Equivalent Cost Minimization Strategy for Plug-In Hybrid Electric Bus with Consideration of an Inhomogeneous Energy Price and Battery Lifespan
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- Lin, Chengtao & Wu, Tian & Ou, Xunmin & Zhang, Qian & Zhang, Xu & Zhang, Xiliang, 2013. "Life-cycle private costs of hybrid electric vehicles in the current Chinese market," Energy Policy, Elsevier, vol. 55(C), pages 501-510.
- Chen, Zheng & Gu, Hongji & Shen, Shiquan & Shen, Jiangwei, 2022. "Energy management strategy for power-split plug-in hybrid electric vehicle based on MPC and double Q-learning," Energy, Elsevier, vol. 245(C).
- Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
- Jiang, C.X. & Jing, Z.X. & Cui, X.R. & Ji, T.Y. & Wu, Q.H., 2018. "Multiple agents and reinforcement learning for modelling charging loads of electric taxis," Applied Energy, Elsevier, vol. 222(C), pages 158-168.
- Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
- Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
- Jinming Xu & Yuan Lin, 2024. "Energy Management for Hybrid Electric Vehicles Using Safe Hybrid-Action Reinforcement Learning," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
- Dongwei Yao & Xinwei Lu & Xiangyun Chao & Yongguang Zhang & Junhao Shen & Fanlong Zeng & Ziyan Zhang & Feng Wu, 2023. "Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
- Karabasoglu, Orkun & Michalek, Jeremy, 2013. "Influence of driving patterns on life cycle cost and emissions of hybrid and plug-in electric vehicle powertrains," Energy Policy, Elsevier, vol. 60(C), pages 445-461.
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