All Outputs (338)

Improved harmonic loss: history gated unit recycling for online state of charge and state of energy co-estimation of lithium-ion batteries for large-scale energy storage stations. (2025)
Journal Article
WANG, S., WEI, J., ZHANG, L., LI, H., FERNANDEZ, C. and BLAABJERG, F. [2025]. Improved harmonic loss: history gated unit recycling for online state of charge and state of energy co-estimation of lithium-ion batteries for large-scale energy storage stations. Energy [online], In Press, article number 139225. Available from: https://doi.org/10.1016/j.energy.2025.139225

Accurate estimation of the state of charge (SOC) and state of energy (SOE) of a battery is critical for battery system management to improve the reliability and safety of battery operation. Since both SOC and SOE are cumulative quantities of time, hi... Read More about Improved harmonic loss: history gated unit recycling for online state of charge and state of energy co-estimation of lithium-ion batteries for large-scale energy storage stations..

A critical review of AI-based battery remaining useful life prediction for energy storage systems. (2025)
Journal Article
YANG, K., WANG, S., ZHOU, L., FERNANDEZ, C. and BLAABJERG, F. 2025. A critical review of AI-based battery remaining useful life prediction for energy storage systems. Batteries [online], 11(10), article number 376. Available from: https://doi.org/10.3390/batteries11100376

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... Read More about A critical review of AI-based battery remaining useful life prediction for energy storage systems..

Separation and analysis of methicillin-sensitive and resistant Staphylococcus strains using vancomycin-modified magnetic nanoparticles. (2025)
Journal Article
WERLE, J., PODHAJSKY, J., BURESOVA, K., PHOLOSI, A., KLAPKOVA, E., NARAYANAN, V.H.B., KOTASKA, K., FERNANDEZ, C., SOCHOR, J., MODISE, S.J., PRUSA, R. and KIZEK, R. 2025. Separation and analysis of methicillin-sensitive and resistant Staphylococcus strains using vancomycin-modified magnetic nanoparticles. Separation science plus [online], 8(10), article number e70135. Available from: https://doi.org/10.1002/sscp.70135

Multi-resistant bacterial strains pose a serious threat in the hospital environment. Management of nosocomial infections requires completely new approaches. Vancomycin (Vanco) is a known antibiotic for the treatment of serious infections, including t... Read More about Separation and analysis of methicillin-sensitive and resistant Staphylococcus strains using vancomycin-modified magnetic nanoparticles..

Development of sustainable, multifunctional, advanced and smart hybrid solid-state electrolyte for structural battery composites. (2025)
Presentation / Conference Contribution
SIDDIQUE, S., FERNANDEZ, C., ELYAN, E., DONELLI, M., GREGORY, D. and NJUGUNA, J. 2025. Development of sustainable, multifunctional, advanced and smart hybrid solid-state electrolyte for structural battery composites. In Chang, F.-K. and Guemes, A. (eds.) Proceedings of the 15th International workshop on structural health monitoring 2025 (IWSHM 2025), 9-11 September 2025, Stanford, CA, USA. Lancaster, PA, USA: DEStech Publications, Inc. [online], pages 237-244. Available from: https://iwshm2025.stanford.edu/iwshm-proceedings-2025

The incremental energy demand exacerbates the global warming issues. One of the main triggering factors for this raising global warming is the transportation sector which causes around 30% of the world's emission. However, EU is facing several obstac... Read More about Development of sustainable, multifunctional, advanced and smart hybrid solid-state electrolyte for structural battery composites..

Enhanced multi-scale signal decomposition transformer neural network for state of health estimation of lithium-ion batteries. (2025)
Journal Article
LI, Y., SHI, H., HUANG, Q., LI, K., LIU, C., NIE, S., JIA, X. and FERNANDEZ, C. 2025. Enhanced multi-scale signal decomposition transformer neural network for state of health estimation of lithium-ion batteries. Journal of energy storage [online], 134(B), article number 118191. Available from: https://doi.org/10.1016/j.est.2025.118191

The accurate estimation of battery state of health (SOH) is important in the fields of electric vehicles, energy storage devices, and renewable energy. To address the accuracy challenges of SOH estimation caused by the phenomenon of small-scale capac... Read More about Enhanced multi-scale signal decomposition transformer neural network for state of health estimation of lithium-ion batteries..

