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@article{205394,
        author = {Varsha M R and Dr. Parth Sarathi Panigrahy},
        title = {An Intelligent CNN-LSTM Framework for Battery Voltage Forecasting in Electric Vehicles},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6265-6271},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205394},
        abstract = {Accurate battery voltage prediction is important for improving the safety, reliability, and efficiency of Battery Management Systems (BMS) in Electric Vehicles (EVs). However, the nonlinear and time-dependent behavior of lithium-ion batteries makes voltage prediction challenging under varying operating conditions. To address this issue, this paper proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model for lithium-ion battery voltage prediction. The CNN layer extracts important features from battery data, while the LSTM network captures temporal dependencies during charging and discharging operations. The model utilizes voltage, current, temperature, and cycle information for training and prediction. By combining feature extraction and sequential learning, the proposed CNN-LSTM framework improves voltage prediction accuracy and stability compared to conventional methods. Experimental results demonstrate that the model achieves reduced prediction error with efficient computational performance, making it suitable for real-time Battery Management System applications in Electric Vehicles.},
        keywords = {Deep Learning, Battery Voltage Estimation, Lithium-Ion Cells, Sequence Learning, Convolutional Neural Network, Long Short-Term Memory Network, Electric Mobility, Battery Health Monitoring, Data-Driven Prediction, Intelligent Battery Systems.},
        month = {June},
        }