This paper presents a vehicle-independent, non-intrusive, and light monitoring system for accurately measuring fuel consumption in road vehicles from longitudinal speed and acceleration derived continuously in time from GNSS and IMU sensors mounted inside the vehicle. In parallel to boosting the transition to zero-carbon cars, there is an increasing interest in low-cost instruments for precise measurement of the environmental impact of the many internal combustion engine vehicles still in circulation. The main contribution of this work is the design and comparison of two innovative black-box algorithms, one based on a reduced complexity physics modeling while the other relying on a feedforward neural network for black-box fuel consumption estimation using only velocity and acceleration measurements. Based on suitable metrics, the developed algorithms outperform the state of the art best approach, both in the instantaneous and in the integral fuel consumption estimation, with errors smaller than 1\% with respect to the fuel flow ground truth. The data used for model identification, testing, and experimental validation is composed of GNSS velocity and IMU acceleration measurements collected during several trips using a diesel fuel vehicle on different roads, in different seasons, and with varying numbers of passengers. Compared to built-in vehicle monitoring systems, this methodology is not customized, uses off-the-shelf sensors, and is based on two simple algorithms that have been validated offline and could be easily implemented in a real-time environment.

翻译：本文提出一种与车辆无关、非侵入式且轻量化的监测系统，通过车载GNSS和IMU传感器连续获取的纵向速度与加速度时间序列，实现对道路车辆燃油消耗的精确测量。在推动零碳排放汽车转型的同时，对仍在运行的大量内燃机车辆环境影响进行精确测量的低成本仪器需求日益增长。本工作的主要贡献在于设计并比较两种创新型黑箱算法：一种基于简化复杂度的物理建模，另一种基于前馈神经网络，仅利用速度和加速度测量值进行黑箱式燃油消耗估算。基于相应评估指标，所提算法在瞬时油耗和积分油耗估算方面均优于当前最优方法，相较于燃油流量真实值误差小于1%。用于模型辨识、测试及实验验证的数据集包含柴油车辆在不同道路、不同季节及不同乘客数量条件下多次行驶采集的GNSS速度与IMU加速度测量数据。相较于内置车辆监测系统，本方法无需定制化配置，采用商用现成传感器，基于两种经离线验证的简易算法，可便捷部署于实时运行环境中。