Energy management of plug-in hybrid electric vehicles based on speed prediction fused driving intention and LIDAR

• Published in: Energy (Oxford) Vol. 284; p. 128535
• Main Authors: Gao, Kai, Luo, Pan, Xie, Jin, Chen, Bin, Wu, Yue, Du, Ronghua
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: data collection; Driving intention; energy; Equivalent consumption minimization strategy (ECMS); fuels; LIDAR; Plug-in hybrid electric vehicle (PHEV); prediction; Speed prediction; LIDAR; Plug-in hybrid electric vehicle (PHEV); Speed prediction; Equivalent consumption minimization strategy (ECMS); Driving intention
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2023.128535

Driving intention and speed prediction are essential factors in the energy management of plug-in hybrid electric vehicles (PHEVs). This paper proposes an improved energy management strategy for the subject vehicle by speed prediction fused with driving intention and LIDAR data in a vehicle-following scenario. A driving intention recognition model is developed based on the gated recurrent unit (GRU), which takes the vehicle speed, throttle opening, and brake pedal force of the subject vehicle as input. Then integrating the LIDAR point cloud data and driving intention result of the subject vehicle to achieve more accurate speed prediction, where joint probabilistic data association and interacting multiple models methods are used to process LIDAR data. The more accurate speed prediction is then applied to design a prediction-informed adaptive equivalent consumption minimization strategy (PIA-ECMS) for real-time energy management optimization. Experimental results demonstrate the recognition accuracy of up to 88%, indicating that the driver’s driving intention can be identified effectively. The speed prediction has an error margin of no more than 5.9 km/h. Compared with existing adaptive ECMS without speed prediction, the proposed PIA-ECMS can enhance fuel economy by 1.3–2.7% while achieving better SOC charge sustainability. •Integration of driving intention recognition and LIDAR into energy management.•Speed prediction is fused by driving intention recognition and LIDAR.•A new prediction-informed adaptive ECMS to achieve better fuel economy.