摘要:由于交通超限治理和计重收费工作的不断深入，汽车动态称重系统得到了越来越广泛的应用。就拿公路汽车动态称重系统来说，汽车的动态称量精度以及汽车通过的速度是最重要的性能指标。目前国内研发的动态称重系统，由于缺乏对影响汽车动态称重的各种因素作系统分析，没有对检测信号作深层次的处理，使得动态称重精度与汽车速度之间的矛盾难以协调。往往测量的精度高，允许的通行速度就比较低;当通行速度高时，测量精度就达不到要求。因此，动态称重系统的开发工作主要解决的问题是测量精度与车辆通行速度之间的矛盾。目前，汽车动态称重系统主要有检测轴载荷和轮载荷两种称重方法，本文使用的是轴载荷称重的方法对动态称重进行研究。

　　　为了达到设计要求，我们首先分析了汽车动态称重系统中影响测量精度的各种因素，得出了影响静态称重的各种因素，其主要包括汽车速度、汽车的振动、地面的不平整等一系列因素，然后提出了从硬件和软件两个方面来提高动态精度的有效措施和动态称重系统的设计思想。

　　　为了提高测量精度，用BP神经网络算法建立了动态称重系统的数学模型，并对动态称重系统的数据做了进一步的处理。 

　　　动态称重系统的硬件部分的设计，包括称重平台的设计以及称重硬件电路的设计。电路部分主要包括传感器的信号调理电路、AD转换接口电路设计、测速电路、显示电路以及与单片机通信接口电路等。

关键词:动态称重系统；称重传感器；单片机；数据采集；BP神经网络

Abstract:Vehicle Weigh-in-Motion (WIM) systems were rapidly developed following transportation investigation, controlling serious vehicle overload and weighing charge. For the highway weigh-in-motion systems, the weighing accuracy and the speed of vehicle moving were the most important standard. At present, when the WIM systems were studied in our country, the contradiction on identifying accuracy and vehicle passing speed can’t be resolved well all along, for the disturbing factors affecting the WIM systems were short of systemic analysis and the inspecting signal wasn’t processed further .So it was important to resolve the contradiction on identifying accuracy and vehicle passing speed.At present, the WIM systems were studied on the base of wheel load and axle load, this paper discussed the way of axle load. 

　　　For improving identifying accuracy, first, this paper analyzed all kinds of influence factors to WIN systems, including vehicle speed, vehicle liberation, road surface, and so on. Second, the restraint ways of disturbance could be realized by both hardware method and software method.

　　　 In order to improve the measurement accuracy, using BP neural network algorithm established mathematical model of dynamic weighing systems, weighing systems and dynamic data to do further processing.

　　　The design of hardware consisted of the design of weighing platform and the design of integrated circuit. The integrated circuit was mainly made of signal disposal, A/D converters, data collection, measuring velocity, display and data communication with personal computer circuit.

Key words: Weigh-in-motion systems; Weigh sensor; MCU; Data acquisition; BP neural network.