25권 2호 305-319 2024 [IJAT]
| 제목 | An Economical Velocity Planning Algorithm for Intelligent Connected Electric Vehicles Based on Real-Time Traffic Information |
|---|---|
| 분야 | ADAS, AI, Autonomous Vehicles |
| 언어 | English |
| 저자 | Mingming Qiu(Hefei University of Technology), Lei Wang(Shanghai Huawei Technology Co., Ltd), Xiaoyu Mu(Hefei University of Technology), Wei Yu(Hefei Oufei Intelligent Vehicle Technology Co., Ltd.), Kang Huang(Hefei University of Technology) |
| Key Words | Intelligent networked vehicle · Energy economy · Global optimal velocity profi le · Cellular automata · Dynamic programming |
| 초록 |
The energy effi ciency of intelligent networked connected electric vehicle (EV) is directly related to its velocity. Aiming at the infl uence of real-time traffi c fl ow information on road speed interval, a two-layer speed planning method is proposed. The upper layer extracts the road speed interval according to the traffi c fl ow information, and based on cellular automata and confi dence interval theory, traffi c information rules are introduced, and a road speed interval extraction method considering traffi c density information is established. The lower layer is used to obtain energy-optimal cruising velocity profi le. Taking the road speed interval as the variable boundary constraint, a dynamic programming algorithm that changes the state quantity boundary in real time is designed, which realizes the effi cient acquisition of the energy-optimized velocity trajectory. To verify the eff ectiveness of proposed approach, the simulation model is formulated based on using collected real traffi c information. The simulation results demonstrate that, compared with the conventional constant speed cruising strategy and dynamic programming (DP) strategy based on road speed interval, the strategy proposed in this study not only improves energy effi ciency and reduces computing time signifi cantly, but also can predict the traffi c conditions ahead to avoid large fl uctuations in velocity. Besides, the biggest signifi cance of this study is the designed economic velocity planning algorithm based on real-time traffi c density information improves the adaptability of intelligent networked connected EV control strategy to actual traffi c conditions, and extends the optimization dimension of eco-driving. |
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