All Outputs (322)

Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation. (2025)
Journal Article
WANG, S., MA, C., GAO, H., DENG, D., FERNANDEZ, C. and BLAABJERG, F. 2025. Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation. Energy [online], 328, article number 136598. Available from: https://doi.org/10.1016/j.energy.2025.136598

At a time when new energy sources are constantly developing, mitigating the safety hazards of lithium batteries and prolonging their lifespan. In this paper, we take a ternary lithium-ion battery as an experimental object and carry out research based... Read More about Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation..

An improved transformer-GRU neural model optimized by polar light optimizer for SOC estimation of lithium-ion batteries under complex operating conditions. (2025)
Journal Article
SHU, X., SHI, H., ZOU, Y., CAO, W. and FERNANDEZ, C. [2025]. An improved transformer-GRU neural model optimized by polar light optimizer for SOC estimation of lithium-ion batteries under complex operating conditions. Ionics [online], Online First. Available from: https://doi.org/10.1007/s11581-025-06353-7

Estimating the battery's state-of-charge (SOC) is essential for determining how safe electric cars are and their remaining range. An SOC estimation technique for lithium-ion batteries based on the Transformer architecture is presented in this paper.... Read More about An improved transformer-GRU neural model optimized by polar light optimizer for SOC estimation of lithium-ion batteries under complex operating conditions..

Deep learning framework designed for high-performance lithium-ion batteries state monitoring. (2025)
Journal Article
TAKYI-ANINAKWA, P., WANG, S., LIU, G., FERNANDEZ, C., KANG, W. and SONG, Y. 2025. Deep learning framework designed for high-performance lithium-ion batteries state monitoring. Renewable and sustainable energy reviews [online], 218, article number 115803. Available from: https://doi.org/10.1016/j.rser.2025.115803

Accurate state of charge (SOC) estimation is crucial for ensuring the safety of batteries, especially in real-time battery management system (BMS) applications. Deep learning methods have become increasingly popular, driving significant advancements... Read More about Deep learning framework designed for high-performance lithium-ion batteries state monitoring..

An improved bidirectional long short-term memory hybrid neural network with Gaussian filtering for multi-temperature state of charge estimation of lithium-ion batteries. (2025)
Journal Article
LIU, Q., SHI, H., ZOU, Y., CAO, W. and FERNANDEZ, C. 2025. An improved bidirectional long short-term memory hybrid neural network with Gaussian filtering for multi-temperature state of charge estimation of lithium-ion batteries. Ionics [online], Latest Articles. Available from: https://doi.org/10.1007/s11581-025-06343-9

The new energy revolution is fundamentally reshaping the global energy structure. Power lithium batteries face issues such as charge-discharge imbalance and limited endurance. To enhance the performance and economic efficiency of power lithium batter... Read More about An improved bidirectional long short-term memory hybrid neural network with Gaussian filtering for multi-temperature state of charge estimation of lithium-ion batteries..

An improved pelican optimization-kernel extreme learning machine for highly accurate state of charge estimation of lithium-ion batteries in energy storage systems. (2025)
Journal Article
LI, S., WANG, S., CAO, W., ZHANG, L. and FERNANDEZ, C. [2025]. An improved pelican optimization-kernel extreme learning machine for highly accurate state of charge estimation of lithium-ion batteries in energy storage systems. Ionics [online], Online First. Available from: https://doi.org/10.1007/s11581-025-06327-9

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is crucial for real-time monitoring and safety control. This paper proposes a novel method for estimating SOC by optimizing the kernel extreme learning machine (KELM) with... Read More about An improved pelican optimization-kernel extreme learning machine for highly accurate state of charge estimation of lithium-ion batteries in energy storage systems..

