Dr Carlos Fernandez's Outputs (279)

High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model. (2024)
Journal Article
FENG, R., WANG, S., YU, C., HAI, N. and FERNANDEZ, C. 2024. High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model. Journal of energy storage [online], 90(A), article number 111834. Available from: https://doi.org/10.1016/j.est.2024.111834

The state of health (SOH) of lithium-ion batteries plays a crucial role in maintaining the stability of electric vehicle systems. To address the issue of low accuracy in existing prediction models, this article introduces an enhanced grey wolf algori... Read More about High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model..

Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction. (2024)
Journal Article
WANG, S., GAO, H., TAKYI-ANINAKWA, P., GUERRERO, J.M., FERNANDEZ, C. and HUANG, Q. 2024. Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction. Protection and control of modern power systems [online], 9(3), pages 157-173. Available from: https://doi.org/10.23919/PCMP.2023.000257

Monitoring various internal parameters plays a core role in ensuring the safety of lithium-ion batteries in power supply applications. It also influences the sustainability effect and online state of charge prediction. An improved multiple feature-el... Read More about Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction..

A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range. (2024)
Journal Article
HAO, X., WANG, S., FAN, Y., LIU, D., LIANG, Y., ZHANG, M. and FERNANDEZ, C. 2024. A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range. Journal of energy storage [online], 89, article number 111820. Available from: https://doi.org/10.1016/j.est.2024.111820

The state of energy (SOE) is a key indicator for lithium-ion battery management systems (BMS). Based on the second-order resistance-capacitance equivalent circuit model and online parameter identification using the dynamic weights particle swarm opti... Read More about A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range..

Engineering triple-phase interfaces around the anode toward practical alkali metal-air batteries. (2024)
Journal Article
GE, B., HU, L., YU, X., WANG, L., FERNANDEZ, C., YANG, N., LIANG, Q. and YANG, Q.-H. 2024. Engineering triple-phase interfaces around the anode toward practical alkali metal-air batteries. Advanced materials [online], 36(27), article number 2400937. Available from: https://doi.org/10.1002/adma.202400937

Alkali metal-air batteries (AMABs) promise ultrahigh gravimetric energy densities, while the inherent poor cycle stability hinders their practical application. To address this challenge, most previous efforts are devoted to advancing the air cathodes... Read More about Engineering triple-phase interfaces around the anode toward practical alkali metal-air batteries..

A novel genetic weight-directed feed forward backpropagation neural network for state of charge estimation of lithium-ion batteries. (2024)
Journal Article
HAI, N., WANG, S., LIU, D., FERNANDEZ, C. and GUERRERO, J.M. 2024. A novel genetic weight-directed feed forward backpropagation neural network for state of charge estimation of lithium-ion batteries. Journal of energy storage [online], 88, article number 111549. Available from: https://doi.org/10.1016/j.est.2024.111549

Precious estimation of state-of-charge has become a more important status to the lithium-ion batteries of electronic vehicles. Basically, a three-layer genetic algorithm based on feed forward backpropagation neural network model is established. Speci... Read More about A novel genetic weight-directed feed forward backpropagation neural network for state of charge estimation of lithium-ion batteries..

An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles. (2024)
Journal Article
ZHU, C., WANG, S., YU, C., ZHOU, H., FERNANDEZ, C. and GUERRERO, J.M. 2024. An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles. Journal of energy storage [online], 88, article number 111552. Available from: https://doi.org/10.1016/j.est.2024.111552

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust... Read More about An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles..

State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model. (2024)
Journal Article
ZHOU, J., WANG, S., CAO, W., XIE, Y. and FERNANDEZ, C. 2024. State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model. Electrochimica acta [online], 487, article number 144146. Available from: https://doi.org/10.1016/j.electacta.2024.144146

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is crucial for the operation and maintenance of new energy electric vehicles. To address this current problem, an improved hybrid neural network model for SOH prediction ba... Read More about State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model..

A novel variable activation function-long short-term memory neural network for high-precision lithium-ion battery capacity estimation. (2024)
Journal Article
WANG, Y., WANG, S., FAN, Y., ZHANG, H., XIE, Y. and FERNANDEZ, C. 2024. A novel variable activation function-long short-term memory neural network for high-precision lithium-ion battery capacity estimation. Ionics [online], 30(5), pages 2609–2625. Available from: https://doi.org/10.1007/s11581-024-05475-8

Capacity estimation of lithium-ion batteries is significant to achieving the effective establishment of the prognostics and health management (PHM) system of lithium-ion batteries. A capacity estimation model based on the variable activation function... Read More about A novel variable activation function-long short-term memory neural network for high-precision lithium-ion battery capacity estimation..

