STATE OF HEALTH AND REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES IN ELECTRIC VEHICLES USING MACHINE LEARNING

Date

2024-9-23

Author

Ghani, Ejaz

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In today’s rapidly evolving industrial landscape, the quest for efficiency, reliability, and cost-effectiveness has spurred a paradigm shift towards proactive maintenance strategies. At the forefront of this transformation lies predictive maintenance, offering the promise of minimizing downtime and maximizing asset lifespan through data-driven insights. This thesis focuses specifically on the forecasting of State of Health (SOH) and Remaining Useful Life (RUL) for lithium-ion (Li-ion) batteries, a critical component in numerous industrial applications.Leveraging the wealth of data available, particularly in the realm of Li-ion battery performance, this research employs advanced machine learning methods, including Deep Neural Networks (DNN) and hybrid models like Convolutional Neural Network-Bidirectional Long Short- Term Memory (CNN-BiLSTM), to develop accurate and reliable predictive maintenance models. The optimized DNN model for SOH prediction showed a significant improvement, with MAE, MSE, and RMSE reduced by 17.54%, 25.86%, and 13.85%, respectively, compared to the base model. For RUL prediction, the best CNN-BiLSTM model (MC-SCNN-BiLSTM) achieved an RMSE of 0.0259, MAE of 0.0183, and MAPE of 1.1951, demonstrating its superior performance.Through systematic experimentation and comprehensive analysis, this study not only improves the predictive capabilities of these models but also advances the efficiency and intelligence of maintenance practices within diverse industrial sectors.

Subject Keywords

Machine learning, Deep learning, AI, SOH, RUL

URI

https://hdl.handle.net/11511/111592

Collections

Northern Cyprus Campus, Thesis