Yankun Qi, Xiaoyu Li, Jinghui Liu, Hanqiu Li and Chen Yang
To systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement…
Abstract
Purpose
To systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement and optimization.
Design/methodology/approach
A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards, railway safety rules and regulations and existing safety data from railway transport enterprises is presented. The system comprises a guideline layer including safety committee formation, work safety responsibility, safety management organization and safety rules and regulations as its components, along with an index layer consisting of 12 quantifiable indexes. Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.
Findings
The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.
Originality/value
Construction of an evaluation index system that is quantifiable, generalizable and accessible, accurately reflects the main aspects of railway transportation enterprises’ basic safety management capability and provides interoperability across various railway transportation enterprises. The application of the game theoretic combination weighting method to derive composite weights which combine experts’ subjective evaluations with the objectivity of data.
Details
Keywords
Cong Li, YunFeng Xie, Gang Wang, XianFeng Zeng and Hui Jing
This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.
Abstract
Purpose
This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.
Design/methodology/approach
Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.
Findings
The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.
Originality/value
The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.