실시간 전기자동차 주행 데이터를 이용한 배터리 건강상태(SOH) 추측 알고리즘 연구

초록

Recently, the number of electric vehicles has increased due to carbon reduction policies. As the number of electric vehicles registrations increases, research is being conducted to evaluate the batteries installed in electric vehicles. In this study, battery evaluation was performed using real-time driving data of electric vehicles. The RLS (Recursive Least Squares) algorithm and the DEKF (Double Extended Kalman Filter) algorithm were applied using MATLABTM, and BMS data and simulation data were compared for the battery SOC and SOH. As a result, the S OC error rate was confirmed to be less than 2.5% and S OH less than 5.0%. We plan to continuously improve the accuracy of the algorithm by collecting operating data for more than one year.

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