A5051211861- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Electric Vehicles and Infrastructure
- Fault Detection and Control Systems
- Fuel Cells and Related Materials
- Supercapacitor Materials and Fabrication
- Advanced Control Systems Design
- Reliability and Maintenance Optimization
- Electric and Hybrid Vehicle Technologies
- Advanced battery technologies research
- Animal Nutrition and Physiology
- Gas Sensing Nanomaterials and Sensors
- Frequency Control in Power Systems
- Aquaculture disease management and microbiota
- Energy Harvesting in Wireless Networks
- Magnetic Field Sensors Techniques
- IoT-based Smart Home Systems
- Antimicrobial Peptides and Activities
- Conducting polymers and applications
- Wireless Power Transfer Systems
- Advanced DC-DC Converters
- Ginger and Zingiberaceae research
- Magnetic properties of thin films
- Characterization and Applications of Magnetic Nanoparticles
Hong Kong Polytechnic University
2023-2025
Shandong University
2025
Beijing Institute of Technology
2016-2023
Foshan University
2023
Swinburne University of Technology
2019-2021
Battery models are the cornerstone to battery state of charge (SOC) estimation and management systems in electric vehicles. This paper proposes a novel fractional-order model for battery, which considers both Butler-Volmer equation fractional calculus constant phase element. The structure characteristics proposed then analyzed, identification method, combines least squares nonlinear optimization algorithm, is proposed. method proven be efficient accurate. Based on model, unscented Kalman...
State-of-health (SOH) estimation is necessary for lithium ion batteries due to ineluctable battery ageing. Existing SOH methods mainly focus on voltage characteristics without considering temperature variation in the process of health degradation. In this article, we propose a novel method based surface temperature. The differential curves during constant charging are analyzed and found be strongly related SOH. Part range adopted establish relationship with using support vector regression....
State of health (SOH) estimation lithium-ion batteries is a key but challengeable technique for the application electric vehicles. Due to ambiguous aging mechanisms and sensitivity applied conditions batteries, recognition SOH monitoring battery might be difficult. A novel mechanism identification method presented in this paper. First, considering dispersion effect, fractional-order model constructed, parameter approach proposed, comparison between integer-order has been done from prospect...
State of health is a critical state which evaluates the degradation level batteries. However, it cannot be measured directly but requires estimation. While accurate estimation has progressed markedly, time- and resource-consuming experiments to generate target battery labels hinder development methods. In this article, we design deep-learning framework enable in absence labels. This integrates swarm deep neural networks equipped with domain adaptation produce We employ 65 commercial...
The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction remaining useful life (RUL). However, this task is challenging due to diverse ageing mechanisms, various operating conditions, limited measured signals. Although data-driven methods are perceived as a promising solution, they ignore intrinsic battery physics, leading compromised accuracy, low efficiency, interpretability. In response, study integrates domain knowledge into deep learning enhance RUL...
Abstract Lithium-ion batteries have always been a focus of research on new energy vehicles, however, their internal reactions are complex, and problems such as battery aging safety not fully understood. In view the preliminary application digital twin in complex systems aerospace, we will opportunity to use solve bottleneck current research. Firstly, this paper arranges development history, basic concepts key technologies twin, summarizes methods challenges modeling, state estimation,...
Lithium-ion batteries (LIBs) have emerged as the preferred energy storage systems for various types of electric transports, including vehicles, boats, trains, and airplanes. The management LIBs in transports all-climate long-life operation requires accurate estimation state charge (SOC) capacity real-time. This study proposes a multi-stage model fusion algorithm to co-estimate SOC capacity. Firstly, based on assumption normal distribution, mean variance residual error from at different...