Predict battery degradation for real world conditions
Recording
8 Feb. 2023
Predict battery degradation for real world conditions
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.
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, oorja has developed a Hybrid approach (Physics + ML) to predict cell and pack level degradation at various C rates, operating temperatures, and real-world drive cycles.
Key Learning Objectives
Predict cell and pack level degradation on the drive cycles
Provide reliable capacity fade warranty on the battery pack
Reduce dependence on tedious cycler experiments
Upto 8X faster predictions for > 95% accuracy
Vineet Dravid
Vineet founded oorja to simplify design for complex engineering problems. Armed with a Ph.D. in Mechanical Engineering from Purdue University, Vineet has worked on cutting-edge simulation and mathematical modelling technologies for two decades.