Dr Carlos Fernandez's Outputs (312)

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..

An improved progressive window-strong tracking multiple fading algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries under variable temperatures. (2024)
Journal Article
ZHU, C., WANG, S., YU, C., HAI, N., FERNANDEZ, C., GUERRERO, J.M. and HUANG, Q. 2024. An improved progressive window-strong tracking multiple fading algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries under variable temperatures. Journal of energy storage [online], 104(part A), article number 114444. Available from: https://doi.org/10.1016/j.est.2024.114444

To ensure the safety of batteries and plan their use reasonably, the accuracy of the state of charge (SOC) of batteries is a key evaluation indicator. State of energy (SOE) is a supplement to SOC to prevent system misjudgment. This paper proposes an... Read More about An improved progressive window-strong tracking multiple fading algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries under variable temperatures..

Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation. (2024)
Journal Article
ZHOU, Y., WANG, S., FENG, R., XIE, Y. and FERNANDEZ, C. 2024. Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation. Energy [online], 312, article number 133596. Available from: https://doi.org/10.1016/j.energy.2024.133596

In this study, we propose a novel hybrid modeling framework for State of Charge (SOC) estimation across a broad temperature spectrum. First, we build a hybrid model to optimize stacked layers of stacked bidirectional long short term memory networks b... Read More about Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation..

Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model. (2024)
Journal Article
FENG, R., WANG, S., YU, C., HAI, N. and FERNANDEZ, C. 2024. Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model. Ionics [online], 30(12), pages 8033-8052. Available from: https://doi.org/10.1007/s11581-024-05893-8

The state of health (SOH) of lithium-ion batteries is a decisive factor in ensuring the stability of electric vehicle systems. To solve the problem of low accuracy and robustness of lithium-ion battery SOH prediction models, this article proposes a d... Read More about Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model..

Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries. (2024)
Journal Article
TAO, J., WANG, S., CAO, W., CUI, Y., FERNANDEZ, C. and GUERRERO, J.M. 2024. Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries. Energy [online], 312, article number 133541. Available from: https://doi.org/10.1016/j.energy.2024.133541

An accurate assessment of lithium-ion (Li-ion) batteries' state of health (SOH) is essential for the safe operation of new energy systems and extended battery life. Health factors were extracted by studying the aging test data of Li-ion batteries to... Read More about Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries..

An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries. (2024)
Presentation / Conference Contribution
XU, X., WANG, S., LIU, D., FERNANDEZ, C. and BLAABJERG, F. 2024. An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries. Journal of physics: conference series [online], 2853: proceedings of the 7th International conference on mechanical, electric and industrial engineering, 21-23 May 2024, Yichang, China, article number 012057. Available from: https://doi.org/10.1088/1742-6596/2853/1/012057

Capacity sorting is the primary prerequisite for the stepwise utilization of decommissioned power batteries. This study proposes an improved relative integral capacity-K-means clustering (RIC-KMC) method for preliminary sorting of decommissioned powe... Read More about An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries..

An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter. (2024)
Journal Article
WANG, S., ZHANG, S., WEN, S. and FERNANDEZ, C. 2024. An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter. Journal of power sources [online], 624, article number 235594. Available from: https://doi.org/10.1016/j.jpowsour.2024.235594

The state of charge (SOC) of lithium-ion batteries (LIBs) is regarded as the fundamental parameter of the battery management system (BMS). In this paper, a parameter optimization method for mobile estimation windows based on particle swarm optimizati... Read More about An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter..

Microalloy Mg-based degradation implant for intra-osteal fixation. (2024)
Journal Article
NIU, T., ZHANG, Y., LIU, S., et al. 2024. Microalloy Mg-based degradation implant for intra-osteal fixation. Materialia [online], 38, article number 102258. Available from: https://doi.org/10.1016/j.mtla.2024.102258

The bottleneck for Mg-based degradable implants lies in the mismatching relationship between mechanical properties and degradable rate, resulting in the rapid failure during the in-vivo degradable process and potential toxic role. Herein microalloy-c... Read More about Microalloy Mg-based degradation implant for intra-osteal fixation..

An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries. (2024)
Journal Article
QIQIAO, W., SHUNLI, W., WEN, C., HAIYING, G., FERNANDEZ, C. and GUERRERO, J.M. 2024. An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries. Ionics [online], 30(11), pages 6969-6984. Available from: https://doi.org/10.1007/s11581-024-05831-8

Accurately estimating lithium-ion batteries' state of charge (SOC) is a vital decision-making technique in battery management systems (BMS), essential to ensuring operational safety and prolonging battery lifespan. The multi-innovation error compensa... Read More about An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries..

An improved particle swarm optimization-cubature Kalman particle filtering method for state-of-charge estimation of large-scale energy storage lithium-ion batteries. (2024)
Journal Article
WANG, C., WANG, S., ZHANG, G., TAKYI-ANINAKWA, P., FERNANDEZ, C. and TAO, J. 2024. An improved particle swarm optimization-cubature Kalman particle filtering method for state-of-charge estimation of large-scale energy storage lithium-ion batteries. Journal of energy storage [online], 100(B), article number 113619. Available from: https://doi.org/10.1016/j.est.2024.113619

With the global demand for large-scale energy storage strategies, lithium-ion batteries with high energy densities have emerged as the primary energy storage systems. State-of-charge (SOC) is a critical state parameter for energy storage systems that... Read More about An improved particle swarm optimization-cubature Kalman particle filtering method for state-of-charge estimation of large-scale energy storage lithium-ion batteries..

A novel back propagation neural network-square root cubature Kalman filtering strategy based on fusion dual factor parameter identification for state-of-charge estimation of lithium-ion batteries. (2024)
Presentation / Conference Contribution
XU, X., WANG, S., WANG, C., FERNANDEZ, C. and BLAABJERG, F. 2024. A novel back propagation neural network-square root cubature Kalman filtering strategy based on fusion dual factor parameter identification for state-of-charge estimation of lithium-ion batteries. In Proceedings of the 4th IEEE (Institute of Electrical and Electronics Engineers) 4th New energy and energy storage system control summit forum 2024 (NEESSC 2024), 29-31 August 2024, Hohhot, China. Piscataway: IEEE [online], pages 120-132. Available from: https://doi.org/10.1109/neessc62857.2024.10733526

Accurate real-time estimation of the state-of-charge (SOC) of the battery is of great significance for promoting the development of electric vehicles. In this research, a novel back propagation neural network-square root cubature Kalman filtering (BP... Read More about A novel back propagation neural network-square root cubature Kalman filtering strategy based on fusion dual factor parameter identification for state-of-charge estimation of lithium-ion batteries..