Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning
Open accessAbstract
In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability.
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Bhattacharjee et al. simplified and expanded the use of CNN in the Encoder of CNN/AE-RNN by replacing varying receptive fields with fixed-length fields to independently extract time-varying information across different channels.
“Bhattacharjee et al. simplified and expanded the use of CNN in the Encoder of CNN/AE-RNN by replacing varying receptive fields with fixed-length fields to independently extract time-varying information across different channels[66].”
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 [66]
Section B, “CNN for SoC Estimation” (page unavailable), describes three differently shaped temporal filters, rather than replacing varying receptive fields with fixed-length ones. Each filter spans voltage, current, and temperature, rather than extracting information independently from each channel. The paper describes a standalone 1D CNN, not an encoder in a CNN/AE-RNN. (LLM, conf=0.89)
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- Authors
- Bhattacharjee, A., Verma, A., Mishra, S., & Saha, T. K.
- Affiliations
- The University of Queensland (AU)
- Indian Institute of Technology Delhi (IN)
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2021-04
- Volume
- 70
- Issue
- 4
- Pages
- 3123-3135
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W3095206811
- Cited by
- 181
- References
- 28
- Citations by year
- 2026: 25 · 2025: 44 · 2024: 44 · 2023: 37 · 2022: 23 · 2021: 8
- FWCI
- 9.09
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Vehicular Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Department of Science and Technology, India (DST/CERI/MI/SG/2017/80(G))
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- state of charge estimation, electric vehicle batteries, 1D convolutional neural network, transfer learning, noise robustness, battery datasets, battery management systems
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