Abstract
When searching for the optimal solution, Equivalent Consumption Minimum Strategy (ECMS) has to calculate and compare the total equivalent fuel rate of huge candidates covered all over the control domain for each time instant. Therefore, this strategy still has a heavy computation burden problem; it is a challenge for ECMS to be implemented online for real-time control. To reduce ECMS’s calculation load, this paper proposes an adaptive Simplified-ECMS-based strategy for a parallel plug-in hybrid electric vehicle (PHEV). A convex piecewise function is applied to fit the total equivalent fuel rate with respect to the motor torque, which is the control variable. Then, the ECMS problem is simplified to calculate and compare only five candidates’ total equivalent fuel rate to determine the optimal torque distribution. Particle swarm optimization (PSO) algorithm is applied to optimize the equivalent factor, and the MAPs of this factor under different driving cycles, driving distances and initial SOC are obtained. Based on this, the adaptive Simplified-ECMS-based strategy is proposed. Simulations were performed, and the results show that the Simplified-ECMS-based strategy can obviously shorten the calculation time compared to ECMS-based strategy, and the adaptive Simplified-ECMS-based strategy can decrease fuel consumption of plug-in hybrid electric vehicle by 16.43% under the testing driving cycle, compared to CD-CS-based strategy. A road test on the prototype vehicle is conducted and the effectiveness of the Simplified-ECMS-based strategy is validated by the test data.
Highlights
Plug-in hybrid electric vehicles (PHEVs) assume an essential role in decreasing fuel consumption, pollutant emissions, and carbon footprint [1]
derivative-free algorithms (DFA)-based-energy management strategy (EMS) mainly concern metaheuristic algorithms inspired in nature and DIRECT deterministic method [12], The main DFA-based-EMSs employed by PHEVs are simulated annealing (SA)-based-EMS [13], genetic algorithm (GA)-based-EMS [16], and particle swarm optimization (PSO)-based-EMS [14]
After gaining the Simplified-Equivalent Consumption Minimum Strategy (ECMS)-based strategy, we introduce the particle swarm optimization (PSO) genetic algorithm to optimize the Simplified-ECMS algorithm’s equivalent factor, instead of optimizing this factor by trial and error method
Summary
Plug-in hybrid electric vehicles (PHEVs) assume an essential role in decreasing fuel consumption, pollutant emissions, and carbon footprint [1]. Hu et al designed QP-based-EMS for a series plug-in hybrid electric bus, and demonstrated its decreasing computational time and the feasibility of real-time control [6]. Simplifications and equations derivation about PMP-based EMS, the local optimization algorithm of ECMS is obtained. ECMS online implementation requires further reduction of the computational time, since candidates of the control variable cover all over the control domain, calculating and comparing the total equivalent fuel of these huge candidates to determine the optimal solution is still a challenge. ECMS-based-EMS is more likely to be applied to PHEVs with complex configuration’s real-time control than other blend-EMSs. we select ECMS algorithm to further simplify it. The adaptive Simplified-ECMS-based EMS is implemented based on the equivalent factor MAPs. The original contribution of this paper is related to the following aspects.
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