Li-Ion Batteries: Modelling and Control from Manufacturing to Performance Evaluation
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D1: Advanced Energy Materials".
Deadline for manuscript submissions: closed (1 June 2021) | Viewed by 17464
Special Issue Editor
Special Issue Information
Dear Colleagues,
We invite you to contribute to this Energies Special Issue on new approaches to modelling and controlling of lithium ion batteries and their applications. The upcoming years are an exciting time for the demand of robust battery models and their applications. Various mathematical models are used to describe and address key challenges facing application of lithium–ion batteries. Multiphysics (electrochemical-thermal-stress) approaches are often utilised in the design optimisation of batteries, improve manufacturing approaches and to predict degradation. Empirical models are typically developed for use with control and systems applications. New research directions are now developed involving reduced-order models and physics-informed machine learning models that builds on the advantages of multiphysics and empirical modelling approaches.
There are several interesting challenges in such approaches, for example, robust parameter estimation schemes, relating model parameters and performance to the manufacturing stages, fast computational approaches, design of experiments for model development and the challenges involved when deploying battery models within the growing application space – battery pack systems in automotive, aerospace and grid applications. This special issue therefore welcomes new research that explores these challenges and will include research papers related to the following topics:
- Multiphysics, data-driven modelling of Lithium-ion batteries
- Battery manufacturing and performance prediction
- Model parameterisation and validation methodologies
- Degradation modelling, prognostics and diagnostics
- Experimental characterisation for modelling and simulation
- State-estimation and optimal control
- Battery pack and battery management systems
- Physics-informed machine learning models
Dr. Dhammika Widanalage
Guest Editor
Manuscript Submission Information
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Keywords
- Lithium ion batteries
- Modelling
- Control
- Battery manufacturing
- Machine-learning
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