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In view of the aggravation of global pollution and greenhouse effects, fuel cell electric vehicles (FCEVs) have attracted increasing attention, owing to their ability to release zero emissions. Extended-range fuel cell vehicles (E-RFCEVs) are the most widely used type of fuel cell vehicles. The powertrain system of E-RFCEV is relatively complex. Bond graph theory was used to model the important parts of the E-RFCEV powertrain system: Battery, motor, fuel cell, DC/DC, vehicle, and driver. In order to verify the control effect of energy management strategy (EMS) in a real-time state, bond graph theory was applied to hardware-in-the-loop (HiL) development. An HiL simulation test-bed based on the bond graph model was built, and the HiL simulation verification of the energy management strategy was completed. Based on the comparison to a power-following EMS, it was found that fuzzy logic EMS is more adaptive to vehicle driving conditions. This study aimed to apply bond graph theory to HiL simulations to verify that bond graph modeling is applicable to complex systems.

Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method