A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle׳ mass for a hybrid electric bus. (February 2016)

• Record Type: Journal Article
• Authors: Sun, Yong; Li, Liang; Yan, Bingjie; Yang, Chao; Tang, Gongyou
• Is Part Of: Mechanical systems and signal processing. Volume 68/69(2016)
• Page Start: 416
• Page End: 430
• Publication Date: 2016-02
• Subjects: Hybrid algorithm with variable structure -- Mass estimation -- Road grade -- Simultaneous optimization -- Hybrid electric bus (HEB); Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.08.015
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper proposes a novel hybrid algorithm for simultaneously estimating the vehicle mass and road grade for hybrid electric bus (HEB). First, the road grade in current step is estimated using extended Kalman filter (EKF) with the initial state including velocity and engine torque. Second, the vehicle mass is estimated twice, one with EKF and the other with recursive least square (RLS) using the estimated road grade. A more accurate value of the estimated mass is acquired by weighting the trade-off between EKF and RLS. Finally, the road grade and vehicle mass thus obtained are used as the initial states for the next step, and two variables could be decoupled from the nonlinear vehicle dynamics by performing the above procedure repeatedly. Simulation results show that in different starting conditions, the proposed algorithm provides higher accuracy and faster convergence speed, compared with the results using EKF or RLS alone. Highlights: A hybrid algorithm of road grade and vehicle mass for parallel HEB with AMT. Integration of extended Kalman filter algorithm and recursive least square algorithm. A simulation validation is carried out using the city bus driving conditions. The algorithm is effectiveness both in uphill and flat starting conditions.