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An Energy Consumption Prediction-Based Optimization Strategy for Eco-driving of Connected Electric Buses
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Yingjiu Pan, Yi Xi, Yansen Liu, Wenpeng Fang, Wenshan Zhang
Automotive Engineering | 2025, 47(5) : 839 - 850
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Automotive Engineering | 2025, 47(5): 839-850
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An Energy Consumption Prediction-Based Optimization Strategy for Eco-driving of Connected Electric Buses
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Yingjiu Pan, Yi Xi, Yansen Liu, Wenpeng Fang, Wenshan Zhang
Affiliations
  • School of Automobile,Chang’an University,Xi’an 710018
Published: 2025-05-25 doi: 10.19562/j.chinasae.qcgc.2025.05.005
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The power system and energy consumption characteristics of electric buses significantly differ from those of traditional buses with internal combustion engines, and conventional ecodriving strategies cannot fully adapt to electric buses. An energy consumption predictionbased deep reinforcement learning model is proposed for ecodriving of connected electric buses, taking into account of signal timing, information from preceding vehicles, energy consumption characteristics and comfort of passengers. Firstly, natural driving data from battery electric buses is collected, and a basic energy consumption model is established using vehicle dynamics, considering the regenerative braking characteristics of electric buses. A system identification model is then constructed to identify and estimate the unknown parameters in the basic energy consumption model. Next, the impact of different signal phases on speed patterns when entering and exiting signalized intersections is analyzed, and state variables that accurately describe traffic environment information are determined. Based on the constructed energy consumption model, a reward function is developed, considering safety, efficiency, energy conservation, and comfort. An optimization model for ecodriving strategies at signalized intersections for electric buses is established using the SAC (soft actor critic) algorithm. Finally, the proposed strategy is compared with the classic intersection passage strategy GLOSA. The results show that the proposed ecodriving strategy ensures vehicle safety across the four defined traffic scenarios. Despite an average increase in travel time of only 7.29%, the strategy enhances comfort by an average of 21.96% and reduces energy consumption by an average of 24.47%.

eco-driving strategy  /  connected electric bus  /  deep reinforcement learning  /  passenger comfort  /  energy consumption prediction  /  signalized intersection
Yingjiu Pan, Yi Xi, Yansen Liu, Wenpeng Fang, Wenshan Zhang. An Energy Consumption Prediction-Based Optimization Strategy for Eco-driving of Connected Electric Buses[J]. Automotive Engineering, 2025 , 47 (5) : 839 -850 . DOI: 10.19562/j.chinasae.qcgc.2025.05.005
Year 2025 volume 47 Issue 5
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Article Info
doi: 10.19562/j.chinasae.qcgc.2025.05.005
  • Receive Date:2024-10-16
  • Online Date:2025-07-08
  • Published:2025-05-25
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  • Received:2024-10-16
  • Revised:2024-12-19
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    School of Automobile,Chang’an University,Xi’an 710018
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表12种不同金属材料的力学参数

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Number of
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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