TY - GEN
T1 - Fuzzy Entropy-Based State of Health Estimation of LiFePO4Batteries Considering Temperature Variation
AU - Sui, Xin
AU - He, Shan
AU - Meng, Jinhao
AU - Teodorescu, Remus
AU - Stroe, Daniel Ioan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, because the Heaviside function is used to access similarity in the definition of SE, SE is sensitive to parameter selection. Hence, the SE shows an obvious change when the battery is aged at different conditions (e.g., temperatures), leading to a decrease in the estimation accuracy. By introducing the concept of fuzzy membership, the generalized version of SE, fuzzy entropy (FE) is weak influenced by parameters and test condition. Therefore, the FE is proposed as a feature to estimate the SOH of battery in terms of aging temperature variation. The FE-SOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified using experimental results.
AB - Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, because the Heaviside function is used to access similarity in the definition of SE, SE is sensitive to parameter selection. Hence, the SE shows an obvious change when the battery is aged at different conditions (e.g., temperatures), leading to a decrease in the estimation accuracy. By introducing the concept of fuzzy membership, the generalized version of SE, fuzzy entropy (FE) is weak influenced by parameters and test condition. Therefore, the FE is proposed as a feature to estimate the SOH of battery in terms of aging temperature variation. The FE-SOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified using experimental results.
KW - Aging temperature variation
KW - Fuzzy entropy
KW - Lithium-ion battery
KW - Sample entropy
KW - State of health estimation
KW - Support vector machine
UR - https://www.scopus.com/pages/publications/85097173005
U2 - 10.1109/ECCE44975.2020.9236267
DO - 10.1109/ECCE44975.2020.9236267
M3 - 会议稿件
AN - SCOPUS:85097173005
T3 - ECCE 2020 - IEEE Energy Conversion Congress and Exposition
SP - 4401
EP - 4406
BT - ECCE 2020 - IEEE Energy Conversion Congress and Exposition
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020
Y2 - 11 October 2020 through 15 October 2020
ER -
