WO2024016496A1 - Method and apparatus for estimating soh of lithium battery - Google Patents
Method and apparatus for estimating soh of lithium battery Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 57
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 46
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 46
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 68
- 241001206881 Myrmeleon inconspicuus Species 0.000 claims abstract description 32
- 239000013598 vector Substances 0.000 claims abstract description 32
- 238000012549 training Methods 0.000 claims abstract description 31
- 238000012360 testing method Methods 0.000 claims abstract description 23
- 230000036541 health Effects 0.000 claims abstract description 20
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 230000006872 improvement Effects 0.000 claims abstract description 7
- 241000258923 Neuroptera Species 0.000 claims description 46
- 241000257303 Hymenoptera Species 0.000 claims description 20
- 238000005295 random walk Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 8
- 230000007423 decrease Effects 0.000 claims description 5
- 238000011156 evaluation Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 abstract description 4
- 229910001416 lithium ion Inorganic materials 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 230000032683 aging Effects 0.000 description 4
- 238000000691 measurement method Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000013480 data collection Methods 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 2
- 238000006731 degradation reaction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 238000012935 Averaging Methods 0.000 description 1
- 241000497425 Myrmeleon formicarius Species 0.000 description 1
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- 238000010219 correlation analysis Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000003487 electrochemical reaction Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
Definitions
- the present invention relates to the field of battery technology, and specifically, to a method and device for estimating the SOH state of a lithium battery.
- Lithium-ion batteries are one of the most widely used energy storage devices at present. As they are continuously charged and discharged, the battery capacity will continue to decrease and the battery will continue to age. This indicator of battery aging is SOH (state of health). , battery health status). Monitoring the battery SOH status can know the battery aging status in time to facilitate timely maintenance of the battery pack and battery replacement. In practical applications, SOH is difficult to measure directly through sensors and requires characteristic parameters to characterize. The capacity of lithium-ion batteries is widely considered to be an indicator of SOH.
- SOH state of health
- the capacity of lithium-ion batteries is widely considered to be an indicator of SOH.
- Direct measurement methods mainly include ampere-hour integration method and coulomb counting method.
- the estimated SOH value is calculated by formula.
- This measurement method has very limited application scenarios and cannot be measured online in actual battery application scenarios. It can only be measured offline using complex equipment in a laboratory environment.
- the model-based method comprehensively considers battery material characteristics, internal chemical mechanisms and load conditions to construct a battery aging model for SOH estimation, which mainly includes electrochemical models, equivalent circuit models and empirical degradation models.
- model-based methods are relatively complex and easily interfered by external dynamic factors, resulting in low accuracy and weak robustness.
- the data-driven method does not need to consider electrochemical reactions, complex external factors and complex models.
- BP Back Propagation Neural Network
- ALO-SVR Ant Lion Optimization Algorithm Support Vector Regression
- the purpose of the invention is to solve the current technical problems of large error in ALO-SVR estimation results, high requirements on the number of iterations, and limited application scope.
- this application provides a method for estimating the SOH state of lithium batteries, which includes the following steps:
- Lithium battery data collection extract health factors to construct feature vectors, generate training samples and test samples, where health factors are elements related to the battery capacity decline trend; health factors include: equal voltage rise time, average discharge voltage and equal voltage drop time; training The proportions of samples and test samples are 60% and 40% respectively.
- the improvement specifically refers to: by adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions, control the search balance in different iteration stages, and output the optimal output parameter group, corresponding to different
- the search balance in the iterative stage is: global search is the main one in the early and middle stages of the iteration, and local search is the main one in the late iteration stage;
- test sample set is predicted through a support vector regression model, and the SOH estimated value is output.
- the definition algorithm parameters include:
- Agents_no 30
- Max_iter 100
- the output parameter group is a space vector (c, ⁇ ) representing the positions of the ants and ant lions.
- the upper limit of c and ⁇ is set to 100, and the lower limit is set to 0.01.
- the search balance in different iteration stages is controlled by the corresponding weight values of the random walks of elite ant lions and ordinary ant lions: the inertial weight ⁇ of ordinary ant lions is nonlinearly dynamically adjusted according to the number of iterations.
- the calculation method is:
- ⁇ max is the maximum value of inertia weight
- ⁇ min is the minimum value of inertia weight
- the optimal output parameter group before outputting the optimal output parameter group, it also includes calculating the fitness value and sorting the moderate values of ants and ant lions.
- the maximum value is set to the elite ant lion, and the corresponding parameter is the optimal output parameter group.
- the fitness value of The calculation method is the fitness function between the actual value and the predicted value of the mean square error (MSE), and its expression is:
- n is the number of training sets.
- prediction of the test sample set through the support vector regression model includes prediction result analysis, including using absolute error and root mean square error as evaluation criteria.
- the calculation method is as follows:
- MAE is the mean absolute error
- RMSE is the root mean square error
- yi are the predicted value and actual value of the i-th SOH respectively
- n is the number of samples.
- This application also provides a device for estimating the SOH status of lithium batteries, including:
- Data processing module used to collect lithium battery data, extract health factors to construct feature vectors, and generate training samples and test samples; the health factors collected by the data processing module include: equal voltage rise time, average discharge voltage and equal voltage drop time; data processing The proportions of module control samples and test samples are 60% and 40% respectively.
- Algorithm control module Prediction algorithm for determining the SOH state of lithium batteries, defining algorithm parameters and output parameter groups; defining algorithm parameters includes defining the population number of ants and ant lions, the dimensionality of the fitness function, and the maximum number of iterations; output parameters The group includes the space vector (c, ⁇ ), the upper limit of c and ⁇ is set to 100, and the lower limit is set to 0.01;
- Algorithm optimization module used to improve the prediction algorithm and output the optimal output parameter group.
- the improvements include adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions;
- Model training and data output module used to predict the test sample set through a support vector regression model and output the SOH estimate.
- the calculation method for adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions defined by the algorithm optimization module is:
- ⁇ max is the maximum value of inertia weight
- ⁇ min is the minimum value of inertia weight
- the algorithm control module includes a fitness value control unit, which is used to calculate the fitness value for the output of the optimal output parameter group, sort the fitness values of ants and ant lions, set the maximum value to the elite ant lion, and set the corresponding parameters to the best Optimal output parameter group, in which the calculation method of fitness value supports the fitness function between the mean square error (MSE) actual value and the predicted value, and its expression is:
- MSE mean square error
- n is the number of training sets.
- the optimized ant lion algorithm is used to optimize the support vector regression (IALO-SVR), support fewer iterations to output the optimal parameter group, and improve the generalization ability and fitting of model training at less cost.
- Figure 1 is a step diagram of a method for estimating the SOH state of a lithium battery according to an embodiment of the present invention
- Figure 2 is a logic flow chart of a method for estimating the SOH state of a lithium battery provided according to an embodiment of the present invention
- Figure 3 is a schematic diagram of the ordinary ant lion inertia weight adjustment process in the method for estimating the SOH state of lithium batteries provided according to an embodiment of the present invention
- Figure 4 is a schematic diagram of the change curve of the actual SOH of the B05 battery and three health factors in the method for estimating the SOH state of the lithium battery provided according to an embodiment of the present invention
- Figure 5 is a comparison diagram of SOH estimation results using different methods in the estimation method of lithium battery SOH state provided according to an embodiment of the present invention
- Figure 6 is a comparison diagram of SOH estimation errors using different methods in the estimation method of lithium battery SOH state provided according to an embodiment of the present invention
- Figure 7 is a comparison diagram of the SOH estimation mean absolute error (MAE) and root mean square error (RMSE) using different methods in the method for estimating the SOH state of lithium batteries provided according to an embodiment of the present invention
- Figure 8 is a structural diagram of a device for estimating the SOH state of a lithium battery provided according to an embodiment of the present invention.
- the technical basis of this application is the application of the mathematical model of vector regression: collecting data from lithium batteries for model training (SVR), and then outputting the prediction of the SOH state of the lithium batteries through correlation analysis.
- SVR model training
- the model needs to be continuously optimized.
- the current mature approach is to construct the Ant Lion Optimization (ALO) algorithm, through which the algorithm outputs a parameter set to provide optimal parameters for the vector regression training model.
- ALO Ant Lion Optimization
- ordinary ant lions mainly affect the global search ability of the algorithm
- elite ant lions mainly affect the local optimization ability of the algorithm.
- the weight of both is 0.5.
- the optimization accuracy controlled by this algorithm cannot meet the requirements.
- this application optimizes the ALO algorithm.
