Research on energy consumption optimization of predictive cruise control considering the state of the leading vehicle

Abstract

To comprehensively assess the economic performance of electric vehicles (EVs) cruise planning and mitigate conflicts with leading vehicles, a dynamic programming-based predictive cruise speed optimization control system was developed. This system comprises two layers: an upper V2X-based LSTM-DP cruise speed planning system and a lower adaptive cruise control system based on sliding mode control (SMC), and the mode switching strategy of the speed and distance tracking controller is designed. Initially, a segmented uniform variable motion algorithm processed the historical driving data of the leading vehicle, significantly enhancing the LSTM neural network's acceleration prediction capability, particularly on inclines, with an improvement in accuracy of 37.8 %. Subsequently, an adaptive cruise tracking controller was implemented to track the planned vehicle speed. Simulation tests on actual roads demonstrated that the LSTM-DP approach not only ensures driving safety but also markedly improves fuel efficiency over conventional constant speed cruising methods when planning is effective. Moreover, even in scenarios where planning fails, the LSTM-DP strategy can still achieve energy savings of up to 37 % without compromising the safety distance between vehicles. Finally, hardware-in-the-loop (HIL) testing confirmed the system's efficacy under real-time operational conditions.