Lithium-ion battery SOC/SOH adaptive estimation via simplified single particle model

Compared with battery Equivalent Circuit Models (ECM), Single Particle Model (SPM) has more appropriate physics representation and higher accuracy theoretically. However, SPM-based parameter estimation performance is restricted by the SPM model complexities. In this paper, a simplified SPM and its corresponding adaptive State of Charge (SOC)/State of Health (SOH) estimation scheme are studied. First, the SPM is simplified from Partial Differential Equation (PDE) to Ordinary Differential Equation (ODE) for a trade-off between model complexity and consistency. Second, an adaptive model observer is proposed to estimate battery parameters, which include a SOC state implying normalized lithium-ion concentration, and a SOH parameter implying the maximum lithium-ion surface concentration, both in the solid surface phase. Because the ODE-based adaptive parameter estimation is capable of avoiding complex identification procedures, this new approach can be implemented in practical applications with high accuracy. Through massive simulation scenarios, the proposed SPM model is validated based on comparison between ODE SPM and PDE SPM, as well as Benchmark Validation. Finally, both simulation and experiment demonstrate the effectiveness of the simplified SPM and the superiority of the proposed SOC/SOH estimation scheme.