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AI-based EV Range Prediction with Personalization in the Vast Vehicle Data Hyundai Motor Company
- Format:
- Book
- Conference/Event
- Author/Creator:
- Joo, Kihyung, author.
- Conference Name:
- WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2024
- Summary:
- It is an important factor in electric vehicles to show customers how much they can drive with the energy of the remaining battery. If the remaining mileage is not accurate, electric vehicle drivers will have no choice but have to feel anxious about the mileage. Additionally, the potential customers have range anxiety when they consider Electric Vehicles. If the remaining mileage to drive is wrong, drivers may not be able to get to the charging station and may not be able to drive because the battery runs out. It is important to show the remaining available driving range exactly for drivers. The previous study proposed an advanced model by predicting the remaining mileage based on actual driving data and based on reflecting the pattern of customers who drive regularly. The Bayesian linear regression model was right model in previous study. In addition, in order to improve performance, the driver's regular driving pattern is recognized in advance before driving and it is reflected in the remaining driving mileage model with Bayesian regression. It could be seen that the performance of the model in previous study was improved 10% better compared to the remaining driving mileage existed in vehicle function. The purpose of this study confirms the robust performance with vast data of the more vehicles in long mileage. The big data are from that almost vehicles took drive for 10k mile range. The vehicles had various experience in location, driving time rage, mileages, driving pattern, charge pattern, and so on. Moreover, the personalized model gets improved in big data to get higher accuracy. This study proposes personalized algorithm enable to embed in vehicles
- Notes:
- Vendor supplied data
- Publisher Number:
- 2024-01-2868
- Access Restriction:
- Restricted for use by site license
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