Improved back-propagation neural network-multi-information gain optimization Kalman filter method for high-precision estimation of state-of-energy in lithium-ion batteries. (2025)
Journal Article
SHI, H., WU, Q., WANG, S., CAO, W., LI, Y., FERNANDEZ, C. and HUANG, Q. 2025. Improved back-propagation neural network-multi-information gain optimization Kalman filter method for high-precision estimation of state-of-energy in lithium-ion batteries. Energy [online], In Press. Available from: https://doi.org/10.1016/j.energy.2025.138214

The accurate estimation of the state-of-energy (SOE) is crucial for extending battery life and improving the performance of electric vehicles. To address the issue of low estimation accuracy of SOE in lithium-ion batteries, a back-propagation neural... Read More about Improved back-propagation neural network-multi-information gain optimization Kalman filter method for high-precision estimation of state-of-energy in lithium-ion batteries..

Synthesis of substituted triazines and evaluation of their corrosion inhibition performance on Fe(100) in 1 M HCl: a combined experimental and DFT study. (2025)
Journal Article
TSHIKHUDO, F., MUGWENA, D.S., MNYAKENI-MOLEELE, S.S., KABANDA, M.M., FERNANDEZ, C. and MURULANA, L.C. 2025. Synthesis of substituted triazines and evaluation of their corrosion inhibition performance on Fe(100) in 1 M HCl: a combined experimental and DFT study. International journal of electrochemical science [online], 20(10), article number 101153. Available from: https://doi.org/10.1016/j.ijoes.2025.101153

This study aimed to evaluate the effectiveness of synthesized substituted triazine, namely 4,6-dichloro-2-morpholine-1,3,5-triazine (DMT), 4,6-dichloro-2-anilino-1,3,5-triazine (DPT), and 4,6-dichloro-N-methylanilino-1,3,5-triazine (DNT), in inhibiti... Read More about Synthesis of substituted triazines and evaluation of their corrosion inhibition performance on Fe(100) in 1 M HCl: a combined experimental and DFT study..

A novel extended Kalman filter-guided long short-term memory algorithm for power lithium-ion battery state of charge estimation at multiple temperatures. (2025)
Journal Article
LIU, D., WANG, S., LI, X., FAN, Y., FERNANDEZ, C. and BLAABJERG, F. 2025. A novel extended Kalman filter-guided long short-term memory algorithm for power lithium-ion battery state of charge estimation at multiple temperatures. Energy [online], 335, article 137973. Available from: https://doi.org/10.1016/j.energy.2025.137973

The power lithium-ion battery's state of charge (SOC) is critical for electric vehicles. However, current neural network-based SOC estimation algorithms fail to consider the effects of feature enhancement in reducing the network training time and imp... Read More about A novel extended Kalman filter-guided long short-term memory algorithm for power lithium-ion battery state of charge estimation at multiple temperatures..

A multi-layer kernel extreme learning machine model based on the fusion algorithm for the remaining useful life prediction of lithium-ion batteries. (2025)
Journal Article
CHEN, L., BAI, L., WEI, X., LI, Y. and FERNANDEZ, C. [2025]. A multi-layer kernel extreme learning machine model based on the fusion algorithm for the remaining useful life prediction of lithium-ion batteries. Ionics [online], Latest Articles. Available from: https://doi.org/10.1007/s11581-025-06597-3

The remaining useful life (RUL) of lithium-ion batteries is a key parameter of battery management systems, and accurate prediction is an important guarantee for the stable and efficient operation of new energy vehicles. A fusion algorithm based on a... Read More about A multi-layer kernel extreme learning machine model based on the fusion algorithm for the remaining useful life prediction of lithium-ion batteries..

Improved dynamic discount function identification strategy for adaptive current transients and capturing complex carrier behavior inside lithium-ion batteries. (2025)
Journal Article
LIN, R., SHI, H., WANG, S., YU, C., NIE, S., FERNANDEZ, C. and KAZMI, M.M. 2025. Improved dynamic discount function identification strategy for adaptive current transients and capturing complex carrier behavior inside lithium-ion batteries. Journal of The Electrochemical Society [online], 172(7), article number 070527. Available from: https://doi.org/10.1149/1945-7111/adeece

In the application of lithium-ion batteries, instantaneous changes in the magnitude and direction of the current cause distortion during online parameter identification, resulting in increased instability and errors. To address this problem, this pap... Read More about Improved dynamic discount function identification strategy for adaptive current transients and capturing complex carrier behavior inside lithium-ion batteries..

