Battery Design and Management, 2nd Edition

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic, “Battery Design and Management” (https://www.mdpi.com/topics/battery). Batteries can be classified into small-scale applications (mobile phones), medium-scale applications (hybrid electric vehicles), and large-scale applications (electric grids) in terms of scale. They are efficient and have high specific energy, featuring a safe and recyclable design. However, concerns about their cost and lifespan have hindered the wider application of battery energy storage. Researchers are constantly developing battery chemistries that cost less and last longer. Battery systems engineering—the intersection of chemistry, dynamic modeling, and systems/control engineering—requires a multidisciplinary approach.

This Topic will highlight recent studies in the field of battery systems engineering, providing the background, models, solution techniques, and system theory required for the development of advanced battery systems.

Topics of interest include, but are not limited to, the following topics:

• Battery materials and battery design;
• Battery and system modeling and simulation;
• Battery status estimation and troubleshooting;
• Battery thermal management and thermal safety;
• Power battery echelon utilization;
• Battery balance;
• Hydrogen fuel cells;
• Battery accident analysis.

Prof. Dr. Quanqing Yu
Prof. Dr. Yonggang Liu
Dr. Xiaopeng Tang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400
Batteries batteries	6.3	9.8	2015	16.4 Days	CHF 2700
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600

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Published Papers (3 papers)

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All Applied Sciences Batteries Energies Sensors

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34 pages, 3542 KB  

Open AccessReview

Thermal Runaway in Lithium-Ion Batteries: A Review of Mechanisms, Prediction Approaches, and Mitigation Strategies

by Zeyu Chen, Jiakai Zhang, Chengxin Liu, Chengyan Yang and Shuxian Chen

Batteries 2026, 12(3), 88; https://doi.org/10.3390/batteries12030088 - 3 Mar 2026

Cited by 6 | Viewed by 9730

Abstract

Thermal runaway is one of the most critical safety challenges limiting the widespread deployment of lithium-ion batteries in electric vehicles, energy storage systems, and aerospace applications. With the continuous increase in battery energy density, the fault-to-failure transition becomes increasingly rapid, which makes early [...] Read more.

Thermal runaway is one of the most critical safety challenges limiting the widespread deployment of lithium-ion batteries in electric vehicles, energy storage systems, and aerospace applications. With the continuous increase in battery energy density, the fault-to-failure transition becomes increasingly rapid, which makes early detection and effective intervention quite difficult. This review systematically summarizes the fundamental mechanisms underlying thermal runaway that drive the escalation of battery hazards. Existing thermal runaway prediction and early warning approaches are comprehensively classified into electrical, thermal, mechanical/gas, and data-driven categories. The detection principles, performance characteristics, and current limitations are critically analyzed. Furthermore, research progress in mitigation and suppression, including system-level thermal management, material-level approach, and structure modification, is discussed. This work aims to support the development of advanced early-warning technologies and to provide guidance for the design of safer next-generation lithium-ion battery systems. Full article

(This article belongs to the Topic Battery Design and Management, 2nd Edition)

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21 pages, 6329 KB  

Open AccessArticle

Transfer Learning-Enhanced Safety Modeling for Lithium-Ion Batteries Under Mechanical Abuse

by Hong Liang, Renjing Gao, Haihe Zhao and Zeyu Chen

Batteries 2026, 12(2), 39; https://doi.org/10.3390/batteries12020039 - 23 Jan 2026

Cited by 1 | Viewed by 1322

Abstract

The widespread adoption of lithium-ion battery-powered electric vehicles has raised increasing concerns regarding battery safety under mechanical abuse conditions. However, mechanical abuse scenarios, such as battery extrusion, are highly diverse, making it impractical to conduct extensive destructive experiments and independent modeling for each [...] Read more.

The widespread adoption of lithium-ion battery-powered electric vehicles has raised increasing concerns regarding battery safety under mechanical abuse conditions. However, mechanical abuse scenarios, such as battery extrusion, are highly diverse, making it impractical to conduct extensive destructive experiments and independent modeling for each specific scenario. In this work, a cross-scenario mechanical safety modeling framework for lithium-ion batteries is proposed based on transfer learning. Three quasi-static mechanical abuse tests, including flat-plate, rigid-rod, and hemispherical compression, are conducted on 18650 lithium-ion batteries. An equivalent mechanical model with a spring–damper parallel structure is employed to characterize the mechanical response and generate simulation data. Based on data from a single mechanical abuse scenario, a backpropagation neural network (BPNN)-based safety model is established to predict the maximum stress in the battery. The learned knowledge is then transferred to other mechanical abuse scenarios using a transfer learning strategy. The results demonstrate that, under limited target-domain data, the transferred models achieve stable prediction performance, with the average relative error controlled within 3.6%, outperforming models trained from scratch under the same conditions. Compared with existing studies that focus on single-scenario modeling, this work explicitly investigates cross-scenario transferability and demonstrates the effectiveness of transfer learning in reducing experimental and modeling effort for battery mechanical safety analysis. Full article

(This article belongs to the Topic Battery Design and Management, 2nd Edition)

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28 pages, 5658 KB  

Open AccessArticle

SOC Estimation for Lithium-Ion Batteries Based on Weighted Multi-Innovation Sage–Husa Adaptive EKF

by Weihua Song, Ranran Liu, Xiaona Jin and Wei Guo

Energies 2025, 18(16), 4364; https://doi.org/10.3390/en18164364 - 16 Aug 2025

Cited by 8 | Viewed by 1797

Abstract

In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this [...] Read more.

In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this paper focuses on the second-order RC equivalent circuit model, firstly designs a simple and reliable improved adaptive forgetting factor (IAFF) regulation mechanism, and proposes the improved adaptive forgetting factor recursive least squares (IAFFRLS) algorithm, which not only improves the accuracy of parameter identification, but also exhibits excellent performance in anti-interference. Secondly, based on the identified model, a weighted multi-innovation improved Sage–Husa adaptive extended Kalman filter (WMISAEKF) algorithm is proposed to solve the problem of filter divergence caused by noise covariance updating. It fully utilizes historical innovations to reasonably allocate innovation weights to achieve accurate SOC estimation. Compared with the VFFRLS algorithm and AFFRLS algorithm, the IAFFRLS algorithm reduces the root mean square error (RMSE) by 29.30% and 19.29%, respectively, and the RMSE under noise interference is decreased by 82.37% and 78.59%, respectively. Based on the identified model for SOC estimation, the WMISAEKF algorithm reduces the RMSE by 77.78%, compared to the EKF algorithm. Furthermore, the WMISAEKF algorithm could still converge under different levels of noise interference and incorrect initial SOC values, which proves that the proposed algorithm has good stability and robustness. Simulation results verify that the parameter identification algorithm proposed in this paper demonstrates higher identification accuracy and anti-interference performance. The proposed SOC estimation algorithm has higher estimation accuracy and good robustness, which provides a new practical support for extending battery life. Full article

(This article belongs to the Topic Battery Design and Management, 2nd Edition)

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