- the main idea is to adjust the global search capability and local optimization capability in different periods: focus on the global search capability in the early and middle stages of the iteration to increase the diversity of the population and avoid falling into the local minimum. Optimal, and a stronger local search is performed in the later stages of the iteration to improve the convergence speed, and ultimately achieve the output of optimal parameters with fewer iterations.
- the SOH state of the lithium battery is estimated through the optimized Ant Lion algorithm optimized support vector regression (IALO-SVR).
- Figure 1 is a step diagram of a method for estimating the SOH state of a lithium battery provided by an embodiment of the present application.
- Figure 2 is a specific logic flow chart. As shown in the figure, it includes the following steps:
- Step S100 Collect lithium battery data, extract health factors to construct feature vectors, and generate training samples and test samples;
- step S210 This step is shown as step S210 in Figure 2, which is the sample processing stage.
- HF2 Average discharge voltage.
- the P420 part of Figure 4 shows the changing trend of the average discharge voltage and actual SOH. There is a strong correlation between the actual SOH and the number of cycles in which the average discharge voltage is above 80%, so the average discharge voltage can be used as a health factor for SOH estimation.
- step S212 shown in Figure 2 the sample set is classified.
- the first 60% of the samples are used as the training sample set, and the last 40% are used as the test sample set.
- Step S110 Determine the prediction algorithm for the SOH state of the lithium battery, and define the algorithm parameters and output parameter group; this step is shown in step S220 in Figure 2.
- the algorithm parameters and output parameter group are also determined:
- the algorithm used in this application is the ant lion optimization algorithm.
- the output parameter group is a set of spatial vectors (c, ⁇ ). Each group of parameters reflects the position of each ant and ant lion. This parameter is finally output to the training model.
- the algorithm process of this step requires the data of the sample set.
- the algorithm construction is as follows:
- N is the number of ant lions and ants
- D is the number of variables
- n is a natural number less than or equal to 1
- d is a natural number less than or equal to D
- L d is the lower limit of the d variable
- U d is the d variable upper limit.
- t k represents the k-th iteration
- k is the maximum number of iterations
- r(t) is a random function, defined as:
- rand is a random number between 0 and 1.
- the standardized position of the i-th variable at the t-th iteration can be expressed as:
- a i and d i represent the minimum and maximum values of the i-th variable random walk, and The minimum and maximum values of the random walk for the i-th variable at the t-th iteration.
- c t and d t are the minimum and maximum values of all variables at the t-th iteration, is the position of the i-th ant lion at the t-th iteration, t and T are the current iteration number and the maximum iteration number respectively.
- step S222 of Figure 2 the ant lion with the best fitness value in each iteration is saved as an elite ant lion, and then an ordinary ant lion is randomly selected through roulette.
- Each ant randomly swims around ordinary ant lions and elite ant lions, and its position update can be expressed as:
- n is the number of training sets.
- the fitness value is calculated according to Equation (20), and the fitness values of ants and ant lions are sorted. The one with the largest value becomes the elite ant lion, and the corresponding parameters are used as the optimal parameters.
- Step S120 Improve the ant lion algorithm and output the optimal output parameter set.
- the improvement plan in this application is: by adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions, control the search balance in different iteration stages, and output the optimal Output parameter group.
- the control method of search balance is as follows: in the early and middle stages of iteration, global search is the main one, and in the late iteration stage, local search is the main one;
- this application adopts the method of dynamically adjusting the inertia weight ⁇ of ordinary ant lions non-linearly with the number of iterations.
- the calculation formula is:
- Figure 3 shows the changing trend of the ordinary ant lion inertia weight ⁇ with iteration.
- IALO Utilizing the nonlinear dynamic adjustment of the ordinary ant lion inertia weight ⁇ , IALO has strong global search capabilities in the early and middle stages of iteration to avoid falling into local optimality. Then, the local search capability is enhanced in the later stage to ensure that the algorithm can find the local optimal solution.
- step S224 The implementation process is shown in step S224 in Figure 2.
- the ordinary ant lion inertia weight ⁇ is adjusted and the ant lion position is updated, an improved ant lion optimization algorithm (IALO) is constructed, and the optimal parameter group (c, ⁇ ) is finally output. .
- Step S130 Use the support vector regression model to predict the test sample set and output the SOH estimated value.
- Support vector regression is an extension of support vector machine in the field of regression. Due to its good generalization ability and fast convergence speed, it is often used to solve nonlinear and small sample problems. Due to the long battery health experiment period and small sample size, SVR is very suitable for SOH prediction. At the same time, the optimal parameter set is obtained through IASO, and C and ⁇ control the generalization ability and fitting ability in the training of the SVR model, that is, controlling the accuracy of prediction and verification accuracy.
- the training model is defined as follows:
- f(x) is the output value
- x, ⁇ , and b are the input value, weight and intercept respectively.
- c is the penalty factor that affects the generalization ability of the SVR model
- ⁇ is the maximum allowable error of regression, which can be obtained by introducing the Lagrangian operator:
- x i and x j are the input vectors in the training sample set and the test sample set respectively.
- the radial basis (RBF) function is selected as the kernel function, which is defined as:
- ⁇ is the bandwidth of the RBF kernel function, which affects the fitting ability of the SVR model.
- mean absolute error (MAE) or root mean square error (RMSE)
- RMSE root mean square error
- the mean absolute error algorithm is:
- the root mean square error algorithm is:
- in and yi are the predicted value and actual value of the i-th SOH respectively, and n is the number of samples.
- This application also provides three methods (Back-Propagation neural network) BP, ALO-SVR and IALO-SVR to compare the SOH of three sets of batteries, corresponding to three lithium batteries, labeled B05, B06 and B07 respectively.
- Figure 5 shows the SOH estimation results of different methods, where P510, P520, and P530 are the estimation results of B05, B06, and B07 respectively.
- P510, P520, and P530 are the estimation results of B05, B06, and B07 respectively.
- the IALO-SVR estimation SOH is the most accurate, while the other two methods do not fit well enough, especially BP.
- IALO-SVR estimates the SOH of B05, B06, and B07 more accurately, while for BP, the estimation ability is seriously degraded when encountering data fluctuations, resulting in a larger deviation in the final estimated SOH.
- ALO-SVR has better estimation accuracy in the early stage, but also gradually deviates from the true SOH in the later stage. In the later period, the prediction accuracy of IALO-SVR is still very high compared with ALO-SVR, which is caused by the dynamic adjustment of the inertial weight of ordinary antlion.
- Figure 6 shows the SOH estimation errors of the three methods.
- Figures P610, P620, and P630 are the estimation errors of B05, B06, and B07 respectively. The estimation errors are all controlled within 2% without large fluctuations.
- Figure 7 plots the performance evaluation indicators of the three algorithms, namely mean absolute error (MAE) and root mean square error (RMSE).
- MAE mean absolute error
- RMSE root mean square error
- FIG. 8 is a structural diagram of the evaluation device provided by the present invention. As shown in the figure, it includes the following modules:
- P810 data processing module used for lithium battery data collection, extracting health factors to construct feature vectors, and generating training samples and test samples;
- the health factors collected in this module include: equal voltage rise time, average discharge voltage and equal voltage drop time; the proportions of generated training samples and test samples are 60% and 40% respectively.
- P820 algorithm control module a prediction algorithm for determining the SOH state of lithium batteries, defining algorithm parameters and output parameter groups;
- the defined algorithm parameters include defining the population size of ants and ant lions, the dimensionality of the fitness function, and the maximum number of iterations ;
- the output parameter group includes a space vector (c, ⁇ ), the upper limit of c and ⁇ is set to 100, and the lower limit is set to 0.01;
- the algorithm control module also includes the P821 fitness value control unit, which is used to calculate the fitness value for the output of the optimal output parameter group, sort the fitness values of ants and ant lions, set the maximum value to the elite ant lion, and the corresponding parameters are optimal Output parameter group, in which the calculation method of fitness value supports the fitness function between the mean square error (MSE) actual value and the predicted value, and its expression is:
- MSE mean square error
- n is the number of training sets.
- P830 algorithm optimization module used to improve the prediction algorithm and output the optimal output parameter group.
- the improvement includes adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions;
- the calculation method for adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions defined by the algorithm optimization module is:
- ⁇ max is the maximum value of inertia weight and is the minimum value of inertia weight.
- P840 model training and data output module used to predict the test sample set through the support vector regression model and output the SOH estimated value.
