of the battery operating state will cause the battery life to decline to a certain extent. In order to reduce the decline, the following effect between the state at the next moment and the original safe state is equivalent to the attraction between opposite charges. The second type is the fault state point. The system should have a tendency to avoid faults, and the distance effect between the state at the next moment and the old fault state is equivalent to the repulsion between like charges.

[0083] The Euclidean distance between two points in the state space is calculated as follows:

[0084] in, Represents the Euclidean distance between any two state points in the state space; Indicates The state point feature values.

[0085] Based on the electrostatic field theory, a potential field analysis sub-model is constructed. The equivalent field strength expression of the potential field analysis sub-model in the state space is as follows:

[0086] in, is the equivalent field strength formed by the fault point and the state point at the current moment at the state point at the next moment in the potential field analysis sub-model, Indicates the fault characteristics. Indicates the state following characteristic, Represents the Euclidean distance between the state point at the next moment and the fault state point in the state space It represents the Euclidean distance between the state point at the next moment in the state space and the original state, that is, the state point at the current moment. and All are correction factors.

[0087] It should be noted that the letters K1, K2, Q1, and Q2 have no specific meanings when separated. They are just aliases. They are related to the characteristics of the fault point and the current state point, respectively. Indicates the fault characteristics, which are positively correlated with the degree of danger of the fault. It represents the state following characteristic, which is positively correlated with the life decay caused by the change of the system state. Considering that the state points have different dimensions, the obtained state points can be normalized to facilitate subsequent calculations.

[0088] In this embodiment, by constructing a potential field analysis sub-model based on the fault entity characteristics and state following characteristics, combining the joint effects of battery failure risk and life degradation, their impact on the battery is equivalent to the electrostatic potential field formed by heterogeneous field source charges in the state space, which can effectively predict the state change of the battery, reduce the dependence on complex algorithm models, reduce the computational cost of battery fault prediction, and improve the accuracy and reliability of battery risk analysis.

[0089] In some embodiments, determining the equivalent field strength of the battery based on the failure risk level and life degradation level of the battery includes: Constructing a mapping relationship between the fault risk level of the battery and the fault entity characteristics; Constructing a mapping relationship between the battery life decay degree and the state following characteristic; The failure risk degree and life decay degree of the battery are input into the potential field analysis sub-model to obtain the equivalent field strength of the battery.

[0090] It is easy to understand that the mapping relationship between the fault danger degree calculated above and the fault entity characteristics is established as follows:

[0091] in, Indicates the fault characteristics. represents the failure risk function, Indicates the degree of danger of the fault.

[0092] The mapping relationship between the life decay rate calculated above and the state following characteristic is established, and the expression is as follows:

[0093] in, Indicates the state following characteristic, represents the lifespan decay function, Represents the lifespan decay rate.

[0094] Finally, the above mapping relationship is brought into the potential field analysis sub-model, and the equivalent field strength of the battery is obtained based on the failure risk level and life degradation level of the battery.

[0095] In this embodiment, by constructing a mapping relationship between the battery's fault risk level and the fault entity characteristics, as well as a mapping relationship between the battery's life degradation level and the state following characteristics, based on these mapping relationships, the battery's fault risk level and life degradation level are input into the potential field analysis sub-model for processing, and the battery's equivalent field strength is obtained, which can reduce the complexity of the battery status assessment process and improve the accuracy and reliability of the battery risk analysis.

[0096] In some embodiments, the risk assessment of the battery based on the equivalent field strength of the battery includes: Determining whether the equivalent field strength is greater than zero; When the equivalent field strength is greater than zero, it is determined that the failure risk level of the battery at the current moment is greater than the life decay level, and there is a risk, and the operating state of the battery is adjusted to a state predicted at the next moment; When the equivalent field strength is less than or equal to zero, it is determined that the fault risk level of the battery at the current moment is smaller than the life decay level, and there is no risk, and the current operating state of the battery is maintained unchanged.

