Abstract
This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles.
| Original language | English |
|---|---|
| Pages (from-to) | 1681-1692 |
| Number of pages | 12 |
| Journal | Energy Conversion and Management |
| Volume | 45 |
| Issue number | 11-12 |
| DOIs | |
| Publication status | Published - Jul 2004 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Adaptive neuro-fuzzy inference system
- Battery residual capacity
- Electric vehicles
- Lithium-ion battery
- State of available capacity
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Nuclear Energy and Engineering
- Fuel Technology
- Energy Engineering and Power Technology
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