Big data-driven decoupling framework enabling quantitative assessments of electric vehicle performance degradation

Abstract

Electric vehicle (EV) performance in terms of the available driving range per charge and the energy consumption rate continuously degrades during its service life. Quantitative assessments of EV performance degradation play an important role in EV residual value analysis, battery management, and battery recycling. However, EV performance degradation is highly sensitive to both ambient temperature and battery aging states; coupled factors make its quantification challenging. Here, a novel big data-driven decoupling framework is proposed to investigate the partial relationships between EV performance degradation and each individual variable (e.g., temperature and total driving distances). The core innovation involves the decoupling process that can enable real-world and large-scale degradation assessments. The basic functionality of the decoupling is achieved by an iterative learning framework where different machine learning-based models can communicate with each other. It achieves the advantages of unsupervised training and high performance; the mean absolute error can be controlled less than 0.1 in the model validation of EV ranges. Its effectiveness is verified using different real-world EV datasets. By utilizing the framework, the changes in the range and energy consumption of EVs across 10 urban areas in China are assessed. The results show that the range and energy consumption rate of EVs are more greatly influenced by ambient temperature than by battery aging. Less consideration of variable decoupling may yield misleading results in EV performance analysis. Our proposed framework opens avenues for quantifying EV performance degradation via real-world EV data, which is critical to onboard and cloud-based EV research.