A5029547618- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Electric Vehicles and Infrastructure
- Fault Detection and Control Systems
- Advanced Battery Materials and Technologies
- Reliability and Maintenance Optimization
- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Electric and Hybrid Vehicle Technologies
- Quantum Chromodynamics and Particle Interactions
- Advanced Algorithms and Applications
- Advanced battery technologies research
- IoT-based Smart Home Systems
- Advanced Computational Techniques and Applications
- Remote Sensing and Land Use
- Optimization and Search Problems
- Machine Learning and ELM
- Algorithms and Data Compression
- Fuel Cells and Related Materials
- Microgrid Control and Optimization
- Optimization and Packing Problems
- Topic Modeling
- Advanced Text Analysis Techniques
- Nuclear reactor physics and engineering
- Biometric Identification and Security
Hefei University of Technology
2018-2025
University of Science and Technology of China
2006-2025
Chinese Academy of Sciences
2025
Purple Mountain Observatory
2025
Anhui University of Technology
2024
Chengdu University of Traditional Chinese Medicine
2024
Southwest Jiaotong University
2022
JAC Motors (China)
2020-2021
Tsinghua University
2021
China Electric Power Research Institute
2019
The state of health (SOH) lithium-ion batteries (LIBs) is a critical parameter the battery management system. Because complex internal electrochemical properties LIBs and uncertain external working environment, it difficult to achieve an accurate SOH determination. In this paper, we have proposed novel estimation method by using prior knowledge-based neural network (PKNN) Markov chain for single LIB. First, extract multiple features capture aging process. Due its effective fitting ability...
Accurately monitoring battery state of charge (SOC) is essential for system safety. However, single and open-loop combination algorithms are mainly used SOC estimation currently, which may have the problems low accuracy poor reliability. Here, a closed-loop algorithm with variance-compensation extended Kalman filter (VCEKF) back-propagation (BP) neural network developed estimation. First, second-order resistance–capacitance model established, model's parameters identified by forgetting...
State-of-health (SOH) of lithium-ion batteries plays a vital role in the safe and reliable operation electric vehicles. However, most existing SOH estimation methods still require large number battery aging data while established model usually lacks generalization. Here, we build data-driven transfer learning to obtain more generality on estimation. First, potential health features are extracted from charging then pruned via importance function. Second, support vector regression (SVR) is...
The widespread use of lithium-ion batteries in electric vehicles has attracted attention both academia and industry. Among them, batteries' prognosis health management are important research problems that need to be resolved urgently. This article proposes a novel computationally efficient data-driven state-of-health (SOH) estimation approach based on an optimized feature selection method. difficulty acquisition is defined voltage data distribution from more than 11 000 charging processes....