HYBRID CNN–LSTM BASED ACCURATE LITHIUM-ION BATTERY PARAMETER ESTIMATION FOR ELECTRIC VEHICLE APPLICATIONS
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Getting battery numbers right helps electric cars run safer and work better. Still, old-school techniques fall short when batteries act unpredictably or wear out differently. A new approach uses smart algorithms to guess SoC, SoH, and RUL more reliably. Instead of one model alone, it mixes two types - one sees patterns across data points, another tracks changes over time. Now here comes a twist - attention mechanisms team up with Bayesian methods
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Nagarajan, P., Gunapriya, D. & Chandrasekaran, V. (2026). Hybrid CNN–LSTM Based Accurate Lithium-ION Battery Parameter Estimation for Electric Vehicle Applications. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.243
Nagarajan, P., et al.. "Hybrid CNN–LSTM Based Accurate Lithium-ION Battery Parameter Estimation for Electric Vehicle Applications." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.243.
Nagarajan, P.,D. Gunapriya, and V. Chandrasekaran. "Hybrid CNN–LSTM Based Accurate Lithium-ION Battery Parameter Estimation for Electric Vehicle Applications." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.243.
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