Abstract
Taking a hybrid energy storage system (HESS) composed of a battery and an ultracapacitor as the study object, this paper studies the energy management strategy (EMS) and optimization method of the hybrid energy storage system in the energy management and control strategy of a pure electric vehicle (EV) for typical driving cycles. The structure and component model of the HESS are constructed. According to the fuzzy control strategy, aimed at the roughness of the membership function in EMS, optimization strategies based on a genetic algorithm (GA) and particle swarm optimization (PSO) are proposed; these use energy consumption as their optimal objective function. Based on the improved EV model, the fuzzy control strategy is studied in MATLAB/Advisor, and two control strategies are obtained. Compared with the simulation results based on three driving cycles, urban dynamometer driving schedule (UDDS), new European driving cycle (NEDC), and ChinaCity, the optimum control strategy were obtained. The theoretical minimum energy consumption of HESS was reached by dynamic programming (DP) algorithm in the same simulation environment. The research shows that, compared with the PSO, the output current peak and current fluctuation of the battery optimized by the GA are lower and more stable, and the total energy consumption is reduced by 3–9% in various simulation case studies. Compared with the theoretical minimum value, the deviation of energy consumption simulated by GA-Fuzzy Control is 0.6%.
Highlights
In recent years, new energy vehicles have become the main development direction of the automobile industry
In order to confirm the effect of algorithm optimization, the fuzzy control strategy of a hybrid energy storage system (HESS) optimized by the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms is examined. e improved electric vehicle (EV) model in MATLAB/ Advisor is used for simulations. e following simulation driving cycles are used: urban dynamometer driving schedule (UDDS), new European driving cycle (NEDC), and ChinaCity
Based on the characteristics of poor life stability and limited battery life of an EV as a new energy vehicle, this paper studied an energy management strategy (EMS) and optimized the management strategy to reduce the energy consumption of the HESS and protect the battery life
Summary
New energy vehicles have become the main development direction of the automobile industry. In [5,6,7,8], by combining a battery and ultracapacitor, an energy control strategy with fuzzy control strategy as the core was proposed to improve the fuel economy and durability of energy system components while maintaining the vehicle power performance. E focus of [16] is the application of driving condition recognition in hybrid electric vehicle intelligent control For this purpose, driving features are identified and used for driving segment clustering, using the k-means clustering algorithm. Based on the above research, this paper uses fuzzy control strategy as an EMS with the composite power supply form of a combined battery and ultracapacitor. An experimental analysis is carried out to compare the battery current output performance and the total energy consumption parameters of the energy system and to evaluate the optimization effect of the algorithm
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