Slope Estimation Method of Electric Vehicles Based on Improved Sage–Husa Adaptive Kalman Filter

Abstract

In order to deal with many influence factors of electric vehicles in driving under complex conditions, this paper establishes the system state equation based on the longitudinal dynamics equation of vehicle. Combined with the improved Sage–Husa adaptive Kalman filter algorithm, the road slope estimation model is established. After the driving speed and rough slope observation are input into the slope estimation model, the accurate road slope estimation at the current time can be obtained. The road slope estimation method is compared with the original Sage–Husa adaptive Kalman filter road slope estimation method through three groups of road tests in different slope ranges, and the accuracy and stability advantages of the proposed algorithm in road conditions with large slopes are verified.

SHAKF-PU: Sage–Husa Adaptive Kalman Filtering-Based Pedestrian Characteristic Parameter Update Mechanism for Enhancing Step Length Estimation in Pedestrian Dead Reckoning

“…SHAKF provides adaptive Q and R values, which can be adjusted by historical values in each estimation interval. Thus, SHAKF is adopted in many applications [9][10][11][12][13][14], such as frequency scanning interferometry [9], motor sensor position [10], slop estimation [11], strapdown inertial navigation [12], radar target tracking [13], and vessel path-following control [14]. These applications require estimating critical values in real time from a noisy environment.…”

“…Besides, CKF and EKF usually adopt two fixed empirical parameters, Q and R. To achieve adaptive Q and R in each positioning interval, the Sage-Husa adaptive Kalman filter (SHAKF) is an ideal solution, and many related applications adopt SHAKF to estimate their core parameters [9][10][11][12][13][14]. These applications show the SHAKF can adaptively estimate core parameters in real time.…”

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

“…In road slope estimation, the traditional Kalman filter (KF) algorithm can be used to denoise nonlinear data [19]; however, this filter can no longer fulfill the current demands of automatic driving because it considers only the effect of Gaussian white noise and exhibits poor denoising ability in complex environments. Instead, the extended Kalman filter (EKF) algorithm can be used to estimate the slope by constructing an approximately linear function [20]; however, when a nonlinear system is approximated to a linear system, the negligence of the higher-order terms trigger filter divergence [21], leading to degradation of the slope estimation accuracy. To overcome this problem, a constrained dual KF based on probability density function truncation was developed to solve filter divergence [22].…”

The Prediction Model for Road Slope of Electric Vehicles Based on Stacking Framework of Deep Learning

“…The other category is the method based on the dynamics model, in which the road slope is estimated by various algorithms based on dynamics model. The Kalman Filter (KF) and its variations [16][17][18][19], as well as the Recursive Least Squares (RLS) method and its variations [20][21], are frequently used; however, because the dynamics equation couples the mass and slope, and because a single algorithm has a poor decoupling performance, the present study focuses primarily on the joint estimation of the road slope and the entire vehicle mass by a variety of methods. Kim filter to first estimate slope, velocity, and acceleration, and then the estimates were used as recursive least squares inputs to estimate the vehicle mass [22]; Sun et al used an extended Kalman filter to estimate the vehicle mass and slope, and then used recursive least squares quadratic estimation to weigh the two estimates to obtain the optimal solution [23]; Chu et al combined high-pass filters with recursive least squares to estimate the whole vehicle mass based on the accurate driving force of electric vehicles, and later estimated the road slope by combining kinematics and dynamics [24]; Chen et al performed slope estimation based on the longitudinal motion characteristics of electric vehicles by fusing slope information from a 1st-order dilation observer with slope information separated from the acceleration sensor using a forgetting factor recursive squares method [25];.…”