基于集总质量模型的柱状锂离子电池荷电状态与核心温度估计

Reliable estimation of the state of charge (SoC) and core temperature (CoT) of battery cells is paramount for formulating efficient energy and thermal management strategies. Focusing on cylindrical Li-ion batteries, this article constructs an equivalent circuit model and a two-state thermal model; then these two different-physics lumped-mass models are close-looped using bridge variables encompassing temperature, heat, and SoC. Notably, in addition to the conventional irreversible thermogenesis of ohmic effect, the generally ignored reversible entropy heat is modeled and experimentally calibrated as well. Then, both the electrical and thermal model parameters are adaptively identified using the variable forgetting factor least square algorithm. Finally, a computationally efficient and nonlinearity-compatible algorithm, namely the singular value decomposition-based Kalman filter, is utilized for the joint estimation of SoC and CoT. Experimental validations under dynamic load excitations demonstrate the robustness and accuracy of the designed scheme, achieving favorable performance with errors as low as 5% for SoC and 0.2 °C for CoT.