A solid-state battery life estimation method based on online parameter identification of a generalization system

Abstract

The application relates to the field of electric vehicle battery life prediction, in particular to a solid-state battery life estimation method based on online parameter identification of a generalization system, which comprises the following steps: constructing a battery system generalization discrete mathematical model through a recursive least square method of parameter estimation; calculating system parameter values according to the battery system generalization discrete mathematical model; and judging the battery life according to the relationship between the obtained system parameter values and a set threshold value. The application constructs the battery system generalization discrete mathematical model through the recursive least square method of parameter estimation, determines the system parameters changing with time according to the constructed model, and judges the battery life of the electric vehicle according to the relationship between the system parameter values and the set threshold value, so that the method is fast in calculation speed, small in occupied memory, capable of online identification, and real-time in system parameter acquisition.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle battery life prediction, and in particular to a method for estimating the life of solid-state batteries based on online parameter identification of a generalized system. Background Technology

[0002] In recent years, research on electric vehicle battery life prediction methods has mainly focused on model-based approaches. These methods analyze the state changes during the charging and discharging process of lithium batteries, relying on electrochemical models, equivalent circuit models, and empirical degradation models to describe the system's state degradation from the perspective of electrochemical reactions. However, electrochemical model-based prediction methods struggle to dynamically track changes in environmental conditions, resulting in poor dynamic accuracy. Furthermore, the testing process for degradation factors is overly complex, making it difficult to establish comprehensive degradation models. Equivalent circuit model-based prediction methods suffer from low accuracy in model parameter identification and long testing cycles. Empirical degradation models typically have fewer parameters and poor adaptability to different batteries and operating conditions. For model-based methods, further research has focused on improving the accuracy of battery physical models, developing more complex models covering different operating conditions, temperatures, and aging mechanisms. However, the enormous computational burden remains a problem that needs to be addressed.

[0003] Existing technologies mainly rely on model-based methods to predict battery life. However, simple models often fail to reflect all the dynamic effects of a battery, which may lead to errors in parameter identification. Complex models always have too many parameters to identify and may suffer from parameter divergence issues. Summary of the Invention

[0004] To improve the accuracy of battery lifetime prediction and reduce computational and time costs during the prediction process, this invention proposes a solid-state battery lifetime estimation method based on online parameter identification of a generalized system, which specifically includes the following steps:

[0005] A generalized discrete mathematical model of the battery system is constructed using the recursive least squares method for parameter estimation.

[0006] Real-time battery operation data of electric vehicles are collected, and the collected data is input into a generalized discrete mathematical model of the battery system for fitting to obtain the first system parameter set and the second system parameter set.

[0007] The effectiveness of the fitted generalized discrete mathematical model of the battery system is determined based on the first set of system parameters. If it is invalid, the estimation ends and battery operating data is collected again for fitting until the model is effective.

[0008] If the fitted generalized discrete mathematical model of the battery system is effective, the remaining lifespan of the first battery and the remaining lifespan of the second battery are calculated based on the obtained second system parameter set. The smaller value between the remaining lifespan of the first battery and the remaining lifespan of the second battery is output as the lifespan prediction of the battery system.

[0009] Furthermore, by sampling real-time battery operation data of the electric vehicle, including data from the current t-th time point and a total of n time points, the fitted generalized discrete mathematical model of the battery system is expressed as:

[0010]

[0011] in, Let u(t) represent the predicted voltage value at time t+1, u(t) represent the voltage value at time t, i(t) represent the current value at time t, and n be the order of the discrete mathematical model; {a1(t+1), a2(t+1), ..., a n {b1(t+1), b2(t+1), ..., b} is the set of first system parameters at time t+1. n (t+1)} is the set of second system parameters at time t+1.

[0012] Furthermore, the process of determining whether the fitted generalized discrete mathematical model of the battery system is valid based on the first system parameters includes:

[0013] |a1(t)|≥|a2(t)|≥|a3(t)|……|a n-1 (t)|≥a n (t)

[0014] Where |·| represents taking the absolute value; a n (t) represents the nth element in the first set of system parameters obtained at time t.

[0015] Furthermore, the process of calculating the remaining lifespan of the first battery based on the obtained second system parameter set includes:

[0016] Remaining battery life = Q1 × |b1(t)|;

[0017] Where Q1 is the first battery life conversion coefficient, and its value is a constant; when |b1(t)|≥W1, the battery system life is determined to be cut off, and W1 is the first life cutoff threshold, and its value is a constant.

