A Multi-Mode Switching Energy Management Strategy Based on Travel Habits for Range-Extended Electric Vehicle

To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range-extended electric vehicle (REEV), a travel habit-based multi-mode switching energy management strategy (M-MSEMS) has been introduced. REEV has the advantages of high power, high efficiency and long driving range, and it is the core technology. An intelligent and efficient EMS must take into account all driving conditions to maximize power utilization while allowing the engine to work in the high-efficiency area to minimize fuel consumption. By statistically analyzing vehicle travel data, this study constructs the urban road network framework as the foundational dataset. Users’ common travelling conditions recognition is accomplished based on Bayesian classifier. The theoretical state of charge (SoC) curve is constructed by analyzing the driving conditions’ distinctive parameters, and an EMS based on driving routes’ trip patterns is then created. The proposed strategy is based on the fuzzy control algorithm, which ensures that the theoretical SoC curve is well followed. Finally, the simulation and experimental results thoroughly indicate that the proposed M-MSEMS with future mileage prediction function can achieve more comprehensive vehicle performance, including improving fuel economy and reducing energy loss, which is that equivalent fuel consumption is reduced by 3.09%, the fuel-to-electric conversion loss rate is reduced by 5.33%, and battery capacity degradation is reduced by 19.01%. This strategy not only optimizes the energy allocation of vehicles under different road conditions, but also improves overall efficiency by reducing the frequency and time of APU system intervention. And this result can be used to develop EMS to improve comprehensive performance levels.