[0097] Figure 4 This is a second flow chart of the battery risk analysis method provided in the embodiment of the present application, such as Figure 4 As shown, a risk analysis model is constructed based on a fault hazard analysis submodel, a life decay analysis submodel and a potential field analysis submodel, the operating parameter data of the battery at the current moment is monitored in real time, and the operating parameter data at the next moment is predicted, and the operating parameter data is input into the risk analysis model to obtain the fault hazard degree and life decay degree of the battery, and the equivalent field strength of the battery is determined based on the fault hazard degree and life decay degree of the battery, and it is judged whether the equivalent field strength is greater than zero. When the equivalent field strength is greater than zero, it is determined that the fault hazard degree of the battery at the current moment is greater than the life decay degree, and there is a risk. The operating state of the battery is adjusted to the state predicted at the next moment. When the equivalent field strength is less than or equal to zero, it is determined that the fault hazard degree of the battery at the current moment is less than the life decay degree, and there is no risk, and the current operating state of the battery is maintained unchanged.

[0098] In this embodiment, by judging whether the equivalent field strength of the battery is greater than zero, and determining the relative relationship between the fault risk level and the life decline level of the battery according to the result of the equivalent field strength, the operating state of the battery is adjusted accordingly based on the judgment result, thereby improving the accuracy and timeliness of the battery state adjustment, and improving the safety and reliability of the battery.

[0099] The battery risk analysis method provided in the embodiment of the present application can be executed by a battery risk analysis device. In the embodiment of the present application, the battery risk analysis device provided in the embodiment of the present application is described by taking the battery risk analysis method executed by the battery risk analysis device as an example.

[0100] The present application also provides a battery risk analysis device, such as Figure 5 As shown, the battery risk analysis device includes: an acquisition module 510 , a processing module 520 , a determination module 530 and an evaluation module 540 .

[0101] An acquisition module 510, configured to acquire multimodal data, wherein the data type of the multimodal data includes at least one of video image data, audio data, and text data; A processing module 520 is used to input the operating parameter data into a risk analysis model to obtain the failure risk degree and life decline degree of the battery, wherein the operating parameter data includes the operating parameter data of the battery at the current moment and the operating parameter data of the battery at the next moment, and the risk analysis model includes a failure risk analysis sub-model, a life decline analysis sub-model and a potential field analysis sub-model; A determination module 530, configured to determine an equivalent field strength of the battery based on a failure risk level and a life degradation level of the battery; The evaluation module 540 is used to evaluate the risk of the battery based on the equivalent field strength of the battery.

[0102] According to the battery risk analysis device provided in the embodiment of the present application, by inputting multimodal data into the risk identification and detection model, multiple first semantic information and first detection results are obtained. The risk identification and detection model includes a trained video image forgery detection sub-model, an audio forgery detection sub-model, a text forgery detection sub-model, a graphic summary sub-model and a speech transcription sub-model, which can comprehensively identify multimodal data, and provide additional knowledge for the risk identification and detection model by calling each sub-model, decomposing the risk content identification and risk summary generation tasks into multiple sub-tasks, and assigning a separate sub-task to each sub-model based on the idea of ​​a risk identification and detection model with a sub-model (multi-model hybrid architecture), thereby greatly reducing the learning cost of the risk identification and detection model, and performing correlation analysis on the semantic information between different modalities. Finally, the risk identification and detection model is assisted in generating risk summaries by retrieving information from a risk knowledge base, thereby improving the accuracy and generalization of risk identification and risk summary generation.

[0103] The battery risk analysis device provided in the embodiment of the present application can achieve Figures 1 to 4 To avoid repetition, the various processes implemented in the battery risk analysis method embodiment are not described here.

[0104] In some embodiments, Figure 6As shown, an embodiment of the present application also provides an electronic device 600, including a processor 601, a memory 602, and a computer program stored in the memory 602 and executable on the processor 601. When the program is executed by the processor 601, each process of the above-mentioned battery risk analysis method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0105] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0106] The embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned battery risk analysis method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0107] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0108] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above-mentioned battery risk analysis method when executed by a processor.

[0109] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0110] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned battery risk analysis method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0111] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0112] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes 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 sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), including a number of instructions for a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the battery risk analysis method of each embodiment of the present application.

[0114] In the description of this application, "first feature" or "second feature" may include one or more of the features.

[0115] In the description of the present application, “plurality” means two or more.

