A Novel Dual H Infinity Filters Based Battery Parameter and State Estimation Approach for Electric Vehicles Application

An accurate battery parameter and state estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. This paper tries to make three aspects of effort. First, to avoid the battery state-of-charge (SoC) estimation inaccuracy brought by the variation of the model parameter under different aging level and operation condition, a novel dual H infinity filters was proposed and employed to execute the online measured data based battery parameter and SoC estimation. Second, to overcome the drawback of the H infinity filters are sensitive to their initial noise information. An adaptive H infinity filter employing the covariance matching approach was proposed and applied to realize a robust SoC estimation. Last, the accurate estimate of battery parameter and SoC were obtained real-timely through model-based dual H infinity filters. A systematic evaluation on the different algorithms based SoC estimation was carried out. Experimental results on various degradation states of lithium-ion polymer battery cells further verified the feasibility of the proposed approach.