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Prediction for state of health of lithium-ion battery by improved grey wolf optimization and support vector regression


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Prediction for state of health of lithium-ion battery by improved grey wolf optimization and support vector regression
Li Longgang1Li Liwei1Yang Yuxin2Luo Yu3
1.College of Electrical Engineering; 2.Library,Qingdao University,Qingdao 266071,China; 3.Weifang Product Quality Inspection Institute,Weifang 261000,China
lithium-ion batteries state of health improved grey wolf optimization algorithm support vector regression parameter joint optimization
To improve the prediction accuracy of state of health(SOH)for lithium-ion batteries,a joint algorithm based on the improved grey wolf optimization and the support vector regression(IGWO-SVR)is proposed by combining improved grey wolf optimization(IGWO)algorithm with the support vector regression(SVR). The core idea of this algorithm is to use the IGWO algorithm to solve the problem of parameter joint optimization in the SVR model. The IGWO-SVR generates a wolf population randomly. The position vector of the wolf is composed of C,σ,ε three parameters of the SVR model. The location information of each grey wolf is used to learn,and the fitness is calculated. The wolves are graded according to the fitness value,and the locations of individual grey wolves are updated. Then the differential evolution operation is carried out to select excellent individuals to enter the next generation population,and the fitness of gray wolf individuals in the new position is recalculated. At the end of the iteration process,the grey wolf location information with optimal fitness in the wolves is extracted and trained as the final SVR model parameters. Experiment on the lithium-ion batteries datasets of NASA demonstrates the effectiveness of the proposed SOH prediction method.


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Last Update: 2020-04-20