The DOI matches its Crossref record; 172 of the 172 references it cites were found in a registry or an index.
Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
Cited by
SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output
Chen, J., Zhang, Y., Wu, J., Cheng, W., & Zhu, Q. (2023) · Energy · doi:10.1016/j.energy.2022.125375 · Cited by 362
Electric vehicle battery pack state of charge estimation using parallel artificial neural networks
Manoharan, A., Sooriamoorthy, D., Begam, K. M., & Aparow, V. R. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108333 · Cited by 128
Self-citationA review on rapid state of health estimation of lithium-ion batteries in electric vehicles
Wang, Z., Zhao, X., Fu, L., Zhen, D., Gu, F., & Ball, A. D. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103457 · Cited by 107
Takyi-Aninakwa, P., Wang, S., Zhang, H., Yang, X., & Fernandez, C. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120043 · Cited by 75
Dini, P., & Paolini, D. (2025) · Batteries · doi:10.3390/batteries11030107 · Cited by 40
Metadata
- Authors
- Manoharan, A., Begam, K. M., Aparow, V. R., & Sooriamoorthy, D.
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 55
- Pages
- 105384
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4289515820
- Cited by
- 293
- References
- 172
- FWCI
- 17.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review. https://app.cochat.ai/papers/10.1016/j.est.2022.105384
- The paper
- Manoharan, A., Begam, K. M., Aparow, V. R., & Sooriamoorthy, D. (2022). Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review. Journal of Energy Storage. https://doi.org/10.1016/j.est.2022.105384
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.
