Battery aging estimation with deep learning

Battery Management System (BMS) is a critical component in EV (Electric Vehicle) powertrains. The precise knowledge of the battery's state of health and capacity impacts the estimation and control strategies of many other EV components. Current battery aging models are physics-based and complex, with limited capability to run in real-time. In this paper, we apply deep learning techniques to design an estimator of battery capacity using a combination of virtual and real battery data, and which can be run in real-time on the EV ECU. The estimator is implemented in an Amesim model of the EV powertrain and experimental results of its performance with standard drive cycles are demonstrated.