A5100367593- Advanced Battery Technologies Research
- Electric and Hybrid Vehicle Technologies
- Electric Vehicles and Infrastructure
- Transportation and Mobility Innovations
- Transportation Planning and Optimization
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Supercapacitor Materials and Fabrication
- Fault Detection and Control Systems
- Smart Parking Systems Research
- Autonomous Vehicle Technology and Safety
- Vehicle emissions and performance
- Advanced battery technologies research
- Advanced Sensor and Energy Harvesting Materials
- Additive Manufacturing and 3D Printing Technologies
- Traffic control and management
- Frequency Control in Power Systems
- Fuel Cells and Related Materials
- Urban Transport and Accessibility
- Embedded Systems and FPGA Design
- Interactive and Immersive Displays
- Reliability and Maintenance Optimization
- Real-time simulation and control systems
- Sharing Economy and Platforms
- Power Systems and Renewable Energy
Sichuan University of Science and Engineering
2015-2025
Xi'an Jiaotong University
2022-2025
Shanghai Institute of Technology
2024
Concordia University
2019-2023
Beijing Institute of Technology
2007-2022
North China Institute of Science and Technology
2022
Concordia University
2022
Southeast University
2022
China Electric Power Research Institute
2022
South China University of Technology
2019-2021
Accurate state of charge (SOC) estimation a battery pack is more meaningful than that cell in practical applications. The existing methods are difficult to provide an accurate SOC under wide range temperature due inconsistency. In this paper, method for series-parallel lithium-ion based on the newly constructed OCV-SOC-temperature relationship was proposed. proposed method, firstly cosine similarity used quantify inconsistency and representative cells determined by largest value. Secondly,...
Electrode material aging leads to a decrease in capacity and/or rise resistance of the whole cell and thus can dramatically affect performance lithium-ion batteries. Furthermore, phenomena are extremely complicated describe due coupling various factors. In this review, we give an interpretation capacity/power fading electrode-oriented mechanisms under cycling storage conditions for metallic oxide-based cathodes carbon-based anodes. For cathode batteries, mechanical stress strain resulting...
<div class="section abstract"><div class="htmlview paragraph">As global energy concerns and environmental challenges intensify, the automotive industry is rapidly transitioning toward more sustainable solutions, with new vehicles, particularly battery electric vehicles (BEVs), at forefront. BEVs depend on lithium-ion batteries due to their high efficiency, large storage capacity, ability support long-range driving. However, maintaining optimal performance, safety, longevity...
The state of charge (SOC) lithium-ion batteries (LIBs) is a pivotal metric within the battery management system (BMS) electric vehicles (EVs). An accurate SOC crucial to ensuring both safety and operational efficiency battery. unscented Kalman filter (UKF) classic commonly used method among various estimation algorithms. However, an transform (UT) utilized in algorithm struggles completely simulate probability density function actual data. Additionally, inaccuracies identification model...
An efficient energy management strategy (EMS) is crucial for the energy-saving and emission-reduction effects of electric vehicles. Research on deep reinforcement learning (DRL)-driven systems (EMSs) has made significant strides in global automotive industry. However, most scholars study only impact a single DRL algorithm EMS performance, ignoring potential improvement optimization objectives that different algorithms can offer under same benchmark. This paper focuses control hybrid storage...
Electric vehicle technologies present promising solutions for achieving energy conservation and emission reduction goals. However, efficiently distributing power across hybrid storage systems (HESSs) remains a major challenge in enhancing overall system performance. To address this, this paper proposes an management strategy (EMS) based on stepwise rules optimized by Particle Swarm Optimization (PSO). The approach begins applying multi-objective optimization method, utilizing the...
The accuracy of the model relies heavily on parameter identification. In order to accurately identify parameters batteries and ultracapacitors, three representative intelligent optimization algorithms: Grey Wolf Optimization Algorithm (GWO), Particle Swarm (PSO), Genetic (GA) are selected in this study battery ultracapacitor models, respectively. results show that algorithm demonstrates significant advantages improving overall prediction supercapacitor models. Specifically, reduces root mean...