Dr Carlos Fernandez's Outputs (306)

High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework. (2024)
Journal Article
FENG, R., WANG, S., YU, C. and FERNANDEZ, C. 2024. High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework. Ionics [online], 30(10), pages 6215-6237. Available from: https://doi.org/10.1007/s11581-024-05740-w

In response to the current issue of low accuracy and robustness in the remaining useful life (RUL) model of lithium-ion batteries. In the framework of AdaBoost, a lithium-ion battery life prediction model based on an improved whale optimization algor... Read More about High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework..

Remaining useful life prediction hybrid model of lithium-ion battery based on improved GWO-LightGBM. (2024)
Presentation / Conference Contribution
ZHOU, Y., WANG, S., SHEN, X., SOJIB, A.S.M. and FERNANDEZ, C. 2024. Remaining useful life prediction hybrid model of lithium-ion battery based on improved GWO-LightGBM. In Proceedings of the 25th IEEE (Institute of Electrical and Electronics Engineers) China conference on system simulation technology and its application 2024 (CCSSTA 2024), 21-23 July 2024, Tianjin, China. Piscataway: IEEE [online], pages 553-556. Available from: https://doi.org/10.1109/CCSSTA62096.2024.10691744

Lithium-ion batteries, as the core of new energy vehicles, determine the safety of new energy vehicles. Remaining useful life of the battery is the most important parameter, and it is particularly important to estimate the remaining life accurately.... Read More about Remaining useful life prediction hybrid model of lithium-ion battery based on improved GWO-LightGBM..

Improved multi-head bi-directional long and short-term memory temporal convolutional network for lithium-ion batteries state of charge estimation in energy storage systems. (2024)
Presentation / Conference Contribution
LI, Y., WANG, S., LIU, D., CUI, Y., FERNANDEZ, C. and BLAABJERG, F. 2024. Improved multi-head bi-directional long and short-term memory temporal convolutional network for lithium-ion batteries state of charge estimation in energy storage systems. In Proceedings of the 25th IEEE (Institute of Electrical and Electronics Engineers) China conference on system simulation technology and its application 2024 (CCSSTA 2024), 21-23 July 2024, Tianjin, China. Piscataway: IEEE [online], pages 581-586. Available from: https://doi.org/10.1109/CCSSTA62096.2024.10691761

Lithium-ion batteries with their high voltage, large capacity, high discharge rate, no memory effect, and green environmental protection advantages are widely used in communication, power stations, backup power, and other energy storage fields. Accur... Read More about Improved multi-head bi-directional long and short-term memory temporal convolutional network for lithium-ion batteries state of charge estimation in energy storage systems..

A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering. (2024)
Journal Article
SHEN, X., WANG, S., YU, C., LI, Z. and FERNANDEZ, C. 2024. A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering. Ionics [online], 30(10), pages 6197-6213. Available from: https://doi.org/10.1007/s11581-024-05716-w

State of energy (SOE) estimation of lithium-ion batteries is the basis of electric vehicle driving range prediction. To improve the estimation accuracy of SOE under complex dynamic working conditions, this paper takes the ternary lithium-ion battery... Read More about A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering..

An improved forgetting factor recursive least square and extended particle filtering algorithm for accurate lithium-ion battery state of energy estimation. (2024)
Journal Article
SHEN, X., WANG, S., YU, C., LI, Z. and FERNANDEZ, C. 2024. An improved forgetting factor recursive least square and extended particle filtering algorithm for accurate lithium-ion battery state of energy estimation. Ionics [online], 30(10), pages 6179-6195. Available from: https://doi.org/10.1007/s11581-024-05698-9

State of energy (SOE) estimation of lithium-ion batteries is the basis for electric vehicle range prediction. To improve the estimation accuracy of SOE under complex dynamic operating conditions. In this paper, ternary lithium-ion batteries are used... Read More about An improved forgetting factor recursive least square and extended particle filtering algorithm for accurate lithium-ion battery state of energy estimation..

An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition. (2024)
Journal Article
ZHU, T., WANG, S., FAN, Y., HAI, N., HUANG, Q. and FERNANDEZ, C. 2024. An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition. Energy, [online], 306, article 132464. Available from: https://doi.org/10.1016/j.energy.2024.132464

Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction mo... Read More about An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition..