A novel multi–input–multi–output–fuzzy uncoupling model parameter identification strategy for the online state of charge estimation of high-power lithium-ion batteries. (2025)
Journal Article
LIU, D., LI, X., WANG, S., FAN, Y. and FERNANDEZ, C. [2025]. A novel multi–input–multi–output–fuzzy uncoupling model parameter identification strategy for the online state of charge estimation of high-power lithium-ion batteries. Ionics [online], Online First. Available from: https://doi.org/10.1007/s11581-025-06330-0

Due to the strong nonlinear characteristics of multi-state coupling of physical fields such as different charge states and discharge rates in lithium-ion batteries, it poses a great challenge for the parameter identification of the model and the esti... Read More about A novel multi–input–multi–output–fuzzy uncoupling model parameter identification strategy for the online state of charge estimation of high-power lithium-ion batteries..

Preferentially-orientated gradient precipitates enable unique strength-ductility synergy in Mg-Sn binary alloys. [Video] (2025)
Data
ZHOU, L., SUN, Y., ZOU, G., HU, H., ZHANG, Y., SU, H., ZHENG, S., ZHU, Y., CHEN, P., FERNANDEZ, C. and PENG, Q. 2025. Preferentially-orientated gradient precipitates enable unique strength-ductility synergy in Mg-Sn binary alloys. [Video]. Journal of materials science and technology [online], 237, pages 298-311. Available from: https://doi.org/10.1016/j.jmst.2025.01.082

Conventional manufacturing approaches, including casting, thermal deformation and annealing, have faced great challenges in achieving both exceptional strength and ductility for Mg alloys. Herein, we report an effective strategy for simultaneously en... Read More about Preferentially-orientated gradient precipitates enable unique strength-ductility synergy in Mg-Sn binary alloys. [Video].

Preferentially-orientated gradient precipitates enable unique strength-ductility synergy in Mg-Sn binary alloys. (2025)
Journal Article
ZHOU, L., SUN, Y., ZOU, G., HU, H., ZHANG, Y., SU, H., ZHENG, S., ZHU, Y., CHEN, P., FERNANDEZ, C. and PENG, Q. 2025. Preferentially-orientated gradient precipitates enable unique strength-ductility synergy in Mg-Sn binary alloys. Journal of materials science and technology [online], 237, pages 298-311. Available from: https://doi.org/10.1016/j.jmst.2025.01.082

Conventional manufacturing approaches, including casting, thermal deformation and annealing, have faced great challenges in achieving both exceptional strength and ductility for Mg alloys. Herein, we report an effective strategy for simultaneously en... Read More about Preferentially-orientated gradient precipitates enable unique strength-ductility synergy in Mg-Sn binary alloys..

Online state of charge estimation for lithium-ion batteries using improved fuzzy C-means sparrow backpropagation algorithm. (2025)
Journal Article
HAI, N., WANG, S., CAO, W., BLAABJERG, F. and FERNANDEZ, C. 2025. Online state of charge estimation for lithium-ion batteries using improved fuzzy C-means sparrow backpropagation algorithm. Journal of energy storage [online], 119, article 116351. Available from: https://doi.org/10.1016/j.est.2025.116351

With the rapid development of new energy vehicles (EVs), cloud-based management of the lithium-ion batteries (LIBs) state of charge (SOC) has become the technological mainstream under increasing intelligence. However, SOC is highly sensitive to the m... Read More about Online state of charge estimation for lithium-ion batteries using improved fuzzy C-means sparrow backpropagation algorithm..

An optimized multiple-weighted adaptive genetic-Kalman hybrid al-gorithm for online state of charge estimation in lithium-ion batteries. (2025)
Journal Article
LONG, G., LI, H., WANG, S., YU, C., GAO, H., CAO, X., ZHU, Y. and FERNANDEZ, C. 2025. An optimized multiple-weighted adaptive genetic-Kalman hybrid al-gorithm for online state of charge estimation in lithium-ion batteries. Journal of the Electrochemical Society [online], 172(3), article number 030520. Available from: https://doi.org/10.1149/1945-7111/adbf4c

As an indispensable aspect of the battery management system, accurate lithium battery state of charge (SOC) estimation has attracted wide attention. This study proposes an innovative adaptive genetic algorithm online intermittent parameter identifica... Read More about An optimized multiple-weighted adaptive genetic-Kalman hybrid al-gorithm for online state of charge estimation in lithium-ion batteries..

