Citation

Rahman, Tawfikur and Deb, Nibedita and Moniruzzaman, Md. and Mengash, Hanan Abdullah and Md. Jizat, Noorlindawaty and Rahman, Abdullah Al Mahfazur and Al-Bawri, Samir Salem (2026) Deep learning-based battery health prediction for enhancing electric vehicle performance. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

Reliable and sustainable battery diagnostics are essential for advancing electric vehicle (EV) technologies and fulfilling Sustainable DevelopmentGoal 7 (SDG 7):Affordable and Clean Energy. This study proposes a hybrid deep learning framework that integrates one-dimensional Convolutional Neural Networks (1D-CNNs),Temporal Convolutional Networks (TCNs), and Long Short-Term Memory (LSTM) layers, along with an attention mechanism, for intelligent EV battery health diagnostics. DifferentialVoltage (dV/dQ), Differential Current (dI/dV), and Incremental CapacityAnalysis (ICA, dQ/ dV) features were extracted and denoised from over 10,000 charge—discharge cycles sourced from the NASA PCoE, Oxford, and CALCE battery degradation datasets.The proposed model achieved a Stateof-Health (SOH) prediction accuracy of R2 = 0.983 and RMSE = 0.021, outperforming conventional CNN, LSTM, and XGBoost baselines by up to 25% in accuracy. With only 0.35 million parameters, the model demonstrated an average inference latency of 6.1 ms and energy consumption of 0.63 mJ per sample—a 27% reduction compared toTransformer-based architectures.These results confirm the framework’s robustness, scalability, and real-time feasibility for embedded Battery Management Systems (BMS). By improving diagnostic precision, extending battery lifespan, and reducing computational energy demand, the proposed method directly contributes to sustainable mobility and the broader goals of energy efficiency and the circular economy under SDG 7

Item Type:	Article
Uncontrolled Keywords:	Electric vehicles, battery diagnostics
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Apr 2026 02:19
Last Modified:	03 Apr 2026 02:19
URII:	http://shdl.mmu.edu.my/id/eprint/15684

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