Abstract
Condition monitoring for batteries involves tracking changes in physical parameters and operational states such as state of health (SOH) and state of charge (SOC), and is fundamentally important for building high-performance and safety-critical battery systems. A model-based condition monitoring strategy is developed in this paper for Lithium-ion batteries on the basis of an electrical circuit model incorporating hysteresis effect. It systematically integrates 1) a fast upper-triangular and diagonal recursive least squares algorithm for parameter identification of the battery model, 2) a smooth variable structure filter for the SOC estimation, and 3) a recursive total least squares algorithm for estimating the maximum capacity, which indicates the SOH. The proposed solution enjoys advantages including high accuracy, low computational cost, and simple implementation, and therefore is suitable for deployment and use in real-time embedded battery management systems (BMSs). Simulations and experiments validate effectiveness of the proposed strategy.
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