Battery multi-time scale fractional-order modeling method for state of charge estimation adaptive to full parameters updating. (2024)
Journal Article
ZENG, J., WANG, S., ZHANG, M., CAO, W., FERNANDEZ, C. and GUERRERO, J.M. 2024. Battery multi-time scale fractional-order modeling method for state of charge estimation adaptive to full parameters updating. Journal of energy storage [online], 86(part B), article number 111283. Available from: https://doi.org/10.1016/j.est.2024.111283

The fractional-order theory has been successfully applied to battery modeling and state of charge (SOC) estimation thanks to the rapid development of smart energy storage and electric vehicles. The fractional-order model (FOM) has high nonlinearity,... Read More about Battery multi-time scale fractional-order modeling method for state of charge estimation adaptive to full parameters updating..

A novel multi-factor fuzzy membership function- adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries. (2024)
Journal Article
LIU, D., WANG, S., FAN, Y., FERNANDEZ, C. and BLAABJERG, F. 2024. A novel multi-factor fuzzy membership function - adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries. Journal of energy storage [online], 86(part A), article number 111222. Available from: https://doi.org/10.1016/j.est.2024.111222

In view of the unmeasurable state parameters of electric-vehicle lithium-ion batteries, this paper investigates a novel multi-factor fuzzy membership function - adaptive extended Kalman filter (MFMF-AEKF) algorithm for the online joint estimation of... Read More about A novel multi-factor fuzzy membership function- adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries..

Nitrogen-anchored boridene enables Mg-CO2 batteries with high reversibility. (2024)
Journal Article
WANG, Y., SUN, Y., WU, F., ZOU, G., GAUMET, J.-J., LI, J., FERNANDEZ, C., WANG, Y. and PENG, Q. 2024. Nitrogen-anchored boridene enables Mg−CO2 batteries with high reversibility. Journal of the American Chemical Society [online], 146(14), pages 9967–9974. Available from: https://doi.org/10.1021/jacs.4c00630

Nanoscale defect engineering plays a crucial role in incorporating extraordinary catalytic properties in two-dimensional materials by varying the surface groups or site interactions. Herein, we synthesized high-loaded nitrogen-doped Boridene (N-Borid... Read More about Nitrogen-anchored boridene enables Mg-CO2 batteries with high reversibility..

Critical review on improved electrochemical impedance spectroscopy-cuckoo search-elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems. (2024)
Journal Article
XIONG, R., WANG, S., TAKYI-ANINAKWA, P., JIN, S., FERNANDEZ, C., HUANG, Q., HU, W. and ZHAN, W. 2024. Critical review on improved electrochemical impedance spectroscopy-cuckoo search-Elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems. Protection and control of modern power systems [online], 9(2), pages 75-100. Available from: https://doi.org/10.23919/PCMP.2023.000234

Efficient and accurate health state estimation is crucial for lithium-ion battery (LIB) performance monitoring and economic evaluation. Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy s... Read More about Critical review on improved electrochemical impedance spectroscopy-cuckoo search-elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems..

Regulating d-orbital hybridization of subgroup-IVB single atoms for efficient oxygen reduction reaction. (2024)
Journal Article
ZHAO, X., SUN, Y., WANG, J., NIE, A., ZOU, G., REN, L., WANG, J., WANG, Y., FERNANDEZ, C. and PENG, Q. 2024. Regulating d-orbital hybridization of subgroup-IVB single atoms for efficient oxygen reduction reaction. Advanced materials [online], 36(21), article number 2312117. Available from: https://doi.org/10.1002/adma.202312117

Highly active single-atom electrocatalysts for the oxygen reduction reaction are crucial for improving the energy conversion efficiency, but they suffer from a limited choice of metal centers and unsatisfactory stabilities. Here, this work reports th... Read More about Regulating d-orbital hybridization of subgroup-IVB single atoms for efficient oxygen reduction reaction..

Improved joint prediction strategy for state of charge and peak power of lithium-ion batteries by considering hysteresis characteristics-current measurement deviation correction. (2024)
Journal Article
QI, C., WANG, S., CAO, W., WANG, Y., LIU, D. and FERNANDEZ, C. 2024. Improved joint prediction strategy for state of charge and peak power of lithium-ion batteries by considering hysteresis characteristics-current measurement deviation correction. Journal of energy storage [online], 84(part A), article number 110726. Available from: https://doi.org/10.1016/j.est.2024.110726

The peak power and state of charge of lithium-ion batteries are closely related to the safety of electric vehicles. Accurate peak power and state of charge prediction can extend battery life while ensuring safe driving. In this paper, a modeling stra... Read More about Improved joint prediction strategy for state of charge and peak power of lithium-ion batteries by considering hysteresis characteristics-current measurement deviation correction..

