Real-Time Estimation of Vehicle Mass and Road Grade Based on Multi-Sensor Data Fusion

2013 IEEE Vehicle Power and Propulsion Conference (VPPC) Pub Date : 2013-11-21 DOI:10.1109/VPPC.2013.6671743

Xiaobin Zhang, Liangfei Xu, Jianqiu Li, M. Ouyang

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引用次数: 19

Abstract

Vehicle mass and road grade are two key parameters for New Energy Vehicles. It plays an important role in the power distribution of multi-energy power systems and braking energy recovery. Using a 4-wheel drive (4WD) electric mini-car as an experimental platform, a road grade and vehicle mass estimation algorithm based on multi-data fusion technology is studied. Firstly, a Simulink model for GPS (Global Positioning System)/INS (Inertial Navigation System)/wheel-speed data fusion is established, taking advantage of the characteristics of a 4WD electric vehicle. An off-line simulation is conducted with data from a real vehicle test to verify the model. Then the verified algorithm is downloaded and successfully implemented in the Vehicle Control Unit based on MPC561 digital core by Simulink Automatic Code Generation technology. Finally, a hardware-inloop simulation based on CANoe and CANalyzer is conducted for the testing and evaluation of the VCU. The result shows that the real-time multi-data fusion algorithm produces a good estimation of the road grade and vehicle mass with an error of 5%, and the convergence and steady-state error meet the need of real vehicle applications.

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基于多传感器数据融合的车辆质量和道路坡度实时估计

车辆质量和道路等级是新能源汽车的两个关键参数。它在多能动力系统的功率分配和制动能量回收中起着重要的作用。以一辆四轮驱动电动微型车为实验平台，研究了一种基于多数据融合技术的道路坡度和车辆质量估计算法。首先，利用四轮驱动电动汽车的特点，建立了GPS /INS /轮速数据融合的Simulink模型;利用实车试验数据进行了离线仿真，验证了模型的正确性。然后下载验证算法，并利用Simulink自动代码生成技术在基于MPC561数字核心的车载控制单元上成功实现。最后，基于CANoe和CANalyzer进行了硬件在环仿真，对VCU进行了测试和评价。结果表明，实时多数据融合算法能较好地估计道路坡度和车辆质量，误差在5%左右，收敛性和稳态误差满足实际车辆应用的需要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 IEEE Vehicle Power and Propulsion Conference (VPPC)

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