Hardware-in-the-loop Implementation of ANFIS based Adaptive SoC Estimation of Lithium-ion Battery for Hybrid Vehicle Applications

This paper presents an effective method to estimate the state of charge (SoC) of a Lithium-ion battery. This parameter is very crucial as it indicates the performance and health of the battery. The battery SoC estimation equivalent circuit provided in MATLAB has been modified by adding the 3-RC pairs in series with its internal resistance. The values of the RC pairs have been calculated mathematically by solving the circuit model, based on charging and discharging dynamics of the battery. The values of these parameters have also been optimized using a lsqnonlin function. The SoC of the battery is estimated using the combination of coulomb counting and open-circuit voltage methods to minimize the error in estimation. The obtained SoC is further corrected for errors using ANFIS based algorithms. The effect of temperature has also been accounted for modelling the battery and in SoC estimation. These obtained SoCs for 3 cases, i.e. without RC/with RC pairs and then tuned with ANFIS based optimization are compared for the same load. The parameter calculation method adopted here results in an efficient and accurate model that keeps track of correct battery SoC. The complete system is validated in real-time using hardware-in-the-loop laboratory setup.