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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Bayesian Inference for Lithium-Ion Cell Parameter Estimation


    Additional title:

    SpringerBriefs in Applied Sciences


    Contributors:


    Publication date :

    2014-01-31


    Size :

    21 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Variance Reduction Estimation in Bayesian Inference

    Li, Chenzhao / Mahadevan, Sankaran | AIAA | 2017


    Bayesian Parameter Estimation of a

    Ray, Jaideep | Online Contents | 2016


    Bayesian Inference

    Prieto Tejedor, Javier | TIBKAT | 2017

    Free access

    Bayesian Inference

    Prieto Tejedor, Javier | GWLB - Gottfried Wilhelm Leibniz Bibliothek | 2017

    Free access