Higher order robust nonlinear controller for energy management in plugin hybrid electric vehicles with a hybrid energy storage system

Abstract

Plugin hybrid electric vehicles (PHEVs) appear an appropriate choice as they offer an extended driving range and an improved fuel economy performance in comparison to conventional pure electric vehicles. Its fuel performance firmly depends on the energy management algorithm. This study proposes an integrated charging mechanism which enhances the overall performance of energy sources in plugin hybrid electric vehicles. The hybrid energy stor age system (HESS) used in this work comprises battery packs as the main energy source, supercapacitor and fuelcell packs as auxiliary sources, each of them coupled to the DC bus through DC-DC converters. Similarly, in the integrated charging approach, a unidirectional buck DC-DC converter has been allocated for controlling the state of charge of the battery. This brief investigates the design of an efficient, adaptive, and fast converging nonlinear control scheme termed as an adaptive integral backstepping controller with adaptive laws for plugin hybrid electric vehicles involving HESS. A genetic algorithm is used for tuning the controller parameters. The primary goal of the proposed controller is to adapt slowly varying parameters of the system, get efficient tracking performance of the battery, supercapacitor, and fuelcell currents to their desired values, and to attain stable regulation of DC bus voltage. The asymptotic stability of the HESS is confirmed by employing the Lyapunov stability criteria. The performance and robustness of the proposed control methodology has been verified by simulating on MATLAB/Simulink environment and the results are then compared with a conventional backstepping and Lyapunov redesign nonlinear controllers. The validity of the suggested framework is further endorsed by testing it on real-time controller hardware in-loop experiments.