Event-Triggered Robust State of Charge Estimation for Lithium-Ion Battery With Cloud-Integrated Battery Management System

The cloud-integrated battery management system (CIBMS) of electric vehicles (EVs) provides an excellent solution for reducing the cost, enabling refined battery management, and implementing advanced algorithms. However, limited network communication bandwidth, non-Gaussian noises, and incomplete measurement information pose significant challenges for CIBMS in accurately estimating the battery states. To overcome these obstacles, this article proposes an event-triggered (ET) robust state of charge (SOC) estimation strategy for CIBMS. First, an ET communication mechanism is designed for CIBMS to ensure that data are transmitted to the cloud on demand. Then, a second-order fractional-order model is introduced, and the discrete-time form of the dynamic equation is derived. Subsequently, the improved fractional-order extended Kalman filter (IFOEKF) is proposed, which reduces the linearization error of measurement equation in FOEKF. Furthermore, for non-Gaussian noises, the robust IFOEKF (RIFOEKF) is developed based on Masreliez–Martin method and adaptive algorithm. Finally, to achieve accurate SOC estimation under incomplete measurement information, the compensation protocol is established and the corresponding ET-RIFOEKF is devised. The proposed method is validated under different EV driving conditions. The experimental results show that the proposed method not only effectively reduces the frequency of transmitting measurement data to the cloud but also has high accuracy and robustness for SOC estimation.