Powertrain components aging model selection for energy efficient vehicles: selection strategy and challenges.

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, leading to reduced 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 model complexity, accuracy, and the volume of data required to maintain the desired precision for the powertrain component models. Based on the selection matrix, an appropriate 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 cyclic 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 cyclic aging dataset is 21%, while the value for the calendar aging dataset is 31%. The electric machine aging model is calibrated using a temperature profile obtained from the experimental dataset.