| Title |
Adaptive battery SOC estimation: temporal attention-enhanced BiLSTM and quantized model comparison for scalable edge solutions |
| Authors |
Das, Opy ; Zafar, Muhammad Hamza ; Rudra, Souman ; Sanfilippo, Filippo |
| DOI |
10.1016/j.egyr.2026.109544 |
| Full Text |
|
| Is Part of |
Energy reports.. Amsterdam : Elsevier. 2026, vol. 16, art. no. 109544, p. 1-16.. ISSN 2352-4847 |
| Keywords [eng] |
Deep learning ; Electric vehicle ; Neural networks ; Quantisation ; State of charge ; Temporal attention mechanism |
| Abstract [eng] |
Lithium-ion batteries are the dominant energy storage technology in modern energy systems, making the accurate state-of-charge (SOC) estimation essential for effective battery management. Reliable SOC estimation helps determine remaining battery capacity, improves operational safety, and supports real-time energy control. However, SOC estimation is challenging due to the nonlinear electrochemical behavior of lithium-ion batteries and performance degradation caused by aging and temperature variations. This study proposes a Bidirectional Long Short-Term Memory network with a Temporal Attention Mechanism (BiLSTM-TAM) to enhance SOC estimation accuracy across a wide temperature range from −20 °C to 40 °C. The model is evaluated under several driving conditions, including the Urban Dynamometer Driving Schedule (UDDS), the US06 aggressive driving cycle, a mixed driving cycle that combines multiple standard cycles, and the Hybrid Pulse Power Characterization (HPPC) test commonly used for battery modeling. Model performance is assessed using root-mean-square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). Results demonstrate that the proposed BiLSTM-TAM model improves SOC estimation accuracy and robustness. To enable practical deployment, post-training quantization (PTQ) is applied to reduce model size and computational requirements while maintaining predictive performance, making it suitable for edge-based electric vehicle applications. |
| Published |
Amsterdam : Elsevier |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
