Abstract
This paper focuses on fault detection based on interclass correlation coefficient (ICC) method for guaranteeing safe and reliable of electric vehicles (EVs). The proposed method calculates ICC values by capturing the off-trend voltage drop and the voltages are extracted from Service and Management Center of electric vehicles. The ICC value is employed to analyze battery fault by ICC principle. The ICC value not only has advanced fault resolution by amplifying the voltage difference, but also can prolong the fault memory by setting moving windows. Moreover, a loop joints the first and last voltages is designed to locate faults in battery pack. In addition, simulation and experiment are employed to validate and analyze the voltage faults. Based on the simulation verification, the appropriate size of moving windows is set to ensuring sensitivity of fault detection method. The experiment results indicate the method can appropriately detect fault signals for EVs.
| Original language | English |
|---|---|
| Pages (from-to) | 402-411 |
| Number of pages | 10 |
| Journal | Measurement: Journal of the International Measurement Confederation |
| Volume | 116 |
| DOIs | |
| Publication status | Published - Feb 2018 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Electric vehicles
- Fault diagnosis
- Interclass correlation coefficient
- Lithium-ion batteries
- Service and management center for electric
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