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Batteries

Batteries is an international, peer-reviewed, open access journal on battery technology and materials published monthly online by MDPI. 
International Society for Porous Media (InterPore) is affiliated with Batteries and their members receive discounts on the article processing charges.
Quartile Ranking JCR - Q2 (Electrochemistry | Materials Science, Multidisciplinary | Energy and Fuels)

All Articles (2,101)

This paper provides a comprehensive review of recent advances in remaining useful life prediction for lithium-ion battery energy storage systems. Existing approaches are generally categorized into model-based methods, data-driven methods, and hybrid methods. A systematic comparison of these three methodological paradigms is presented, with hybrid methods further divided into filter-based hybrids and data-driven hybrids, followed by a comparative analysis of remaining useful life prediction accuracy. The literature analysis indicates that data-driven hybrid methods, by integrating the strengths of physical mechanism modeling and machine learning algorithms, exhibit superior robustness under complex operating conditions. Among them, the hybrid framework combining long short-term memory networks with an eXtreme Gradient Boosting model optimized by the Binary Firefly Algorithm demonstrates the highest stability and accuracy in the reviewed studies, achieving a root mean squared error below 2% and a mean absolute percentage error below 1%. Future research may further enhance the generalization capability of this framework, reduce computational cost, and improve model interpretability.

15 October 2025

Mechanism of capacity attenuation of lithium-ion batteries.

Rapid and accurate prediction of the maximum remaining life of lithium-ion batteries is a critical technical challenge for enhancing battery management system reliability and enabling the efficient secondary utilization of retired batteries. Traditional approaches that rely on full charge–discharge cycles or complex electrochemical models often suffer from long detection time and limited adaptability, making them unsuitable for fast testing scenarios. To address these limitations, this study proposes a novel capacity prediction method that integrates charging segment feature extraction with a back-propagation neural network (BPNN) co-optimized using the genetic algorithm (GA) and dung beetle optimizer (DBO). Leveraging the public CALCE datasets, key degradation-related features were extracted from partial charging segments to serve as inputs to the prediction framework. The hybrid GA_DBO algorithm is employed to jointly optimize the BPNN’s weights, learning rate, and activation thresholds. A comparative analysis is conducted across various charging durations (900 s, 1800 s, and 2700 s) to evaluate performance under different input lengths. Results reveal that the model using 1800 s charging segment features achieves the best overall accuracy, with a test set mean squared error (MSE) of 0.0001 Ah2, mean absolute error (MAE) of 0.0092 Ah, root mean square error (RMSE) of 0.0122 Ah, and a coefficient of determination (R2) of 99.66%, demonstrating strong robustness and predictive capability. This research overcomes the traditional reliance on full cycles, demonstrating the effectiveness of short charging segments combined with intelligent optimization algorithms. The proposed method offers a high-precision, low-cost solution for online battery health monitoring and rapid sorting of retired batteries, highlighting its significant engineering application potential.

13 October 2025

Schematic diagram of the CS2 battery test setup (adapted from the CALCE Battery Research Group webpage) [19]. Note: Jellyroll configuration wrapped around the “length” axis, indicated by red arrows.

The rapid development and spread of electric vehicles is fundamentally revolutionizing transportation in the European Union and around the world. With the diffusion of electric vehicles, issues related to the batteries that power them have also become more prominent. Given that the production of these components is one of the most environmentally burdensome processes, the need for their secondary use has quickly become evident. Based on the Eurostat database, this article analyzes the indicators that may influence the prospects for the secondary use of batteries. It examines the relationship between the GDP (Gross Domestic Product) of European Union member states and the number of electric vehicles, the share of renewable energy, and household electricity consumption. The results show that electric vehicle penetration and the use of renewable energy vary greatly among EU member states. The second part of the article examines battery data from an electric vehicle, the solar panel production of a family home, and electricity consumption using a linear programming model on a monthly basis. The objective function of the model makes it possible to minimize the amount of energy purchased from the grid. The resulting savings can be quantified. The article focuses on providing a foundation for the opportunities offered by the secondary-use battery market.

13 October 2025

Electric vehicle share and GDP in 2024.

The structure, chemical composition, thermal stability, and abuse responses of cathode materials are critical to the safety and economy of lithium-ion batteries (LIBs). This review systematically summarizes advances in research on how cathode materials influence LIB thermal runaway (TR) behavior. It analyzes the oxygen release from cathodes in TR mechanisms and the hazards of such oxygen generation during TR, expounds on how differences in cathode structure, chemical composition, and thermal stability affect TR behavior, and summarizes the thermal characteristics of LIBs with different cathodes under mechanical, electrical, and thermal abuse. Results indicate that oxygen released from cathode decomposition during TR oxidizes electrolytes, releasing substantial heat and gas and causing more severe TR hazards. Structural instability of cathodes leads to accelerated release of lattice oxygen, speeding up TR initiation. Chemical composition regulates thermal stability, phase transition pathways, and gas generation rates during TR, while elemental ratios affect the ease of TR triggering. Cathodes with poor thermal stability have lower thermal decomposition onset temperatures, making TR more likely to occur and intensifying reaction severity. All three abuse types trigger inherent risks of cathodes, inducing TR and significantly increasing its occurrence probability. Differences in intrinsic properties further extend to the system level, also influencing thermal runaway propagation and fire dynamics at the module level. Future research focusing on the intrinsic properties of cathodes and external abuse is of great significance for addressing LIB TR behavior.

12 October 2025

Thermal runaway mechanism properties of cathode materials, intrinsic properties of cathode materials, and three kinds of abuse triggering thermal runaway.

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Transportation Electrification Key Applications
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Transportation Electrification Key Applications

Battery Storage System, DC/DC Converter, Wireless Charging, Sensors
Editors: Xiaoyu Li, Jinhao Meng, Xu Liu
Towards a Smarter Battery Management System
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Towards a Smarter Battery Management System

2nd Edition
Editors: Chris Mi, Zhi Cao, Naser Vosoughi Kurdkandi

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Batteries - ISSN 2313-0105Creative Common CC BY license