An ANFIS-Based ECMS for Energy Optimization of Parallel Hybrid Electric Bus

摘要

The fuel economy of hybrid electric vehicles is very closely associated with the energy management strategy (EMS). In this paper, a practicality-oriented adaptive EMS for a parallel hybrid electric bus is presented, which combines the adaptive neuro-fuzzy inference system (ANFIS) and equivalent consumption minimization strategy (ECMS). Considering the regular and fixed route of the city bus, the optimal control trajectories can be attained by the dynamic programming in advance. Using the rolling optimization method, a group of optimal equivalent factors is extracted from aforementioned control trajectories and used as the training samples. Then, a trained ANFIS model that produces the optimal equivalent factor online is constructed, showcasing striking superiority in self-learning and inference. By applying the derived equivalent factor in the framework of the ECMS, an adaptive energy management controller is available to achieve desirable power distribution online. Finally, the simulation and hardware in the loop (HIL) tests are used to validate the effectiveness and feasibility of the controller. The results demonstrate that, compared with other strategies, the fuel economy with the proposed strategy can be effectively improved.