Associate professor
Supervisor of Master's Candidates
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Journal:JOURNAL OF MECHANICAL ENGINEERING
Key Words:lithium-ion battery; state of health estimation; feature selection; random forest algorithm
Abstract:State of health (SOH) estimation plays a significant role in the battery management system for electric vehicles. Accurate estimation of the SOH is conducive to extending the lifespan of lithium-ion batteries and ensuring vehicles' safe and reliable operation. Aiming at the problem that the previous data-driven methods cannot balance the accuracy of SOH estimation and the cost of model calculation, a solution based on feature optimization is proposed. Firstly, several features are extracted based on the partial charging voltage curve and incremental capacity curve. Moreover, the importance of each feature is calculated by the Gini coefficient in the random forest algorithm. Then, the optimal feature subset is selected by considering the estimation accuracy of the model and the feature number of the selected subset. Finally, the random forest algorithm is employed to establish the battery aging model and estimate the SOH. The results show that the mean absolute error and root mean square error of the proposed SOH estimation method are within 0.4 % and 0.5 %, respectively. Here, the most relevant feature set can be selected by the developed feature optimization strategy. Hence, combined with the random forest algorithm, it can achieve higher SOH estimation accuracy while reducing the calculation cost of the model.
Indexed by:Journal paper
Discipline:Engineering
Document Type:J
Page Number:Early Access
Translation or Not:no
Date of Publication:2022-06-15
Included Journals:EI
