Hongling Liu
Research Subjects
Research Interests
Hongling Liu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Chuanyu Bie
Wuhan Power Battery Recycling Technology Co., Ltd. 431400 Wuhan China
1 shared publication - Fan Luo
Wuhan Power Battery Recycling Technology Co., Ltd., Wuhan 431400, China
1 shared publication - Jianqiang Kang
Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
1 shared publication - Yuping Zhang
GEM Co., Ltd., Shenzhen 518101, China
1 shared publication
Latest Publications
- Open Access
Rapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis
In order to solve the problems of long-time consumption and high energy consumption in existing capacity detection methods of retired Ni-MH batteries, a fast and reliable capacity prediction method for retired Ni-MH batteries by multi-parameter driven analysis was proposed in this paper. This method mainly obtains several parameters through short-time measurement and pulse rapid nondestructive testing. Then, Pearson correlation coefficient and KS-test were used to analyze the correlation between the two parameters and verify the same distribution. Finally, SVR was used to predict the battery discharge capacity. The results show that the volume expansion thickness difference Δd, AC internal resistance R, terminal voltage U of the battery, charge and discharge polarization internal resistance Rf1 and Rf2 and pulse charging power P2 of the battery are strongly negatively correlated with the discharge capacity, and these characteristic parameters can effectively and reliably reflect the internal structural characteristics of the battery. Additionally, the mean relative error of the established capacity model is 5.87%, and the lowest error is 1.32%. The prediction effect is good, which provides a certain reference value for the subsequent consistent sorting method.
Open AccessRapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis
In order to solve the problems of long-time consumption and high energy consumption in existing capacity detection methods of retired Ni-MH batteries, a fast and reliable capacity prediction method for retired Ni-MH batteries by multi-parameter driven analysis was proposed in this paper. This method mainly obtains several parameters through short-time measurement and pulse rapid nondestructive testing. Then, Pearson correlation coefficient and KS-test were used to analyze the correlation between the two parameters and verify the same distribution. Finally, SVR was used to predict the battery discharge capacity. The results show that the volume expansion thickness difference Δd, AC internal resistance R, terminal voltage U of the battery, charge and discharge polarization internal resistance Rf1 and Rf2 and pulse charging power P2 of the battery are strongly negatively correlated with the discharge capacity, and these characteristic parameters can effectively and reliably reflect the internal structural characteristics of the battery. Additionally, the mean relative error of the established capacity model is 5.87%, and the lowest error is 1.32%. The prediction effect is good, which provides a certain reference value for the subsequent consistent sorting method.