Multi-objective decision analysis for data-driven based estimation of battery states: A case study of remaining useful life estimation

Abstract

Data-driven methods, which can explore the relationship among battery external parameters and battery states automatically without establishing complicated battery model, have been intensively applied to estimate state of charge (SOC), state of health (SOH) and remaining useful life (RUL) etc. Nevertheless, relatively few researches have been done on the selection of data-driven model parameters and the determination of model with the optimal comprehensive performance. To address these questions, this paper presents a multi-objective decision method for data-driven based estimation of battery states. This method adopts the combination of the analytic hierarchy process and the entropy weight method together with integrating subjective and objective weights. The mean absolute error and root squared mean error of training-set, validation-set and test-set are used as accuracy indexes, and modeling time is seen as computation burden index. These seven indexes are applied as objective criteria for the multi-objective evaluation method, successfully evaluating the comprehensive performance of estimation model. Moreover, with three cases for RUL estimation, the specific application process of selecting the model with the optimal comprehensive performance by the proposed method is presented in detail.