Abstract
The accurate estimation of battery state of charge (SOC) and state of health (SOH) is essential for the battery management system in automotive and stationary energy storage systems. However, the nonlinear dynamics of battery characteristics due to temperature and aging seriously degrade the state estimation accuracy. In this paper, an advanced fusion estimation method for battery SOC and SOH is proposed considering the effects of temperature and aging. Firstly, to reduce the computational complexity and achieve a stable identification of polarization parameters, an offline and online combined parameter identification method is proposed. Second, the adaptive unscented Kalman filter is adopted for estimation of battery SOC and capacity with the noise covariances updated adaptively by Sage-Husa algorithm. Different from the existing SOH determination method, the back propagation neural network (BPNN) is employed to characterize the relationship of SOH with the estimated capacity and temperature. Finally, the proposed fusion method is thoroughly verified with dynamic profiles at varied temperatures and aging statuses. The experimental results present the superiority of the proposed method with the RMSE of SOC and SOH estimation <1.2% and 2.5%, respectively.
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