Paul Takyi‐Aninakwa

TL;DR: In this paper , an improved feedforward-long short-term memory (FF-LSTM) modeling method is proposed to realize an accurate whole-life-cycle state of charge (SOC) prediction by effectively considering the current, voltage, and temperature variations.

TL;DR: The convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutionic neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction.

TL;DR: In this paper , a strong tracking adaptive fading-extended Kalman filter (STAF•EKF) based on the second-order resistor-capacitor equivalent circuit model (2RC•ECM) is proposed for accurate state of charge estimation of lithium-ion batteries under different working conditions and ambient temperatures.

TL;DR: Based on the second-order resistor-capacitor equivalent circuit model and online parameter identification using variable forgetting factor recursive least square (VFF-RLS), a fuzzy adaptive controller is proposed to improve the convergence speed of the cubature Kalman filter (CKF) for the SOC estimation as mentioned in this paper .

TL;DR: In this article , a feedback correction-adaptive Kalman filtering (FC-AKF) method is proposed for the online battery state co-prediction, which is adaptive to the whole-life-cycle of the lithium-ion battery based on the improved second-order equivalent circuit model (SO-ECM).