Abstract
To ensure the reliability and sustainability of the energy storage system, it is important to accurately estimate the state of charge of the battery management system. The Li-ion battery is established based on fractional-order model, and the model parameters are identified online using particle swarm optimization combined with the forgetting factor recursive least square method. On this basis, a novel fractional-order extended Kalman filter method for on-line joint state estimation and parameter identification is proposed. This method can update the parameter model of Li-ion battery in real-time, which not only improves the accuracy of the battery model but also improves the accuracy of SOC estimation. Finally, to verify the accuracy and superiority of the method, the integral order extended Kalman filter, fractional-order extended Kalman filter are compared with the proposed method under the BBDST test schedule. Experimental results show that the algorithm has the highest SOC estimation accuracy and the smallest estimation error (1.5 %.). The results indicate that the fractional-order model can better describe the dynamic characteristics of Li-ion battery, and the adaptive scheme can significantly suppress noise measurement errors and battery model errors. The algorithm realizes online parameter identification and can be used in engineering applications.
Highlights
In recent years, to improve the ecological environment and respond to the fossil energy utilization crisis, the development and utilization of renewable energy systems have become very important [1, 2]
The results show that fractional-order model (FOM) has a higher model accuracy than integer order model (IOM), which improves the accuracy of SOC estimation results
To improve the performance of the battery management system and enhance the reliability and sustainability of the energy storage system, a novel online joint state estimation and parameter identification method for Li-ion battery based on adaptive fractional-order extended Kalman filtering is proposed
Summary
To improve the ecological environment and respond to the fossil energy utilization crisis, the development and utilization of renewable energy systems have become very important [1, 2]. The fractional-order model (FOM) uses impedance elements such as constant phase element (CPE) and Warburg element to accurately describe the charge-transfer between solid electrode/electrolyte interface and double layer effect in the electrochemical process, and can better simulate the dynamic characteristics of the system to reduce modeling errors [25]. [31, 32] proposed a FOM containing 2RC networks, which improved the accuracy of the model, but ignored the engineering application, making online parameter identification difficult to achieve These studies have proved that for different types of batteries, FOM is superior to IOM in terms of voltage simulation and SOC estimation. To solve the above problems, a novel fractional-order extended Kalman filter method for on-line joint state estimation and parameter identification of the high power Li-ion batteries is proposed to improve the battery management system performance and enhance the reliability and sustainability of the storage system. The operating current Ik is the input of the system, and the discrete state space equation is obtained as shown in Eq (7)
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