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Abstract
A method for estimating the SOH of a lithium battery. The method comprises the following steps: collecting data of a lithium battery, extracting health factors to construct feature vectors, and generating training samples and test samples (S100); determining an estimation algorithm for the SOH of the lithium battery to be an ant lion optimization algorithm, and defining algorithm parameters and an output parameter group (S110); improving the estimation algorithm to output an optimal output parameter group (S120), wherein the improvement refers to controlling search balance in different iteration stages by means of adjusting weight values corresponding to the random walking of an elite ant lion and an ordinary ant lion, so as to output the optimal output parameter group; and on the basis of the optimal output parameter group, predicting a test sample set by means of a support vector regression model, so as to output an estimated value of the SOH (S130). By using the estimation method, outputting an optimal parameter group with fewer iterations can be supported, and the generalization capability and the fitting capability for model training are improved with a lower cost, such that the SOH of a lithium ion battery is estimated accurately in real time, and the accuracy and the convergence precision are improved.
Description
本发明涉及电池技术领域,具体而言,涉及一种锂电池SOH状态的预估方法和装置。The present invention relates to the field of battery technology, and specifically, to a method and device for estimating the SOH state of a lithium battery.
锂离子电池是目前最广泛使用的储能设备之一,随着其不断的充放电循环使用,电池容量会不断的减小,电池不断的老化,这个判断电池老化的指标就是SOH(state of health,电池健康状态)。监测电池SOH状态可以及时知道电池老化状态以便于及时对电池组进行维护和更换电池。在实际应用中,SOH难以通过传感器直接测量,需要特征参数来表征,而锂离子电池的容量被广泛认为是表征SOH的指标。目前,现有技术使用的SOH估计方法主要有三种:直接测量法、基于模型的方法和数据驱动的方法。直接测量方法主要包括安培小时积分法和库仑计数法,通过离线直接测量电池容量、内阻等老化特性,公式计算得出估计的SOH值。这种测量方法的应用场景非常有限,无法在电池实际应用场景中在线测量,只能在实验室环境中使用复杂的设备离线测量。基于模型的方法综合考虑电池材料特性、内部化学机理和负荷条件,构建电池老化模型进行SOH估算,主要包括电化学模型、等效电路模型和经验退化模型。但是,基于模型的方法比较复杂,容易受到外部动态因素的干扰,以至于精度不高,鲁棒性不强。基于数据驱动方法不需要考虑电化学反应、复杂的外部因素和复杂的模型,只需要从电池循环数据中挖掘电池降解规律,找到能够映射SOH的健康因子,建立非线性映射关系,实现SOH的精确估计。目前数据驱动的主要方法有:1)BP(Back Propagation Neural Network)神经网络估计SOH;2)基于蚁狮优化算法的支持向量回归(ALO-SVR)的SOH估 计方法。Lithium-ion batteries are one of the most widely used energy storage devices at present. As they are continuously charged and discharged, the battery capacity will continue to decrease and the battery will continue to age. This indicator of battery aging is SOH (state of health). , battery health status). Monitoring the battery SOH status can know the battery aging status in time to facilitate timely maintenance of the battery pack and battery replacement. In practical applications, SOH is difficult to measure directly through sensors and requires characteristic parameters to characterize. The capacity of lithium-ion batteries is widely considered to be an indicator of SOH. Currently, there are three main SOH estimation methods used in the existing technology: direct measurement method, model-based method and data-driven method. Direct measurement methods mainly include ampere-hour integration method and coulomb counting method. By directly measuring battery capacity, internal resistance and other aging characteristics offline, the estimated SOH value is calculated by formula. This measurement method has very limited application scenarios and cannot be measured online in actual battery application scenarios. It can only be measured offline using complex equipment in a laboratory environment. The model-based method comprehensively considers battery material characteristics, internal chemical mechanisms and load conditions to construct a battery aging model for SOH estimation, which mainly includes electrochemical models, equivalent circuit models and empirical degradation models. However, model-based methods are relatively complex and easily interfered by external dynamic factors, resulting in low accuracy and weak robustness. The data-driven method does not need to consider electrochemical reactions, complex external factors and complex models. It only needs to mine battery degradation rules from battery cycle data, find health factors that can map SOH, establish non-linear mapping relationships, and achieve accurate SOH estimate. The current main data-driven methods are: 1) BP (Back Propagation Neural Network) neural network estimates SOH; 2) SOH estimation method based on Ant Lion Optimization Algorithm Support Vector Regression (ALO-SVR).
基于蚁狮优化算法的支持向量回归(ALO-SVR)的SOH估计方法,虽然克服了直接测量法、基于模型的方法当中精度、鲁棒性的问题,但是由于其在迭代过程中易陷入局部最优解的问题,导致估算结果误差大,应用范围有一定的局限,算法的收敛精度也不够高。Although the SOH estimation method based on the support vector regression (ALO-SVR) of the Ant Lion optimization algorithm overcomes the accuracy and robustness problems of direct measurement methods and model-based methods, it is prone to falling into local minima during the iterative process. The problem of optimal solutions leads to large errors in estimation results, certain limitations in the scope of application, and the convergence accuracy of the algorithm is not high enough.
发明内容Contents of the invention
本发明的目的在于解决目前ALO-SVR的估算结果误差大,对迭代次数要求高,应用范围局限的技术问题。The purpose of the invention is to solve the current technical problems of large error in ALO-SVR estimation results, high requirements on the number of iterations, and limited application scope.
第一方面,为实现上述目的,本申请提供了一种锂电池SOH状态的预估方法,包括以下步骤:In the first aspect, in order to achieve the above purpose, this application provides a method for estimating the SOH state of lithium batteries, which includes the following steps:
锂电池数据采集,提取健康因子构建特征向量,生成训练样本和测试样本,其中健康因子为电池容量衰退趋势相关的元素;健康因子包括:等压升时间、平均放电电压和等压降时间;训练样本和测试样本的占比分别为60%和40%。Lithium battery data collection, extract health factors to construct feature vectors, generate training samples and test samples, where health factors are elements related to the battery capacity decline trend; health factors include: equal voltage rise time, average discharge voltage and equal voltage drop time; training The proportions of samples and test samples are 60% and 40% respectively.
确定锂电池SOH状态的预估算法,定义算法参数和输出参数组;确定预估算法为蚁狮优化算法;Determine the prediction method for the SOH state of the lithium battery, define the algorithm parameters and output parameter group; determine the prediction method as the Ant Lion optimization algorithm;
改进预估算法,输出最优输出参数组,改进内容具体指:通过调整精英蚁狮和普通蚁狮的随机游走对应权重值,控制不同迭代阶段搜索平衡,输出最优输出参数组,对应不同迭代阶段搜索平衡为:迭代前期、中期以全局搜索为主,迭代后期以局部搜索为主;Improve the prediction algorithm and output the optimal output parameter group. The improvement specifically refers to: by adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions, control the search balance in different iteration stages, and output the optimal output parameter group, corresponding to different The search balance in the iterative stage is: global search is the main one in the early and middle stages of the iteration, and local search is the main one in the late iteration stage;
结合所述最优输出参数组,通过支持向量回归模型对所述测试样本集进行预测,输出所述SOH预估值。Combined with the optimal output parameter group, the test sample set is predicted through a support vector regression model, and the SOH estimated value is output.
其中,定义算法参数包括:Among them, the definition algorithm parameters include:
蚂蚁和蚁狮的种群数量设为Agents_no=30,The population size of ants and ant lions is set to Agents_no=30,
适应度函数的维数设为dim=2,The dimension of the fitness function is set to dim=2,
最大迭代次数设为Max_iter=100,The maximum number of iterations is set to Max_iter=100,
输出参数组为代表和蚂蚁和蚁狮的位置的空间向量(c,σ),所述c和σ的上限设为100,下限设为0.01。The output parameter group is a space vector (c, σ) representing the positions of the ants and ant lions. The upper limit of c and σ is set to 100, and the lower limit is set to 0.01.
其中,通过精英蚁狮和普通蚁狮的随机游走对应权重值控制不同迭代阶段搜索平衡指:根据迭代次数非线性动态调整普通蚁狮惯性权重γ,计算方法为:Among them, the search balance in different iteration stages is controlled by the corresponding weight values of the random walks of elite ant lions and ordinary ant lions: the inertial weight γ of ordinary ant lions is nonlinearly dynamically adjusted according to the number of iterations. The calculation method is:
其中,γ
max为惯性权重最大值,γ
min为惯性权重最小值。
Among them, γ max is the maximum value of inertia weight, and γ min is the minimum value of inertia weight.
