Abstract
The lithium ion battery pack, which is filled with cells, is an important part in electric vehicles (EVs), also the main fault source. The inconsistent cells or the design and assembly fail of the pack could affect its performance and life or even endanger vehicles security in extreme situation, which makes the early fault diagnosis is essential. For further analysis, we introduce an equivalent circuit model (ECM) to identify the cell characteristics parameters, which supports the fault diagnosis by simulating the fault battery performance in dynamic cycle. According the battery working mechanism and the practical experience, via collecting data and preprocessing the typical data, a diagnostic method and model based on fuzzy neural network is proposed to discover the battery pack fault related to irreversible or reversible capacity loss.
| Original language | English |
|---|---|
| Pages (from-to) | 2066-2070 |
| Number of pages | 5 |
| Journal | Energy Procedia |
| Volume | 61 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 6th International Conference on Applied Energy, ICAE 2014 - Taipei, Taiwan, Province of China Duration: 30 May 2014 → 2 Jun 2014 |
Keywords
- Capacity fade
- Electric vehicle
- Fault diagnosis
- Fuzzy neural network
- Lithium ion battery