An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model. (2024)
Journal Article
ZHU, C., WANG, S., YU, C., HAI, N., FERNANDEZ, C. and GUERRERO, J.M. 2024. An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model. Energy [online], 306, article number 132465. Available from: https://doi.org/10.1016/j.energy.2024.132465

The accurate estimation of battery state of charge (SOC) and state of energy (SOE) are the key technologies in researching battery management systems. Due to the uncertainty of prior noise in practical engineering, traditional cubature Kalman filters... Read More about An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model..

A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems. (2024)
Journal Article
TAO, J., WANG, S., CAO, W., TAKYI-ANINAKWA, P., FERNANDEZ, C. and GUERRERO, J.M. 2024. A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems. Ionics [online], 30(10), pages 5903-5927. Available from: https://doi.org/10.1007/s11581-024-05686-z

With the gradual transformation of energy industries around the world, the trend of industrial reform led by clean energy has become increasingly apparent. As a critical link in the new energy industry chain, lithium-ion (Li-ion) battery energy stora... Read More about A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems..

Improved adaptive feedback particle swarm optimization-multi-innovation singular decomposition unscented Kalman filtering for high accurate state of charge estimation of lithium-ion batteries in energy storage systems. (2024)
Journal Article
LI, Y., WANG, S., LIU, D., LIU, C., FERNANDEZ, C. and WANG, X. 2024. Improved adaptive feedback particle swarm optimization-multi-innovation singular decomposition unscented Kalman filtering for high accurate state of charge estimation of lithium-ion batteries in energy storage systems. Ionics [online], 30(9), pages 5411-5427. Available from: https://doi.org/10.1007/s11581-024-05663-6

Accurate estimation of the state of charge (SOC) of lithium-ion batteries is very important for the development of energy storage systems. However, batteries are subject to characteristic changes in complex environments, making it difficult to accura... Read More about Improved adaptive feedback particle swarm optimization-multi-innovation singular decomposition unscented Kalman filtering for high accurate state of charge estimation of lithium-ion batteries in energy storage systems..

Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model. (2024)
Journal Article
FENG, R., WANG, S., YU, C. and FERNANDEZ, C. 2024. Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model. Ionics [online], 30(9), pages 5449-5471. Available from: https://doi.org/10.1007/s11581-024-05685-0

The remaining useful life (RUL) of lithium-ion batteries is a decisive factor in the stability of electric vehicle systems. Aiming at the problem of limited robustness of Deep Extreme Learning Machine (DELM) in lithium-ion battery RUL prediction, an... Read More about Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model..

High-precision state of charge estimation of electric vehicle lithium-ion battery energy storage system based on multi-scale optimized time-varying bounded smoothing variable structure filtering algorithm. (2024)
Journal Article
WU, F., WANG, S., LIU, D. and FERNANDEZ, C. 2024 High-precision state of charge estimation of electric vehicle lithium-ion battery energy storage system based on multi-scale optimized time-varying bounded smoothing variable structure filtering algorithm. Ionics [online], 30(9), pages 5429-5447. Available from: https://doi.org/10.1007/s11581-024-05678-z

State of charge (SOC) is a crucial parameter in evaluating the remaining power of commonly used lithium-ion battery energy storage systems, and the study of high-precision SOC is widely used in assessing electric vehicle power. This paper proposes a... Read More about High-precision state of charge estimation of electric vehicle lithium-ion battery energy storage system based on multi-scale optimized time-varying bounded smoothing variable structure filtering algorithm..

Battery lumped fractional-order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time-varying core temperature conditions. (2024)
Journal Article
ZENG, J., WANG, S., TAKYI-ANINAKWA, P., ZHANG, M., CAO, W., FERNANDEZ, C. and GUERRERO, J.M. 2024. Battery lumped fractional-order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time-varying core temperature conditions. International journal of circuit theory and applications [online], Early View. Available from: https://doi.org/10.1002/cta.4138

As electric vehicles become more common, there is increasing concern regarding their battery reliability and safety. The estimation accuracy is strongly correlated with the performance of the battery model. The lumped fractional-order hysteresis ther... Read More about Battery lumped fractional-order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time-varying core temperature conditions..