Improved particle swarm optimization-adaptive dual extended Kalman filtering for accurate battery state of charge and state of energy joint estimation with efficient core factor feedback correction. (2025)
Journal Article
WANG, S., WU, Y., ZHOU, H., ZHANG, Q., FERNANDEZ, C. and BLAABJERG, F. 2025. Improved particle swarm optimization-adaptive dual extended Kalman filtering for accurate battery state of charge and state of energy joint estimation with efficient core factor feedback correction. Energy [online], 322, article number 135686. Available from: https://doi.org/10.1016/j.energy.2025.135686

With the rapid development of electric vehicles, the accuracy requirement for lithium-ion battery state feedback is increasing. However, traditional algorithms cannot achieve the desired accuracy. For this purpose, this article focuses on the ternary... Read More about Improved particle swarm optimization-adaptive dual extended Kalman filtering for accurate battery state of charge and state of energy joint estimation with efficient core factor feedback correction..

Joint state of charge and state of energy estimation of special aircraft lithium-ion batteries by optimized genetic marginalization-extended particle filtering. (2025)
Journal Article
WANG, S., LUO, T., HAI, N., BLAABJERG, F. and FERNANDEZ, C. 2025. Joint state of charge and state of energy estimation of special aircraft lithium-ion batteries by optimized genetic marginalization-extended particle filtering. Journal of energy storage [online], 115, article number 116001. Available from: https://doi.org/10.1016/j.est.2025.116001

With the continuous development and widespread application of special aircraft, accurately estimating the performance and status of battery systems has become crucial. This paper focuses on the joint estimation of State of Charge (SOC) and State of E... Read More about Joint state of charge and state of energy estimation of special aircraft lithium-ion batteries by optimized genetic marginalization-extended particle filtering..

Improved volumetric noise-adaptive H-infinity filtering for accurate state of power estimation of lithium-ion batteries with multi-parameter constraint considering low-temperature influence. (2025)
Journal Article
WANG, S., HU, B., ZHOU, L., LIU, Y., FERNANDEZ, C. and BLAABJERG, F. 2025. Improved volumetric noise-adaptive H-infinity filtering for accurate state of power estimation of lithium-ion batteries with multi-parameter constraint considering low-temperature influence. Journal of energy storage [online], 115, article number 115999. Available from: https://doi.org/10.1016/j.est.2025.115999

Currently, the field of new energy is booming. Batteries containing lithium-ion have become an important component of new energy vehicles. The key parameters to accurately estimate the battery state depend on the State of Charge (SOC) and the State o... Read More about Improved volumetric noise-adaptive H-infinity filtering for accurate state of power estimation of lithium-ion batteries with multi-parameter constraint considering low-temperature influence..

Battery pack capacity estimation based on improved cooperative co-evolutionary strategy and LightGBM hybrid models using indirect health features. (2025)
Journal Article
ZHOU, Y., WANG, S., LI, Z., FENG, R. and FERNANDEZ, C. 2025. Battery pack capacity estimation based on improved cooperative co-evolutionary strategy and LightGBM hybrid models using indirect health features. Journal of energy storage [online], 114(Part B), article number 115914. Available from: https://doi.org/10.1016/j.est.2025.115914

Focuses on the accurate estimation of battery pack capacity under real-world operating conditions, which is critical to improving the reliability of battery-powered systems, extending battery life, and optimizing health management strategies. This pa... Read More about Battery pack capacity estimation based on improved cooperative co-evolutionary strategy and LightGBM hybrid models using indirect health features..

