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	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Batteries batteries	4.8	6.6	2015	18.5 Days	CHF 2700	Submit
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit

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Published Papers (1 paper)

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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

Viewed by 387

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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