N-decorated main-group MgAl2O4 spinel: unlocking exceptional oxygen reduction activity for Zn-air batteries. (2024)
Journal Article
ZHAO, X., WU, F., HU, H., LI, J., SUN, Y., WANG, J., ZOU, G., CHEN, X., WANG, Y., FERNANDEZ, C. and PENG, Q. 2024. N-decorated main-group MgAl2O4 spinel: unlocking exceptional oxygen reduction activity for Zn-air batteries. Small [online], 20(28), article number 2311268. Available from: https://doi.org/10.1002/smll.202311268

The development of economical and efficient oxygen reduction reaction (ORR) catalysts is crucial to accelerate the widespread application rhythm of aqueous rechargeable zinc-air batteries (ZABs). Here, a strategy is reported that the modification of... Read More about N-decorated main-group MgAl2O4 spinel: unlocking exceptional oxygen reduction activity for Zn-air batteries..

Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries. (2024)
Journal Article
XIONG, R., WANG, S., HUANG, Q., YU, C., FERNANDEZ, C., XIAO, W., JIA, J. and GUERRERO, J.M. 2024. Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries. Energy [online], 292, article number 130594. Available from: https://doi.org/10.1016/j.energy.2024.130594

At present, the accurate establishment of the battery model and the effective state of health (SOH) estimation under actual energy storage conditions have become the main problems in new energy storage stations. Therefore, a SOH estimation method bas... Read More about Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries..

High-strong-ductile magnesium alloys by interactions of nanoscale quasi-long period stacking order unit with twin. (2024)
Journal Article
ZHOU, L., NIU, T., ZOU, G., SU, H., HE, S., ZHENG, S., ZHU, Y., CHEN, P., FERNANDEZ, C. and PENG, Q. 2024. High-strong-ductile magnesium alloys by interactions of nanoscale quasi-long period stacking order unit with twin. Journal of magnesium and alloys [online], 12(12), pages 4953-4965. Available from: https://doi.org/10.1016/j.jma.2024.01.015

Magnesium alloys with high strength in combination of good ductility are especially desirable for applications in transportation, aerospace and bio-implants owing to their high stiffness, abundant raw materials, and environmental friendliness. Howeve... Read More about High-strong-ductile magnesium alloys by interactions of nanoscale quasi-long period stacking order unit with twin..

An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries. (2024)
Journal Article
WU, F., WANG, S., LIU, D., CAO, W., FERNANDEZ, C. and HUANG, Q. 2024. An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries. Journal of energy storage [online], 82, article 110574. Available from: https://doi.org/10.1016/j.est.2024.110574

State of charge (SOC) and state of energy (SOE) are the key factors that reflect the safe and range driving of new energy vehicles. This paper proposes an optimized convolutional neural network-bidirectional gate recurrent unit (CNN-BiGRU) and an imp... Read More about An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries..

State of health prediction of lithium-ion batteries using combined machine learning model based on nonlinear constraint optimization. (2024)
Journal Article
LIANG, Y., WANG, S., FAN, Y., HAO, X., LIU, D. and FERNANDEZ, C. 2024. State of health prediction of lithium-ion batteries using combined machine learning model based on nonlinear constraint optimization. Journal of the Electrochemical Society [online], 171(1), article number 010508. Available from: https://doi.org/10.1149/1945-7111/ad18e1

Accurate State of Health (SOH) estimation of battery systems is critical to vehicle operation safety. However, it's difficult to guarantee the performance of a single model due to the unstable quality of raw data obtained from lithium-ion battery agi... Read More about State of health prediction of lithium-ion batteries using combined machine learning model based on nonlinear constraint optimization..

A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction. (2024)
Journal Article
CHEN, Y., DUAN, W., HE, Y., WANG, S. and FERNANDEZ, C. 2024. A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction. Green energy and intelligent transportation [online], 3(2), article number 100160. Available from: https://doi.org/10.1016/j.geits.2024.100160

Battery life prediction is of great significance to the safe operation, and the maintenance costs are reduced. This paper proposed a hybrid framework considering feature extraction to solve the problem of data backward, large sample data and uneven d... Read More about A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction..