Xu Jun, Wang Jing, Li Shiying, Cao Binggang
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2016 Aug 19;16(8):1328. doi: 10.3390/s16081328.
Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.
近年来,能量状态(SOE)已成为电动汽车电池管理系统最基本的参数之一。然而,当前信息在SOE估计中至关重要,通常利用电流传感器来获取最新的电流信息。然而,如果电流传感器发生故障,SOE估计可能会面临较大误差。因此,本文试图做出以下贡献:同时实现电流传感器故障检测和SOE估计方法。通过使用基于比例积分观测器(PIO)的方法,可以准确估计电流传感器故障。利用准确估计的电流传感器故障,可以消除和补偿由电流传感器故障引起的影响。结果,SOE估计结果受故障的影响很小。此外,建立了仿真和实验工作台来验证所提出的方法。结果表明,可以准确估计电流传感器故障。同时,也可以准确估计SOE,并且估计误差受故障的影响很小。即使电流传感器故障导致的大电流误差仍然存在,最大SOE估计误差也小于2%。