A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory

In recent years, hybrid energy storage systems have been widely used in electric vehicle and smart grid applications. Real-time and robust modeling and state estimation are essential to the reliable and safe operation of the hybrid energy storage system. Although there exists a considerable mass of research on the modeling and state estimation of the lithium-ion batteries, a survey focusing on the remaining discharge time prognostic for the hybrid power source system has not been conducted. To fill this gap, this paper handles the problem of fractional-order modeling and the remaining discharge time prognostic of the lithium-ion battery and ultra-capacitor hybrid energy storage system. First, the fractional-order models for the lithium-ion batteries and ultra-capacitors are presented, where the particle swarm optimization Algorithm with the Chaos theory is employed for parameter identification in the time domain. Second, a Markov load trajectory prediction is proposed for enhancing the reliability and robustness of the remaining discharge time prognostic. Third, the prognostic framework of the hybrid energy storage system is presented based on the Bayesian method. The results with urban dynamometer driving schedule are analyzed and discussed, which indicate that the proposed method has high accuracy and robustness.