[0018] Furthermore, the process of calculating the remaining lifespan of the second battery based on the obtained set of second system parameters includes:

[0019]

[0020] Where Q2 is the second battery life conversion coefficient, its value is a constant; when To determine the end of the battery system's lifespan, W2 is the second lifespan end-of-life threshold, which is the sum of all elements in the second system parameter set when the first element b1(t) in the second system parameter set equals W1.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] ① This invention improves the accuracy of battery life prediction. It employs a recursive least squares method to progressively update model parameters to adapt to changes in battery life across different charging cycles. This helps improve model accuracy, thereby enabling more precise prediction of battery life.

[0023] ② This invention enables real-time monitoring of battery health. The recursive least squares method used in this invention can dynamically update model parameters as new data is continuously received, thus enabling real-time battery life prediction. This is very useful for applications that require timely adjustment of charging strategies or monitoring of battery health.

[0024] ③ This invention has strong applicability. The parameter estimation method of this invention is generally quite flexible, and the appropriate model structure and parameter estimation method can be selected according to the actual situation. This makes the method adaptable to different types and specifications of batteries, and can be customized as needed.

[0025] ④ This invention enables visual analysis. By plotting a constant coefficient curve, this invention can intuitively display the trend of battery life changing with the number of charging cycles. By observing the characteristics of the constant coefficient curve, the patterns and trends of battery life degradation can be identified, providing a reference for developing more effective battery management strategies.

[0026] ⑥ This invention can save costs. By accurately predicting battery life, this invention can avoid unnecessary battery replacement or repair costs, which helps reduce maintenance costs and improve the reliability of the battery system, and has important theoretical research value and practical engineering significance. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of a preferred implementation process of a solid-state battery lifetime estimation method based on online parameter identification of a generalized system according to the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention proposes a method for estimating the lifetime of solid-state batteries based on online parameter identification of a generalized system, specifically including the following steps:

[0030] A generalized discrete mathematical model of the battery system is constructed using the recursive least squares method for parameter estimation.

[0031] Real-time battery operation data of electric vehicles are collected, and the collected data is input into a generalized discrete mathematical model of the battery system for fitting to obtain the first system parameter set and the second system parameter set.

[0032] The effectiveness of the fitted generalized discrete mathematical model of the battery system is determined based on the first set of system parameters. If it is invalid, the estimation ends and battery operating data is collected again for fitting until the model is effective.

[0033] If the fitted generalized discrete mathematical model of the battery system is effective, the remaining lifespan of the first battery and the remaining lifespan of the second battery are calculated based on the obtained second system parameter set. The smaller value between the remaining lifespan of the first battery and the remaining lifespan of the second battery is output as the lifespan prediction of the battery system.

[0034] In this embodiment, real-time sampling of the electric vehicle's battery operating data is used. The recorded current and voltage values ​​from the first n time points are used to construct a generalized discrete mathematical model of the battery system at time t using the least squares method. The constructed model is then used to perform online analysis of the collected data. The system parameter values ​​changing over time are obtained through recursive least squares parameter estimation. The relationship between the system parameter values ​​and a set threshold is used to determine the battery life. Specifically, the following steps are included:

[0035] Step 1: Based on the recorded current and voltage values ​​at the first n time points, construct a generalized discrete mathematical model of the battery system at time t, which is expressed as follows:

[0036]

[0037] In the formula, t+1 represents the time point to be predicted, and the current time is t, that is, the data that can be collected at present is the data at time t and the data before time t; The voltage data predicted at time t+1 is obtained by fitting the voltage data from the previous n time points; u(t) represents the actual voltage data at time t; i(t) represents the actual current data at time t; n is the model order, which is determined according to the number of time points collected in this invention. In this invention, the data from the previous n consecutive time points of the time point to be predicted are used. The data at each time point includes the voltage and current values ​​of the battery system at that time point; {a1(t+1), a2(t+1), ..., a n (t+1)} represents the first set of system parameters fitted when predicting the battery life at time t+1, where the element a in this set... n (t+1) represents the coefficient corresponding to the voltage data at the nth sampling time used when predicting the battery life at time t+1; {b1(t+1),b2(t+1),...,b n (t+1)} represents the set of second system parameters fitted when predicting the battery life at time t+1, where the element b in this set... n (t+1) represents the coefficient corresponding to the current data at the nth sampling time used when predicting the battery life at time t+1.

[0038] Step 2: For the constructed generalized discrete mathematical model of the battery system, the voltage value at time t+1 is related not only to the voltage value u(t) at time t, the voltage value u(t-1) at time t-1, ..., the voltage value u(1) at time 1, but also to the current value i(t) at time t, the current value i(t-1) at time t-1, ..., the current value i(1) at time 1, but the degree of correlation is different. Therefore, the system coefficient a is used. i (t+1), b i (t+1)(i=1,2,…,n) is used to characterize the correlation between the voltage and current values ​​from time 1 to time t and the voltage value at time t+1.

