Particle swarm optimization based sliding mode controllers for electric vehicle onboard charger

High-performance onboard chargers are needed to proliferate the popularity of electric vehicles considering the charging convenience and flexibility. In this study, a sliding mode controller (SMC) for power converters is presented to enhance the performance of the onboard battery charger. The SMC parameters are selected using the particle swarm optimization (PSO) algorithm. A two-stage charging topology is adopted with independent controllers. The grid side AC–DC converter mitigates the harmonic content in the grid current and smoothens the voltage and current in the dc bus. The bidirectional DC–DC converter at the battery side regulates the charging characteristics of the Li-ion battery with a constant current–constant voltage (CC–CV) profile. The steady-state and dynamic behavior of the charging system is presented. Hardware in the loop real-time emulator is utilized to confirm the validity of this study. Experiments show that optimization-based SMC contributes to the overall dynamic performance of the onboard battery charger. [Display omitted] •Sliding mode controller is designed to charge Li-ion battery in electric vehicles.•Particle swarm optimization is used to select the sliding mode parameters.•Analysis is performed under constant-current–constant-voltage charging profile.•Optimized controller resulted in robust performance of onboard battery charger.•Experimental validation is implemented using hardware in the loop platform.