For the driving safety of electric vehicle (EV), intelligent diagnosis based on artificial hydrocarbon networks (AHNs) is proposed to detect mechanical faults of in-wheel motor (IWM) which is a promising force pattern of EV. AHNs, a novel mathematical model of supervised learning algorithm, can encapsulate or inherit or mix any information, then are adapted to deal with serious external interference and the variable operating conditions. Based on the basic AHNs, complex error function is proposed to optimize more information of classification targets, and distance error ratio is defined to evaluate the performance. Then, the improved AHNs is employed to build two intelligent diagnosis systems namely one-stop diagnosis and sequential diagnosis, which select the same and different symptom parameters as the object of a follow-on process, respectively. The effectiveness of the proposed methods is validated by two case studies of Case Western Reserve University dataset and mechanical faults data from IWM’s test bench.

为了电动汽车（EV）的行驶安全，提出了基于人工碳氢化合物网络（AHNs）的智能诊断来检测轮毂电机（IWM）的机械故障，这是一种很有前途的电动汽车受力模式。AHNs是一种新颖的监督学习算法数学模型，可以封装、继承或混合任何信息，从而适应严重的外部干扰和多变的运行条件。在基本的AHNs的基础上，提出了复杂的误差函数来优化分类目标的更多信息，并定义距离误差率来评估性能。然后，利用改进的AHNs构建了一站式诊断和顺序诊断两个智能诊断系统，分别选择相同和不同的症状参数作为后续过程的对象。