Outputs (39)

A novel multiple training-scale dynamic adaptive cuckoo search optimized long short-term memory neural network and multi-dimensional health indicators acquisition strategy for whole life cycle health evaluation of lithium-ion batteries. (2022)
Journal Article
REN, P., WANG, S., CHEN, X., ZHOU, H., FERNANDEZ, C. and STROE, D.-I. 2022. A novel multiple training-scale dynamic adaptive cuckoo search optimized long short-term memory neural network and multi-dimensional health indicators acquisition strategy for whole life cycle health evaluation of lithium-ion batteries. Electrochimica Acta [online], 435, article 141404. Available from: https://doi.org/10.1016/j.electacta.2022.141404

State of health evaluation of lithium-ion batteries has become a significant research direction in related fields attributed to the crucial impact on the reliability and safety of electric vehicles. In this research, a dynamic adaptive cuckoo search... Read More about A novel multiple training-scale dynamic adaptive cuckoo search optimized long short-term memory neural network and multi-dimensional health indicators acquisition strategy for whole life cycle health evaluation of lithium-ion batteries..

Current development and future challenges in microplastic detection techniques: a bibliometrics-based analysis and review. (2022)
Journal Article
JIN, M., LIU, J., YU, J., ZHOU, Q., WU, W., FU, L., YIN, C., FERNANDEZ, C. and KARIMI-MALEH, H. 2022. Current development and future challenges in microplastic detection techniques: a bibliometrics-based analysis and review. Science progress [online], 105(4), pages 1-22. Available from: https://doi.org/10.1177/003685042211321

Microplastics have been considered a new type of pollutant in the marine environment and have attracted widespread attention worldwide in recent years. Plastic particles with particle size less than 5 mm are usually defined as microplastics. Because... Read More about Current development and future challenges in microplastic detection techniques: a bibliometrics-based analysis and review..

Application of thermal spray coatings in electrolysers for hydrogen production: advances, challenges, and opportunities. (2022)
Journal Article
FAISAL, N.H., PRATHURU, A., AHMED, R. et al. 2022. Application of thermal spray coatings in electrolysers for hydrogen production: advances, challenges, and opportunities. ChemNanoMat [online], 8(12), article number e202200384. Available from: https://doi.org/10.1002/cnma.202200384

Thermal spray coatings have the advantage of providing thick and functional coatings from a range of engineering materials. The associated coating processes provide good control of coating thickness, morphology, microstructure, pore size and porosity... Read More about Application of thermal spray coatings in electrolysers for hydrogen production: advances, challenges, and opportunities..

Improved compound correction-electrical equivalent circuit modeling and double transform-unscented Kalman filtering for the high-accuracy closed-circuit voltage and state-of-charge co-estimation of whole-life-cycle lithium-ion batteries. (2022)
Journal Article
WANG, S., TAKYI-ANINAKWA, P., YU, C., JIN, S. and FERNANDEZ, C. 2022. Improved compound correction-electrical equivalent circuit modeling and double transform-unscented Kalman filtering for the high-accuracy closed-circuit voltage and state-of-charge co-estimation of whole-life-cycle lithium-ion batteries. Energy technology [online], 10(12), article 2200921. Available from: https://doi.org/10.1002/ente.202200921

For complex energy storage conditions, it is necessary to monitor the state-of-charge (SOC) and closed-circuit voltage (CCV) status accurately for the reliable power supply application of lithium-ion batteries. Herein, an improved compound correction... Read More about Improved compound correction-electrical equivalent circuit modeling and double transform-unscented Kalman filtering for the high-accuracy closed-circuit voltage and state-of-charge co-estimation of whole-life-cycle lithium-ion batteries..

