A5070409552- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Reliability and Maintenance Optimization
- Glass properties and applications
- Fault Detection and Control Systems
- Luminescence Properties of Advanced Materials
- Electric Vehicles and Infrastructure
- Optical properties and cooling technologies in crystalline materials
- Sensor Technology and Measurement Systems
- Advanced Battery Materials and Technologies
- Microwave Dielectric Ceramics Synthesis
- Industrial Automation and Control Systems
- Power Systems and Renewable Energy
- Embedded Systems and FPGA Design
- EEG and Brain-Computer Interfaces
- Pigment Synthesis and Properties
- Extraction and Separation Processes
Changchun University of Science and Technology
2023-2024
University of Huddersfield
2022-2024
The state of health (SOH) estimation lithium-ion batteries is essential to ensure the safety electric vehicles. Electrochemical impedance spectroscopy (EIS) measurement can provide valuable ageing information and pave way for battery SOH estimation. However, semicircle's overlapping in EIS during degradation introduces large uncertainty electrochemical process, causing difficulty identifying features. Therefore, this paper proposes a hybrid intelligent model extraction highly influential...
With the rapid development of clean energy technologies, lithium-ion batteries have emerged as dominant power source. It is great significance to monitor state charge (SOC) and health (SOH) accurately efficiently for ensuing high safety reliability. This paper proposes an active acoustic emission (AE) sensing technology demonstrates feasibility co-estimation SOC SOH battery. The proposed method aims achieve a fast monitoring by putting insights into variation material properties during...
With the dramatic increase in electric vehicles (EVs) globally, demand for lithium-ion batteries has grown dramatically, resulting many being retired future. Developing a rapid and robust capacity estimation method is challenging work to recognize battery ageing level on service provide regroup strategy of retied secondary use. There are still limitations current methods, such as direct internal resistance (DCIR) electrochemical impedance spectroscopy (EIS), terms efficiency robustness. To...
The accurate lithium-ion battery capacity estimation is vital for ensuring the safe and reliable operation of battery-powered systems. Existing data-driven methods heavily rely on fixed charging stages feature extractions, posing significant limitations in real-world applications. This paper proposes an adaptable approach utilising short-duration random voltages during constant-current stage leveraging convolutional neural networks (CNNs). Based user-friendly "Vstart−tend" strategy, two...
Lithium-ion battery capacity estimation is crucial to ensure the operational reliability and safety of electric vehicles. Electrochemical Impedance Spectroscopy (EIS) can provide rich physical degradation information battery, which makes EIS-based data-driven method a promising solution for accurate estimation. However, batteries tend present diverse patterns due operating conditions manufacturing, resulting in large errors practical applications. Therefore, this paper proposes transfer...
The state of health (SOH) estimation lithium-ion batteries is crucial for the operational reliability and safety electric vehicles. However, traditional data-driven methods face problems label shortage domain discrepancy caused by diverse battery types operating conditions. This paper proposes a label-free SOH method based on adversarial multi-domain adaptation technique relaxation voltage (RV). Firstly, raw RV integral are proposed to construct two-dimensional input sequence ensure high...
Lithium-ion battery state of health (SOH) estimation remains a significant challenge in management systems due to the sophisticated electrochemical processes within battery. As model-free method, data-driven-based method has shown great potential SOH estimation. However, existing data-driven approach requires large dataset and shows low model adaptability among different samples. To address issues, this paper proposes transfer learning (TL)-based technique coupled with multi-layer perceptron...
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With the rapid development of clean energy technologies, lithium-ion batteries have emerged as dominant power source. It is great significance to monitor state charge (SOC) and health (SOH) accurately efficiently for ensuing high safety reliability. This paper proposes an active acoustic emission (AE) sensing technology demonstrates feasibility co-estimation SOC SOH battery. The proposed method aims achieve a fast monitoring by putting insights into variation material properties during...