Dr Carlos Fernandez's Outputs (304)

An improved robust function correction-adaptive extended Kalman filtering algorithm for SOC estimation of lithium-ion batteries. (2023)
Presentation / Conference Contribution
ZHU, C., WANG, S., YU, C., HAI, N., FERNANDEZ, C. and SUN, Z. 2023. An improved robust function correction-adaptive extended Kalman filtering algorithm for SOC estimation of lithium-ion batteries. In Proceedings of the 3rd New energy and energy storage system control summit forum 2023 (NEESSC 2023), 26-28 September 2023, Mianyang, China. Piscataway: IEEE [online], pages 358-362. Available from: https://doi.org/10.1109/NEESSC59976.2023.10349263

State of Charge (SOC) is one of the key indicators for evaluating the state of electric vehicles. In order to cope with the uncertainty of random noise in nonlinear systems, an improved robust function correction-adaptive extended Kalman filtering (R... Read More about An improved robust function correction-adaptive extended Kalman filtering algorithm for SOC estimation of lithium-ion batteries..

An improved genetic-backpropagation neural network for state of charge estimation of lithium-ion batteries. (2023)
Presentation / Conference Contribution
WANG, S., HAI, N., YANG, J. and FERNANDEZ, C. 2023. An improved genetic-backpropagation neural network for state of charge estimation of lithium-ion batteries. In Proceedings of the 3rd New energy and energy storage system control summit forum 2023 (NEESSC 2023), 26-28 September 2023, Mianyang, China. Piscataway: IEEE [online], pages 369-372. Available from: https://doi.org/10.1109/NEESSC59976.2023.10349297

The state of charge estimation with high precision plays an important role in the usage of lithium-ion batteries in electronic vehicles. An improved genetic-backpropagation neural network (GA-BPNN) is proposed to predict the state of charge with high... Read More about An improved genetic-backpropagation neural network for state of charge estimation of lithium-ion batteries..

An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries. (2023)
Journal Article
WANG, S., WANG, C., TAKYI-ANINAKWA, P., JIN, S., FERNANDEZ, C. and HUANG, Q. 2024. An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries. Journal of energy storage [online], 80, article number 110222. Available from: https://doi.org/10.1016/j.est.2023.110222

The State estimation and determination of time-varying model parameters are crucial for ensuring the safe management of lithium-ion batteries. This paper designs a limited memory recursive least square algorithm to improve the accuracy of online para... Read More about An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries..

An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries. (2023)
Journal Article
LI, Z., WANG, S., YU, C., QI, C., SHEN, X. and FERNANDEZ, C. 2024. An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries. Ionics [online], 30(1), pages 311-334. Available from: https://doi.org/10.1007/s11581-023-05272-9

In the battery management system (BMS), the state of charge (SOC) of lithium-ion batteries is an indispensable part, and the accuracy of SOC estimation has attracted wide attention. Accurate SOC estimation can improve the efficiency of battery use wh... Read More about An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries..

A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range. (2023)
Journal Article
CHEN, L, WANG, S., JIANG, H. and FERNANDEZ, C. 2024. A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range. Applied energy [online], 355, article number 122225. Available from: https://doi.org/10.1016/j.apenergy.2023.122225

Lithium-ion batteries are one of the best choices as energy storage devices for self-powered nodes in wireless sensor networks (WSN) due to their advantages of no memory effect, high energy density, long cycle life, and being pollution-free after bei... Read More about A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range..

Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries. (2023)
Journal Article
SHI, H., WANG, S., HUANG, Q., FERNANDEZ, C., LIANG, J., ZHANG, M., QI, C. and WANG, L. 2024. Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries. Applied energy [online], 353, part B, article 122174. Available from: https://doi.org/10.1016/j.apenergy.2023.122174

Unraveling the kinetic behavior inside the battery is essential to break through the limitations of mechanistic studies and to optimize the control of the integrated management system. Given this fact that the battery system is multi-domain coupled a... Read More about Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries..

Valorisation of whisky distillery waste as a sustainable source of antioxidant and antibacterial properties with neuroprotective potential. (2023)
Journal Article
BLAIKIE, L., WELGAMAGE DON, A., FRANZEN, X., FERNANDEZ, C., FAISAL, N. and KONG THOO LIN, P. 2024. Valorisation of whisky distillery waste as a sustainable source of antioxidant and antibacterial properties with neuroprotective potential. Waste and biomass valorization [online], 15(4), pages 2333-2343. Available from: https://doi.org/10.1007/s12649-023-02292-4

Waste by-products such as pot ale are abundantly produced during the whisky distillation process and are conventionally used as livestock feed, however a significant proportion continues to require land and sea disposal. Here, the novel potential of... Read More about Valorisation of whisky distillery waste as a sustainable source of antioxidant and antibacterial properties with neuroprotective potential..

A novel genetic marginalized particle filter method for state of charge and state of energy estimation adaptive to multi-temperature conditions of lithium-ion batteries. (2023)
Journal Article
JIA, X., WANG, S., CAO, J., QIAO, J., YANG, X., LI, Y. and FERNANDEZ, C. 2023. A novel genetic marginalized particle filter method for state of charge and state of energy estimation adaptive to multi-temperature conditions of lithium-ion batteries. Journal of energy storage [online], 74(part A), article number 109291. Available from: https://doi.org/10.1016/j.est.2023.109291

Power lithium-ion batteries are widely used in various fields, the battery management system (BMS) is the main object of battery energy management and safety monitoring, so the accurate collaboration of state of charge (SoC) and state of energy (SoE)... Read More about A novel genetic marginalized particle filter method for state of charge and state of energy estimation adaptive to multi-temperature conditions of lithium-ion batteries..

An improved random drift particle swarm optimization-feed forward backpropagation neural network for high-precision state-of-charge estimation of lithium-ion batteries. (2023)
Journal Article
HAI, N., WANG, S., LIU, D., GAO, H. and FERNANDEZ, C. 2023. An improved random drift particle swarm optimization-feed forward backpropagation neural network for high-precision state-of-charge estimation of lithium-ion batteries. Journal of energy storage [online], 73(part D), article number 109286. Available from: https://doi.org/10.1016/j.est.2023.109286

A predictive model with high accuracy and stability of the state of charge (SOC) estimation for lithium-ion batteries plays a significant role in electric vehicles. An improved random drift particle swarm optimization-feed forward backpropagation neu... Read More about An improved random drift particle swarm optimization-feed forward backpropagation neural network for high-precision state-of-charge estimation of lithium-ion batteries..

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..