HIGH-PRECISION MODELLING-BASED POWER BATTERY STATE ESTIMATION AND ACTIVE BALANCING

Ye, Xin and Song, Yongwen and Csiszár, Csaba and Csonka, Bálint and Földes, Dávid (2024) HIGH-PRECISION MODELLING-BASED POWER BATTERY STATE ESTIMATION AND ACTIVE BALANCING. INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 20 (3). pp. 965-978. ISSN 1349-4198

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Abstract

Power battery state estimation is the cornerstone of BMS (Battery Management System), and active balancing technology is the core function of BMS. The accuracy of state estimation is limited by the accuracy of the model. To improve the accuracy of state estimation, the solid-state diffusion in the electrochemical mechanism is considered to update the open-circuit voltage, and a high-precision battery model is obtained. Next, the applicability of three estimation algorithms is compared, and joint estimation of SOC (State of Charge) and SOH (State of Health) is achieved based on the high-precision model and dual-capacity Kalman filtering, and the effect of model improvement and algorithm optimization is simulated and verified under multiple dynamic working conditions. Finally, a “peak shaving and valley filling” parallel balancing strategy is formulated using battery SOC as the variable, and simulation is conducted on a bidirectional flyback converter balancing system for four single cells under different degrees of imbalance with a discreteness value of 10%. The results show that battery state estimation based on a highprecision model can effectively reduce estimation errors throughout the full SOC range and improve inconsistency among the cells in the battery pack with the active balancing technology. © 2024, ICIC International. All rights reserved.

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