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.
褚文博,罗禹贡,罗剑,李克强. 电驱动车辆的整车质量与路面坡度估计[J]. 清华大学学报（自然科学版）, 2014, 54(6): 724-728.
Wenbo CHU,Yugong LUO,Jian LUO,Keqiang LI. Vehicle mass and road slope estimates for electric vehicles. Journal of Tsinghua University(Science and Technology), 2014, 54(6): 724-728.
Bae H S, Gerdes J C. Parameter estimation and command modification for longitudinal control of heavy vehicles [C]// Proceedings of the International Symposium on Advanced Vehicle Control. Ann Arbor, MI, USA, 2000.
[2]
Bae H S, Ryu J, Gerdes J C. Road grade and vehicle parameter estimation for longitudinal control using GPS [C]// Proceedings of the IEEE Conference on Intelligent Transportation Systems. Oakland, CA, USA, 2001.
[3]
Johansson K. Road Slope Estimation with Standard Truck Sensors[M]. Stockholm, Sweden: KTH Royal Institute of Technology, 2005.
[4]
Sahlholm P, Johansson K H. Road grade estimation for look-ahead vehicle control using multiple measurement runs[J]. Control Engineering Practice, 2010, 18(11):1328-1341.
[5]
Parviainen J, Hautamäki J, Collin J, et al. Barometer-aided road grade estimation [C]// Proceedings of the World Congress of the International Association of Institutes of Navigation. Stockholm, Sweden, 2009.
[6]
Zhang T, Yang D, Li T, et al. Vehicle state estimation system aided by inertial sensors in GPS navigation [C]// Proceedings of the International Conference on Electrical and Control Engineering. Wuhan, China, 2010.
[7]
Vahidi A, Stefanopoulou A, Peng H. Recursive least squares with forgetting for online estimation of vehicle mass and road grade: Theory and experiments[J]. Vehicle System Dynamics, 2005, 43(1): 57-75.
[8]
McIntyre M L, Ghotikar T J, Vahidi A, et al. A two-stage Lyapunov-based estimator for estimation of vehicle mass and road grade[J]. IEEE Transactions on Vehicular Technology, 2009, 58(7): 3177-3185.
[9]
Lingman P, Schmidtbauer B. Road slope and vehicle mass estimation using Kalman filtering[J]. Vehicle System Dynamics, 2002, 37(S1): 12-23.
[10]
Eriksson A. Implementation and Evaluation of a Mass Estimation Algorithm[M]. Stockholm, Sweden: KTH Royal Institute of Technology, 2009.
[11]
Madsen C K, Zhao J H. Optical Filter Design and Analysis: A Signal Processing Approach[M]. New York, NJ, USA: Wiley, 1999.
[12]
Gibbs B P. Advanced Kalman Filtering, Least-Squares and Modeling[M]. Hoboken, NJ, USA: Wiley, 2011.
[13]
Parkum J E, Poulsen N K, Holst J. Recursive forgetting algorithms[J]. International Journal of Control, 1992, 55(1): 109-128.
[14]
王博, 罗禹贡, 邹广才, 等. 四轮独立电驱动越野车辆研究实验平台[J]. 清华大学学报: 自然科学版, 2009, 49(11): 183-1842. WANG Bo, LUO Yugong, ZOU Guangcai, et al.Four wheel independent electric drive off road vehicle test bed[J]. Journal of Tsinghua University: Science and Technology, 2009, 49(11): 1838-1842. (in Chinese)
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