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Meeting the demand for reliable energy storage, this work presents a machine-learning model for precise cycle life prediction in lithium-ion batteries (LIB). It explores battery aging features, utilizes data-driven methods for health assessment, and applies machine learning to predict cycle life. To address data limitations, synthetic data generation is employed, enhancing prediction accuracy. The presentation concludes by demonstrating the practical deployment of the proposed ML model for accelerated degradation prediction (for battery cell development and manufacturing feedback) and onboard deployment of low data AI on in-operation energy management. Discussions cover crucial aspects such as battery aging, data-driven health measurement, and the model’s versatility in handling accidental effects during operation.
This webinar will focus on the following key topics:
• Accelerated degradation based on low data AI for battery development for targeted applications
• Data-driven insights: machine learning for battery state of health assessment
• Prediction of rejection thresholds during cell manufacturing for application oriented cell development
• Prediction of targeted C-Rates for specific device applications
• Real-world impact: practical deployment of low data ML during real time device operation
Presenter
Dr. Vikas Tomar – Professor at Purdue University
Prof. Tomar’s interests lie in directed cell development using low data AI and vertical integration of targeted cells in c-rate and energy density specific devices. His research group has published extensively in topics related to developing data-driven models for agnostic BMS in UAVs and EVs, predicting degradation of COTS Li-ion batteries. The technology is now part of a startup, Primordis Inc., focused on launching vertically integrated Li-ion cells in autonomous systems within the framework of autonomous energy intelligence using an ASIC technology.
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