Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on a New Adaptive Nonlinear Observer

Estimation of accurate state of charge (SOC) and state of health (SOH) of lithium-ion batteries has become more difficult in electric vehicles due to various uncertainties in the battery. The main objective of this paper is to estimate the accurate and robust SOC and SOH of the lithium-ion battery. Here, a first-order resistor-capacitor (RC) electrical equivalent circuit model is considered for the analysis and modeling, an adaptive nonlinear observer (ANO) is proposed to convert nonlinear equations into linearized equations. The transfer function is obtained from the linearized equations to estimate the actual parameters and the internal states of the battery. When comparing the proposed ANO to the conventional method extended Kalman filter, the new ANO gives better dynamic results, less SOC error, and high convergence capability. An extra random variable noise is added to the system; the proposed model is suitable for the SOC at current noise factors. The convergence capability of the observer is analyzed with the linear criterion. The simulation results of the proposed ANO are validated through the MATLAB/Simulink platform under the hybrid pulse power characteristics test.