A Multi-model Battery State of Charge Fusion Estimation Method for Electric Vehicles

What is it about?

Uncertainty prediction of Lithium-ion battery state-of-charge (SOC) is a key in battery management systems (BMS) for electric vehicles (EV). Aiming at the shortcomings of a single equivalent circuit model (ECM) and traditional SOC fusion estimation algorithms, this paper proposes a novel multi-model SOC fusion method. Initially, three sub-models are established. Then, an adaptive extended Kalman filter (AEKF) is applied to each sub-model in parallel to predict the battery terminal voltage and SOC simultaneously. Next, based on the ordered weighted averaging (OWA) operator theory, the real covariance matrix of the output voltage error of each model is obtained, and the weight factor of each sub-model is calculated by using this matrix. Finally, the SOC estimation of each model is weighted and synthesized to realize the SOC fusion estimation. The experimental results demonstrated that the proposed multi-model SOC fusion estimation method has superior comprehensive performance than the single model and traditional SOC fusion estimation.

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Photo by Michael Marais on Unsplash

Photo by Michael Marais on Unsplash

Why is it important?

Aiming at increasing battery SOC estimation accuracy, a novel multi-model SOC fusion estimation method, which adopt ordered weighted averaging (OWA) operator, is proposed in this paper.

Perspectives