Abstract It is difficult for existing methods to solve the real-time accuracy problem of battery module-level state of charge (SoC) and the impact of single battery inconsistency at the same time under dynamic operating conditions. The integration of data-driven technology and traditional algorithms is insufficient, resulting in limited error compensation effect. In view of the accuracy of the SoC estimation of electric vehicle (EV) battery packs under dynamic driving conditions, this paper proposes a hybrid SoC estimation method for battery management system (BMS) based on cloud master-slave architecture. The hybrid framework combines direct measurement methods (Coulomb counting method, open circuit voltage method), state estimation algorithms (extended Kalman filtering, traceless Kalman filtering), and data-driven technologies (neural networks, NARMA L-2 models), and verifies its effectiveness through hardware-in-the-loop experiments. The research results show that under dynamic operating conditions, the hybrid Coulomb counting+neural network (CC+NN) method has the fastest error convergence speed and is better than other methods. In addition, the proposed cloud master-slave BMS architecture significantly improves system reliability through real-time cross-verification of the SoC data of the advanced algorithms of the on-board BMS (slave device) and the master device. The experiment is based on the FTP-75 driving cycle and verifies the high efficiency of this method in practical applications.The final analysis shows that the CC+NN combination exhibits optimal error suppression ability in complex scenarios, and provides a high-precision solution for electric vehicle battery management.
Bose et al. (Tue,) studied this question.
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