Optimization Of Fast-Charging Lithium-Ion Battery Systems Using Generative Ai Models A Data-Driven Approach To Energy Efficiency And Thermal Management

Dr. Sunil Kumar Srivastava; Ramu Ratlavath; Dr John T Abraham; Ashima Airan; Dr. Ramakrishnaprabu. G; Dipanjan Guha Chowdhury; Shubham Gade

doi:10.29284/hdyge754

Authors

Dr. Sunil Kumar Srivastava Associate Professor, Teerthanker Mahaveer College of Pharmacy, Teerthanker Mahaveer University, Moradabad
Ramu Ratlavath Assistant Professor, Automobile Engineering, Thermal Engineering, VNR Vignana Jyothi Institute of Engineering and Technology (VNRVJIET), Hyderabad
Dr John T Abraham Dean and HoD of Computer Science, Bharatamata College, Kochi
Ashima Airan Assistant Professor, Bharati Vidyapeeth College of Engineering, New Delhi
Dr. Ramakrishnaprabu. G Director & Associate Professor, Department of Electrical and Electronics Engineering, Jayalakshmi Institute of Technology, Thoppur, Tamil Nadu
Dipanjan Guha Chowdhury Assistant Professor, Computer Science & Engineering (CS&DS), Brainware University, Barasat
Shubham Gade Artificial Intelligence and Machine Learning Ops Engineer, EnerSys

DOI:

https://doi.org/10.29284/hdyge754

Keywords:

Fast charging, Lithium-ion batteries, Generative AI, Thermal management.

Abstract

Fast charging in lithium-ion batteries is an important problem due to high heat that is generated, low energy efficiency and high rate of degradation at high rate of charging conditions. This paper proposes a generative artificial intelligence-based optimization framework, which allows modelling and optimizing fast charging using variational auto encoder train on the real charging data. The model exhibited a good reconstruction accuracy with overall R2 of 0.945, RMSE of 169.5 and MAE of 41.9, which reveal that the model is able to capture the electro-thermal dynamics which is nonlinear. The training with synthetic charging profiles from the latent space showed an almost perfect distributional correspondence with the real pattern on the temperature, efficiency and thermal stress, thus making possible to search for optimal states of operation not covered by the empirical data. Through multi-objective screening the framework found two optimized charging profiles which, at the same time, provided high charging efficiency (around 0,86), low thermal stress levels and safe temperature operation (38-43 degC) without extremely low degradation indicators (around 4x10-4). These numerical results show that the generative models were able to reduce thermal stress and facilitate fast charging (charging safety) without sacrificing performance. The results of this study are of great potential for integration into battery management systems, which allows potential for adaptive and data-driven charging control. Overall, the proposed framework points to a promising direction towards safer, faster and efficient fast charging strategies and as a foundation for further developments for AI assisted battery optimization strategies.

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