基于速度预测与庞特里亚金最小值原理的电池-超级电容电动车最优在线能量管理系统

The uncertain driving and road conditions complicate the design of Energy Management System (EMS) of Electric vehicles (EVs). To improve the performance of Battery/Supercapacitor (SC) based EV, an adaptive EMS based on Pontryagin's Minimum Principle (PMP) considering actual road slope and velocity prediction through Artificial Neural Network (ANN) based method is proposed. An adaptive EMS is designed by considering the solution obtained from PMP based EMS. The solution obtained by PMP is based on a constant costate variable which cannot be applied to uncertain driving conditions. To solve this problem, the costate variable is adjusted in real time using a novel adaptation law which considers the future power demand of the vehicle, actual road slope and battery conditions. The integration of ANN based velocity prediction model with EMS results in an enhanced technique that can adapt automatically to changing road and vehicle conditions and thus improves the efficiency of the EV. The entire system is tested on three actual road slope conditions and different driving cycles. A Rule Based Strategy (RBS) and PMP based technique without velocity prediction are also studied to compare and analyse the performance of the proposed Adaptive EMS based on PMP (APMP). Simulation results show that the proposed Adaptive EMS based on PMP increases the participation of SC in the EMS and thereby improves battery health.