Energy management for hybrid electric vehicles using rule based strategy and PI control tuned by particle swarming optimization algorithm

Abstract

Recently, hybrid electric vehicles are increasingly being used to replace conventional vehicles. In this paper, a control methodology is designed that can reduce fuel consumption and improve the vehicle’s dynamic response. As the control unit based on this methodology consists of two levels, the first depends on the application of a rule-based strategy for energy management between the main components of the vehicle, and this strategy is based on a set of rules that are activated according to parameters such as vehicle speed and the battery state of charge (SOC) that control the activation/deactivation of the internal combustion engine (ICE), motor, and generator. This level also makes ICE operate at operation points with high efficiency, which is represented by the optimal operating line (OOL). The second level is called the low control level, and it consists of two proportional-integral (PI) controllers used to control the speed of each ICE and the motor to obtain the appropriate torque for both of them to drive the vehicle properly. The particle swarming optimization (PSO) algorithm is utilized to tune the parameters of the PI controllers. The obtained results have effectively minimized fuel consumption and improved the performance of the vehicle.