Spectrophotometric and chromatographic analysis of creatine: creatinine crystals in urine. (2024)
Journal Article
WERLE, J., BURESOVA, K., CEPOVA, J., BJØRKLUND, G., FORTOVA, M., PRUSA, R., FERNANDEZ, C., DUNOVSKA, K., KLAPKOVA, E., KIZEK, R. and KOTASKA, K. 2024. Spectrophotometric and chromatographic analysis of creatine: creatinine crystals in urine. Spectrochimica acta part A: molecular and biomolecular spectroscopy [online], 322, article number 124689. Available from: https://doi.org/10.1016/j.saa.2024.124689

Creatinine is the end product of the catabolism of creatine and creatine phosphate. Creatine phosphate serves as a reservoir of high-energy phosphate, especially in skeletal and cardiac muscle. Besides typical known changes in serum and urinary creat... Read More about Spectrophotometric and chromatographic analysis of creatine: creatinine crystals in urine..

A review of data-driven whole-life state of health prediction for lithium-ion batteries: data preprocessing, aging characteristics, algorithms, and future challenges. (2024)
Journal Article
XIE, Y., WANG, S., ZHANG, G., TAKYI-ANINAKWA, P., FERNANDEZ, C. and BLAABJERG, F. 2024. A review of data-driven whole-life state of health prediction for lithium-ion batteries: data preprocessing, aging characteristics, algorithms, and future challenges. Journal of energy chemistry [online], 97, pages 630-649. Available from: https://doi.org/10.1016/j.jechem.2024.06.017

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems (BMSs) that efficiently manage the batteries. This not only ensures the safety performance of the batteries but also sign... Read More about A review of data-driven whole-life state of health prediction for lithium-ion batteries: data preprocessing, aging characteristics, algorithms, and future challenges..

An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures. (2024)
Journal Article
LIU, D., WANG, S., FAN, Y., FERNANDEZ, C. and BLAABJERG, F. 2024. An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures. Energy [online], 304, article number 132048. Available from: https://doi.org/10.1016/j.energy.2024.132048

With the development of intelligentization and network connectivity of new energy vehicles, the estimation of power lithium-ion battery state of charge (SOC) using artificial intelligence methods is becoming a research hotspot. This paper proposes an... Read More about An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures..

An improved grey wolf optimization–double adaptive extended Kalman filtering algorithm for co-estimation of state of charge and state of health for lithium-ion batteries based on temperature-dependent second-order RC model. (2024)
Journal Article
CHEN, L, WANG, S., CHEN, L., GAO, H. and FERNANDEZ, C. 2024. An improved grey wolf optimization–double adaptive extended Kalman filtering algorithm for co-estimation of state of charge and state of health for lithium-ion batteries based on temperature-dependent second-order RC model. Ionics [online], 30(8), pages 4631-4646. Available from: https://doi.org/10.1007/s11581-024-05610-5

Accurately estimating the state of charge (SOC) and state of health (SOH) of lithium-ion batteries is crucial for the safe operation of electric vehicle battery management systems (BMS). This paper proposes an improved grey wolf optimization–double a... Read More about An improved grey wolf optimization–double adaptive extended Kalman filtering algorithm for co-estimation of state of charge and state of health for lithium-ion batteries based on temperature-dependent second-order RC model..

Electrochemical sensors and biosensors for identification of viruses: a critical review. (2024)
Journal Article
HOSNEDLOVA, B., WERLE, J., CEPOVA, J., NARAYANAN, V.H.B., VYSLOUZILOVA, L., FERNANDEZ, C., PARIKESIT, A.A., KEPINSKA, M., KLAPKOVA, E., KOTASKA, K., STEPANKOVA, O., BJORKLUND, G., PRUSA, R. and KIZEK, R. 2024. Electrochemical sensors and biosensors for identification of viruses: a critical review. Critical reviews in analytical chemistry [online], Online First. Available from: https://doi.org/10.1080/10408347.2024.2343853

Due to their life cycle, viruses can disrupt the metabolism of their hosts, causing diseases. If we want to disrupt their life cycle, it is necessary to identify their presence. For this purpose, it is possible to use several molecular-biological and... Read More about Electrochemical sensors and biosensors for identification of viruses: a critical review..

Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm. (2024)
Journal Article
ZHOU, Y., WANG, S., XIE, Y., ZENG, J. and FERNANDEZ, C. 2024. Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm. Energy [online], 300, article number 131575. Available from: https://doi.org/10.1016/j.energy.2024.131575

Due to the large-scale application of electric vehicles, the remaining service life prediction and health status diagnosis of lithium-ion batteries as their power core is particularly important, and the essence of RUL prediction and SOH diagnosis is... Read More about Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm..

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..