An online state of charge estimation for Lithium-ion and supercapacitor in hybrid electric drive vehicle

Abstract

Abstract Hybrid Energy Storage Systems (HESSs), which are mainly based on Lithium-ion ( L i − i o n ) batteries and supercapacitors (SCs), are extensively investigated for large-scale application such as autonomous trucks, autonomous mobile robots, delivery drones and more precisely Hybrid Electric Vehicle (HEV) applications. This hybridization combines the superior energy density characteristics from L i − i o n battery and the quick ability of SC for energy storage with a virtually unlimited number of charge and discharge cycles. The combination of L i − i o n battery with SC adds more HEV autonomy but increases the complexity of the power management system (PMS). Generally, a good estimation for the state of charge (SOC) of these two power sources creates an opportunity for optimizing the PMS and the safety of HEV. This paper presents a real time state of charge estimation for L i − i o n battery (SOCb) and for SC (SOCsc) using an Extended Kalman Filter (EKF). Whereas, a powerful E K F − S O C estimator needs a precise dynamic battery/SC model. So, an hybrid model that benefits from the advantages of direct measurement Open Circuit Voltage (OCV) method and a Recursive Least Square (RLS) with a forgetting factor is used in this work in order to obtain a globally optimal estimating performance. Nevertheless, this work try to simplify the hard task of L i − i o n battery/SC SOC estimation for any modeling needs, just with few preliminary experimental tests. Results show the efficiency of EKF using a R L S − O C V hybrid method for identifying L i − i o n battery/SC parameters. Finally, an implementation of this reliable hybrid method had been established in test HEV platform.