Yuanyuan Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Yuanyuan Li has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Daniel-Ioan Stroe
Aalborg University
2 shared publications - Jinhao Meng
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2 shared publications - Remus Teodorescu
Aalborg University
2 shared publications - Xinrong Huang
School of Energy and Electrical Engineering, Changan University, Xian, China
2 shared publications - Anirudh Budnar Acharya
Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
1 shared publication - Kaibo Shi
School of Information Science and Engineering Chengdu University Chengdu China
1 shared publication
Latest Publications
- Open Access
State of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test
Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.
Open AccessState of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test
Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.
- Open Access
A Review of Pulsed Current Technique for Lithium-ion Batteries
Lithium-ion (Li-ion) batteries have been competitive in Electric Vehicles (EVs) due to their high energy density and long lifetime. However, there are still issues, which have to be solved, related to the fast-charging capability of EVs. The pulsed current charging technique is expected to improve the lifetime, charging speed, charging/discharging capacity, and the temperature rising of Li-ion batteries. However, the impact of the pulsed current parameters (i.e., frequency, duty cycle, and magnitude) on characteristics of Li-ion batteries has not been fully understood yet. This paper summarizes the existing pulsed current modes, which are positive Pulsed Current Mode (PPC) and its five extended modes, and Negative Pulsed Current (NPC) mode and its three extended modes. An overview of the impact of pulsed current techniques on the performance of Li-ion batteries is presented. Then the main impact factors of the PPC strategy and the NPC strategy are analyzed and discussed. The weight of these impact factors on lifetime, charging speed, charging/discharging capacity, and the temperature rising of batteries is presented, which provides guidance to design advanced charging/discharging strategies as well as to determine future research gaps.
Open AccessA Review of Pulsed Current Technique for Lithium-ion Batteries
Lithium-ion (Li-ion) batteries have been competitive in Electric Vehicles (EVs) due to their high energy density and long lifetime. However, there are still issues, which have to be solved, related to the fast-charging capability of EVs. The pulsed current charging technique is expected to improve the lifetime, charging speed, charging/discharging capacity, and the temperature rising of Li-ion batteries. However, the impact of the pulsed current parameters (i.e., frequency, duty cycle, and magnitude) on characteristics of Li-ion batteries has not been fully understood yet. This paper summarizes the existing pulsed current modes, which are positive Pulsed Current Mode (PPC) and its five extended modes, and Negative Pulsed Current (NPC) mode and its three extended modes. An overview of the impact of pulsed current techniques on the performance of Li-ion batteries is presented. Then the main impact factors of the PPC strategy and the NPC strategy are analyzed and discussed. The weight of these impact factors on lifetime, charging speed, charging/discharging capacity, and the temperature rising of batteries is presented, which provides guidance to design advanced charging/discharging strategies as well as to determine future research gaps.