[0116] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0117] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0118] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A battery risk analysis method, characterized in that: The method comprises: Acquire the operating parameter data of the battery at the current moment, and determine the operating parameter data of the battery at the next moment based on the unscented Kalman filter algorithm; Inputting the operating parameter data into a risk analysis model to obtain the failure risk degree and life decay degree of the battery, wherein the operating parameter data includes the operating parameter data of the battery at a current moment and the operating parameter data of the battery at a next moment, and the risk analysis model includes a failure risk analysis sub-model, a life decay analysis sub-model and a potential field analysis sub-model; Determining the equivalent field strength of the battery based on the failure risk level and life degradation level of the battery; Based on the equivalent field strength of the battery, the risk of the battery is evaluated.

2. The battery risk analysis method according to claim 1, characterized in that: The determining of the operating parameter data of the battery at the next moment based on the unscented Kalman filter algorithm includes: Constructing a state space equation of the battery based on the operating parameter data of the battery at the current moment; Generate a sigma point at the current moment based on the operating parameter data and covariance matrix of the battery at the current moment; Based on the state space equation of the battery, the sigma point at the current moment is propagated to the next moment to obtain the estimated state value of the battery at the next moment; The state estimation value of the battery at the next moment is adjusted based on the Kalman gain to obtain the operating parameter data of the battery at the next moment.

3. The battery risk analysis method according to claim 1, characterized in that: The step of inputting the operating parameter data into a risk analysis model to obtain the failure risk level and life degradation level of the battery includes: Inputting the operating parameter data into a fault risk analysis sub-model to obtain a fault risk degree of the battery; The operating parameter data is input into a life decay analysis sub-model to obtain the life decay degree of the battery, wherein the life decay analysis sub-model is constructed based on a long short-term memory recurrent neural network.

4. The battery risk analysis method according to claim 1, characterized in that: The battery life degradation degree can be calculated by the following formula: ； in, is the life decay rate, for The remaining useful life of the moment, for The remaining useful life of the moment, For the time interval.

5. The battery risk analysis method according to claim 1, characterized in that: The potential field analysis sub-model is a physical model constructed based on the fault entity characteristics and state following characteristics. The expression corresponding to the potential field analysis sub-model is as follows: ； in, is the equivalent field strength, is the fault body characteristic, is the state following characteristic, is the Euclidean distance between the next state point and the fault state point in the state space, is the Euclidean distance between the state point at the next moment and the state point at the current moment in the state space, is the fault correction factor, is the state correction factor.

6. The battery risk analysis method according to claim 1, characterized in that: The determining of the equivalent field strength of the battery based on the failure risk level and life degradation level of the battery includes: Constructing a mapping relationship between the fault risk level of the battery and the fault entity characteristics; Constructing a mapping relationship between the battery life decay degree and the state following characteristic; The failure risk degree and life decay degree of the battery are input into the potential field analysis sub-model to obtain the equivalent field strength of the battery.

7. The battery risk analysis method according to claim 1, characterized in that: The risk assessment of the battery based on the equivalent field strength of the battery includes: Determining whether the equivalent field strength is greater than zero; When the equivalent field strength is greater than zero, it is determined that the failure risk level of the battery at the current moment is greater than the life decay level, and there is a risk, and the operating state of the battery is adjusted to a state predicted at the next moment; When the equivalent field strength is less than or equal to zero, it is determined that the fault risk level of the battery at the current moment is smaller than the life decay level, and there is no risk, and the current operating state of the battery is maintained unchanged.

8. A battery risk analysis device, implemented by the battery risk analysis method according to any one of claims 1 to 7, characterized in that: The device comprises: An acquisition module, used to acquire multimodal data, wherein the data type of the multimodal data includes at least one of video image data, audio data, and text data; a processing module, configured to input the operating parameter data into a risk analysis model to obtain a failure risk degree and a life decay degree of the battery, wherein the operating parameter data includes operating parameter data of the battery at a current moment and operating parameter data of the battery at a next moment, and the risk analysis model includes a failure risk analysis sub-model, a life decay analysis sub-model and a potential field analysis sub-model; A determination module, configured to determine an equivalent field strength of the battery based on a failure risk level and a life degradation level of the battery; An evaluation module is used to evaluate the risk of the battery based on the equivalent field strength of the battery.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the battery risk analysis method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the battery risk analysis method according to any one of claims 1 to 7 is implemented.

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