随着电动汽车的普及,直流充电桩的计量准确性成为保障用户权益和充电服务公平性的关键。本文依据JJG 1149—2022《电动汽车非车载充电机（试行）》,针对直流充电桩计量误差展开研究,分析纹波、温度、硬件特性等多因素对计量精度的影响,提出基于信号分解与神经网络的误差分析方法,并通过试验验证方法的有效性。研究结果为直流充电桩的计量性能优化和检定规程实施提供技术支撑。

With the popularization of electric vehicles, the metering accuracy of DC charging piles has become the key to ensuring user rights and the fairness of services. This paper is based on JJG 1149—2022 "Electric Vehicle Off-Board Charger (Trial)", focusing on the metering error of DC charging pile, analyzing the influence of multiple factors such as ripple, temperature and hardware characteristics on metering accuracy, and proposes an error analysis method based on signal decomposition and neural network, and verifies the effectiveness of the method through experiments. The research results provide technical support for the optimization of metering performance and the implementation of procedures for DC charging piles.