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Volume 13 | Issue 10 | Year 2026 | Article Id. IJEEE-V13I10P110 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I10P110

Aging-Aware Attention-Driven Transformer Reinforcement Learning Framework for Sustainable Hybrid Battery Energy Management in Electric Vehicles

P.G. Raghavendran, V. Hamsadhwani

Received Revised Accepted Published
29 Jul 2026 08 Sep 2026 01 Oct 2026 28 Oct 2026

Citation :

P.G. Raghavendran, V. Hamsadhwani, "Aging-Aware Attention-Driven Transformer Reinforcement Learning Framework for Sustainable Hybrid Battery Energy Management in Electric Vehicles," International Journal of Electrical and Electronics Engineering, vol. 13, no. 10, pp. 134-159, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I10P110

Abstract

Battery degradation is another important constraint of the hybrid electric vehicle systems, where the standard approach to energy management focuses on short-term fuel efficiency and does not consider the long-term health of the battery. In this paper, a proposal has been made on an Aging-Aware Attention-Driven Transformer Reinforcement Learning (AAT-DRL) framework of intelligent hybrid battery energy management. Transformer-based temporal feature encoding, along with actor-critic reinforcement learning architecture, is combined in the model to encode patterns that are sensitive to degradation such as temperature, current stress and depth-of-discharge. The multi-objective reward is developed with an aim to optimize the energy efficiency, thermal safety and battery life. The framework is tested at standard driving cycles (UDDS, NEDC and WLTP). In the results, it is shown that the degradation index has reduced by 38%, the SOC has become more stable and thermal fluctuations have been reduced, with a peak efficiency of 95.3%, which is a better performance than the state-of-the-art DRL methods. The solution offers a healthy, scalable and battery management system for the next generation based on the proposed approach.

Keywords

Battery degradation mitigation, Electric Vehicles, Energy management strategy, Hybrid battery systems, Reinforcement learning, Transformer architecture.

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