基于多模型耦合的电动汽车三电系统安全性估计方法

The safety of power battery, drive motor and electronic control system is essential for the normal operation of electric vehicles and the safety of occupant's life and property. A novel safety estimation method for electric system in electric vehicles is proposed based on multiple model coupling. The method only needs sparse data collected by onboard sensors as input and can detect the fault vehicles with the same specification. Firstly, a safety estimation scheme of electric system is proposed, which is constructed by multiple layers “from top to bottom”. Then, a multi-model coupling method is proposed, consisting of gaussian mixture, entropy weight calculation and safety score computation. Gaussian mixture can obtain the distribution of safety indicators in safety estimation scheme and output the probability density. This can avoid the error caused by the subjective interval division of entropy weight; The proposed entropy weight calculation can determine the weight of each indicator based on probability density, and calculate the total safety indicator of each vehicle/system according to the safety estimation scheme. This can avoid the subjective determination of the importance of each indicator; The safety scores of each vehicle/system are then computed based on statistics and data normalization. Finally, the method is verified by the data of ten real-world electric vehicles, including vehicle safety estimation, electric system safety estimation and robustness in different seasons. The results show that the accuracies of the proposed method for normal and fault vehicle/electric system classification are 40%/26.7% higher than analytic hierarchy process, and it will not misjudge normal vehicles in different seasons.