Jiling Song¹, Hua Wang^2,*, Jianbing Guo¹, Minghua Lin², Bin Zheng^2,*, Jiqiang Wu^3,*

Journal of Polymer Materials, Vol.42, No.4, pp. 1143-1157, 2025, DOI:10.32604/jpm.2025.073414 - 26 December 2025

Abstract The pursuit of safer energy storage systems is driving the development of advanced electrolytes for lithium-ion batteries. Traditional liquid electrolytes pose flammability risks, while solid-state alternatives often suffer from low ionic conductivity. Gel polymer electrolytes (GPEs) emerge as a promising compromise, combining the safety of solids with the ionic conductivity of liquids. Cellulose, an abundant and eco-friendly polymer, presents an ideal base material for sustainable GPEs due to its biocompatibility and mechanical strength. This study systematically investigates how drying methods affect cellulose-based GPEs. Cellulose hydrogels were synthesized through dissolution-crosslinking and processed using vacuum drying (VD),… More >

Pu Yang^1,*, Wanning Yan¹, Rong Li¹, Lei Chen², Lijie Guo²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 699-725, 2025, DOI:10.32604/cmc.2025.064947 - 09 June 2025

Abstract Lithium-ion batteries (LIBs) have been widely used in mobile energy storage systems because of their high energy density, long life, and strong environmental adaptability. Accurately estimating the state of health (SOH) for LIBs is promising and has been extensively studied for many years. However, the current prediction methods are susceptible to noise interference, and the estimation accuracy has room for improvement. Motivated by this, this paper proposes a novel battery SOH estimation method, the Beluga Whale Optimization (BWO) and Noise-Input Gaussian Process (NIGP) Stacked Model (BGNSM). This method integrates the BWO-optimized Gaussian Process Regression (GPR)… More >

Yi-Feng Luo¹, Guan-Jhu Chen^2,*, Chun-Liang Liu³, Yen-Tse Chung⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 823-844, 2025, DOI:10.32604/cmc.2025.061498 - 26 March 2025

Abstract As lithium-ion batteries become increasingly prevalent in electric scooters, vehicles, mobile devices, and energy storage systems, accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability. Unlike traditional methods that rely on static alternating internal resistance (SAIR) measurements in an open-circuit state, this study presents a real-time state of charge (SOC) estimation method combining dynamic alternating internal resistance (DAIR) with artificial neural networks (ANN). The system simultaneously measures electrochemical impedance |Z| at various frequencies, discharge C-rate, and battery surface temperature during the discharge process, using these parameters for ANN training. The… More >

Yi-Feng Luo^1,*, Jyuan-Fong Yen², Wen-Cheng Su³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3069-3087, 2025, DOI:10.32604/cmes.2025.061180 - 03 March 2025

Abstract This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article More >

Liyao Yang¹, Hongyan Ma^1,2,3,*, Yingda Zhang¹, Wei He¹

Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >

Yu Zhang, Daoyu Zhang^*, Tiezhou Wu

Energy Engineering, Vol.122, No.1, pp. 203-220, 2025, DOI:10.32604/ee.2024.056244 - 27 December 2024

Abstract Precisely estimating the state of health (SOH) of lithium-ion batteries is essential for battery management systems (BMS), as it plays a key role in ensuring the safe and reliable operation of battery systems. However, current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation. Additionally, the Elman neural network, which is commonly employed for SOH estimation, exhibits several drawbacks, including slow training speed, a tendency to become trapped in local minima, and the initialization of weights and thresholds using pseudo-random numbers, leading to unstable model performance.… More >

J. Liu^a, Y. X. Liu^b, J. N. Ding^b, W. Yan^b, Y. C. Wei^b, J. Xu^b,*

Chalcogenide Letters, Vol.21, No.7, pp. 499-511, 2024, DOI:10.15251/CL.2024.217.499

Abstract With society's rapid progress, there is an increasing need for electricity among individuals, but the prevailing source of electricity is still mainly obtained through the burning of nonrenewable fossil fuels, which are not renewable and pose serious environmental hazards. Lithium-ion batteries are used widely for excellent rechargeable capabilities and high energy density. Recently, researchers have become increasingly interested in transition metal sulfides owing to their cost-effectiveness and remarkable specific capacity. However, their commercialization has been hindered by the expansion of material volume and low electrical conductivity during charging and discharging. We have successfully designed and More >

Yuhang Lyu¹, Shaohai Dong¹, Li Ting Gao¹, Zhan-Sheng Guo^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.012815

Abstract Drying and calendering processes are crucial in electrode manufacturing, as they significantly affect the mechanical and electrochemical performances of lithium-ion batteries. In this study, we established a three-dimensional (3D) representative volume element (RVE) of electrodes composed of active material particles, carbon binder domain particles, solvent, and different particle contact types. We continuously simulated the 3D macroscopic and microscopic structural evolutions of the RVE during drying and calendering using the discrete element method (DEM). Based on the evolution of the particle coordination numbers and contact networks during drying, we propose a three-stage-drying scheme, consistent with the More >

Qi Wang^1,2,3, Yandong Gu^1,*, Tao Zhu¹, Lantian Ge¹, Yibo Huang¹

Energy Engineering, Vol.121, No.11, pp. 3221-3239, 2024, DOI:10.32604/ee.2024.053758 - 21 October 2024

Abstract Accurately estimating the State of Health (SOH) of batteries is of great significance for the stable operation and safety of lithium batteries. This article proposes a method based on the combination of Capacity Incremental Curve Analysis (ICA) and Whale Optimization Algorithm-Radial Basis Function (WOA-RBF) neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries. Firstly, preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage (Q-V) curve, convert the Q-V curve into an IC curve and denoise it, analyze the parameters… More >

Tianqing Yuan^1,2, Na Li^1,2, Hao Sun³, Sen Tan^4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4497-4512, 2024, DOI:10.32604/cmc.2024.056061 - 12 September 2024

Abstract To improve the estimation accuracy of state of charge (SOC) and state of health (SOH) for lithium-ion batteries, in this paper, a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm (GA) combined with back propagation (BP) neural network is proposed, the research addresses the issue of data manipulation resulting from cyber-attacks. Firstly, anomalous data stemming from cyber-attacks are identified and eliminated using the isolated forest algorithm, followed by data restoration. Secondly, the incremental capacity (IC) curve is derived from the restored data using the Kalman filtering algorithm, with… More >