动力锂电池组充放电电流检测与滤波方法研究

Detection of Charge and Discharge Current in Power lithium battery pack and Study on Filtering Method

交通领域的快速发展和能源消耗加剧了全球的能源危机和环境污染,低消耗、绿色环保的无人飞机产业、电动汽车产业已成为全球交通能源转型的发展方向。锂离子电池具有比能量大、循环寿命长和无污染等优点,必将成为未来电动汽车、无人飞机动力系统的首选。而锂电池组的充放电电流管理、实时数据采样就显得尤为重要。本文采用电阻采样方案对动力锂电池组进行充放电电流检测和使用微处理器STM32对数据进行处理,并使用卡尔曼滤波算法对数据进行分析并在MATLAB进行仿真及实验研究,保证采集的电流数据误差在1.00%。锂电池的充放电电流不仅是一个表征锂电池组工作状态的重要外部参数,也是荷电状态(SOC)在线估算所需要的重要参数,高精度的数据为其提供重要基础。

The rapid development of transportation and energy consumption have exacerbated the global energy crisis and environmental pollution, low consumption and green unmanned aircraft industry. The electric vehicle industry has become the development direction of global transportation energy transformation. Lithium-ion batteries with energy, long cycle life and pollution-free advantages, will become the future of electric vehicles, unmanned aircraft power system of choice. The lithium battery charge and discharge current management, real-time data sampling is particularly important. The resistance sampling scheme was used to detect the charging and discharging current of the power Li-ion battery pack and to process the data using the microprocessor STM32. The data were analyzed by Kalman filter algorithm and simulated and experimentally studied in MATLAB. Ensure that the current data collected error of 1.00%. The charge and discharge current of lithium battery is not only an important external parameter to characterize the working state of lithium battery, but also an important parameter needed for on-line estimation of state of charge （SOC）. High-precision data provides an important fotmdation for that.