Development of a representative urban driving cycle construction methodology for electric vehicles: A case study in Xi’an

Zhao, Xuan; Zhao, Xiangmo; Yu, Qiang; Ye, Yiming; Yu, Man

doi:10.1016/j.trd.2020.102279

Cited by 121 publications

(54 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…is the nonlinear mapping function, reflecting the relationship between the state at the current time and the previous time; H= [1,0] is the state observation matrix; k  and k  are process noise and measurement noise, and their covariance matrices are Q and R, respectively.…”

Section: B Road Grade Estimator Based Ekpf In the Second Stepmentioning

confidence: 99%

“…With the improving demand of the market for vehicle safety and the energy economy, the active safety and intelligent control technology in vehicles have been widely developed and applied [1]. Knowledge of vehicle parameters and road conditions, especially vehicle mass and road grade, is of great significance to achieve optimal control performance [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade

Zhao

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Section: B Road Grade Estimator Based Ekpf In the Second Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade

Zhao

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

“…With the development of the automobile industry, the four in-wheel motor independent drive electric vehicles (4WIDEVs) have attracted increasing attention due to their contribution to energy saving and environmental protection [1][2][3]. Moreover, the 4WIDEV has a lot of advantages compared with the centralized drive electric vehicles (CDEVs) [4], where the independent control of each wheel is of great benefit for the improvement of the vehicle stability control [5].…”

Section: Introductionmentioning

confidence: 99%

A Modified Unscented Kalman Filter Combined with Ant Lion Optimization for Vehicle State Estimation

Zhang

Zhao

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Accurate estimation of vehicle states is extremely crucial for vehicle stability control. As a reliable estimation methodology, the unscented Kalman filter (UKF) has been widely utilized in vehicle control. However, the estimation accuracy still needs to be improved caused by the unpredictable measurement and process noise. In this paper, a novel modified UKF state estimation methodology combined with the ant lion optimization (ALO) is proposed for the stability control of a four in-wheel motor independent drive electric vehicle (4WIDEV). First, the optimal performance of the ALO algorithm is analyzed, where both unimodal and multimodal optimization test functions are selected and optimized by GA, PSO, and ALO, respectively. The results indicate that the ALO algorithm has good global optimization capability and applicability. Second, the ALO algorithm is merged into the UKF to adjust the statistical properties of noise information for the ALOUKF estimator design without extra sensor signals. At last, the simulations on the Matlab/Simulink-CarSim co-simulation platform and the road test based on an A&D 5435 rapid prototyping experiment platform (RPP) are carried out to verify the proposed method. The simulation and experiment results demonstrate that the ALOUKF estimator can improve state estimation accuracy and resist the vehicle nonlinearity even in the case of the complicated and emergency maneuvers.

show abstract

“…With the increasing complexity of road traffic environment, advanced driver assistance systems have been widely researched and deployed to avoid or mitigate collision, improve driving safety, and reduce accidents and fatalities [1][2][3][4]. As more intelligent driver assistants, in order to avoid the occurrence of false interventions and mismatch situations, and increase the driver's adaptability and trust to the vehicle, these systems should effectively obtain the driver's intentions [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network

Wang

Zhao

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Driving intention identification is a key technology which can improve the adaptability of the intelligent driver assistance systems and the energy efficiency of electric vehicles. This article proposes a novel method for identifying the driver braking intention. In order to improve the identification accuracy of driving intention, a braking intention identification model based on Long Short-Term Memory (LSTM) Network is constructed. The data of slight braking, normal braking and hard braking that can use for offline training are obtained through tests on real vehicle at Chang'an University vehicle performance testing ground. Support vector machine-recursive feature elimination (SVM-RFE) algorithm is used to select the characteristic parameter of braking intention identification model. The random search is subsequently used to optimize the hyper-parameters of LSTM. LSTM-based and General Hidden Markov Model (GHMM)-based model under different time window are used to identify braking intention of slight braking, normal braking and hard braking respectively. The results show that the Precision, Recall, F-measure, Accuracy of the braking intention identification model which propose in this paper based on LSTM are better than that of the braking intention identification model based on GHMM. Moreover, the Recall and Accuracy of the LSTM-based braking intention identification models are above 0.95, indicating the good ability of intention identification.

show abstract

Development of a representative urban driving cycle construction methodology for electric vehicles: A case study in Xi’an

Cited by 121 publications

References 39 publications

Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade

Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade

A Modified Unscented Kalman Filter Combined with Ant Lion Optimization for Vehicle State Estimation

Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network

Contact Info

Product

Resources

About