[0039] Step 3: Using the battery system current and voltage measurement data from the previous n time steps, recursively estimate the time-varying system parameter 'a' of the generalized discrete mathematical model of the battery system. i The value of (t)(i=1,2,…,n) quantifies the remaining lifespan of the electric vehicle battery system. The generalized discrete mathematical model of the battery system is considered valid if the following conditions are met.

[0040] |a1(t)|≥|a2(t)|≥|a3(t)|……|a n-1 (t)|≥a n (t)

[0041] Where |·| represents taking the absolute value; a n(t) represents the system parameters obtained at time t regarding the nth voltage data.

[0042] Step 4: Using the battery system current and voltage measurement data from the previous n time steps, recursively estimate the time-varying system parameters b of the generalized discrete mathematical model of the battery system. i The value of (t)(i=1,2,…,n) is based on the obtained system parameter b. i We can estimate the remaining battery life at time t by using the value of b1(t) (i = 1, 2, ..., n). The formula for calculating the remaining battery life is:

[0043] Remaining battery life = Q1 × |b1(t)|

[0044] Q1 is the conversion factor for the remaining life of the first battery. Its value is a constant, and those skilled in the art can determine the optimal value based on experiments or set a threshold based on experience.

[0045] When |b1(t)|≥W1, the battery system lifespan is determined to be over, where W1 is the first lifespan overdue threshold, which is a constant. Those skilled in the art can determine the optimal value based on experiments or set the threshold based on experience.

[0046] This embodiment can also be calculated. The remaining battery life at time t is estimated using the value of , and the formula for calculating the remaining battery life is:

[0047]

[0048] Q2 is the conversion factor for the remaining life of the second battery. Its value is a constant, and those skilled in the art can determine the optimal value based on experiments or set a threshold based on experience.

[0049] when Determine the end of the battery system's lifespan; where W2 is the second lifespan end-of-life threshold, and its value is a constant. As an optional implementation, this embodiment sets the threshold to the sum of all elements in the second system parameter set when the first element b1(t) in the second system parameter set is equal to W1.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for solid-state battery life estimation based on online parameter identification of a generalized system, characterized by, Specifically comprising the following steps: A battery system generalized discrete mathematical model is constructed by a recursive least square method of parameter estimation; Real-time collection of battery operation data of an electric vehicle, including data at a current tth time point and a total of n time points in the recent time, input of the collected data into the constructed battery system generalized discrete mathematical model for fitting, obtaining of a first system parameter set and a second system parameter set, and representation of the fitted battery system generalized discrete mathematical model as: wherein, represents a voltage prediction value at a t+1th time point, represents a voltage value at a tth time point; represents a current value at a tth time point; n is a discrete mathematical model order; is a first system parameter set fitted when predicting the battery life at the t+1th time point, an element in the set represents a coefficient corresponding to the voltage data at the nthsampling time point used when predicting the battery life at the t+1th time point; is a second system parameter set fitted when predicting the battery life at the t+1th time point, an element in the set represents a coefficient corresponding to the current data at the nthsampling time point used when predicting the battery life at the t+1th time point; Determination of whether the fitted battery system generalized discrete mathematical model is effective according to the first system parameter set, ending of the estimation if not, and continuous collection of battery operation data for fitting until the model is effective; the process of determining whether the fitted battery system generalized discrete mathematical model is effective according to the first system parameter set comprising: ≥ ≥ …… ≥ (t) wherein, denotes taking the absolute value; (t) denotes the nth element of the first set of system parameters obtained at the tth time point. The process of calculating the first battery residual life according to the obtained second system parameter set comprising: Battery remaining life = Q1 ; Wherein Q1 is a first battery life conversion coefficient; When the fitted battery system generalized discrete mathematical model is effective, the first battery residual life and the second battery residual life are calculated according to the obtained second system parameter set, comprising: Battery remaining life = Q2 x ; Wherein Q2 is a second battery life conversion coefficient; The smaller value of the first battery residual life and the second battery residual life is output as the life prediction of the battery system.

2. The method of claim 1, wherein the method is based on a generalized system online parameter identification for solid-state battery lifetime estimation. When the first battery remaining life output is as a battery remaining life, if ≥ W1, determining a battery system life cutoff, where W1 is a first life cutoff threshold.

3. The solid-state battery lifetime estimation method based on generalized system online parameter identification of claim 2, wherein, When the second battery remaining life output is as the battery remaining life, if ≥ W2, determine the battery system life cutoff, where W2 is a second life cutoff threshold having a value that is the sum of all elements in the second system parameter set when the first element in the second system parameter set equals W1.

Images