A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm

This paper proposes a simple and easily optimizable mathematical representation of an energy management strategy (EMS) for the hybrid energy storage system (HESS) in EV. The power of each device in the HESS is provided as a continuous function of load power called γ. Two strategies based on the proposed method, one incorporating fixed coefficients of the γ function (GBS) and one with coefficients optimized by a genetic algorithm (GAS) in real-time using a backward time window, are tested and compared to the rule-based strategy (RBS) and battery storage system. The calculations are made for an electric car with a LiFePO4 battery-supercapacitor HESS. The analyzed parameters are: energy consumption, RMS and maximum current rates of the battery, and the cycle cost of an EV with HESS and a battery-powered EV. The analysis is made in dependence on drive cycle speed and an internal resistance of the battery module. The obtained results show that the GBS and the GAS are able to reduce the RMS current rate by 40% in the NEDC in comparison to battery-powered EV, as well as that maximum current rates do not exceed nominal values. The GAS aims at the minimization of energy consumption. It obtains best results in low speed cycles.