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Li-Ion Batteries: Modelling and Control from Manufacturing to Performance Evaluation

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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 14279

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Special Issue Editor

Dr. Dhammika Widanalage

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Guest Editor

WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: System Identification; Li-ion Batteries; Reduced-order modelling

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Lithium ion batteries
Modelling
Control
Battery manufacturing
Machine-learning

Published Papers (3 papers)

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Research

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16 pages, 506 KiB

Open AccessArticle

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

by Alireza Rastegarpanah, Jamie Hathaway and Rustam Stolkin

Energies 2021, 14(9), 2597; https://doi.org/10.3390/en14092597 - 01 May 2021

Cited by 16 | Viewed by 2656

Abstract

The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes. Full article

(This article belongs to the Special Issue Li-Ion Batteries: Modelling and Control from Manufacturing to Performance Evaluation)

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15 pages, 7876 KiB

Open AccessArticle

Elbows of Internal Resistance Rise Curves in Li-Ion Cells

by Calum Strange, Shawn Li, Richard Gilchrist and Gonçalo dos Reis

Energies 2021, 14(4), 1206; https://doi.org/10.3390/en14041206 - 23 Feb 2021

Cited by 16 | Viewed by 3302

Abstract

The degradation of lithium-ion cells with respect to increases of internal resistance (IR) has negative implications for rapid charging protocols, thermal management and power output of cells. Despite this, IR receives much less attention than capacity degradation in Li-ion cell research. Building on recent developments on ‘knee’ identification for capacity degradation curves, we propose the new concepts of ‘elbow-point’ and ‘elbow-onset’ for IR rise curves, and a robust identification algorithm for those variables. We report on the relations between capacity’s knees, IR’s elbows and end of life for the large dataset of the study. We enhance our discussion with two applications. We use neural network techniques to build independent state of health capacity and IR predictor models achieving a mean absolute percentage error (MAPE) of 0.4% and 1.6%, respectively, and an overall root mean squared error below

0.0061

. A relevance vector machine, using the first 50 cycles of life data, is employed for the early prediction of elbow-points and elbow-onsets achieving a MAPE of 11.5% and 14.0%, respectively. Full article

(This article belongs to the Special Issue Li-Ion Batteries: Modelling and Control from Manufacturing to Performance Evaluation)

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Review

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18 pages, 610 KiB

Open AccessReview

A Review of Pulsed Current Technique for Lithium-ion Batteries

by Xinrong Huang, Yuanyuan Li, Anirudh Budnar Acharya, Xin Sui, Jinhao Meng, Remus Teodorescu and Daniel-Ioan Stroe

Energies 2020, 13(10), 2458; https://doi.org/10.3390/en13102458 - 13 May 2020

Cited by 49 | Viewed by 7481

Abstract

Lithium-ion (Li-ion) batteries have been competitive in Electric Vehicles (EVs) due to their high energy density and long lifetime. However, there are still issues, which have to be solved, related to the fast-charging capability of EVs. The pulsed current charging technique is expected to improve the lifetime, charging speed, charging/discharging capacity, and the temperature rising of Li-ion batteries. However, the impact of the pulsed current parameters (i.e., frequency, duty cycle, and magnitude) on characteristics of Li-ion batteries has not been fully understood yet. This paper summarizes the existing pulsed current modes, which are positive Pulsed Current Mode (PPC) and its five extended modes, and Negative Pulsed Current (NPC) mode and its three extended modes. An overview of the impact of pulsed current techniques on the performance of Li-ion batteries is presented. Then the main impact factors of the PPC strategy and the NPC strategy are analyzed and discussed. The weight of these impact factors on lifetime, charging speed, charging/discharging capacity, and the temperature rising of batteries is presented, which provides guidance to design advanced charging/discharging strategies as well as to determine future research gaps. Full article

(This article belongs to the Special Issue Li-Ion Batteries: Modelling and Control from Manufacturing to Performance Evaluation)

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