State of energy estimation in electric propulsion systems with lithium-sulfur batteries

Lithium-Sulfur (Li-S) batteries are an emerging and appealing electrical energy storage technology. The literature on the Stateof- charge (SoC) estimation of Li-S is readily available. In real-world, battery operated vehicles and equipment need to monitor the electrical energy. This paper focuses on State-of-Eneergy (SoE) estimation of Li-S battery based electric propulsion system. This paper bridges literature gap of the SoE estimation of Li-S battery. While comparing mathematically, the definition of the SoC and SoE batteries are different. Reviewing the SoC estimation, this paper compares the SoC and SoE estimation for same data set. The challenges in Li-S SoC and SoE estimation include battery modelling and time-varying parameters and nonlinear voltage measurement, which has deeply skewed high-plateau and flatted low-plateau characteristics. Modelling Li-S battery as a Thevenin’s equivalent circuit network (ECN), the battery parameters are estimated using Predict Error Minimization (PEM) approach. For estimate SoC and SoE, the extended Kalman filter (EKF) is used. Since the parameters are high sensitive to battery current, the estimators use parameters obtained by polynomial fitting model. A simple switching logic based on SoCmeasurement voltage is used to join the high- and low-plateau. The degree of observability analysis is used to investigate the performance of SoE estimation by the EKF. Using experiment test data, simulation results demonstrate the performance of both SoC and SoE estimators. Results show that the SoE estimation is as close to the SoC estimation