Improved hybrid neural network based on CNN-BiLSTM-attention for co-estimation of SOC and SOE in lithium-ion batteries. (2025)
Journal Article
LUO, T., SHI, H., LI, K., LI, H., WANG, S., YU, C. and FERNANDEZ, C. 2025. Improved hybrid neural network based on CNN-BiLSTM-attention for co-estimation of SOC and SOE in lithium-ion batteries. Journal of energy storage [online], 131(Part B), article number 117651. Available from: https://doi.org/10.1016/j.est.2025.117651

As the core of modern energy storage technology, lithium-ion batteries are widely used in fields such as electric vehicles, renewable energy storage, and portable electronic devices. Accurately estimating the state-of-charge (SOC) and state-of-energy... Read More about Improved hybrid neural network based on CNN-BiLSTM-attention for co-estimation of SOC and SOE in lithium-ion batteries..

A hybrid squeeze excitation gate recurrent unit-autoregressive integrated moving average model for long-term state of health estimation of lithium-ion batteries with adaptive enhancement ability. (2025)
Journal Article
WU, W., WANG, S., FAN, Y., LIU, D., LONG, G. and FERNANDEZ, C. 2025. A hybrid squeeze excitation gate recurrent unit-autoregressive integrated moving average model for long-term state of health estimation of lithium-ion batteries with adaptive enhancement ability. Journal of energy storage [online], 131(part B), article number 117600. Available from: https://doi.org/10.1016/j.est.2025.117600

The accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safe operation and effective management. Since the data acquired from the lithium-ion battery aging experiment is abundant in electrochemical... Read More about A hybrid squeeze excitation gate recurrent unit-autoregressive integrated moving average model for long-term state of health estimation of lithium-ion batteries with adaptive enhancement ability..

Boron-doped hexagonal boridene-derived sulfides unlock high reversible capacity for rechargeable magnesium batteries. (2025)
Journal Article
WU, F., YANG, W., WANG, Y., GAO, W., LIU, D., WANG, P., SUN, Y., LIU, S., ZOU, G., WANG, J., FERNANDEZ, C. and PENG, Q. [2025]. Boron-doped hexagonal boridene-derived sulfides unlock high reversible capacity for rechargeable magnesium batteries. Advanced functional materials [online], Early View, article number e10635. Available from: https://doi.org/10.1002/adfm.202510635

Rechargeable magnesium batteries (RMBs) are emerging as compelling alternatives for next-generation high-energy-density storage systems, owing to magnesium's abundance, safety, and high volumetric capacity. However, the divalent nature of magnesium i... Read More about Boron-doped hexagonal boridene-derived sulfides unlock high reversible capacity for rechargeable magnesium batteries..

Adversarial multi-source domain generalization approach for power prediction in unknown photovoltaic systems. (2025)
Journal Article
LIU, S., QI, Y., LI, D., LIU, L., WANG, S., FERNANDEZ, C. and GAO, X. 2025. Adversarial multi-source domain generalization approach for power prediction in unknown photovoltaic systems. Applied soft computing [online], 181, article number 113495. Available from: https://doi.org/10.1016/j.asoc.2025.113495

Accurate forecasting of power output for previously unseen photovoltaic installations is of critical importance to the reliability and efficiency of renewable-energy management systems. Existing data-driven PV prediction techniques rely primarily on... Read More about Adversarial multi-source domain generalization approach for power prediction in unknown photovoltaic systems..

Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries. (2025)
Journal Article
ZOU, Y., WANG, S., HAI, N., BLAABJERG, F., FERNANDEZ, C. and CAO, W. 2025. Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries. Energy [online], 332, article number137201. Available from: https://doi.org/10.1016/j.energy.2025.137201

The state of charge (SOC) estimation accuracy of lithium-ion batteries directly affects the reliability and management efficiency of clean energy storage systems. However, due to the nonlinear characteristics of batteries and complex working conditio... Read More about Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries..

Improved adaptive fusion parameter identification and chaotic gravitational search-Kalman particle filtering method for state-of-energy accurate estimation of lithium-ion batteries. (2025)
Journal Article
WANG, C., WANG, S., ZHANG, G., CHEN, L., SHI, H., LIN, R. and FERNANDEZ, C. 2025. Improved adaptive fusion parameter identification and chaotic gravitational search-Kalman particle filtering method for state-of-energy accurate estimation of lithium-ion batteries. Journal of power sources [online], 650, article number 237495. Available from: https://doi.org/10.1016/j.jpowsour.2025.237495

State-of-energy (SOE) is an important parameter in the battery management system, which determines the current maximum possible range of electric vehicles. In this study, an improved chaotic gravitational search-Kalman particle filtering method for S... Read More about Improved adaptive fusion parameter identification and chaotic gravitational search-Kalman particle filtering method for state-of-energy accurate estimation of lithium-ion batteries..

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..