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.

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