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Batteries
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Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power...
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Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions

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: closed (31 January 2026) | Viewed by 39978

Special Issue Editors

Prof. Dr. Xiaogang Wu

E-Mail Website
Guest Editor
School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, China
Interests: battery management; energy management
Special Issues, Collections and Topics in MDPI journals
Dr. Jiu-Yu Du

E-Mail Website
Guest Editor
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: electric vehicles; plug-in hybrid electric vehicle; traction battery; energy storage; fuel cell stack; thermal management; battery management; battery safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of the social economy, energy security and environmental protection have become urgent issues facing mankind. The use of renewable energy to generate electricity can alleviate the above problems to a certain extent. In the development and application of renewable power generation systems, such as wind power generation and photovoltaic power generation, new energy storage and energy conversion technologies are vital. The application of advanced energy storage/conversion technologies can promote the continuous and stable generation of power by renewable energy sources, while reducing wind and solar abandonment rates. Among them, the rapid development of battery technology is crucial to the realization of the efficient use of renewable energy and low-carbon as well as low-emission operation.

The Special Issue, titled “Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions”, is focused on the integration and management of fuel cells or other hybrid energy systems with battery storage at their core, suitable for large-scale applications and sustainable complex energy systems.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Battery energy storage in renewable energy;
  • Solar PV–battery hybrid systems (design, optimization, and applications);
  • Fuel cell integration and hybrid energy systems (e.g., hydrogen production, fuel cell–battery hybrids);
  • Application of artificial intelligence in battery management systems;
  • Battery safety management;
  • Battery thermal management.

Prof. Dr. Xiaogang Wu
Dr. Jiu-Yu Du
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 250 words) can be sent to the Editorial Office for assessment.

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

  • renewable energy
  • battery
  • battery system
  • battery management system
  • integration

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Related Special Issue

Published Papers (6 papers)

