A5101435833- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Electric Vehicles and Infrastructure
- Nonlinear Dynamics and Pattern Formation
- Neural dynamics and brain function
- Advanced battery technologies research
- Fuel Cells and Related Materials
- Non-Destructive Testing Techniques
- stochastic dynamics and bifurcation
- Magnetic Bearings and Levitation Dynamics
- Industrial Automation and Control Systems
- Fault Detection and Control Systems
- Multi-Criteria Decision Making
- Machine Learning in Materials Science
- Neural Networks Stability and Synchronization
- Hydraulic and Pneumatic Systems
- Magnetic Field Sensors Techniques
- Robotic Mechanisms and Dynamics
- Optimization and Mathematical Programming
- Time Series Analysis and Forecasting
- Reliability and Maintenance Optimization
- Network Time Synchronization Technologies
- Optical Systems and Laser Technology
- Teleoperation and Haptic Systems
Qingdao University
2025
Beihang University
2013-2024
North China University of Technology
2019-2023
Chongqing University of Posts and Telecommunications
2010-2022
Air Force Engineering University
2020-2022
Aviation Industry Corporation of China (China)
2020-2022
Taiyuan Heavy Industry (China)
2022
Wuhan University
2014-2016
McGovern Institute for Brain Research
2014
Beijing Normal University
2011-2013
An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control (BMS) improved performance. In this paper, general framework utilizing an end-edge-cloud architecture cloud-based BMS proposed, composition function each link described....
Abstract The safety, durability and power density of lithium‐ion batteries (LIBs) are currently inadequate to satisfy the continuously growing demand emerging battery markets. Rapid progress has been made from material engineering system design, combining experimental results simulations enhance LIB performance. Limited by spatial temporal resolution, state‐of‐the‐art advanced characterization techniques fail fully reveal complex multi‐scale degradation mechanism in LIBs. Strengthening...
Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, reliability, long service life. It is essential estimate state of health (SOH) ensure safety, optimize better efficiency enhance battery life-cycle management. This paper presents a comprehensive review SOH estimation methods, including experimental approaches, model-based machine learning algorithms. A critical in-depth analysis...
Abstract The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application lithium‐ion batteries (LIBs). This paper reviews multiscale modeling techniques their applications battery analysis, including atomic scale computational chemistry, particle reaction simulations, electrode structural models, macroscale electrochemical data‐driven models at system level. Multiscale offers a profound insight into material behavior aging process...
The accurate estimation of the state charge (SOC) plays a crucial role in ensuring range electric vehicles (EVs) and reliability EVs battery. However, due to dynamic working conditions implementation limitation onboard BMS computational force, it is challenging achieve reliable, high-accuracy real-time online battery SOC under diverse scenarios. Therefore, this study proposes an end-cloud collaboration approach lithium-ion batteries estimate SOC. On cloud-side, deep learning model...
Safety issues are one of the main limitations for further application lithium-ion batteries, and battery degradation is an important causative factor. However, current state-of-health (SOH) estimation methods mostly developed a single feature operating condition as well material system, which consequently makes it difficult to guarantee robustness generalization. This paper proposes data-driven multi-feature collaborative SOH method based on equal voltage interval discharge time, incremental...
The multi-scale modeling of lithium-ion battery (LIB) is difficult and necessary due to its complexity. However, it capture the aging behavior batteries, coupling mechanism between multiple scales still incomplete. In this paper, a simplified electrochemical model (SEM) kinetic Monte Carlo (KMC)-based solid electrolyte interphase (SEI) film growth are used study characteristics LIBs. single-particle SEM (SP-SEM) described for macro scale, simple self-consistent multi-particle (MP-SEM)...
Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory consisting excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated seemingly random patterns can be viewed as well-organized target waves propagating from along the shortest paths, loops passing through both their driver play role oscillation sources. Analyzing simple are able to understand predict...
With the aggravation of environmental pollution and energy crisis, lithium-ion batteries are widely regarded as promising. However, current distribution in parallel battery pack branches is highly heterogeneous. Charging strategies based on models can be adopted to prevent side reactions that may lead severe degradation or even thermal runaway under various ambient temperatures. In this study, a model for single cell established by coupling particle with electrolyte, model, model. Besides,...