In this paper, an enabling multi-sensor fusion-based longitudinal vehicle speed estimator is proposed for four-wheel-independently-actuated electric vehicles using a Global Positioning System and Beidou Navigation Positioning (GPS-BD) module, and a low-cost Inertial Measurement Unit (IMU). For accurate vehicle speed estimation, an approach combing the wheel speed and the GPS-BD information is firstly put forward to compensate for the impact of road gradient on the output horizontal velocity of the GPS-BD module, and the longitudinal acceleration of the IMU. Then, a multi-sensor fusion-based longitudinal vehicle speed estimator is synthesized by employing three virtual sensors which generate three longitudinal vehicle speed tracks based on multiple sensor signals. Finally, the accuracy and reliability of the proposed longitudinal vehicle speed estimator are examined under a diverse range of driving conditions through hardware-in-the-loop tests. The results show that the proposed method has high estimation accuracy, robustness, and real-time performance.

中文翻译：

---

基于多传感器融合的四轮独立驱动电动汽车纵向车速估计

在本文中，为使用全球定位系统和北斗导航定位 (GPS-BD) 模块的四轮独立驱动电动汽车提出了一种基于多传感器融合的纵向车速估计器，以及一种低成本的惯性测量单元 (IMU)。为了精确估计车速，首先提出了一种结合车轮速度和GPS-BD信息的方法来补偿道路坡度对GPS-BD模块输出水平速度和IMU纵向加速度的影响。然后，通过采用三个虚拟传感器合成基于多传感器融合的纵向车速估计器，这些虚拟传感器基于多个传感器信号生成三个纵向车速轨迹。最后，通过硬件在环测试，在各种驾驶条件下检查了所提出的纵向车速估计器的准确性和可靠性。结果表明，该方法具有较高的估计精度、鲁棒性和实时性。