The DOI matches its Crossref record; 191 of the 191 references it cites were found in a registry or an index.
A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution
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Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010) · Communications of the ACM · doi:10.1145/1721654.1721672 · Cited by 9,464
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
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Li-ion battery materials: present and future
Nitta, N., Wu, F., Lee, J. T., & Yushin, G. (2015) · Materials Today · doi:10.1016/j.mattod.2014.10.040 · Cited by 7,389
In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
Cited by
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., et al. (2023) · Journal of Big Data · doi:10.1186/s40537-023-00727-2 · Cited by 907
The fuel cell electric vehicles: The highlight review
Pramuanjaroenkij, A., & Kakaç, S. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.11.103 · Cited by 469
Sustainable energies and machine learning: An organized review of recent applications and challenges
Ifaei, P., Nazari-Heris, M., Tayerani Charmchi, A. S., Asadi, S., & Yoo, C. (2023) · Energy · doi:10.1016/j.energy.2022.126432 · Cited by 79
Metadata
- Authors
- Ganesh, A. H., & Xu, B.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 154
- Pages
- 111833
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W3213163478
- Cited by
- 285
- References
- 191
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution. https://app.cochat.ai/papers/10.1016/j.rser.2021.111833
- The paper
- Ganesh, A. H., & Xu, B. (2022). A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2021.111833
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
