The efficiency in energy consumption of an electric vehicle (EV) has significant value to both vehicle manufacturers and vehicle owners. Such efficiency will directly impact the cost of energy and vehicle range while relieving the stringent requirements on the DC motor and battery specs. Nowadays, with the development of advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), drivers enjoy a much safer driving experience. ADAS capabilities in sensory, computing and communication can be leveraged in EVs for the purpose of optimizing energy consumption.
This paper introduces an energy-optimized ACC platform, which utilizes a forecast of the speed profile of the host vehicle in a short (few seconds) horizon. Such speed information can be available through ADAS or similar systems. This paper focuses on optimization in longitudinal tracks. We consider ten different drive-cycles in several driving scenarios, such as highways, urban areas, and test tracks with multiple stops. We study the average energy consumption and performance in all the scenarios through simulation experiments. Our results show significant improvement in the overall energy consumption in a drive-cycle compared with a baseline vehicle that only uses ACC.
We can optimize the energy consumption by 2.30% on average in a random driving scenario (Highway, Urban area, or test tracks with multiple stops) utilizing the proposed method compared to only using ACC.