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Recently, researches on electric vehicle are focused on motion control and state estimation. These works require a model that imitate a real vehicle as close as possible. We propose in this paper a signal hardware-in-the-loop model of electric vehicles that includes driving system and vehicle model running in real-time in dSPACE-DS1103 control card. All the required states of the electric vehicle are monitored in Matlab/Simulink environment. The model is validated by implementing representative test cases. Results of this paper can be a useful tool for further electric vehicle researches.

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Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle.