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A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries. (2020)
Journal Article
WANG, S., FERNANDEZ, C., FAN, Y., FENG, J., YU, C., HUANG, K. and XIE, W. 2020. A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle-adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries. Energy science and engineering [online], 8(5), pages 1484-1500. Available from: https://doi.org/10.1002/ese3.606

The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its energy management and safety protection. A new equivalent modeling method is proposed for the ma... Read More about A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries..

A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions. (2020)
Journal Article
WANG, S., STROE, D.-I., FERNANDEZ, C., YU, C., ZOU, C. and LI, X. 2020. A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions. Journal of power sources [online], 450, article ID 227652. Available from: https://doi.org/10.1016/j.jpowsour.2019.227652

The dynamic model of the ternary lithium battery is a time-varying nonlinear system due to the polarization and diffusion effects inside the battery in its charge-discharge process. Based on the comprehensive analysis of the energy management methods... Read More about A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions..