School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
ISA Trans. 2019 Nov;94:326-337. doi: 10.1016/j.isatra.2019.04.008. Epub 2019 Apr 20.
The accurate state of charge (SOC) estimation can protect the battery from overcharging and over-discharging, and it is useful to make an effective dispatching strategy. The extended Kalman filter (EKF) method is used to estimate SOC widely. But it does not consider the SOC constraints. Moreover, the convergence is influenced by the uncertain initial SOC, which may lead to false alarm, unwanted operation of protection, error dispatching and poor robustness of the system. This paper presents an improved extended Kalman filter (IEKF) method to estimate SOC for vanadium redox battery (VRB) by introducing a gain factor. It can be adjusted automatically according to the output error and SOC boundary. To implement IEKF estimator, a VRB state space model is established and its parameters are identified by recursive least square (RLS) method. Then a VRB of 5kW/30kWh experimental platform is built. Finally, the IEKF method is validated and compared with EKF against unknown initial value through the experiments. The results have shown that IEKF method is superior to EKF in terms of accuracy, convergence speed and robustness. And the estimated SOC remains bounded by using IEKF method. It is more suitable for SOC estimation than EKF algorithm in the industrial applications.
准确的荷电状态 (SOC) 估计可以保护电池免受过充和过放的影响，并且有助于制定有效的调度策略。扩展卡尔曼滤波 (EKF) 方法被广泛用于估计 SOC。但它没有考虑 SOC 约束。此外，SOC 的初始值不确定会影响收敛，可能导致误报、保护装置误动作、错误调度以及系统鲁棒性差。本文提出了一种改进的扩展卡尔曼滤波 (IEKF) 方法，通过引入增益因子来估计 VRB 的 SOC。它可以根据输出误差和 SOC 边界自动调整。为了实现 IEKF 估计器，建立了 VRB 状态空间模型，并通过递归最小二乘法 (RLS) 对其参数进行了识别。然后建立了一个 5kW/30kWh 的 VRB 实验平台。最后，通过实验验证了 IEKF 方法，并与 EKF 方法进行了比较，实验中 SOC 的初始值未知。结果表明，IEKF 方法在准确性、收敛速度和鲁棒性方面均优于 EKF 方法。并且使用 IEKF 方法可以使估计的 SOC 保持有界。在工业应用中，IEKF 方法比 EKF 算法更适合 SOC 估计。
