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WEVJ
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Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control
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Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3274

Special Issue Editor

Dr. Haijun Ruan

E-Mail Website
Guest Editor
Centre for E-Mobility and Clean Growth Research, Coventry University, Coventry CV1 5FB, UK
Interests: batteries; battery management systems; machine learning; optimization and control; energy storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Batteries are deployed across a wide range of applications, such as portable electronics, electric vehicles, and stationary power storage systems, due to their high energy and power densities and long lifetime. With emerging technology such as artificial intelligence and blockchain technology, smart battery systems that integrate state-of-the-art battery hardware with advanced battery management processes are moving rapidly from a research field to a requirement for technology’s functionality. Advanced modeling, state estimation, prognostics, and control are the key parts of smart battery systems and contribute to extending battery lifetime and enhancing battery safety. This Special Issue is a dedicated outlet for up-to-date research on all aspects of advanced modeling, state estimation, prognostics, and control of smart battery systems. Manuscripts from cross-disciplinary fields, such as artificial intelligence, blockchain, electrochemistry, power electronics, and thermal and mechanical technologies are warmly welcome. We would particularly like to encourage the submission of papers that bridge the gap between theoretical research and the practical deployment of batteries.

We invite the submission of original research, reviews, and perspective articles, and the topics of particular interest to us include (but are not limited to) the following:

  • The smart management of different batteries, including lithium-ion batteries, sodium-ion batteries, solid-state batteries, etc.
  • Advanced battery modeling.
  • Next-generation battery model parameterization techniques.
  • Battery state estimation: state of charge, state of power, temperature, and state of health.
  • Advanced thermal management: the cooling and heating of lithium-ion batteries.
  • Battery charging methods.
  • Battery degradation, faults, and safety management.
  • Battery diagnostics and prognostics.
  • Machine learning, big data, and battery fusion methods.

Dr. Haijun Ruan
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. World Electric Vehicle Journal 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 1400 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

  • battery
  • modeling
  • state estimation
  • control
  • machine learning
  • big data
  • diagnostics
  • prognostics
  • charging
  • thermal management
  • heating
  • cooling
  • safety management
  • health management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

