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DimensionsSainaath, R. and Thiyagesan, M. and Isabella, L.A. and Vanitha, R. and Akash, G. and Sourav Krishna, R. (2025) Multimodal Data Fusion using EKF and Deep Learning for Battery Fault Prediction. In: 6th International Conference on Electronics and Sustainable Communication Systems, ICESC 2025, 2025-09-10 through 2025-09-12, Coimbatore.
Full text not available from this repository.Abstract
The model is validated using MATLAB/Simulink simulations of a 4S3P lithium-ion battery pack under diverse operating profiles. Results show that the hybrid method achieves an R of 0.9996 for SoC and 0.9981 for SoH, with RMSE values of 0.0044 and 0.0061, respectively, outperforming standalone EKF and LSTM approaches. This work demonstrates a lowcomplexity, high-accuracy solution for next-generation Battery Management Systems (BMS) and provides insights into future hardware implementation strategies. This work demonstrates a low-complexity, high-accuracy solution for next-generation Battery Management Systems (BMS) and provides insights into future hardware implementation strategies. © 2025 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Depositing User: | Unnamed user with email techsupport@mosys.org |
| Date Deposited: | 16 Dec 2025 09:58 |
| Last Modified: | 16 Dec 2025 10:02 |
| URI: | https://ir.vmrfdu.edu.in/id/eprint/5634 |
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