All Outputs (271)

Co-estimation of state-of-charge and state-of-health for high-capacity lithium-ion batteries. (2023)
Journal Article
XIONG, R., WANG, S., FENG, F., YU, C., FAN, Y., CAO, W. and FERNANDEZ, C. 2023. Co-estimation of state-of-charge and state-of-health for high-capacity lithium-ion batteries. Batteries [online], 9(10), article number 509. Available from: https://doi.org/10.3390/batteries9100509

To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursiv... Read More about Co-estimation of state-of-charge and state-of-health for high-capacity lithium-ion batteries..

Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference. (2023)
Journal Article
YANG, X., WANG, S., TAKYI-ANINAKWA, P., YANG, X. and FERNANDEZ, C. 2023. Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference. Journal of energy storage [online], 73(B), article number 108974. Available from: https://doi.org/10.1016/j.est.2023.108974

Strong electromagnetic interference, which has a significant impact on the performance and safety of the lithium-ion battery, usually affects the accurate state of charge (SOC). Different optimization strategies are used to estimate the model paramet... Read More about Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference..

Improved forgetting factor recursive least square and adaptive square root unscented Kalman filtering methods for online model parameter identification and joint estimation of state of charge and state of energy of lithium-ion batteries. (2023)
Journal Article
ZHU, T., WANG, S., FAN, Y., ZHOU, H., ZHOU, Y. and FERNANDEZ, C. 2023. Improved forgetting factor recursive least square and adaptive square root unscented Kalman filtering methods for online model parameter identification and joint estimation of state of charge and state of energy of lithium-ion batteries. Ionics [online], 29(12), pages 5295-5314. Available from: https://doi.org/10.1007/s11581-023-05205-6

The estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is very important for the battery management system (BMS) and the analysis of the causes of equipment failures. Aiming at many problems such as the changes... Read More about Improved forgetting factor recursive least square and adaptive square root unscented Kalman filtering methods for online model parameter identification and joint estimation of state of charge and state of energy of lithium-ion batteries..

High-precision state of charge estimation of lithium-ion batteries based on improved particle swarm optimization-backpropagation neural network-dual extended Kalman filtering. (2023)
Journal Article
CHEN, L., WANG, S., CHEN, L., QIAO, J. and FERNANDEZ, C. 2024. High-precision state of charge estimation of lithium-ion batteries based on improved particle swarm optimization-backpropagation neural network-dual extended Kalman filtering. International journal of circuit theory and application [online], 52(3), pages 1192-1209. Available from: https://doi.org/10.1002/cta.3788

High precision state of Charge (SOC) estimation is essential for battery management systems (BMS). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm is combined with the backpropagation neural network (PSO-BPNN-... Read More about High-precision state of charge estimation of lithium-ion batteries based on improved particle swarm optimization-backpropagation neural network-dual extended Kalman filtering..

Improved backward smoothing square root cubature Kalman filtering and fractional order-battery equivalent modeling for adaptive state of charge estimation of lithium-ion batteries in electric vehicles. (2023)
Journal Article
ZHOU, J., WANG, S., CAO, W., XIE, Y. and FERNANDEZ, C. 2023. Improved backward smoothing square root cubature Kalman filtering and fractional order-battery equivalent modeling for adaptive state of charge estimation of lithium-ion batteries in electric vehicles. Energy technology [online], 11(12), article number 2300765. Available from: https://doi.org/10.1002/ente.202300765

The accuracy of lithium-ion battery state of charge (SOC) estimation affects the driving distance, battery life, and safety performance of electric vehicles. Herein, the polarization reaction inside the battery is modeled using a second-order fractio... Read More about Improved backward smoothing square root cubature Kalman filtering and fractional order-battery equivalent modeling for adaptive state of charge estimation of lithium-ion batteries in electric vehicles..

Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction. (2023)
Journal Article
CAO, J., WANG, S. and FERNANDEZ, C. 2024. Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction. Journal of solid state electrochemistry [online], 28(1), pages 19-32. Available from: https://doi.org/10.1007/s10008-023-05650-3

Remaining useful life (RUL) of lithium-ion batteries is an important indicator for battery health management, and accurate prediction can promote reliable battery system design, as well as safety and effectiveness of practical use. Therefore, we extr... Read More about Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction..

Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries. (2023)
Journal Article
LI, Y., WANG, S., CHEN, L., QI, C. and FERNANDEZ, C. 2023. Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries. Energy [online], 282, article number 128776. Available from: https://doi.org/10.1016/j.energy.2023.128776

High precision state of health (SOH) estimation of lithium-ion batteries (LIBs) is a research hotspot in battery management system (BMS). To achieve this goal, an improved integrated algorithm based on multiple layer kernel extreme learning machine (... Read More about Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries..

Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm. (2023)
Journal Article
ZHOU, Y., WANG, S., XIE, Y., SHEN, X. and FERNANDEZ, C. 2023. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm. Energy [online], 285, article 128761. Available from: https://doi.org/10.1016/j.energy.2023.128761

The prediction of SOH for Lithium-ion battery systems determines the safety of Electric vehicles and stationary energy storage devices powered by LIBs. State of health diagnosis and remaining useful life prediction also rely significantly on excellen... Read More about Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm..

A hybrid algorithm based on beluga whale optimization-forgetting factor recursive least square and improved particle filter for the state of charge estimation of lithium-ion batteries. (2023)
Journal Article
SHEN, X., WANG, S., YU, C., QI, C., LI, Z. and FERNANDEZ, C. 2023. A hybrid algorithm based on beluga whale optimization-forgetting factor recursive least square and improved particle filter for the state of charge estimation of lithium-ion batteries. Ionics [online], 29(10), pages 4351-4363. Available from: https://doi.org/10.1007/s11581-023-05147-z

Battery state of charge (SOC) is crucial in power battery management systems for improving the efficiency of battery use and its safety performance. In this paper, we propose a forgotten factor recursive least squares (FFRLS) method based on the belu... Read More about A hybrid algorithm based on beluga whale optimization-forgetting factor recursive least square and improved particle filter for the state of charge estimation of lithium-ion batteries..

Improved fractional-order hysteresis-equivalent circuit modeling for the online adaptive high-precision state of charge prediction of urban-electric-bus lithium-ion batteries. (2023)
Journal Article
ZENG, J., WANG, S., CAO, W., ZHANG, M., FERNANDEZ, C. and GUERRERO, J.M. 2024. Improved fractional-order hysteresis-equivalent circuit modeling for the online adaptive high-precision state of charge prediction of urban-electric-bus lithium-ion batteries. International journal of circuit theory and application [online], 52(1), pages 420-438. Available from: https://doi.org/10.1002/cta.3767

Accurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second-order RC equivalent circuit model and Grunwald-Letnikov (G-L) definition, the high-preci... Read More about Improved fractional-order hysteresis-equivalent circuit modeling for the online adaptive high-precision state of charge prediction of urban-electric-bus lithium-ion batteries..

A complete ensemble empirical mode decomposition with adaptive noise deep autoregressive recurrent neural network method for the whole life remaining useful life prediction of lithium-ion batteries. (2023)
Journal Article
ZHANG, C., WANG, S., YU, C., WANG, Y. and FERNANDEZ, C. 2023. A complete ensemble empirical mode decomposition with adaptive noise deep autoregressive recurrent neural network method for the whole life remaining useful life prediction of lithium-ion batteries. Ionics [online], 29(10), pages 4337-4349. Available from: https://doi.org/10.1007/s11581-023-05152-2

The real-time prediction of the remaining useful life (RUL) of lithium-ion batteries provides an effective mean of preventing accidents. An improved adaptive noise-reduction deep learning method is applied to achieve adaptive noise-reduction decompos... Read More about A complete ensemble empirical mode decomposition with adaptive noise deep autoregressive recurrent neural network method for the whole life remaining useful life prediction of lithium-ion batteries..

Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations. (2023)
Journal Article
WANG, S., WU, F., TAKYI-ANINAKWA, P., FERNANDEZ, C., STROE, D.-I. and HUANG, Q. 2023. Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations. Energy [online], 284, article 128677. Available from: https://doi.org/10.1016/j.energy.2023.128677

For the development of low-temperature power systems in aviation, the transport synergistic carrier optimization of lithium-ions and electrons is conducted to improve the low-temperature adaptability of lithium-ion batteries. In this paper, an improv... Read More about Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations..

High-precision joint estimation of the state of charge and state of energy for new energy electric vehicle lithium-ion batteries based on improved singular value decomposition-adaptive embedded cubature Kalman filtering. (2023)
Journal Article
ZHOU, J., WANG, S., CAO, W., XIE, Y. and FERNANDEZ, C. 2023. High-precision joint estimation of the state of charge and state of energy for new energy electric vehicle lithium-ion batteries based on improved singular value decomposition-adaptive embedded cubature Kalman filtering. Journal of solid state electrochemistry [online], 27(12), pages 3293-3306. Available from: https://doi.org/10.1007/s10008-023-05594-8

Accurate online estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries are essential for efficient and reliable energy management of new energy electric vehicles (EVs). To improve the accuracy and stability of the... Read More about High-precision joint estimation of the state of charge and state of energy for new energy electric vehicle lithium-ion batteries based on improved singular value decomposition-adaptive embedded cubature Kalman filtering..

Microstructure and corrosion resistance of ultrahigh pressure Mg-8Li based alloys. (2023)
Journal Article
YANG, M., FENG, J., HU, H., NIU, T., GAO, W., ZOU, G., FERNANDEZ, C., REN, L. and PENG, Q. 2023. Microstructure and corrosion resistance of ultrahigh pressure Mg-8Li based alloys. Journal of alloys and compounds [online], 966, article ID 171543. Available from: https://doi.org/10.1016/j.jallcom.2023.171543

High anti-corrosion Mg alloys is desirable to expand their industrial applications. Herein we report an outstanding corrosion resistance of dual-phase Mg-8Li-Y alloy using ultrahigh pressure (UHP) technology. The average corrosion rate is ~0.47 mm/y,... Read More about Microstructure and corrosion resistance of ultrahigh pressure Mg-8Li based alloys..

An improved comprehensive learning: particle swarm optimization: extended Kalman filtering method for the online high-precision state of charge and model parameter co-estimation of lithium-ion batteries. (2023)
Journal Article
SHEN, X., WANG, S., YU, C., QI, C., LI, Z. and FERNANDEZ, C. 2023. An improved comprehensive learning: particle swarm optimization: extended Kalman filtering method for the online high-precision state of charge and model parameter co-estimation of lithium-ion batteries. Journal of The Electrochemical Society [online], 170(7), article 070522. Available from: https://doi.org/10.1149/1945-7111/ace555

The precise assessment of the state of charge (SOC) of lithium-ion batteries (LIBs) is critical in battery management systems. This work offers a comprehensive learning particle swarm optimization (CLPSO) and extended Kalman filter (EKF) technique to... Read More about An improved comprehensive learning: particle swarm optimization: extended Kalman filtering method for the online high-precision state of charge and model parameter co-estimation of lithium-ion batteries..

A sodiophilic amyloid fibril modified separator for dendrite-free sodium metal batteries. (2023)
Journal Article
WANG, J., GAO, Y., LIU, D., ZOU, G., LI, L., FERNANDEZ, C., ZHANG, Q., and PENG, Q. 2024. A sodiophilic amyloid fibril modified separator for dendrite-free sodium metal batteries. Advanced materials [online], 36(11), article 2304942. Available from: https://doi.org/10.1002/adma.202304942

Sodium (Na) batteries are being considered as prospective candidates for the next generation of secondary batteries in contrast to lithium-based batteries, due to their high raw material abundance, low cost, and sustainability. However, the unfavorab... Read More about A sodiophilic amyloid fibril modified separator for dendrite-free sodium metal batteries..

