TY - GEN
T1 - Reliable Real-Time Charging Profile Estimation for Fast EV Chargers Under Faulty Conditions
AU - Sharida, Ali
AU - Kamal, Naheel Faisal
AU - Bayhan, Sertac
AU - Abu-Rub, Haitham
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper proposes a real-time charging profile forecasting method for fast electric vehicle (EV) chargers. The proposed method aims to estimate voltage, current, power, and state-of-charge (SoC) profiles in the event of sensors faults, communication faults, or both. The healthy measurements are initially used to develop a dynamic model. When a fault occurs, this model forecasts the charging profile for the remainder of the session without relying on additional measurements. The proposed method considers the model of the battery as a black box, and utilizes an adaptive recursive least squares (RLS) algorithm to estimate the internal parameters of the battery model. This ensures a reliable reconstruction of missing sensor's data under post-fault conditions. To evaluate the effectiveness of the proposed approach, various mathematical and approximation models are analyzed and compared for accuracy using Matlab.
AB - This paper proposes a real-time charging profile forecasting method for fast electric vehicle (EV) chargers. The proposed method aims to estimate voltage, current, power, and state-of-charge (SoC) profiles in the event of sensors faults, communication faults, or both. The healthy measurements are initially used to develop a dynamic model. When a fault occurs, this model forecasts the charging profile for the remainder of the session without relying on additional measurements. The proposed method considers the model of the battery as a black box, and utilizes an adaptive recursive least squares (RLS) algorithm to estimate the internal parameters of the battery model. This ensures a reliable reconstruction of missing sensor's data under post-fault conditions. To evaluate the effectiveness of the proposed approach, various mathematical and approximation models are analyzed and compared for accuracy using Matlab.
KW - Charging profile
KW - EV charging
KW - State of charge forecasting
UR - https://www.scopus.com/pages/publications/105016135042
U2 - 10.1109/ISIE62713.2025.11124711
DO - 10.1109/ISIE62713.2025.11124711
M3 - Conference contribution
AN - SCOPUS:105016135042
SN - 979-8-3503-7480-3
T3 - Proceedings Of The Ieee International Symposium On Industrial Electronics
BT - 2025 Ieee 34th International Symposium On Industrial Electronics, Isie
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE International Symposium on Industrial Electronics, ISIE 2025
Y2 - 20 June 2025 through 23 June 2025
ER -