Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model
Bin Ma(Jilin University), Xinhua Liu(Beihang University), Shichun Yang(Beihang University), Xianbin Yang(Jilin University), Wentao Wang(Beihang University), Lisheng Zhang(Beihang University), Siyan Chen(Jilin University), Haicheng Xie(Jilin University), Huizhi Wang(Imperial College London), Hanqing Yu(Beihang University)
Cited by 107
Related Papers
Designing the next generation of proton-exchange membrane fuel cells
|Nature|2021|2.7k
A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
|Energies|2023|92
Identifying nitrate sources and transformation in groundwater in a large subtropical basin under a framework of groundwater flow systems
|Journal of Hydrology|2022|62
Electric vehicle lifecycle carbon emission reduction: A review
|Carbon Neutralization|2023|62
Vehicle routing in urban areas based on the Oil Consumption Weight ‐Dijkstra algorithm
|IET Intelligent Transport Systems|2016|61