Enhanced transformer encoder long short-term memory hybrid neural network for multiple temperature state of charge estimation of lithium-ion batteries. (2025)
Journal Article
ZOU, Y., WANG, S., CAO, W., HAI, N. and FERNANDEZ, C. 2025. Enhanced transformer encoder long short-term memory hybrid neural network for multiple temperature state of charge estimation of lithium-ion batteries. Journal of power sources [online], 632, article number 236411. Available from: https://doi.org/10.1016/j.jpowsour.2025.236411

Accurate state of charge (SOC) estimation for lithium-ion batteries remains a critical challenge in battery management systems. Existing methods based on machine learning may cause data leakage and inaccuracy during neural network training. Here, thi... Read More about Enhanced transformer encoder long short-term memory hybrid neural network for multiple temperature state of charge estimation of lithium-ion batteries..

A multi-timescale estimator for state of energy and maximum available energy of lithium-ion batteries based on variable order online identification. (2025)
Journal Article
CHEN, L., WANG, S., CHEN, L., FERNANDEZ, C. and BLAABJERG, F. 2025. A multi-timescale estimator for state of energy and maximum available energy of lithium-ion batteries based on variable order online identification. Journal of energy storage [online], 110, article number 115350. Available from: https://doi.org/10.1016/j.est.2025.115350

The dynamic adjustment of the fractional orders for fractional-order models can improve the accuracy of modeling lithium-ion batteries, as a bridge between the state of energy (SOE) and the terminal voltage, the maximum available energy value will de... Read More about A multi-timescale estimator for state of energy and maximum available energy of lithium-ion batteries based on variable order online identification..

An innovative multitask learning: long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current conditions. (2024)
Journal Article
TAO, J., WANG, S., CAO, W., FERNANDEZ, C., BLAABJERG, F. and CHENG, L. 2025. An innovative multitask learning: long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current conditions. Energy [online], 314, article number 134272. Available from: https://doi.org/10.1016/j.energy.2024.134272

As the new industrial revolution accelerates, new energy storage systems are becoming increasingly vital to the industrial chain. The overall performance of the battery management system can be improved by using a long short-term memory neural networ... Read More about An innovative multitask learning: long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current conditions..

A comprehensive review of multiple physical and data-driven model fusion methods for accurate lithium-ion battery inner state factor estimation. (2024)
Journal Article
TAO, J., WANG, S., CAO, W., FERNANDEZ, C. and BLAABJERG, F. 2024. A comprehensive review of multiple physical and data-driven model fusion methods for accurate lithium-ion battery inner state factor estimation. Batteries [online], 10(12), article 442. Available from: https://doi.org/10.3390/batteries10120442

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, h... Read More about A comprehensive review of multiple physical and data-driven model fusion methods for accurate lithium-ion battery inner state factor estimation..

Improved lithium battery state of health estimation and enhanced adaptive capacity of innovative kernel extreme learning machine optimized by multi-strategy dung beetle algorithm. (2024)
Journal Article
MO, D., WANG, S., ZHANG, M., FAN, Y., WU, W., FERNANDEZ, C. and SU, Q. 2025. Improved lithium battery state of health estimation and enhanced adaptive capacity of innovative kernel extreme learning machine optimized by multi-strategy dung beetle algorithm. Ionics [online], 31(1), pages 329-343. Available from: https://doi.org/10.1007/s11581-024-05914-6

Accurate estimation of the state of health (SOH) of lithium batteries is crucial to ensure the reliable and safe operation of lithium batteries. Aiming at the problems of low accuracy of extreme learning machine and poor mapping ability of convention... Read More about Improved lithium battery state of health estimation and enhanced adaptive capacity of innovative kernel extreme learning machine optimized by multi-strategy dung beetle algorithm..

An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries. (2024)
Journal Article
WANG, S., DANG, Q., GAO, Z., LI, B., FERNANDEZ, C. and BLAABJERG, F. 2024. An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries. Journal of energy storage [online], 104(part B), article number 114555. Available from: https://doi.org/10.1016/j.est.2024.114555

In the context of the thriving development of new energy vehicles, lithium-ion batteries, as a crucial component of the power storage system, will increasingly contribute to the strategic advancement of the industry, while this paper addresses three... Read More about An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries..