TY - GEN
T1 - Impact of Loss Model Selection on Power Semiconductor Lifetime Prediction in Electric Vehicles
AU - Xia, Hongjian
AU - Zhang, Yi
AU - Zhou, Dao
AU - Chen, Minyou
AU - Lai, Wei
AU - Wei, Yunhai
AU - Wang, Huai
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/12
Y1 - 2022/12
N2 - Power loss estimation is an indispensable procedure to conduct lifetime prediction for power semiconductor device. The previous studies successfully perform steady-state power loss estimation for different applications, but which may be limited for the electric vehicles (EVs) with high dynamics. Based on two EV standard driving cycle profiles, this paper gives a comparative study of power loss estimation models with two different time resolutions, i.e., the output period average and the switching period average. The correspondingly estimated power losses, thermal profiles, and lifetime clearly pointed out that the widely applied power loss model with the output period average is limited for EV applications, in particular for the highly dynamic driving cycle. The difference in the predicted lifetime can be up to 300 times due to the unreasonable choice the loss model, which calls for the industry attention on the differences of the EVs and the importance of loss model selection in lifetime prediction.
AB - Power loss estimation is an indispensable procedure to conduct lifetime prediction for power semiconductor device. The previous studies successfully perform steady-state power loss estimation for different applications, but which may be limited for the electric vehicles (EVs) with high dynamics. Based on two EV standard driving cycle profiles, this paper gives a comparative study of power loss estimation models with two different time resolutions, i.e., the output period average and the switching period average. The correspondingly estimated power losses, thermal profiles, and lifetime clearly pointed out that the widely applied power loss model with the output period average is limited for EV applications, in particular for the highly dynamic driving cycle. The difference in the predicted lifetime can be up to 300 times due to the unreasonable choice the loss model, which calls for the industry attention on the differences of the EVs and the importance of loss model selection in lifetime prediction.
KW - electric vehicle
KW - lifetime
KW - loss model
KW - power semiconductor device
UR - https://www.scopus.com/pages/publications/85143891516
U2 - 10.1109/IECON49645.2022.9968430
DO - 10.1109/IECON49645.2022.9968430
M3 - Conference article published in proceeding or book
AN - SCOPUS:85143891516
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 1
EP - 7
BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Y2 - 17 October 2022 through 20 October 2022
ER -