Degradation prediction in Li-ion batteries is a challenging and highly nonlinear problem. Traditional physics-based approaches and tools need several designs, electrochemical, degradation parameters, and long computation times, which make it a tedious and expensive approach.
To solve the challenge quickly and accurately, a new Hybrid approach (Physics + ML) has been developed to predict cell and pack level degradation at various C rates, operating temperatures, and real-world drive cycles. Battery degradation also impacts power delivery and performance. Using this new Range app, estimate the impact of real-life driving conditions, road conditions and temperature on vehicle range and power delivery over the life of the vehicle.
Topic Area
- Hybrid approach (Physics + ML) for faster and more accurate prediction
- Predict Cell Level and Pack level capacity fade for real-life drive cycles
- Understand degradation at varying operating conditions
- SoH and power predictions based on cyclic and calendar aging
Registration Procedure
This event is free of charge: registration is required to attend.
It uses a web platform that does not require local software installation. You can join the session via: Mac, PC, or any mobile device.
Registered participants will receive the link and credentials to participate at the email address provided during registration.
You can ask questions and participate in the discussion via the chat function in the web platform.
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