Towards an ultra-long lifespan Li-CO2: electron structure and charge transfer pathway regulation on hierarchical architecture. (2022)
Journal Article
WANG, Y., WANG, J., WANG, J., YANG, M., ZOU, G., LI, L., TSE, J.S., FERNANDEZ, C. and PENG, Q. 2022. Towards an ultra-long lifespan Li-CO2: electron structure and charge transfer pathway regulation on hierarchical architecture. Chemical engineering journal [online], 451(Part 3), article 138953. Available from: https://doi.org/10.1016/j.cej.2022.138953

Lithium-CO2 batteries are recognized as an essential strategy for efficient carbon sequestration and energy storage to achieve carbon neutrality. Their cycle-ability and polarization voltage, however, are hindered by high decomposition voltage (≈4.3–... Read More about Towards an ultra-long lifespan Li-CO2: electron structure and charge transfer pathway regulation on hierarchical architecture..

Improved particle swarm optimization-extreme learning machine modeling strategies for the accurate lithium-ion battery state of health estimation and high-adaptability remaining useful life prediction. (2022)
Journal Article
ZHANG, C.-Y., WANG, S.-L., YU, C.-M., XIE, Y.-X. and FERNANDEZ, C. [2022]. Improved particle swarm optimization-extreme learning machine modeling strategies for the accurate lithium-ion battery state of health estimation and high-adaptability remaining useful life prediction. Journal of the Electrochemical Society [online], 169(8), article 080520. Available from: https://doi.org/10.1149/1945-7111/ac8a1a

To ensure the secure and stable operation of lithium-ion batteries, the state of health (SOH) and the remaining useful life (RUL) are the critical state parameters of lithium-ion batteries, which need to be estimated precisely. A joint SOH and RUL es... Read More about Improved particle swarm optimization-extreme learning machine modeling strategies for the accurate lithium-ion battery state of health estimation and high-adaptability remaining useful life prediction..

A novel adaptive back propagation neural network-unscented Kalman filtering algorithm for accurate lithium-ion battery state of charge estimation. (2022)
Journal Article
WANG, Y., WANG, S., FAN, Y., XIE, Y. and FERNANDEZ, C. 2022. A novel adaptive back propagation neural network-unscented Kalman filtering algorithm for accurate lithium-ion battery state of charge estimation. Metals [online], 12(8), article 1369. Available from: https://doi.org/10.3390/met12081369

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling m... Read More about A novel adaptive back propagation neural network-unscented Kalman filtering algorithm for accurate lithium-ion battery state of charge estimation..

An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries. (2022)
Journal Article
TAKYI-ANINAKWA, P., WANG, S., ZHANG, H., LI, H., XU, W. and FERNANDEZ, C. 2022. An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries. Energy [online], 260, article 125093. Available from: https://doi.org/10.1016/j.energy.2022.125093

Accurate state of charge (SOC) estimation of lithium-ion batteries by the battery management system (BMS) plays a prominent role in ensuring their reliability, safe operation, and acceptable durability in smart devices, electric vehicles, etc. In thi... Read More about An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries..

A novel equivalent modeling method combined with the splice-electrochemical polarization model and prior generalized inverse least-square parameter identification for UAV lithium-ion batteries. (2022)
Journal Article
PENG, J., SHI, H., WANG, S., WANG, L., FERNANDEZ, C., XIONG, X. and DABLU, B.E. 2022. A novel equivalent modeling method combined with the splice-electrochemical polarization model and prior generalized inverse least-square parameter identification for UAV lithium-ion batteries. Energy science and engineering [online], 10(10), pages 3727-3740. Available from: https://doi.org/10.1002/ese3.1268

The accuracy of lithium-ion battery state estimation is critical to the safety of unmanned aerial vehicles (UAVs). In this paper, aiming at the high-fidelity modeling of the UAV lithium-ion battery, a splice-electrochemical polarization model (S-EPM)... Read More about A novel equivalent modeling method combined with the splice-electrochemical polarization model and prior generalized inverse least-square parameter identification for UAV lithium-ion batteries..

Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries. (2022)
Journal Article
SHI, H., WANG, S., FERNANDEZ, C., YU, C., XU, W., DABLU, B.E. and WANG, L. 2022. Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries. Applied energy [online], 324, article 119789. Available from: https://doi.org/10.1016/j.apenergy.2022.119789

The rapid development of new energy fields such as electric vehicles and smart grids has put forward higher requirements for the power management of battery integrated systems. Considering that internal temperature and parameter consistency are impor... Read More about Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries..