Computer Modeling and Parameter Estimation of Power Battery Performance for New Energy Vehicles under Hot Working Conditions

Abstract

With the aggravation of environmental pollution problems and the reduction of non-renewable energy sources such as oil, new energy vehicles have gradually become the focus of attention, and the application of their power batteries has become more and more widespread. The state of energy (SOE) of the power battery is an important basis for energy scheduling. Therefore, the study used computer technology to develop an analogous model of the power battery and evaluated its properties at various temperatures in order to precisely analyze the performance of the battery under thermal conditions. At the same time, to address the limitations in parameter estimation, the study uses the improved Kalman filter (KF) algorithm to optimize it. The results revealed that the estimation errors of the improved cubature Kalman filter (CKF) algorithm were reduced by 0.52%, 2.91% and 3.10% compared with the traditional CKF algorithm, EKF algorithm and UKF algorithm, respectively. In summary, the research on computer modeling and parameter estimation of the performance of new energy vehicle power batteries under hot working conditions provides important support and reference for the efficient operation and safety of new energy power batteries under hot working conditions.