进一步的,输出最优输出参数组前还包括计算适应度值,对蚂蚁和蚁狮的适度值排序,最大值设为精英蚁狮,对应参数为最优输出参数组,其中,适应度值的计算方法为均方误差(MSE)实际值和预测值之间的适应度函数,其表达式为:Furthermore, before outputting the optimal output parameter group, it also includes calculating the fitness value and sorting the moderate values of ants and ant lions. The maximum value is set to the elite ant lion, and the corresponding parameter is the optimal output parameter group. Among them, the fitness value of The calculation method is the fitness function between the actual value and the predicted value of the mean square error (MSE), and its expression is:
其中
为第i个预测值,y
i为第i个实际值,n是训练集数量。
in is the i-th predicted value, yi is the i -th actual value, and n is the number of training sets.
进一步的,通过支持向量回归模型对测试样本集进行预测包括预测结果分析,包括使用绝对误差和均方根误差作为评价标准,计算方式如下:Further, prediction of the test sample set through the support vector regression model includes prediction result analysis, including using absolute error and root mean square error as evaluation criteria. The calculation method is as follows:
其中:MAE为平均绝对误差,RMSE为均方根误差,
和y
i分别是第i个SOH的预测值和实际值,n为样本数。
Among them: MAE is the mean absolute error, RMSE is the root mean square error, and yi are the predicted value and actual value of the i-th SOH respectively, and n is the number of samples.
本申请还提供了一种锂电池SOH状态的预估装置,包括:This application also provides a device for estimating the SOH status of lithium batteries, including:
数据处理模块:用于锂电池数据采集,提取健康因子构建特征向量,生成训练样本和测试样本;数据处理模块采集的健康因子包括:等压升时间、 平均放电电压和等压降时间;数据处理模块控制所述样本和测试样本的占比分别为60%和40%。Data processing module: used to collect lithium battery data, extract health factors to construct feature vectors, and generate training samples and test samples; the health factors collected by the data processing module include: equal voltage rise time, average discharge voltage and equal voltage drop time; data processing The proportions of module control samples and test samples are 60% and 40% respectively.
算法控制模块:用于确定锂电池SOH状态的预估算法,定义算法参数和输出参数组;定义算法参数包括定义蚂蚁和蚁狮的种群数量、适应度函数的维数、最大迭代次数;输出参数组包括空间向量(c,σ),c和σ的上限设为100,下限设为0.01;Algorithm control module: Prediction algorithm for determining the SOH state of lithium batteries, defining algorithm parameters and output parameter groups; defining algorithm parameters includes defining the population number of ants and ant lions, the dimensionality of the fitness function, and the maximum number of iterations; output parameters The group includes the space vector (c, σ), the upper limit of c and σ is set to 100, and the lower limit is set to 0.01;
算法优化模块:用于改进预估算法,输出最优输出参数组,改进内容包括调整精英蚁狮和普通蚁狮的随机游走对应权重值;Algorithm optimization module: used to improve the prediction algorithm and output the optimal output parameter group. The improvements include adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions;
模型训练和数据输出模块:用于通过支持向量回归模型对所述测试样本集进行预测,输出所述SOH预估值。Model training and data output module: used to predict the test sample set through a support vector regression model and output the SOH estimate.
进一步的,算法优化模块定义的调整精英蚁狮和普通蚁狮的随机游走对应权重值的计算方法为:
Furthermore, the calculation method for adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions defined by the algorithm optimization module is:
其中,γ
max为惯性权重最大值,γ
min为惯性权重最小值。
Among them, γ max is the maximum value of inertia weight, and γ min is the minimum value of inertia weight.
进一步的,算法控制模块包括适应度值控制单元,用于为输出最优输出参数组计算适应度值,对蚂蚁和蚁狮的适度值排序,将最大值设为精英蚁狮,对应参数为最优输出参数组,其中,适应度值的计算方法支持均方误差(MSE)实际值和预测值之间的适应度函数,其表达式为:Further, the algorithm control module includes a fitness value control unit, which is used to calculate the fitness value for the output of the optimal output parameter group, sort the fitness values of ants and ant lions, set the maximum value to the elite ant lion, and set the corresponding parameters to the best Optimal output parameter group, in which the calculation method of fitness value supports the fitness function between the mean square error (MSE) actual value and the predicted value, and its expression is:
其中
为第i个预测值,y
i为第i个实际值,n是训练集数量。
in is the i-th predicted value, yi is the i -th actual value, and n is the number of training sets.
根据本发明,用优化后的蚁狮算法优化支持向量回归(IALO-SVR),支持更少的迭代次数输出最优的参数组,以更少的代价,提高模型训练的泛化能力和拟合能力,以实现精确、实时地对锂离子电池SOH进行估计,提高准确度和收敛精度。According to the present invention, the optimized ant lion algorithm is used to optimize the support vector regression (IALO-SVR), support fewer iterations to output the optimal parameter group, and improve the generalization ability and fitting of model training at less cost. Ability to achieve accurate and real-time estimation of lithium-ion battery SOH, improving accuracy and convergence precision.
图1是根据本发明实施例提供的锂电池SOH状态的预估方法步骤图;Figure 1 is a step diagram of a method for estimating the SOH state of a lithium battery according to an embodiment of the present invention;
图2是根据本发明实施例提供的锂电池SOH状态的预估方法逻辑流程图;Figure 2 is a logic flow chart of a method for estimating the SOH state of a lithium battery provided according to an embodiment of the present invention;
图3是根据本发明实施例提供的锂电池SOH状态的预估方法中普通蚁狮惯性权重调整过程示意图;Figure 3 is a schematic diagram of the ordinary ant lion inertia weight adjustment process in the method for estimating the SOH state of lithium batteries provided according to an embodiment of the present invention;
图4是根据本发明实施例提供的锂电池SOH状态的预估方法中B05电池实际SOH与三个健康因子的变化曲线示意图;Figure 4 is a schematic diagram of the change curve of the actual SOH of the B05 battery and three health factors in the method for estimating the SOH state of the lithium battery provided according to an embodiment of the present invention;
图5是根据本发明实施例提供的锂电池SOH状态的预估方法中采用不同方法的SOH估计结果比较图;Figure 5 is a comparison diagram of SOH estimation results using different methods in the estimation method of lithium battery SOH state provided according to an embodiment of the present invention;
图6是根据本发明实施例提供的锂电池SOH状态的预估方法中采用不同方法的SOH估计误差比较图;Figure 6 is a comparison diagram of SOH estimation errors using different methods in the estimation method of lithium battery SOH state provided according to an embodiment of the present invention;
图7是根据本发明实施例提供的锂电池SOH状态的预估方法中采用不同方法的SOH估计平均绝对值误差(MAE)和均方根误差(RMSE)比较图;Figure 7 is a comparison diagram of the SOH estimation mean absolute error (MAE) and root mean square error (RMSE) using different methods in the method for estimating the SOH state of lithium batteries provided according to an embodiment of the present invention;
图8是根据本发明实施例提供的锂电池SOH状态的预估装置结构图。Figure 8 is a structural diagram of a device for estimating the SOH state of a lithium battery provided according to an embodiment of the present invention.
本申请的技术基础是对向量回归的数学模型的应用:从锂电池采集数据进行模型训练(SVR),再通过相关性分析输出锂电池SOH状态的预测。在模型训练的过程中需要对其模型的不断优化,目前成熟的做法是构建蚁狮优化(ALO)的算法,通过该算法输出参数组为向量回归的训练模型提供最优参数。在ALO算法中,普通蚁狮主要影响算法的全局搜索能力,精英蚁狮主要影响算法的局部寻优能力,更新蚂蚁位置时两者的权重均为0.5。这种算法控制的寻优精度还不能达到需求。The technical basis of this application is the application of the mathematical model of vector regression: collecting data from lithium batteries for model training (SVR), and then outputting the prediction of the SOH state of the lithium batteries through correlation analysis. During the process of model training, the model needs to be continuously optimized. The current mature approach is to construct the Ant Lion Optimization (ALO) algorithm, through which the algorithm outputs a parameter set to provide optimal parameters for the vector regression training model. In the ALO algorithm, ordinary ant lions mainly affect the global search ability of the algorithm, and elite ant lions mainly affect the local optimization ability of the algorithm. When updating the ant position, the weight of both is 0.5. The optimization accuracy controlled by this algorithm cannot meet the requirements.
因此,本申请对ALO算法进行优化,其主要思想是在不同时期对全局搜索能力和局部寻优能力进行调整:在迭代的前中期注重全局搜索能力, 以增加种群的多样性,避免陷入局部最优,而迭代的后期进行更强的局部搜索,以提高收敛速度,最终实现用较少的迭代次数输出最优的参数。通过优化后的蚁狮算法优化支持向量回归(IALO-SVR),对锂电池SOH状态进行预估。Therefore, this application optimizes the ALO algorithm. The main idea is to adjust the global search capability and local optimization capability in different periods: focus on the global search capability in the early and middle stages of the iteration to increase the diversity of the population and avoid falling into the local minimum. Optimal, and a stronger local search is performed in the later stages of the iteration to improve the convergence speed, and ultimately achieve the output of optimal parameters with fewer iterations. The SOH state of the lithium battery is estimated through the optimized Ant Lion algorithm optimized support vector regression (IALO-SVR).
下面结合说明书附图对本发明的具体实现方式做详细描述。The specific implementation manner of the present invention will be described in detail below with reference to the accompanying drawings.
图1为本申请实施例提供的锂电池SOH状态的预估方法步骤图,与之结合,图2为具体的逻辑流程图,如图所示,包括以下步骤:Figure 1 is a step diagram of a method for estimating the SOH state of a lithium battery provided by an embodiment of the present application. Combined with it, Figure 2 is a specific logic flow chart. As shown in the figure, it includes the following steps:
步骤S100:锂电池数据采集,提取健康因子构建特征向量,生成训练样本和测试样本;Step S100: Collect lithium battery data, extract health factors to construct feature vectors, and generate training samples and test samples;
本步骤在图2中如步骤S210,为样本处理阶段。This step is shown as step S210 in Figure 2, which is the sample processing stage.
首先,在锂电池数据采集方面,可以通过电压、电流、内阻等充放电数据,提取出跟锂电池容量衰退趋势相关的健康因子作为样本。本申请提出的方案,采集的数据可以为:等压升时间、平均放电电压和等压降时间,这三种数据都可以很好地反映SOH地衰退,如图4所示:First of all, in terms of lithium battery data collection, health factors related to the decline trend of lithium battery capacity can be extracted as samples through charge and discharge data such as voltage, current, and internal resistance. In the scheme proposed by this application, the data collected can be: equal voltage rise time, average discharge voltage and equal voltage drop time. These three data can well reflect the decline of SOH, as shown in Figure 4:
HF1:等电压升时间,等电压上升时间与实际SOH的关系如图4的P410部分所示,随着循环次数的增加,两者的下降趋势大致相同。HF1: Equal voltage rise time. The relationship between the equal voltage rise time and the actual SOH is shown in the P410 part of Figure 4. As the number of cycles increases, the downward trends of the two are roughly the same.
HF2:平均放电电压,图4的P420部分显示了平均放电电压和实际SOH的变化趋势。实际SOH与平均放电电压在80%以上的循环次数存在较强的相关性,因此平均放电电压可以作为SOH估计的健康因子。HF2: Average discharge voltage. The P420 part of Figure 4 shows the changing trend of the average discharge voltage and actual SOH. There is a strong correlation between the actual SOH and the number of cycles in which the average discharge voltage is above 80%, so the average discharge voltage can be used as a health factor for SOH estimation.
HF3:等压降时间,以恒定电流将4.2v的电压放电到截止电压,所花费的时间与实际SOH的对比如图4的P430部分所示,这两者具有极其相似的下降趋势。HF3: With equal voltage drop time, the voltage of 4.2v is discharged to the cut-off voltage with a constant current. The comparison between the time taken and the actual SOH is shown in the P430 part of Figure 4. The two have very similar downward trends.
因此,在实际应用中,可以根据实际情况选择其中之一作为数据样本。Therefore, in practical applications, one of them can be selected as a data sample according to the actual situation.
在图2所示步骤S212中,对样本集进行分类,对于采集的数据,前60% 的样子作为训练样本集,后40%用作为测试样本集。In step S212 shown in Figure 2, the sample set is classified. For the collected data, the first 60% of the samples are used as the training sample set, and the last 40% are used as the test sample set.
步骤S110:确定锂电池SOH状态的预估算法,定义算法参数和输出参数组;本步骤在图2中见步骤S220。Step S110: Determine the prediction algorithm for the SOH state of the lithium battery, and define the algorithm parameters and output parameter group; this step is shown in step S220 in Figure 2.
首先在本步骤中还确定算法参数和输出参数组:First, in this step, the algorithm parameters and output parameter group are also determined:
本申请中采用的算法为蚁狮优化算法,算法参数的定义包括将蚂蚁和蚁狮的种群数量设为Agents_no=30,适应度函数的维数设为dim=2,最大迭代次数设为Max_iter=100,c和σ的上下限分别设为100和0.01;The algorithm used in this application is the ant lion optimization algorithm. The definition of the algorithm parameters includes setting the population number of ants and ant lions to Agents_no=30, the dimension of the fitness function to dim=2, and the maximum number of iterations to Max_iter= 100, the upper and lower limits of c and σ are set to 100 and 0.01 respectively;
输出参数组是一组空间向量(c,σ),每组参数是每只蚂蚁和蚁狮的位置的体现,该参数最终输出至训练模型。The output parameter group is a set of spatial vectors (c, σ). Each group of parameters reflects the position of each ant and ant lion. This parameter is finally output to the training model.
本步骤的算法过程需要带入样本集的数据,算法构建具体如下:The algorithm process of this step requires the data of the sample set. The algorithm construction is as follows:
1)首先,如图2的S221所示,将蚁狮和蚂蚁的位置x
n,d随机初始化,初始化方法见公式(1),x
n,d每个位置代表问题的一个解。最终该数据会根据适应度函数计算对应的适应度值,选取适应度值最好的作为精英蚁狮,记为R
E:
1) First, as shown in S221 of Figure 2, the positions x n and d of the ant lions and ants are randomly initialized. The initialization method is shown in formula (1). Each position x n and d represents a solution to the problem. Finally, the data will calculate the corresponding fitness value according to the fitness function, and the one with the best fitness value will be selected as the elite ant lion, recorded as RE :
X
n,d=L
d+rand(U
d-L
d) (1)
X n,d =L d +rand(U d -L d ) (1)
其中N是蚁狮和蚂蚁的数量,D是变量的数量,n为小于等于1的自然数,d为小于等于D的自然数,L
d是第d个变量的下限,U
d是第d个变量的上限。
Where N is the number of ant lions and ants, D is the number of variables, n is a natural number less than or equal to 1, d is a natural number less than or equal to D, L d is the lower limit of the d variable, U d is the d variable upper limit.
2)接下来,ALO算法通过一个随机函数来模拟蚂蚁的游走过程,其随机游荡步长集合可以表示为:2) Next, the ALO algorithm simulates the walking process of ants through a random function, and its random wandering step set can be expressed as:
X(t)={0,cumsum[2r(t
1)-1],...,cumsum[2r(t
k)-1]} (2)
X(t)={0, cumsum[2r(t 1 )-1],..., cumsum[2r(t k )-1]} (2)
其中cumsum用于计算累计和,t
k代表第k次迭代,k为最大迭代次数;r(t)作为随机函数,定义为:
Among them, cumsum is used to calculate the cumulative sum, t k represents the k-th iteration, k is the maximum number of iterations; r(t) is a random function, defined as:
其中rand是0到1之间的随机数。where rand is a random number between 0 and 1.
第i个变量在第t次迭代便准化后的位置可以表示为:The standardized position of the i-th variable at the t-th iteration can be expressed as:
其中a
i和d
i表示第i个变量随机游走的最小值和最大值,
和
第i个变量在第t次迭代时随机游走的最小值和最大值。
where a i and d i represent the minimum and maximum values of the i-th variable random walk, and The minimum and maximum values of the random walk for the i-th variable at the t-th iteration.
3)设
和
定义的超球面为蚂蚁随机游走的搜索空间,随机游走空间会不断缩小,以加快捕获过程,此过程定义为:
3) Assume and The defined hypersphere is the search space for random walks of ants. The random walk space will continue to shrink to speed up the capture process. This process is defined as:
其中c
t和d
t是所有变量在第t次迭代时的最小值和最大值,
是第i个蚁狮在第t次迭代时的位置,t和T分别是当前迭代次数和最大迭代次数。
where c t and d t are the minimum and maximum values of all variables at the t-th iteration, is the position of the i-th ant lion at the t-th iteration, t and T are the current iteration number and the maximum iteration number respectively.
4)如图2步骤S222所示,每次迭代中适应度值最好的蚁狮被保存为精英蚁狮,然后通过轮盘赌随机选择一只普通蚁狮。每只蚂蚁在普通蚁狮和精英蚁狮周围随机游动,其位置更新可以表示为:4) As shown in step S222 of Figure 2, the ant lion with the best fitness value in each iteration is saved as an elite ant lion, and then an ordinary ant lion is randomly selected through roulette. Each ant randomly swims around ordinary ant lions and elite ant lions, and its position update can be expressed as:
为第i个蚂蚁在第t次迭代时的位置,
为轮盘赌选择的普通蚁狮在第t次迭代时的位置,
为轮盘赌选择的精英蚁狮在第t次迭代时的位置,随机游走位置由式(2)到式(4)更新,通过这种方式,获取蚁狮和精英蚁狮的位置。
is the position of the i-th ant at the t-th iteration, The position of the common antlion selected for the roulette wheel at iteration t, The position of the elite ant lion selected for roulette at the t-th iteration, the random walk position is updated from equation (2) to equation (4). In this way, the positions of the ant lion and elite ant lion are obtained.
获取位置后,需要判断最优参数,即设置适应度函数:设置均方误差(MSE)实际值和预测值之间的适应度函数,其表达式为:After obtaining the position, you need to determine the optimal parameters, that is, set the fitness function: set the fitness function between the actual value and the predicted value of the mean square error (MSE), and its expression is:
其中
是第i个预测值,y
i是第i个实际值,n是训练集数量。
in is the i-th predicted value, yi is the i -th actual value, and n is the number of training sets.
如图2的S223所示,根据式(20)计算适应度值,对蚂蚁和蚁狮的适应度值进行排序,值最大的成为精英蚁狮,对应的参数作为最优参数。As shown in S223 of Figure 2, the fitness value is calculated according to Equation (20), and the fitness values of ants and ant lions are sorted. The one with the largest value becomes the elite ant lion, and the corresponding parameters are used as the optimal parameters.
5)如果蚂蚁的适应度值高于捕食它的蚁狮,那么蚁狮的位置就会更新为被捕食蚂蚁的位置,陷阱就会在被捕食蚂蚁的位置重建,新的陷阱将有更高的机会抓住蚂蚁,其规则表示如下:5) If the fitness value of the ant is higher than that of the ant lion that preyed on it, then the position of the ant lion will be updated to the position of the prey ant, the trap will be rebuilt at the location of the prey ant, and the new trap will have a higher Opportunity to catch ants, the rules are expressed as follows:
以上步骤构建了传统的ALO算法,其中,普通蚁狮主要影响算法的全局搜索能力,精英蚁狮主要影响算法的局部寻优能力,ALO算法过程中,蚂蚁位置时两者的权重均为0.5。The above steps construct the traditional ALO algorithm. Among them, ordinary ant lions mainly affect the global search ability of the algorithm, and elite ant lions mainly affect the local optimization ability of the algorithm. During the ALO algorithm process, the weight of both ants is 0.5.
步骤S120:改进蚁狮算法,输出最优输出参数组,本申请中的改进方案为:通过调整精英蚁狮和普通蚁狮的随机游走对应权重值,控制不同迭代阶段搜索平衡,输出最优输出参数组。其中搜索平衡的控制方式为:在迭代前期、中期以全局搜索为主,迭代后期以局部搜索为主;Step S120: Improve the ant lion algorithm and output the optimal output parameter set. The improvement plan in this application is: by adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions, control the search balance in different iteration stages, and output the optimal Output parameter group. The control method of search balance is as follows: in the early and middle stages of iteration, global search is the main one, and in the late iteration stage, local search is the main one;
为实现这一控制方法,本申请采用迭代次数非线性动态调整普通蚁狮惯性权重γ的方法,计算公式为:In order to realize this control method, this application adopts the method of dynamically adjusting the inertia weight γ of ordinary ant lions non-linearly with the number of iterations. The calculation formula is:
其中γ
max=0.9和γ
min=0.1,分别为惯性权重的最大值和最小值,那么结合公式(9)、公式(10),改进后的蚁狮位置可以表达为:
Among them, γ max =0.9 and γ min =0.1 are the maximum and minimum values of the inertia weight respectively. Then combined with formula (9) and formula (10), the improved ant lion position can be expressed as:
图3显示了普通蚁狮惯性权重γ随迭代的变化趋势。利用普通蚁狮惯性权重γ的非线性动态调整,IALO在迭代初期和中期具有较强的全局搜索能力,避免陷入局部最优。然后在后期增强局部搜索能力,保证算法能够找到局部最优解。Figure 3 shows the changing trend of the ordinary ant lion inertia weight γ with iteration. Utilizing the nonlinear dynamic adjustment of the ordinary ant lion inertia weight γ, IALO has strong global search capabilities in the early and middle stages of iteration to avoid falling into local optimality. Then, the local search capability is enhanced in the later stage to ensure that the algorithm can find the local optimal solution.
实现过程如图2的步骤S224所示,迭代过程中调整普通蚁狮惯性权重γ后更新蚁狮位置,构建改进后的蚁狮优化算法(IALO),最终输出最优参数组(c,σ)。The implementation process is shown in step S224 in Figure 2. During the iterative process, the ordinary ant lion inertia weight γ is adjusted and the ant lion position is updated, an improved ant lion optimization algorithm (IALO) is constructed, and the optimal parameter group (c, σ) is finally output. .
在以上步骤中,对锂电池SOH状态的预估方法中涉及的算法进行构建和优化。接下来是在模型训练中的应用:In the above steps, the algorithms involved in the estimation method of lithium battery SOH status are constructed and optimized. Next is the application in model training:
步骤S130:通过支持向量回归模型对所述测试样本集进行预测,输出所述SOH预估值。Step S130: Use the support vector regression model to predict the test sample set and output the SOH estimated value.
支持向量回归(support vector regression,SVR)是支持向量机在回归领域的一种扩展,由于其泛化能力好、收敛速度快,常用于解决非线性、小样本问题。由于电池健康实验周期较长,样本量较小,SVR非常适合用于SOH预测。同时,通过IASO获取最优参数组,C和σ在SVR模型的训练中控制泛化能力和拟合能力,即控制预测的准确度和验证的准确度。Support vector regression (SVR) is an extension of support vector machine in the field of regression. Due to its good generalization ability and fast convergence speed, it is often used to solve nonlinear and small sample problems. Due to the long battery health experiment period and small sample size, SVR is very suitable for SOH prediction. At the same time, the optimal parameter set is obtained through IASO, and C and σ control the generalization ability and fitting ability in the training of the SVR model, that is, controlling the accuracy of prediction and verification accuracy.
对训练模型的定义如下:The training model is defined as follows:
给定一个样本集
其中x
i是第i个输入特征向量,y
i是对应的输出值,n为样本集个数总量。支持向量回归的表达式为:
Given a sample set Where x i is the i-th input feature vector, y i is the corresponding output value, and n is the total number of sample sets. The expression of support vector regression is:
f(x)=ωφ(x)+b (13)f(x)=ωφ(x)+b (13)
其中f(x)是输出值,x,ω,b分别是输入值、权重和截距。where f(x) is the output value, x, ω, and b are the input value, weight and intercept respectively.
其中,c是影响SVR模型泛化能力的惩罚因子,ε是回归的最大允许误差,通过引入拉格朗日算子可得:Among them, c is the penalty factor that affects the generalization ability of the SVR model, and ε is the maximum allowable error of regression, which can be obtained by introducing the Lagrangian operator:
其中i=1,2,…,n,
和α
i是拉格朗日算子,通过引入核函数,SVR方程转换为:
where i=1,2,…,n, and α i are Lagrangian operators. By introducing the kernel function, the SVR equation is transformed into:
其中K(x
i,x
j)=φ(x
i)φ(x
j)是核函数,x
i和x
j分别是训练样本集和测试样本集中的输入向量。选择径向基(RBF)函数作为核函数,其定义为:
Among them, K( xi , x j )=φ( xi )φ(x j ) is the kernel function, x i and x j are the input vectors in the training sample set and the test sample set respectively. The radial basis (RBF) function is selected as the kernel function, which is defined as:
其中σ是RBF核函数的带宽,影响SVR模型的拟合能力。where σ is the bandwidth of the RBF kernel function, which affects the fitting ability of the SVR model.
由于c和σ是为IALO中计算生成的最优参数,在SVR模型中对预测的误差和验证的准确进行较好的控制。Since c and σ are optimal parameters calculated and generated in IALO, the prediction error and verification accuracy are better controlled in the SVR model.
为了验证本申请方案的性能,可以采用平均绝对误差(MAE)或者均方根误差(RMSE)的方式进行评价。它们都可以通过以下方式计算:In order to verify the performance of the solution of this application, the mean absolute error (MAE) or root mean square error (RMSE) can be used for evaluation. They can all be calculated by:
平均绝对误差算法为:The mean absolute error algorithm is:
均方根误差算法为:The root mean square error algorithm is:
其中
和y
i分别是第i个SOH的预测值和实际值,n是样本数。
in and yi are the predicted value and actual value of the i-th SOH respectively, and n is the number of samples.
运行算法直到迭代结束,用对应的精英蚁狮输出参数(c,σ),然后用所输出的参数估计电池SOH。Run the algorithm until the end of the iteration, use the corresponding elite antlion output parameters (c, σ), and then use the output parameters to estimate the battery SOH.
本申请还提供了(Back-Propagation神经网络)BP、ALO-SVR和IALO-SVR三种方法对三组电池SOH进行比校,对应三块锂电池,标号分别为B05、B06和B07。This application also provides three methods (Back-Propagation neural network) BP, ALO-SVR and IALO-SVR to compare the SOH of three sets of batteries, corresponding to three lithium batteries, labeled B05, B06 and B07 respectively.
图5为不同方法的SOH估计结果,其中P510、P520、P530分别为B05、B06和B07的估计结果,当前60%的样本被作为训练集时,从图中可以看出,IALO-SVR估计的SOH是最准确的,而其他两种方法的拟合度不够高,尤其是BP。IALO-SVR对B05、B06和B07的SOH估计得更准确,而对于BP,在遇到数据波动时,估计能力严重下降,导致最终估计的SOH偏差较大。ALO-SVR在早期有较好的估计精度,但在后期也逐渐偏离真实的SOH。在后期,IALO-SVR的预测精度与ALO-SVR相比仍然很高,这是由于普通蚁狮惯性权重的动态调整造成的。Figure 5 shows the SOH estimation results of different methods, where P510, P520, and P530 are the estimation results of B05, B06, and B07 respectively. When the first 60% of the samples are used as the training set, it can be seen from the figure that the IALO-SVR estimation SOH is the most accurate, while the other two methods do not fit well enough, especially BP. IALO-SVR estimates the SOH of B05, B06, and B07 more accurately, while for BP, the estimation ability is seriously degraded when encountering data fluctuations, resulting in a larger deviation in the final estimated SOH. ALO-SVR has better estimation accuracy in the early stage, but also gradually deviates from the true SOH in the later stage. In the later period, the prediction accuracy of IALO-SVR is still very high compared with ALO-SVR, which is caused by the dynamic adjustment of the inertial weight of ordinary antlion.
图6显示了三种方法的SOH估计误差,其中图P610、P620、P630分别为B05、B06和B07的估计误差,其估计误差均控制在2%以内,没有大的波动。Figure 6 shows the SOH estimation errors of the three methods. Figures P610, P620, and P630 are the estimation errors of B05, B06, and B07 respectively. The estimation errors are all controlled within 2% without large fluctuations.
图7绘制了三种算法的性能评价指标,即平均绝对值误差(MAE)和均方根误差(RMSE),对应值越小,即误差越小,性能越好。与ALO-SVR和BP相比,IALO-SVR的估计性能明显提高。对于三种类型的电池,IALO-SVR的最大MAE和RMSE分别为0.64%和0.70%。与ALO-SVR相比,IALO-SVR的MAE和RMSE平均减少0.998和1.394。此外,与BP相比,IALO-SVR的MAE和RMSE也大大降低,平均为2.1422和2.5587。此外,从图中我们还可以看出,在B05的SOH估计性能中,BP和ALO-SVR与IALO-SVR的差异相对较小,而B06和B07 的SOH估计性能明显落后于IALO-SVR,这是因为B06和B07的SOH曲线波动较大,所以BP和ALO-SVR的拟合能力和泛化能力不如IALO-SVR。Figure 7 plots the performance evaluation indicators of the three algorithms, namely mean absolute error (MAE) and root mean square error (RMSE). The smaller the corresponding value, that is, the smaller the error, the better the performance. Compared with ALO-SVR and BP, the estimation performance of IALO-SVR is significantly improved. For the three battery types, the maximum MAE and RMSE of IALO-SVR are 0.64% and 0.70%, respectively. Compared with ALO-SVR, the MAE and RMSE of IALO-SVR are reduced by 0.998 and 1.394 on average. In addition, compared with BP, the MAE and RMSE of IALO-SVR are also greatly reduced, averaging 2.1422 and 2.5587. In addition, we can also see from the figure that in the SOH estimation performance of B05, the difference between BP and ALO-SVR and IALO-SVR is relatively small, while the SOH estimation performance of B06 and B07 lags significantly behind IALO-SVR. This It is because the SOH curves of B06 and B07 fluctuate greatly, so the fitting ability and generalization ability of BP and ALO-SVR are not as good as IALO-SVR.
图8是本发明提供的评估装置结构图,如图所示,包括以下模块:Figure 8 is a structural diagram of the evaluation device provided by the present invention. As shown in the figure, it includes the following modules:
P810数据处理模块:用于锂电池数据采集,提取健康因子构建特征向量,生成训练样本和测试样本;P810 data processing module: used for lithium battery data collection, extracting health factors to construct feature vectors, and generating training samples and test samples;
本模块中采集的健康因子包括:等压升时间、平均放电电压和等压降时间;生成的训练样本和测试样本的占比分别为60%和40%。The health factors collected in this module include: equal voltage rise time, average discharge voltage and equal voltage drop time; the proportions of generated training samples and test samples are 60% and 40% respectively.
P820算法控制模块:用于确定锂电池SOH状态的预估算法,定义算法参数和输出参数组;所述定义算法参数包括定义蚂蚁和蚁狮的种群数量、适应度函数的维数、最大迭代次数;所述输出参数组包括空间向量(c,σ),所述c和σ的上限设为100,下限设为0.01;P820 algorithm control module: a prediction algorithm for determining the SOH state of lithium batteries, defining algorithm parameters and output parameter groups; the defined algorithm parameters include defining the population size of ants and ant lions, the dimensionality of the fitness function, and the maximum number of iterations ;The output parameter group includes a space vector (c, σ), the upper limit of c and σ is set to 100, and the lower limit is set to 0.01;
算法控制模块还包括P821适应度值控制单元,用于为输出最优输出参数组计算适应度值,对蚂蚁和蚁狮的适度值排序,将最大值设为精英蚁狮,对应参数为最优输出参数组,其中,适应度值的计算方法支持均方误差(MSE)实际值和预测值之间的适应度函数,其表达式为:The algorithm control module also includes the P821 fitness value control unit, which is used to calculate the fitness value for the output of the optimal output parameter group, sort the fitness values of ants and ant lions, set the maximum value to the elite ant lion, and the corresponding parameters are optimal Output parameter group, in which the calculation method of fitness value supports the fitness function between the mean square error (MSE) actual value and the predicted value, and its expression is:
其中
为第i个预测值,y
i为第i个实际值,n是训练集数量。
in is the i-th predicted value, yi is the i -th actual value, and n is the number of training sets.
P830算法优化模块:用于改进所述预估算法,输出最优输出参数组,所述改进内容包括调整精英蚁狮和普通蚁狮的随机游走对应权重值;P830 algorithm optimization module: used to improve the prediction algorithm and output the optimal output parameter group. The improvement includes adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions;
算法优化模块定义的调整精英蚁狮和普通蚁狮的随机游走对应权重值的计算方法为:
The calculation method for adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions defined by the algorithm optimization module is:
其中,γ
max为惯性权重最大值,为惯性权重最小值。
Among them, γ max is the maximum value of inertia weight and is the minimum value of inertia weight.
P840模型训练和数据输出模块:用于通过支持向量回归模型对所述测试 样本集进行预测,输出所述SOH预估值。P840 model training and data output module: used to predict the test sample set through the support vector regression model and output the SOH estimated value.
通过本申请提出的对锂电池SOH状态的预估方案,可以减少迭代次数,有效地提升SOH预测精度,改善了电池管理系统的安全性与稳定性。Through the prediction scheme for lithium battery SOH status proposed in this application, the number of iterations can be reduced, the SOH prediction accuracy can be effectively improved, and the safety and stability of the battery management system can be improved.