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Research

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14 pages, 637 KB  
Article
Research on the State of Charge Estimation of Electric Forklift Batteries Based on an Improved Transformer Model
by Jia Wang, Shenglong Zhang and Xia Hu
Batteries 2026, 12(2), 41; https://doi.org/10.3390/batteries12020041 - 23 Jan 2026
Viewed by 445
Abstract
The state of charge (SoC) is one of the critical parameters in battery management systems, as it directly determines the operational safety and reliability of batteries. To accurately predict the SoC of an electric forklift under varying operating conditions, two surrogate models, an [...] Read more.
The state of charge (SoC) is one of the critical parameters in battery management systems, as it directly determines the operational safety and reliability of batteries. To accurately predict the SoC of an electric forklift under varying operating conditions, two surrogate models, an improved Transformer and an improved Transformer 2, are developed. The experimental data obtained through real-vehicle tests are multi-dimensional and contain multiple sources of noise, resulting in poor prediction accuracy when only a single preprocessing algorithm is used. Therefore, this paper first discusses the effect of the preprocessing algorithms on SoC estimation. Compared with the original experimental data and the Kalman filter algorithm, the Kalman filter–principal component analysis (PCA) method is more suitable for preprocessing the original electric forklift data. The mean absolute error (MAE) and root mean square error (RMSE) of the improved Transformer model obtained using the Kalman filter – PCA method are reduced by 26.32% and 27.73% respectively, compared to the single Kalman method. Then, this study investigates the impact of data with different dimensions on the prediction performance of the improved Transformer mode. The results show that five-dimensional data can more effectively train the improved Transformer model, since the MAE decreases by 14.63% and 19.54%, and the RMSE decreases by 14.85% and 20.37% compared to three-dimensional and seven-dimensional data. Through the analysis of the improved Transformer model, an improved Transformer 2 model with higher prediction accuracy is obtained. Then, the improved Transformer 2 model is compared with the LSTM and CNN algorithms. The results indicate that the improved Transformer 2 model can predict SoC more stably and accurately than the single LSTM and CNN algorithms. Specifically, compared with the LSTM model, the proposed Transformer 2 model reduces the MAE by 77.16% and the RMSE by 91.75%. In comparison with the CNN model, the MAE is reduced by 71.81% and the RMSE by 80%. Full article
(This article belongs to the Special Issue Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions)
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33 pages, 3089 KB  
Article
Designing a Sustainable Off-Grid EV Charging Station: Analysis Across Urban and Remote Canadian Regions
by Muhammad Nadeem Akram and Walid Abdul-Kader
Batteries 2026, 12(1), 17; https://doi.org/10.3390/batteries12010017 - 1 Jan 2026
Viewed by 1314
Abstract
Electric vehicles are becoming more commonplace as we shift towards cleaner transportation. However, current charging infrastructure is immature, especially in remote and off-grid regions, making electric vehicle adoption challenging. This study presents an architecture for a standalone renewable energy-based electric vehicle charging station. [...] Read more.
Electric vehicles are becoming more commonplace as we shift towards cleaner transportation. However, current charging infrastructure is immature, especially in remote and off-grid regions, making electric vehicle adoption challenging. This study presents an architecture for a standalone renewable energy-based electric vehicle charging station. The proposed renewable energy system comprises wind turbines, solar photovoltaic panels, fuel cells, and a hydrogen tank. As an energy storage system, second-life electric vehicle batteries are considered. This study investigates the feasibility and performance of the charging station with respect to two vastly different Canadian regions, Windsor, Ontario (urban), and Eagle Plains, Yukon (remote). In modeling these two regions using HOMER Pro software, this study concludes that due to its higher renewable energy availability, Windsor shows a net-present cost of $2.80 million and cost of energy of $0.201/kWh as compared to the severe climate of Eagle Plains, with a net-present cost of $3.61 million and cost of energy of $0.259/kWh. In both cases, we see zero emissions in off-grid configurations. A sensitivity analysis shows that system performance can be improved by increasing wind turbine hub heights and solar photovoltaic panel lifespans. With Canada’s goal of transitioning towards 100% zero-emission vehicle sales by 2035, this study provides practical insights regarding site-specific resource optimization for electric vehicle infrastructure that does not rely on grid energy. Furthermore, this study highlights a means to progress the sustainable development goals, namely goals 7, 9, and 13, through the development of more accessible electric vehicle charging stations. Full article
(This article belongs to the Special Issue Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions)
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22 pages, 18294 KB  
Article
Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles
by Yujia Chang, Ran Li, Hao Sun and Xiaoyu Zhang
Batteries 2024, 10(5), 154; https://doi.org/10.3390/batteries10050154 - 30 Apr 2024
Cited by 3 | Viewed by 4763
Abstract
This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust [...] Read more.
This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles. Full article
(This article belongs to the Special Issue Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions)
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19 pages, 7178 KB  
Article
Experimental and Model Analysis of the Thermal and Electrical Phenomenon of Arc Faults on the Electrode Pole of Lithium-Ion Batteries
by Chuanyou Dong, Bin Gao, Yalun Li and Xiaogang Wu
Batteries 2024, 10(4), 127; https://doi.org/10.3390/batteries10040127 - 9 Apr 2024
Cited by 6 | Viewed by 3721
Abstract
Aiming at the electrical safety problem of a high-voltage lithium-ion battery system caused by an arc, and based on the establishment of a battery arc fault experimental platform, the evolution law of safety caused by an arc in the negative terminal of a [...] Read more.
Aiming at the electrical safety problem of a high-voltage lithium-ion battery system caused by an arc, and based on the establishment of a battery arc fault experimental platform, the evolution law of safety caused by an arc in the negative terminal of a battery system under different working conditions is discussed. On this basis, a battery arc evolution model based on magnetohydrodynamics is established to analyze the arc’s electro-thermal coupling characteristics to further obtain the distribution of the arc’s multi-physical field. The results show that the arc generated by the high-voltage grade battery pack will break down the cell’s shell and form a hole, resulting in electrolyte leakage. When the loop current is 10 A, the evolution law of arc voltage and current is basically the same under different supply voltages, charges, and discharges. The accuracy of the battery arc simulation model is verified by comparing the simulation with the experimental results. The research in this paper provides a theoretical basis for the electrical safety design of lithium-ion batteries caused by the arc, fills the gaps in the field of battery system arc simulation, and is of great significance for improving the safety performance of arc protection. Full article
(This article belongs to the Special Issue Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions)
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23 pages, 7954 KB  
Article
The Polarization and Heat Generation Characteristics of Lithium-Ion Battery with Electric–Thermal Coupled Modeling
by Jiayong Guo, Qiang Guo, Jie Liu and Hewu Wang
Batteries 2023, 9(11), 529; https://doi.org/10.3390/batteries9110529 - 25 Oct 2023
Cited by 20 | Viewed by 15932
Abstract
This paper investigates the polarization and heat generation characteristics of batteries under different ambient temperatures and discharge rates by means of using a coupled electric–thermal model. This study found that the largest percentage of polarization is ohmic polarization, followed by concentration polarization and [...] Read more.
This paper investigates the polarization and heat generation characteristics of batteries under different ambient temperatures and discharge rates by means of using a coupled electric–thermal model. This study found that the largest percentage of polarization is ohmic polarization, followed by concentration polarization and electrochemical polarization. The values of the three types of polarization are generally small and stable under normal-temperature environments and low discharge rates. However, they increase significantly in low-temperature environments and at high discharge rates and continue to rise during the discharge process. Additionally, ohmic heat generation and polarization generation also increase significantly under these conditions. Reversible entropy heat is less sensitive to ambient temperature but increases significantly with the increase in the discharge rate. Ohmic heat generation and polarization heat generation contribute to the total heat generation of the battery at any ambient temperature, while reversible entropy heat only contributes to the total heat generation of the battery at the end of discharge. Full article
(This article belongs to the Special Issue Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions)
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Review

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51 pages, 4099 KB  
Review
Artificial Intelligence and Digital Twin Technologies for Intelligent Lithium-Ion Battery Management Systems: A Comprehensive Review of State Estimation, Lifecycle Optimization, and Cloud-Edge Integration
by Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Hicham Chaoui, Saad Mekhilef, Shi Xue Dou and Khay See
Batteries 2025, 11(8), 298; https://doi.org/10.3390/batteries11080298 - 5 Aug 2025
Cited by 43 | Viewed by 11748
Abstract
The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery [...] Read more.
The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article
(This article belongs to the Special Issue Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions)
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