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Applied Sciences
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Advances in Lithium-Ion Automobile Batteries
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Advances in Lithium-Ion Automobile Batteries

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 14620

Special Issue Editors

Dr. Daniela Chrenko

E-Mail Website
Guest Editor
FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS, 90000 Belfort, France
Interests: battery systems; energy demand in real use; battery aging and second use
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Julia Kowal

E-Mail Website
Guest Editor
Technical University of Berlin, Electrical Energy Storage Technology, Berlin, Germany
Interests: batteries; lithium-ion; lead-acid; supercaps; lithium-air; zinc-air; simulation; impedance spectroscopy; ageing; battery management system; state determination
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Battery systems are one of the key contributors to sustainable mobility, and automobile batteries are expected to be vastly applied. Even though numerous electric and hybrid electric vehicles are already on the road, considerable research is still required to improve the life cycle sustainability of automotive batteries, including, among others, thermal management, mass production, reduction or raw materials, reuse and recycling, repeatable and representative accelerated aging, storage and use of battery data over a lifetime, etc. Those topics have to be approached both by experiments but also by modeling and should be enriched by knowhow acquired in the field. Moreover, new sustainable business models have to be created for lithium-ion automobile batteries. In this Special Issue on “Advances in Lithium-Ion Automobile Batteries”, the journal Applied Sciences will address all those interesting and cross-cutting topics in this field with growing interest and importance.

Prof. Dr. Daniela Chrenko
Prof. Dr. Julia Kowal
Guest Editors

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. Applied Sciences 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 2400 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

  • thermal management
  • mass production
  • reduction in raw materials
  • reuse and recycling
  • repeatable and representative accelerated aging
  • storage and use of battery data over a lifetime
  • second life
  • sustainable business models
  • field test result

Published Papers (7 papers)

