A Battery Capacity Estimation Framework Combining Hybrid Deep Neural Network and Regional Capacity Calculation Based on Real-World Operating Data
Qiushi Wang, Zhenpo Wang, Lei Zhang, Peng Liu, Litao Zhou
Abstract
Efficient battery capacity estimation is of utmost importance for safe and reliable operations of electric vehicles (EVs). This article proposes a battery capacity estimation framework based on real-world EV operating data collected from forty electric buses of the same model operating in two cities. First, a reference capacity calculation method is presented by combining the Coulomb counting method with the incremental capacity analysis method. Then, the impacts of temperature, current, and state-of-charge on battery degradation are quantitatively analyzed. Using the historical probability distributions as battery health features, a hybrid deep neural network model that combines a convolutional neural network with a fully connected neural network is proposed for battery capacity estimation. The validation results show that the proposed model outperforms the state-of-the-art methods and reaches a mean absolute percentage error of 2.79%, while maintaining low computational cost.