Real-Time State of Charge Prediction Model for Electric Two-Wheeler
- Title
- Real-Time State of Charge Prediction Model for Electric Two-Wheeler
- Creator
- Jose P.; Alex A.J.; Iyyanam A.G.; Varghese A.; Athiyalil A.A.; Raj A.
- Description
- To maximise the efficiency and performance of electric vehicles, traction battery State of Charge (SoC) must be accurately predicted. In this work, a prediction model for traction battery State of Charge estimation is developed in real time. The traction battery powers an electric two-wheeler through a predetermined drive cycle. To produce accurate state-of-charge forecasts, the predictive model considers several input characteristics, such as temperature, voltage, and current. This research is crucial for fostering effective energy management and improving the safety and dependability of electric two-wheelers. Open-circuit voltage (OCV) and coulomb counting are two commonly utilised techniques used to evaluate the state of charge prediction model. These techniques act as standards for assessing the developed Neural Network model prediction, the model's dependability and accuracy. The model's usefulness and its potential to outperform the current State of Charge estimating techniques are demonstrated by comparing the state-of-charge predictions from the model with these standard methods. 2024 IEEE.
- Source
- 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- E-2W; Neural Network; OCV; SoC
- Coverage
- Jose P., Christ University, Department of Eee, Bengaluru, India; Alex A.J., Christ University, Department of Eee, Bengaluru, India; Iyyanam A.G., Christ University, Department of Eee, Bengaluru, India; Varghese A., Christ University, Department of Eee, Bengaluru, India; Athiyalil A.A., Christ University, Department of Eee, Bengaluru, India; Raj A., Christ University, Department of Eee, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038399-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jose P.; Alex A.J.; Iyyanam A.G.; Varghese A.; Athiyalil A.A.; Raj A., “Real-Time State of Charge Prediction Model for Electric Two-Wheeler,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19083.