College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.
School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel). 2022 Dec 8;22(24):9637. doi: 10.3390/s22249637.
As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process.
由于单一特征参数无法全面评估电池的健康状况，因此针对车载电池的健康评估问题，提出了一种基于高斯混合模型和贝叶斯推断距离的多源信息融合方法。该方法对实际车辆运行中的缺失和异常数据进行预处理，提取决定电池性能的敏感特征参数。利用无故障状态数据建立正常状态高斯混合模型，同时构建贝叶斯推断距离作为定量评估电池性能状态的指标。为了解决测量数据中可能存在异常数据并将其引入评估结果的问题，制定了异常数据的判定规则。实际车辆运行数据的验证结果表明，所提方法可以准确评估车载电池的状态，降低电动汽车正常运行过程中的安全隐患。