以上公开的仅为本发明的几个具体实施例,但是,本发明并非局限于此,任何本领域的技术人员能思之的变化都应落入本发明的保护范围。The above disclosures are only a few specific embodiments of the present invention. However, the present invention is not limited thereto. Any changes that can be thought of by those skilled in the art should fall within the protection scope of the present invention.
Claims (10)
- 一种锂电池SOH状态的预估方法,其特征在于,包括以下步骤:A method for estimating SOH status of lithium batteries, which is characterized by including the following steps:锂电池数据采集,提取健康因子构建特征向量,生成训练样本和测试样本,所述健康因子为电池容量衰退趋势相关的元素;Collect lithium battery data, extract health factors to construct feature vectors, and generate training samples and test samples. The health factors are elements related to the battery capacity decline trend;确定锂电池SOH状态的预估算法为蚁狮优化算法,定义算法参数和输出参数组;The prediction method for determining the SOH state of lithium batteries is the Ant Lion optimization algorithm, which defines algorithm parameters and output parameter groups;改进所述预估算法,输出最优输出参数组,所述改进指:通过调整精英蚁狮和普通蚁狮的随机游走对应权重值,控制不同迭代阶段搜索平衡,输出最优输出参数组,所述不同迭代阶段搜索平衡为:迭代前期、中期以全局搜索为主,迭代后期以局部搜索为主;Improve the prediction method and output the optimal output parameter group. The improvement means: by adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions, control the search balance in different iteration stages, and output the optimal output parameter group, The search balance in different iteration stages is: global search is the main focus in the early and middle stages of the iteration, and local search is the main focus in the late iteration stage;结合所述最优输出参数组,通过支持向量回归模型对所述测试样本集进行预测,输出所述SOH预估值。Combined with the optimal output parameter group, the test sample set is predicted through a support vector regression model, and the SOH estimated value is output.
- 根据权利要求1所述的锂电池SOH状态的预估方法,其特征在于,所述定义算法参数包括:The method for estimating the SOH state of a lithium battery according to claim 1, wherein the defined algorithm parameters include:蚂蚁和蚁狮的种群数量设为Agents_no=30,The population size of ants and ant lions is set to Agents_no=30,适应度函数的维数设为dim=2,The dimension of the fitness function is set to dim=2,最大迭代次数设为Max_iter=100,The maximum number of iterations is set to Max_iter=100,所述输出参数组为代表和蚂蚁和蚁狮的位置的空间向量(C,σ),所述C和σ的上限设为100,下限设为0.01。The output parameter group is a space vector (C, σ) representing the positions of ants and ant lions. The upper limit of C and σ is set to 100, and the lower limit is set to 0.01.
- 根据权利要求2所述的锂电池SOH状态的预估方法,其特征在于,所述通过精英蚁狮和普通蚁狮的随机游走对应权重值控制不同迭代阶段搜索平衡指:根据迭代次数非线性动态调整普通蚁狮惯性权重γ,计算方法为:The method for estimating the SOH state of lithium batteries according to claim 2, characterized in that the weight value corresponding to the random walk of elite ant lions and ordinary ant lions controls the search balance in different iteration stages: non-linear according to the number of iterations Dynamically adjust the inertia weight γ of the ordinary ant lion. The calculation method is:其中,γ max为惯性权重最大值,γ min为惯性权重最小值。 Among them, γ max is the maximum value of inertia weight, and γ min is the minimum value of inertia weight.
- 根据权利要求1所述的锂电池SOH状态的预估方法,其特征在于,所述输出最优输出参数组前还包括计算适应度值,对蚂蚁和蚁狮的适度值排序,最大值设为精英蚁狮,对应参数为最优输出参数组,其中,适应度值的计算方法为均方误差(MSE)实际值和预测值之间的适应度函数,其表达式为:The method for estimating SOH status of lithium batteries according to claim 1, characterized in that before outputting the optimal output parameter group, the method further includes calculating fitness values, sorting the fitness values of ants and ant lions, and the maximum value is set to For Elite Ant Lion, the corresponding parameter is the optimal output parameter group, in which the fitness value is calculated as the fitness function between the mean square error (MSE) actual value and the predicted value, and its expression is:
- 根据权利要求1所述的锂电池SOH状态的预估方法,其特征在于,所述健康因子包括:等压升时间、平均放电电压和等压降时间;The method for estimating the SOH state of a lithium battery according to claim 1, wherein the health factors include: equal voltage rise time, average discharge voltage and equal voltage drop time;所述训练样本和测试样本的占比分别为60%和40%。The proportions of training samples and test samples are 60% and 40% respectively.
- 根据权利要求1所述的的锂电池SOH状态的预估方法,其特征在于,所述通过支持向量回归模型对所述测试样本集进行预测包括预测结果分析,包括使用绝对误差和均方根误差作为评价标准,计算方式如下:The method for estimating the SOH state of a lithium battery according to claim 1, wherein the prediction of the test sample set through a support vector regression model includes prediction result analysis, including using absolute error and root mean square error. As the evaluation criteria, the calculation method is as follows:
- 一种锂电池SOH状态的预估装置,其特征在于,包括:A device for estimating the SOH state of a lithium battery, which is characterized by including:数据处理模块:用于锂电池数据采集,提取健康因子构建特征向量,生成训练样本和测试样本;Data processing module: used to collect lithium battery data, extract health factors to construct feature vectors, and generate training samples and test samples;算法控制模块:用于确定锂电池SOH状态的预估算法,定义算法参数和输出参数组;所述定义算法参数包括定义蚂蚁和蚁狮的种群数量、适应度函数的维数、最大迭代次数;所述输出参数组包括空间向量(C,σ),所述C和σ的上限设为100,下限设为0.01;Algorithm control module: a prediction algorithm for determining the SOH state of lithium batteries, defining algorithm parameters and output parameter groups; the defined algorithm parameters include defining the population size of ants and ant lions, the dimensionality of the fitness function, and the maximum number of iterations; The output parameter group includes a space vector (C, σ), the upper limit of C and σ is set to 100, and the lower limit is set to 0.01;算法优化模块:用于改进所述预估算法,输出最优输出参数组,所述改 进内容包括调整精英蚁狮和普通蚁狮的随机游走对应权重值;Algorithm optimization module: used to improve the prediction algorithm and output the optimal output parameter group. The improvement includes adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions;模型训练和数据输出模块:用于通过支持向量回归模型对所述测试样本集进行预测,输出所述SOH预估值。Model training and data output module: used to predict the test sample set through a support vector regression model and output the SOH estimate.
- 根据权利要求7所述的锂电池SOH状态的预估装置,其特征在于,所述算法优化模块定义的调整精英蚁狮和普通蚁狮的随机游走对应权重值的计算方法为: The device for estimating the SOH state of a lithium battery according to claim 7, wherein the calculation method defined by the algorithm optimization module for adjusting the weight values corresponding to the random walks of elite ant lions and ordinary ant lions is:其中,γ max为惯性权重最大值,γ min为惯性权重最小值,且γ max+γ min=1。 Among them, γ max is the maximum value of the inertia weight, γ min is the minimum value of the inertia weight, and γ max + γ min =1.
- 根据权利要求7所述的锂电池SOH状态的预估装置,其特征在于,所述算法控制模块包括适应度值控制单元,用于为输出最优输出参数组计算计算适应度值,对蚂蚁和蚁狮的适度值排序,将最大值设为精英蚁狮,对应参数为最优输出参数组,其中,适应度值的计算方法支持均方误差(MSE)实际值和预测值之间的适应度函数,其表达式为:The device for estimating the SOH state of a lithium battery according to claim 7, wherein the algorithm control module includes a fitness value control unit for calculating the fitness value for outputting the optimal output parameter group, for ants and The fitness value of ant lions is sorted, and the maximum value is set as the elite ant lion, and the corresponding parameter is the optimal output parameter group. Among them, the calculation method of the fitness value supports the fitness between the mean square error (MSE) actual value and the predicted value. function, its expression is:
- 根据权利要求7所述的一种锂电池SOH状态的预估装置,其特征在于,所述数据处理模块采集的所述健康因子包括:等压升时间、平均放电电压和等压降时间;A device for estimating the SOH state of a lithium battery according to claim 7, wherein the health factors collected by the data processing module include: equal voltage rise time, average discharge voltage and equal voltage drop time;所述数据处理模块控制所述样本和测试样本的占比分别为60%和40%。The data processing module controls the proportions of the samples and test samples to be 60% and 40% respectively.
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