Electric vehicle battery pack state of charge estimation using parallel artificial neural networks
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Manoharan et al. employed ensemble learning with a BiLSTM-based SOC estimator and achieved an RMSE under 0.74% in three dynamic discharge tests at 45 °C.
“Manoharan et al. employed this approach, with the BiLSTM-based SOC estimator, and achieved an RMSE under 0.74% in three dynamic discharge tests at 45 °C[88].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [88]
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References 52 · Cited by 128
- Authors
- Manoharan, A., Sooriamoorthy, D., Begam, K. M., & Aparow, V. R.
- Affiliations
- University of Nottingham Malaysia Campus (MY)
- Taylor's University (MY)
- Corresponding author
- Manoharan, Aaruththiran
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2023-11
- Volume
- 72
- Pages
- 108333
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4384306254
- Cited by
- 128
- References
- 52
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 67 · 2023: 8
- FWCI
- 8.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Energy Storage (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- state of charge estimation, lithium-ion battery packs, electric vehicles, bidirectional LSTM, battery temperature, battery life
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