Vehicle mass and road slope estimates for electric vehicles

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Abstract

This paper presents a vehicle mass estimation method based on high-frequency information extraction to improve existing mass estimation methods that are susceptible to the influence of road slope with poor real-time performance. Accurate estimates are needed to accurately predict the driving force provided by the electrical drive systems. A high-pass filter is used to extract high-frequency longitudinal driving force and acceleration information. Then, a recursive least squares algorithm estimates the vehicle mass. Then, the road slope is estimated based on a combined kinematic and dynamic model. This method solves the problem that road slope estimates require an accurate vehicle dynamic model and are susceptible to acceleration sensor bias. The algorithm combines the dynamic method in a recursive least squares algorithm with a factor to neglect some previous information to estimate the road slope and a kinematic method that uses the relationship between the longitudinal vehicle acceleration and the acceleration sensor to calculate the road slope. Experimental tests show that this method is robust and can accurately estimate the vehicle mass and road slope in real-time.

Keywords electric vehicle vehicle mass estimate road slope estimate dynamic method kinematic method

Issue Date: 15 June 2014

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	Wenbo CHU
	Yugong LUO
	Jian LUO
	Keqiang LI

Cite this article:

Wenbo CHU,Yugong LUO,Jian LUO, et al. Vehicle mass and road slope estimates for electric vehicles[J]. Journal of Tsinghua University(Science and Technology), 2014, 54(6): 724-728.

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http://jst.tsinghuajournals.com/EN/ OR http://jst.tsinghuajournals.com/EN/Y2014/V54/I6/724

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