Multi-objective autonomous eco-driving strategy: A pathway to future green mobility
With the wide popularity of electric vehicles in the market and advancements in autonomous driving technology, intelligent electric vehicles (iEVs) equipped with comprehensive eco-driving capabilities are expected to play a pivotal role in energy conservation and emission reduction of future mobilit...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Green Energy and Intelligent Transportation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773153725000295 |
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| Summary: | With the wide popularity of electric vehicles in the market and advancements in autonomous driving technology, intelligent electric vehicles (iEVs) equipped with comprehensive eco-driving capabilities are expected to play a pivotal role in energy conservation and emission reduction of future mobility. This paper proposes an intelligent eco-driving strategy (IEDS) to address the safety and eco-driving concerns with the parallel hybrid electric vehicle (PHEV). The IEDS is a data-driven autonomous driving solution to effectively control vehicle motion and energy management, developed based on refined deep reinforcement learning (DRL) algorithms, integrating safety and efficiency knowledge in autonomous driving through a multi-head deep Q network (DQN) with elaborate rewards for potentially dangerous collisions and fuel consumption. In the case studies, the simulations show that the IEDS is able to achieve excellent energy-saving performance through stable and safe driving manners. Compared with the baselines, its obstacle avoidance and energy-saving performance are 2.10% and 5.83% ahead, achieving 97.07% of the optimal energy management result. |
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| ISSN: | 2773-1537 |