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Research

13 pages, 3818 KiB  
Article
Lithium-Ion Battery Aging Analysis of an Electric Vehicle Fleet Using a Tailored Neural Network Structure
by Thomas Lehmann and Frances Weiß
Appl. Sci. 2023, 13(7), 4448; https://0-doi-org.brum.beds.ac.uk/10.3390/app13074448 - 31 Mar 2023
Viewed by 1012
Abstract
Within the presented research study we want to estimate the State of Health (SOH) of a fleet of electric vehicles solely using field data. This information may not only help operators during mission planning, but it can reveal causes of accelerated aging. For [...] Read more.
Within the presented research study we want to estimate the State of Health (SOH) of a fleet of electric vehicles solely using field data. This information may not only help operators during mission planning, but it can reveal causes of accelerated aging. For this purpose, we use a customized neural network that is able to process the data of all fleet vehicles simultaneously. Thus, information between batteries of the different vehicles is transferred and the extrapolation properties are enhanced. We firstly show results with data gathered from a fleet of 25 electric buses. A prediction accuracy of below 5 mV could be obtained for most validation sections. Furthermore, a proof-of-concept experiment illustrates the advantages of the fleet learning approach. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Automobile Batteries)
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13 pages, 5066 KiB  
Article
Thermal Characterisation of Automotive-Sized Lithium-Ion Pouch Cells Using Thermal Impedance Spectroscopy
by Dominik Droese and Julia Kowal
Appl. Sci. 2023, 13(5), 2870; https://0-doi-org.brum.beds.ac.uk/10.3390/app13052870 - 23 Feb 2023
Cited by 1 | Viewed by 1436
Abstract
This study used thermal impedance spectroscopy to measure a 46 Ah high-power lithium-ion pouch cell, introducing a testing setup for automotive-sized cells to extract the relevant thermal parameters, reducing the time for thermal characterisation in the complete operational range. The results are validated [...] Read more.
This study used thermal impedance spectroscopy to measure a 46 Ah high-power lithium-ion pouch cell, introducing a testing setup for automotive-sized cells to extract the relevant thermal parameters, reducing the time for thermal characterisation in the complete operational range. The results are validated by measuring the heat capacity using an easy-to-implement calorimetric measurement method. For the investigated cell at 50% state of charge and an ambient temperature of 25 °C, values for the specific heat capacity of 1.25 J/(gK) and the cross-plane thermal conductivity of 0.47 W/(mK) are obtained. For further understanding, the values were measured at different states of charge and at different ambient temperatures, showing a notable dependency only on the thermal conductivity from the temperature of −0.37%/K. Also, a comparison of the cell with a similar-sized 60 Ah high-energy cell is investigated, comparing the influence of the cell structure to the thermal behaviour of commercial cells. This observation shows about 15% higher values in heat capacity and cross-plane thermal conductivity for the high-energy cell. Consequently, the presented setup is a straightforward implementation to accurately obtain the required model parameters, which could be used prospectively for module characterisation and investigating thermal propagation through the cells. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Automobile Batteries)
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23 pages, 9124 KiB  
Article
Lithium-Ion Battery Health Estimation Using an Adaptive Dual Interacting Model Algorithm for Electric Vehicles
by Richard Bustos, S. Andrew Gadsden, Mohammad Al-Shabi and Shohel Mahmud
Appl. Sci. 2023, 13(2), 1132; https://0-doi-org.brum.beds.ac.uk/10.3390/app13021132 - 14 Jan 2023
Cited by 7 | Viewed by 1379
Abstract
To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS’s main task is to accurately estimate a battery’s available power, referred to as the state of charge (SOC). Unfortunately, the SOC cannot be measured directly due to [...] Read more.
To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS’s main task is to accurately estimate a battery’s available power, referred to as the state of charge (SOC). Unfortunately, the SOC cannot be measured directly due to its structure, and so must be estimated using indirect measurements. In addition, the methods used to estimate SOC are highly dependent on the battery’s available capacity, known as the state of health (SOH), which degrades as the battery is used, resulting in a complex problem. In this paper, a novel adaptive battery health estimation method is proposed. The proposed method uses a dual-filter architecture in conjunction with the interacting multiple model (IMM) algorithm. The dual filter strategy allows for the model’s parameters to be updated while the IMM allows access to different degradation rates. The well-known Kalman filter (KF) and relatively new sliding innovation filter (SIF) are implemented to estimate the battery’s SOC. The resulting methods are referred to as the dual-KF-IMM and dual-SIF-IMM, respectively. As demonstrated in this paper, both algorithms show accurate estimation of the SOC and SOH of a lithium-ion battery under different cycling conditions. The results of the proposed strategies will be of interest for the safe and reliable operation of electrical systems, with particular focus on electric vehicles. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Automobile Batteries)
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15 pages, 1083 KiB  
Article
Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements
by Daniel Schürholz, Bernhard Schweighofer, Markus Neumayer and Hannes Wegleiter
Appl. Sci. 2022, 12(23), 11876; https://0-doi-org.brum.beds.ac.uk/10.3390/app122311876 - 22 Nov 2022
Cited by 3 | Viewed by 1623
Abstract
Due to the long life of lithium ion cells, it is difficult to measure their low capacity degradation from cycle to cycle. In order to accelerate the measurements, cells are often exposed to extreme stress conditions, which usually means elevated temperatures and high [...] Read more.
