Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications
Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications
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DOI：
10.1109/tie.2019.2941151
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发表时间：
2020-09
影响因子：
7.7
通讯作者：
F. Naseri;E. Farjah;T. Ghanbari;Z. Kazemi;E. Schaltz;J. Schanen
中科院分区：
文献类型：
--
作者：
F. Naseri;E. Farjah;T. Ghanbari;Z. Kazemi;E. Schaltz;J. Schanen
在电动汽车(EV)应用中，对超级电容器健康状态(SOH)和能量状态(SOE)的在线准确估计对于实现高效的能量管理和实时状态监测至关重要。本文首次将无迹卡尔曼滤波(UKF)用于超级电容器的在线参数和状态估计。在该方法中，建立了超级电容器的非线性状态空间模型，该模型考虑了电容变化和自放电效应。所考虑的模型的可观测性使用图解方法进行了分析确认。然后基于设计的UKF的超级电容器在线辨识模型估计SOH和SOE。由于避免了滤波过程中的线性化误差，该方法比卡尔曼滤波(KF)和扩展KF算法具有更好的估计精度。通过在实验室试验台上的多个实验，验证了该方法的有效性。该方法的总体估计误差小于0.5%。此外，还进行了半实物实验，保证了该方法的实时可行性。
Online accurate estimation of supercapacitor state-of-health (SoH) and state-of-energy (SoE) is essential to achieve efficient energy management and real-time condition monitoring in electric vehicle (EV) applications. In this article, for the first time, unscented Kalman filter (UKF) is used for online parameter and state estimation of the supercapacitor. In the proposed method, a nonlinear state-space model of the supercapacitor is developed, which takes the capacitance variation and self-discharge effects into account. The observability of the considered model is analytically confirmed using a graphical approach. The SoH and SoE are then estimated based on the supercapacitor online identified model with the designed UKF. The proposed method provides better estimation accuracy over Kalman filter (KF) and extended KF algorithms since the linearization errors during the filtering process are avoided. The effectiveness of the proposed approach is demonstrated through several experiments on a laboratory testbed. An overall estimation error below 0.5% is achieved with the proposed method. In addition, hardware-in-the-loop experiments are conducted and real-time feasibility of the proposed method is guaranteed.