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..