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Powertrain Components Aging Model Selection for Energy Efficient Vehicles: Selection Strategy and Challenges The Ohio State University
- Format:
- Book
- Conference/Event
- Author/Creator:
- Rownak, Md Ragib, author.
- Conference Name:
- WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2025
- Summary:
- The long-term performance of powertrain components in energy-efficient vehicles, particularly in Class 8 heavy-duty applications, is crucial for sustaining energy efficiency. However, these components degrade over time, impacting performance and highlighting the need for appropriate aging models to estimate the impact of aging. This study aims to identify and select appropriate aging models for two critical powertrain components: battery and electric machine. Through a comprehensive literature review, the primary aging processes, key influencing factors, and available aging models for these components are identified. A selection matrix is established, considering the model complexity, the model accuracy, and the volume of data required while maintaining the desired precision for the powertrain component models. Based on the selection matrix, an appropriate battery aging model is chosen for the vehicle's battery. This model was selected for its ability to effectively capture the aging process and estimate capacity degradation with reasonable accuracy while remaining computationally efficient. For the electric machine, a thermal-based aging model is chosen to account for dynamic operations and temperature effects, which are crucial for understanding the aging behavior of the electric machine. The selected battery aging model is calibrated and validated using experimental cycling and calendar aging datasets. The performance metric, relative standard error of prediction (RSEP), is used to measure the efficacy of this model compared to the experimental data. The RSEP value obtained from the cycling aging dataset is 14.05%, while the value for the calendar aging dataset is 31.34%. The electric machine aging model is implemented using the temperature profile obtained from an experimental dataset
- Notes:
- Vendor supplied data
- Publisher Number:
- 2025-01-8541
- Access Restriction:
- Restricted for use by site license
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