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.
Bayesian Inference for Lithium-Ion Cell Parameter Estimation
SpringerBriefs in Applied Sciences
2014-01-31
21 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Variance Reduction Estimation in Bayesian Inference
AIAA | 2017
|Bayesian Parameter Estimation of a
Online Contents | 2016
|Recursive Bayesian filtering framework for lithium-ion cell state estimation
Online Contents | 2016
|Bayesian Parameter Estimation for Heavy-Duty Vehicles
British Library Conference Proceedings | 2017
|Bayesian Parameter Estimation for Heavy-Duty Vehicles
SAE Technical Papers | 2017
|