A robust BCNMCC based variance smoothing approach for SPV based grid integrated EV battery charging system

The demand for good quality charging infrastructure is increasing rapidly as the number of electric vehicles (EVs) are increasing day by day. Nowadays, the contribution of non-conventional and distributed energy sources is rising. Consequently, the solar energy-based charging station is anticipated to grow. Therefore, this paper pre-sents a multifunctional solar photovoltaic (SPV) based single-phase grid-integrated charging station reinforced with an EV battery energy storage (EVBES). The support of EVBES also improves the viability and reliability of the charging station. The voltage source converter (VSC) facilitates harmonic mitigation, reactive power compensation (RPC) and power factor correction (PFC), under different practical situations besides providing the charging to the EVBES. The robust bias-compensated normalized maximum correntropy criterion (BCNMCC) based adaptive filtering approach is utilized to compute the real power component of the nonlinear load current. It minimizes the mean square error and improves dynamic response in varying ambiance situations. The per-formance of the charging system is observed in the MATLAB-Simulink platform. An experimental prototype of proposed model is developed and tested at various practical situations. The recorded results demonstrate satisfactory operation of charging station in laboratory environment.

Automated summary

The demand for high-quality charging infrastructure is increasing due to the rising number of electric vehicles. Solar energy-based charging stations are expected to grow as non-conventional and distributed energy sources gain more importance. This paper introduces a multifunctional solar photovoltaic based charging station with EV battery energy storage, which improves the viability and reliability of the station. The voltage source converter provides harmonic mitigation, reactive power compensation, and power factor correction, while the adaptive filtering approach minimizes errors and enhances dynamic response.