Lithium-ion batteries (LIBs) play a vital role in the advancement of electric vehicles and sustainable energy solutions. They are favored over other secondary energy storage systems due to their high energy density, long cycle life, high nominal voltage, and low self-discharge rate. However, the latency of LIBs’ internal states makes it difficult to predict their performance and ensure they are being operated safely. Fortunately, battery management systems (BMS) can use battery models to predict the internal states of a battery. This webinar explores:
- How to manage trade-offs between accuracy and computational costs for battery management systems (BMS)
- How a digital twin framework can capture the accuracy of high-fidelity electrochemical models while meeting the computational constraints imposed by the BMS
- How this can be achieved using a lower-fidelity model in real-time to accurately predict slower dynamics such as the state of health and more dynamic states such as voltage, temperature, and state of charge
Speakers:
Pantelis Dimitrakopoulos, Staff Application Engineer – Mobility Systems and Integration, Gamma Technologies
Nikhil Biju, Staff Application Engineer, Gamma Technologies