27 pages, 895 KiB  
Article
Optimizing State of Charge Estimation in Lithium–Ion Batteries via Wavelet Denoising and Regression-Based Machine Learning Approaches
by Mohammed Isam Al-Hiyali, Ramani Kannan and Hussein Shutari
World Electr. Veh. J. 2025, 16(6), 291; https://doi.org/10.3390/wevj16060291 (registering DOI) - 24 May 2025
Abstract
Accurate state of charge (SOC) estimation is key for the efficient management of lithium–ion (Li-ion) batteries, yet is often compromised by noise levels in measurement data. This study introduces a new approach that uses wavelet denoising with a machine learning regression model to [...] Read more.
Accurate state of charge (SOC) estimation is key for the efficient management of lithium–ion (Li-ion) batteries, yet is often compromised by noise levels in measurement data. This study introduces a new approach that uses wavelet denoising with a machine learning regression model to enhance SOC prediction accuracy. The application of wavelet transform in data pre-processing is investigated to assess the impact of denoising on SOC estimation accuracy. The efficacy of the proposed technique has been evaluated using various polynomial and ensemble regression models. For empirical validation, this study employs four Li-ion battery datasets from NASA’s prognostics center, implementing a holdout method wherein one cell is reserved for testing to ensure robustness. The results, optimized through wavelet-denoised data using polynomial regression models, demonstrate improved SOC estimation with RMSE values of 0.09, 0.25, 0.28, and 0.19 for the respective battery datasets. In particular, significant improvements (p-value < 0.05) with variations of 0.18, 0.20, 0.16, and 0.14 were observed between the original and wavelet-denoised SOC estimates. This study proves the effectiveness of wavelet-denoised input in minimizing prediction errors and establishes a new standard for reliable SOC estimation methods. Full article
(This article belongs to the Special Issue Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control)
21 pages, 4231 KiB  
Article
A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization
by Wuke Li, Ying Xiong, Shiqi Zhang, Xi Fan, Rui Wang and Patrick Wong
World Electr. Veh. J. 2025, 16(5), 268; https://doi.org/10.3390/wevj16050268 - 14 May 2025
Viewed by 132
Abstract
This paper shows that lithium-ion battery model parameters are vital for state-of-health assessment and performance optimization. Traditional evolutionary algorithms often fail to balance global and local search. To address these challenges, this study proposes the Elite Opposition-Based Learning Snake Optimization (EOLSO) algorithm, which [...] Read more.
This paper shows that lithium-ion battery model parameters are vital for state-of-health assessment and performance optimization. Traditional evolutionary algorithms often fail to balance global and local search. To address these challenges, this study proposes the Elite Opposition-Based Learning Snake Optimization (EOLSO) algorithm, which uses an elite opposition-based learning mechanism to enhance diversity and a non-monotonic temperature factor to balance exploration and exploitation. The algorithm is applied to the parameter identification of the second-order RC equivalent circuit model. EOLSO outperforms some traditional optimization methods, including the Gray Wolf Optimizer (GWO), Honey Badger Algorithm (HBA), Golden Jackal Optimizer (GJO), Enhanced Snake Optimizer (ESO), and Snake Optimizer (SO), in both standard functions and HPPC experiments. The experimental results demonstrate that EOLSO significantly outperforms the SO, achieving reductions of 43.83% in the Sum of Squares Error (SSE), 30.73% in the Mean Absolute Error (MAE), and 25.05% in the Root Mean Square Error (RMSE). These findings position EOLSO as a promising tool for lithium-ion battery modeling and state estimation. It also shows potential applications in battery management systems, electric vehicle energy management, and other complex optimization problems. The code of EOLSO is available on GitHub. Full article
(This article belongs to the Special Issue Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control)
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19 pages, 1544 KiB  
Article
Patent Analysis of the Electric Vehicle Battery Management Systems Based on the AHP and Entropy Weight Method
by Dan Wan, Ling Peng and Hao Zhan
World Electr. Veh. J. 2025, 16(4), 218; https://doi.org/10.3390/wevj16040218 - 5 Apr 2025
Viewed by 873
Abstract
With the rapid development of the electric vehicle (EV) industry, the importance of battery management systems (BMS) in ensuring the safety, reliability, and efficiency of batteries has significantly increased. This study explores the technological development trends and market layout of EV BMS through [...] Read more.
With the rapid development of the electric vehicle (EV) industry, the importance of battery management systems (BMS) in ensuring the safety, reliability, and efficiency of batteries has significantly increased. This study explores the technological development trends and market layout of EV BMS through patent analysis, focusing on patent quantity, geographic distribution, and technical classification. By integrating the analytic hierarchy process (AHP) and entropy weight method, a patent value evaluation model was constructed to identify key patents and assess their quality across four dimensions: technical, market, economic, and legal. The results reveal that BMS patents are primarily concentrated in China, the United States, and South Korea, with major contributors including LG Energy Solution, BYD, and Hyundai. While BMS patent applications grew rapidly from 2015 to 2020, the pace has slowed since 2021, indicating a possible shift in market focus. The analysis identified 14 high-quality patents, mainly focused on battery safety and compactness, while fewer patents addressed battery lifespan extension and anti-interference capabilities. The study suggests that although significant progress has been made in BMS technology, there is still substantial room for innovation, particularly in areas such as battery lifespan management, charging efficiency, and intelligent energy scheduling. This research provides valuable insights for future technological innovation and market decision-making in the EV BMS sector. Full article
(This article belongs to the Special Issue Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control)
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26 pages, 4125 KiB  
Article
An RNN-CNN-Based Parallel Hybrid Approach for Battery State of Charge (SoC) Estimation Under Various Temperatures and Discharging Cycle Considering Noisy Conditions
by Md. Shahriar Nazim, Md. Minhazur Rahman, Md. Ibne Joha and Yeong Min Jang
World Electr. Veh. J. 2024, 15(12), 562; https://doi.org/10.3390/wevj15120562 - 4 Dec 2024
Cited by 1 | Viewed by 1661
Abstract
With the increasing use of lithium-ion (Li-ion) batteries in electric vehicles (EVs), accurately measuring the state of charge (SoC) has become crucial for ensuring battery reliability, performance, and safety. In addition, EVs operate in different environmental conditions with different driving styles, which also [...] Read more.
With the increasing use of lithium-ion (Li-ion) batteries in electric vehicles (EVs), accurately measuring the state of charge (SoC) has become crucial for ensuring battery reliability, performance, and safety. In addition, EVs operate in different environmental conditions with different driving styles, which also cause inaccurate SoC estimation resulting in reduced reliability and performance of battery management systems (BMSs). To address this issue, this work proposes a new hybrid method that integrates a gated recurrent unit (GRU), temporal convolution network (TCN), and attention mechanism. The TCN and GRU capture both long-term and short-term dependencies and the attention mechanism focuses on important features within input sequences, improving model efficiency. With inputs of voltage, current, and temperature, along with their moving average, the hybrid GRU-TCN-Attention (GTA) model is trained and tested in a range of operating cycles and temperatures. Performance metrics, including average RMSE (root mean squared error), MAE (mean absolute error), MaxE (maximum error), and R2 score indicates the model is performing well, with average values of 0.512%, 0.354%, 1.98%, and 99.94%, respectively. The proposed model performs well under both high and low noise conditions, with an RMSE of less than 2.18%. The proposed hybrid approach is consistently found to be superior when compared against traditional baseline models. This work offers a potential method for accurate SoC estimation in Li-ion batteries, which has an important impact on clean energy integration and battery management systems in EVs. Full article
(This article belongs to the Special Issue Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control)
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