6. Bayesian Inference for Lithium-Ion Cell Parameter Estimation

The optimization of lithium-ion cells is becoming increasingly important. Using models that reflect the fundamental electrochemical processes is advantageous for this purpose. These models are typically computationally expensive and hard to invert using optimization methods. Additionally, deterministic optimization methods do not yield information regarding parameter uncertainties in the presence of noise. To overcome this problem, it is possible to apply Bayesian methods. This chapter provides an overview of parameter estimation. After a brief introduction to the model, parameter selection and modelling of the prior is presented. Finally, we present the results of a synthetic fitting problem solved by a parallel adaptive Markov chain Monte Carlo method. We validate the approach and compare it to realistic noisy data and a separated method.