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Batteries
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Machine Learning for Advanced Battery Systems
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Machine Learning for Advanced Battery Systems

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: 21 July 2024 | Viewed by 5682

Special Issue Editors

Dr. Benben Jiang

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: AI for energy; Bayesian machine learning; lithium-ion battery; fault diagnosis
Dr. Guannan He

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China
Interests: energy system; mobility; energy storage; optimization
Dr. Xiang Chen

E-Mail Website
Guest Editor
Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
Interests: energy storage; batteries; DFT

Special Issue Information

Dear Colleagues,

Machine learning has significant potential to enable a more economic, efficient, and reliable low-carbon transition of energy systems, such as improving generation and load forecasting, accelerating the design of next-generation battery chemistries, enhancing distributed energy resources coordination, and advancing battery management systems, including battery lifetime prediction, capacity fade estimation, and optimal charge design. The purpose of this Special Issue is to provide an overview of the state of the art, to present new research results and to discuss the promising future research directions at the interface between energy and machine learning.

Potential topics include, but are not limited to, the following:

  • Machine learning for battery management system including battery lifetime prediction, capacity fade estimation, and optimal charge design;
  • Machine learning and reinforcement learning for distributed optimization and control of large-scale energy systems;
  • Physics-informed machine learning for battery system optimization;
  • Machine-learning-based time aggregation method for energy system planning;
  • Electrochemical energy system optimization with machine learning;
  • Battery system fault diagnosis with data-driven methods;
  • Battery materials design assisted by machine learning.

Dr. Benben Jiang
Dr. Guannan He
Dr. Xiang Chen
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. Batteries is an international peer-reviewed open access monthly 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 2700 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

  • machine learning
  • reinforcement learning
  • data-driven prediction
  • data-driven optimization
  • energy systems
  • battery management systems

Published Papers (3 papers)

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Research

23 pages, 2489 KiB  
Article
Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries
by Alireza Rastegarparnah, Mohammed Eesa Asif and Rustam Stolkin
Batteries 2024, 10(3), 106; https://doi.org/10.3390/batteries10030106 - 15 Mar 2024
Viewed by 1096
Abstract
With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern [...] Read more.
With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)
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20 pages, 2565 KiB  
Article
Enhancing Lithium-Ion Battery Manufacturing Efficiency: A Comparative Analysis Using DEA Malmquist and Epsilon-Based Measures
by Chia-Nan Wang, Fu-Chiang Yang, Nhut T. M. Vo and Van Thanh Tien Nguyen
Batteries 2023, 9(6), 317; https://doi.org/10.3390/batteries9060317 - 06 Jun 2023
Cited by 28 | Viewed by 2115
Abstract
Innovative carbon reduction and sustainability solutions are needed to combat climate change. One promising approach towards cleaner air involves the utilization of lithium-ion batteries (LIB) and electric power vehicles, showcasing their potential as innovative tools for cleaner air. However, we must focus on [...] Read more.
Innovative carbon reduction and sustainability solutions are needed to combat climate change. One promising approach towards cleaner air involves the utilization of lithium-ion batteries (LIB) and electric power vehicles, showcasing their potential as innovative tools for cleaner air. However, we must focus on the entire battery life cycle, starting with production. By prioritizing the efficiency and sustainability of lithium-ion battery manufacturing, we can take an essential step toward mitigating climate change and creating a healthier planet for future generations. A comprehensive case study of the leading LIB manufacturers demonstrates the usefulness of the suggested hybrid methodology. Initially, we utilized the Malmquist model to evaluate these firms’ total efficiency while dissecting their development into technical and technological efficiency change components. We employed the Epsilon-Based Measure (EBM) model to determine each organization’s efficiency and inefficiency scores. The findings show that the EBM approach successfully bridged the gap in the LIB industry landscape. Combined with the Malmquist model, the resulting framework offers a powerful and equitable evaluation paradigm that is easily applicable to any domain. Furthermore, it accurately identifies the top-performing organizations in specific aspects across the research period of 2018–2021. The EBM model demonstrates that most organizations have attained their top level, except for A10, which has superior technology adoption but poor management. A1, A2, A4, A6, A8, A9, and A10 were unable to meet their targets because of the COVID-19 pandemic, despite productivity improvements. A12 leads the three highest-scoring enterprises in efficiency and total productivity changes, while A3 and A5 should focus on innovative production techniques and improved management. The managerial implications provide vital direction for green energy practitioners, enhancing their operational effectiveness. Concurrently, consumers can identify the best LIB manufacturers, allowing them to invest in long-term green energy solutions confidently. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)
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16 pages, 1629 KiB  
Article
Deep Reinforcement Learning-Based Method for Joint Optimization of Mobile Energy Storage Systems and Power Grid with High Renewable Energy Sources
by Yongkang Ding, Xinjiang Chen and Jianxiao Wang
Batteries 2023, 9(4), 219; https://doi.org/10.3390/batteries9040219 - 05 Apr 2023
Cited by 3 | Viewed by 1762
Abstract
The joint optimization of power systems, mobile energy storage systems (MESSs), and renewable energy involves complex constraints and numerous decision variables, and it is difficult to achieve optimization quickly through the use of commercial solvers, such as Gurobi and Cplex. To address this [...] Read more.
The joint optimization of power systems, mobile energy storage systems (MESSs), and renewable energy involves complex constraints and numerous decision variables, and it is difficult to achieve optimization quickly through the use of commercial solvers, such as Gurobi and Cplex. To address this challenge, we present an effective joint optimization approach for MESSs and power grids that consider various renewable energy sources, including wind power (WP), photovoltaic (PV) power, and hydropower. The integration of MESSs could alleviate congestion, minimize renewable energy waste, fulfill unexpected energy demands, and lower the operational costs for power networks. To model the entire system, a mixed-integer programming (MIP) model was proposed that considered both the MESSs and the power grid, with the goal of minimizing costs. Furthermore, this research proposed a highly efficient deep reinforcement learning (DRL)-based method to optimize route selection and charging/discharging operations. The efficacy of the proposed method was demonstrated through many numerical simulations. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)
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