Due to the long life of lithium ion cells, it is difficult to measure their low capacity degradation from cycle to cycle. In order to accelerate the measurements, cells are often exposed to extreme stress conditions, which usually means elevated temperatures and high charging currents. This raises doubts as to whether the results obtained in this way are representative for real world applications. This work shows that, with the help of very precise capacity measurements, it is possible to determine cell aging in a few days even under normal operating conditions from cycle to cycle. To verify this, a self-built measurement system is used. After demonstrating the capabilities of the system, two different cycling schemes are used simultaneously to determine the various causes of aging—namely cycle aging, calendrical aging and self-discharge due to leakage currents. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Automobile Batteries)
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27 pages, 13834 KiB  
Article
Aging Study of In-Use Lithium-Ion Battery Packs to Predict End of Life Using Black Box Model
by Daniela Chrenko, Manuel Fernandez Montejano, Sudnya Vaidya and Romain Tabusse
Appl. Sci. 2022, 12(13), 6557; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136557 - 28 Jun 2022
Cited by 4 | Viewed by 1662
Abstract
In order to study the state of health (SOH) of unbalanced battery packs in real life, a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are [...] Read more.
In order to study the state of health (SOH) of unbalanced battery packs in real life, a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are studied, leading to the identification of five key parameters to indicate aging, as well as parameters influencing aging. Based on the measurement results, a simple black box model using evolutionary genetic algorithm is presented, which is used as end-of-life prediction model of the battery pack, successfully providing an approximate estimation of aging. This approach might thus be used for the supervision of battery systems during real-life use. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Automobile Batteries)
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14 pages, 2027 KiB  
Article
Forecasting the Global Battery Material Flow: Analyzing the Break-Even Points at Which Secondary Battery Raw Materials Can Substitute Primary Materials in the Battery Production
by Michael Neidhardt, Jordi Mas-Peiro, Magnus Schulz-Moenninghoff, Josep O. Pou, Rafael Gonzalez-Olmos, Arno Kwade and Benedikt Schmuelling
Appl. Sci. 2022, 12(9), 4790; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094790 - 09 May 2022
Cited by 13 | Viewed by 3884
Abstract
Growing numbers of electric vehicles (EVs) as well as controversial discussions on cost, scarcity and the environmental and social sustainability of primary raw materials that are needed for battery production together emphasize the necessity for battery recycling in the future. Nonetheless, the market [...] Read more.
Growing numbers of electric vehicles (EVs) as well as controversial discussions on cost, scarcity and the environmental and social sustainability of primary raw materials that are needed for battery production together emphasize the necessity for battery recycling in the future. Nonetheless, the market for battery recycling is not fully understood and captured in data today. The underlying reasons are found in both market size and various parameters such as the battery-technology mix, the resulting material demand and expected battery lifetime. In consequence, the question of when secondary-material availability from battery recycling is sufficient to satisfy the global cobalt demand for EV applications has not yet been clarified. To address this question, this study estimates the global battery raw-material demand together with the expected amount of the recycled materials by 2035, taking into account a number of parameters affecting future battery material flows. While focusing on cobalt, nickel, lithium, and manganese, the results indicate that the global cobalt demand can be satisfied from secondary sources by the early 2030s in three out of four different technology forecast scenarios. Furthermore, a sensitivity analysis highlights the amount of waste occurring during battery production and battery lifetime as the main drivers for secondary-material availability by 2035. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Automobile Batteries)
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17 pages, 5369 KiB  
Article
A Strategic Pathway from Cell to Pack-Level Battery Lifetime Model Development
by Md Sazzad Hosen, Ashkan Pirooz, Theodoros Kalogiannis, Jiacheng He, Joeri Van Mierlo and Maitane Berecibar
Appl. Sci. 2022, 12(9), 4781; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094781 - 09 May 2022
Cited by 4 | Viewed by 2577
Abstract
The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work [...] Read more.
The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work aims to provide a guideline for pack-level lifetime model development that could facilitate battery maintenance, ensuring a safe and reliable operational lifespan. The first of the twofold approach is a cell-level empirical lifetime model that is developed from a lab-level aging dataset of commercial LTO cells. The model is validated with an exhaustive sub-urban realistic driving cycle yielding a root-mean-square error of 0.45. The model is then extended to a 144S1P modular architecture for pack-level simulation. The second step provides the pack electro-thermal simulation results that are upscaled from a cell-level and validated 1D electrical model coupled with a 3D thermal model. The combined simulation framework is online applicable and considers the relevant aspects into account in predicting the battery system’s lifetime that results in over 350,000 km of suburban driving. This robust tool is a collaborative research outcome from two Horizon2020 EU projects—GHOST and Vision xEV, showcasing outstanding cell-level battery modeling accuracies. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Automobile Batteries)
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