An improved compression factor particle swarm optimization-unscented particle filter algorithm for accurate lithium-ion battery state of energy estimation. (2023)
Journal Article
HAO, X., WANG, S., FAN, Y., LIANG, Y., WANG, Y. and FERNANDEZ, C. 2023. An improved compression factor particle swarm optimization-unscented particle filter algorithm for accurate lithium-ion battery state of energy estimation. Journal of The Electrochemical Society [online], 170(7), article 070507. Available from: https://doi.org/10.1149/1945-7111/acdf8a

Accurate prediction of the remaining range remains a challenge for electric vehicles. The state of energy (SOE) is a state parameter representing the remaining mileage and remaining charge of a lithium-ion battery, which is related to the prediction... Read More about An improved compression factor particle swarm optimization-unscented particle filter algorithm for accurate lithium-ion battery state of energy estimation..

An ASTSEKF optimizer with nonlinear condition adaptability for accurate SOC estimation of lithium-ion batteries. (2023)
Journal Article
TAKYI-ANINAKWA, P., WANG, S., ZHANG, H., LI, H., YANG, X. and FERNANDEZ, C. 2023. An ASTSEKF optimizer with nonlinear condition adaptability for accurate SOC estimation of lithium-ion batteries. Journal of energy storage [online], 70, article 108098. Available from: https://doi.org/10.1016/j.est.2023.108098

Safe and reliable operations of lithium-ion batteries in electric vehicles (EVs), etc., highly depend on the accurate state of charge (SOC) estimated by the battery management system (BMS). However, due to the battery's nonlinear operating conditions... Read More about An ASTSEKF optimizer with nonlinear condition adaptability for accurate SOC estimation of lithium-ion batteries..

State of energy estimation for lithium-ion batteries using adaptive fuzzy control and forgetting factor recursive least squares combined with AEKF considering temperature. (2023)
Journal Article
LIU, D., WANG, S., FAN, Y., LIANG, Y., FERNANDEZ, C., STROE, D.I. 2023. State of energy estimation for lithium-ion batteries using adaptive fuzzy control and forgetting factor recursive least squares combined with AEKF considering temperature. Journal of energy storage [online], 70, article 108040. Available from: https://doi.org/10.1016/j.est.2023.108040

As the main energy storage component of electric vehicles (EV), lithium-ion battery state estimation is an essential part of the battery management system (BMS). State of Energy (SOE) is one of the important state parameters, and its accurate estimat... Read More about State of energy estimation for lithium-ion batteries using adaptive fuzzy control and forgetting factor recursive least squares combined with AEKF considering temperature..

High-precision state of charge estimation of lithium-ion batteries based on joint compression factor particle swarm optimization-forgetting factor recursive least square-adaptive extended Kalman filtering. (2023)
Journal Article
YANG, J., WANG, S., CHEN, L., QIAO, J., FERNANDEZ, C. and GUERRERO, J.M. 2023. High-precision state of charge estimation of lithium-ion batteries based on joint compression factor particle swarm optimization-forgetting factor recursive least square-adaptive extended Kalman filtering. Journal of The Electrochemical Society [online], 170(6), article 060527. Available from: https://doi.org/10.1149/1945-7111/acd815

Accurate state of charge (SOC) estimation is an important basis for battery energy management and the applications of lithium-ion batteries. In this paper, an improved compression factor particle swarm optimization-forgetting factor recursive least s... Read More about High-precision state of charge estimation of lithium-ion batteries based on joint compression factor particle swarm optimization-forgetting factor recursive least square-adaptive extended Kalman filtering..