scholarly journals The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 52 ◽  
Author(s):  
Nan Lin ◽  
Changfu Zong ◽  
Shuming Shi
Keyword(s):  
Colored Noise ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Cruise Control ◽  
Unknown Parameters ◽  
System Error ◽  
Mass Estimation ◽  
Longitudinal Dynamics ◽  
Mass Error ◽  
Vehicle Mass

Vehicle mass is a critical parameter for economic cruise control. With the development of active control, vehicle mass estimation in real-time situations is becoming notably important. Normal state estimators regard system error as white noise, but many sources of error, such as the accuracy of measured parameters, environment and vehicle motion state, cause system error to become colored noise. This paper presents a mass estimation method that considers system error as colored noise. The system error is considered an unknown parameter that must be estimated. The recursive least squares algorithm with two unknown parameters is used to estimate both vehicle mass and system error. The system error of longitudinal dynamics is analyzed in both qualitative and quantitative aspects. The road tests indicate that the percentage of mass error is 16%, and, if the system error is considered, the percentage of mass error is 7.2%. The precision of mass estimation improves by 8.8%. The accuracy and stability of mass estimation obviously improves with the consideration of system error. The proposed model can offer online mass estimation for intelligent vehicle, especially for heavy-duty vehicle (HDV).

Truck mass estimation method based on the on-board sensor

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2429-2443
Author(s):  
Beijia Wang ◽  
Hongliang Wang ◽  
Lei Wu ◽  
Liuliu Cai ◽  
Dawei Pi ◽  
...  
Keyword(s):  
Error Correction ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Correction Method ◽  
Mass Estimation ◽  
The Road ◽  
Vehicle Mass ◽  
Error Correction Method ◽  
Road Gradient

Vehicle mass estimation is the key technology to improve vehicle stability. However, the existing mass estimation accuracy is easily affected by the change of road gradient, and there are few studies on the mass estimation method of the light truck. Aiming at this problem, this paper uses sensors to measure road gradient and rear suspension deformation and proposes a sensor-based vehicle mass estimation algorithm. First, factors that affect the mass estimation are analyzed, road gradient error correction method and mass estimation error correction method are established. Besides, the suspension deformation is decoupled from the road gradient. Second, the mass estimation algorithm model was established in Matlab/Simulink platform and compared with the mass estimation iterative algorithm. Finally, the road test was carried out under various conditions, the results show that the proposed mass estimation algorithm is robust, and the accuracy of the mass estimation will not be affected by the sudden change of road gradient.

Longitudinal Motion Based Lightweight Vehicle Payload Parameter Real-Time Estimations

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Xiaoyu Huang ◽  
Junmin Wang
Keyword(s):  
Real Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Location Estimation ◽  
Torque Control ◽  
Recursive Least Squares ◽  
Longitudinal Motion ◽  
Unknown Parameters ◽  
Payload Mass ◽  
Planar Location

This paper proposes a longitudinal motion based payload parameter estimator (PPE) design for four-wheel-independently driven lightweight vehicles (LWVs), whose dynamics and control are substantially affected by their payload variations due to the LWVs' significantly reduced sizes and weights. Accurate and real-time estimation of payload parameters, including payload mass and its onboard planar location, will be helpful for LWV control (particularly under challenging driving conditions) and load monitoring. The proposed estimation method consists of three steps in sequential: tire effective radius identification for undriven wheels at constant speed driving; payload mass estimation during acceleration–deceleration period; and payload planar location estimation (PPLE). The PPLE is divided into two parts: a tire nominal normal force estimator (NNFE) based on a recursive least squares algorithm using signals generated by the redundant inputs, and a parameter calculator combining these estimated nominal normal forces. The prototype LWV is a lightweight electric ground vehicle (EGV) with separable torque control of the four wheels enabled by four in-wheel motors, which allow redundant input injections in the designed maneuvers. Experimental results obtained on an EGV road test show that the proposed PPE is capable of accurately estimating payload parameters, and it is independent of other unknown parameters such as tire-road friction coefficient.

An Extended Kalman Filter implementation for estimating the mass of a passenger vehicle

2020 ◽  
Author(s):  
Stephanie L. J. Galvão ◽  
Raony M. Fontes ◽  
Daniel D. Santana ◽  
Reiner Requião
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Method ◽  
Passenger Vehicle ◽  
Passenger Cars ◽  
Engine Torque ◽  
Mass Estimation ◽  
Vehicle Mass ◽  
Simulation Results ◽  
Filter Approach

The vehicle mass is an important information to guarantee drivability, performance feel, fuel economy and safety. In this sense, the application of strategies to reach these goals depends on how accurate the vehicle operating information is, requiring a precise and robust algorithm. This paper proposes an offline mass estimation method to passenger cars, using the Extended Kalman Filter. The method combines the filter approach whereby only the speed is provided as a measurement with a parameter estimation to adjust possible modelling errors and to calculate a proposed engine torque. Tests in city routes were done in controlled dynamics to guarantee the effectiveness of the estimation and the simulation results presented errors below 5%.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals Software Sensor to Enhance Online Parametric Identification for Nonlinear Closed-Loop Systems for Robotic Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3653
Author(s):  
Lilia Sidhom ◽  
Ines Chihi ◽  
Ernest Nlandu Kamavuako
Keyword(s):  
Degrees Of Freedom ◽  
Closed Loop ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Parametric Identification ◽  
Recursive Least Squares ◽  
Unknown Parameters ◽  
Closed Loop Identification ◽  
Input Variables ◽  
Robotic Applications

This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differentiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algorithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

New targets number estimation method under colored noise background

2012 ◽  
Vol 23 (6) ◽  
pp. 831-837 ◽  
Author(s):  
Jing Wang ◽  
Jianguo Huang ◽  
Jing Han ◽  
Zhenhua Xu
Keyword(s):  
Colored Noise ◽  
Estimation Method ◽  
Noise Background ◽  
New Targets
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