All Outputs (25)

Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries. (2024)
Journal Article
XIONG, R., WANG, S., HUANG, Q., YU, C., FERNANDEZ, C., XIAO, W., JIA, J. and GUERRERO, J.M. 2024. Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries. Energy [online], 292, article number 130594. Available from: https://doi.org/10.1016/j.energy.2024.130594

At present, the accurate establishment of the battery model and the effective state of health (SOH) estimation under actual energy storage conditions have become the main problems in new energy storage stations. Therefore, a SOH estimation method bas... Read More about Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries..

High-strong-ductile magnesium alloys by interactions of nanoscale quasi-long period stacking order unit with twin. (2024)
Journal Article
ZHOU, L., NIU, T., ZOU, G., SU, H., HE, S., ZHENG, S., ZHU, Y., CHEN, P., FERNANDEZ, C. and PENG, Q. 2024. High-strong-ductile magnesium alloys by interactions of nanoscale quasi-long period stacking order unit with twin. Journal of magnesium and alloys [online], In Press. Available from: https://doi.org/10.1016/j.jma.2024.01.015

Magnesium alloys with high strength in combination of good ductility are especially desirable for applications in transportation, aerospace and bio-implants owing to their high stiffness, abundant raw materials, and environmental friendliness. Howeve... Read More about High-strong-ductile magnesium alloys by interactions of nanoscale quasi-long period stacking order unit with twin..

An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries. (2024)
Journal Article
WU, F., WANG, S., LIU, D., CAO, W., FERNANDEZ, C. and HUANG, Q. 2024. An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries. Journal of energy storage [online], 82, article 110574. Available from: https://doi.org/10.1016/j.est.2024.110574

State of charge (SOC) and state of energy (SOE) are the key factors that reflect the safe and range driving of new energy vehicles. This paper proposes an optimized convolutional neural network-bidirectional gate recurrent unit (CNN-BiGRU) and an imp... Read More about An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries..

State of health prediction of lithium-ion batteries using combined machine learning model based on nonlinear constraint optimization. (2024)
Journal Article
LIANG, Y., WANG, S., FAN, Y., HAO, X., LIU, D. and FERNANDEZ, C. 2024. State of health prediction of lithium-ion batteries using combined machine learning model based on nonlinear constraint optimization. Journal of the Electrochemical Society [online], 171(1), article number 010508. Available from: https://doi.org/10.1149/1945-7111/ad18e1

Accurate State of Health (SOH) estimation of battery systems is critical to vehicle operation safety. However, it's difficult to guarantee the performance of a single model due to the unstable quality of raw data obtained from lithium-ion battery agi... Read More about State of health prediction of lithium-ion batteries using combined machine learning model based on nonlinear constraint optimization..

A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction. (2024)
Journal Article
CHEN, Y., DUAN, W., HE, Y., WANG, S. and FERNANDEZ, C. 2024. A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction. Green energy and intelligent transportation [online], 3(2), article number 100160. Available from: https://doi.org/10.1016/j.geits.2024.100160

Battery life prediction is of great significance to the safe operation, and the maintenance costs are reduced. This paper proposed a hybrid framework considering feature extraction to solve the problem of data backward, large sample data and uneven d... Read More about A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction..