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All Journal International Journal of Applied Power Engineering (IJAPE)
Teerdala Rakesh
St. Martin’s Engineering College
Electrical & Electronics Engineering

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Advanced AI-driven battery state estimation using deep learning and ensemble models N. Sumana Keerthi; B. Jyothi; M. Sharanya; CH. Srinivas; K. Shravani; V. Sanjeeva Rao; Teerdala Rakesh; Nittala Ramchandra; Malligunta Kiran Kumar; K. V. Govardhan Rao
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1072-1093

Abstract

Accurate estimation of the state of charge (SOC) and state of health (SOH) of lithium-ion batteries is essential for improving the performance, safety, and lifespan of electric vehicles (EVs). Traditional estimation methods often face challenges such as high computational complexity, limited adaptability to battery aging, and reduced accuracy under varying operating conditions. To overcome these limitations, this paper presents a hybrid data-driven framework that combines machine learning and deep learning techniques for SOC and SOH prediction. For SOC estimation, linear regression, recurrent neural networks (RNN), gated recurrent unit (GRU), and stacked long short-term memory (LSTM) models are employed. For SOH prediction, ensemble learning methods, including stacking regressor, tree-based pipeline optimization tool (TPOT) regressor, and hybrid GRU-LSTM models, are utilized. The proposed models are evaluated using publicly available lithium-ion battery datasets under different charge-discharge conditions. Results show that deep learning approaches achieve superior performance, with GRU-LSTM and stacked LSTM models providing highly accurate SOC estimation (R² ≈ 0.993, RMSE ≈ 0.015), while the TPOT-based ensemble model delivers near-perfect SOH prediction (R² ≈ 1.0). A web-based implementation further enables real-time battery monitoring, demonstrating the framework’s practicality for advanced battery management systems (BMS) in EV applications.
Co-Authors B. Jyothi CH. Srinivas K. Shravani K. V. Govardhan Rao M. Sharanya Malligunta Kiran Kumar N. Sumana Keerthi Nittala Ramchandra V. Sanjeeva Rao