Abstract
The slip ratio control is an important research topic in in-wheel-motored electric vehicles (EVs). Traditional control methods are usually designed for some specified modes. Therefore, the optimal slip ratio control cannot be achieved while vehicles work under various modes. In order to achieve the optimal slip ratio control, a novel model predictive controller-based optimal slip ratio control system (MPC-OSRCS) is proposed. The MPC-OSRCS includes three parts, a road surface adhesion coefficient identifier, an operation mode recognizer, and an MPC based-optimal slip ratio control. The current working road surface is identified by the road surface adhesion coefficient identifier, and a modified recursive Bayes theorem is used to compute the matching degree between current road surfaces and reference road surfaces. The current operation state is recognized by the operation mode recognizer, and a fuzzy logic method is applied to compute the matching degree between actual operation state and reference operation modes. Then, a parallel chaos optimization algorithm (PCOA)-based MPC is used to achieve the optimal control under various operation modes and different road surfaces. The MPC-OSRCS for EV is verified on simulation platform and simulation results under various conditions to show the significant performance.
Highlights
With the development of social, the environment is getting worse [1]
In order to solve this problem, a novel MPC-OSRCS is proposed in this paper. e MPC-OSRCS includes three parts, a road surface adhesion coefficient identifier, an operation mode recognizer, and an MPC based-optimal slip ratio control. e current working road surface is identified by the road surface adhesion coefficient identifier
E main contributions of this paper cover the following points. (1) e state of electric vehicles (EVs) is divided into fifteen kinds of typical modes, and the reference model is established for these fifteen modes, which separately represent the link of five road surfaces and three operation modes. (2) e identifier is designed for recognizing road surface adhesion coefficient and operation mode, respectively. e output of the identifier presents the matching degree between the state of actual EV and each typical model
Summary
With the development of social, the environment is getting worse [1]. Compared with traditional vehicle, EVs have great advantage in decreasing environment pollution. erefore, more and more scholars are involved in the research of EVs [2, 3]. In order to achieve the multiobjective optimization control, an MPC-based slip control system for EVs was proposed [7]. E MPC-OSRCS includes three parts, a road surface adhesion coefficient identifier, an operation mode recognizer, and an MPC based-optimal slip ratio control. In order to accurately describe the operation state, three operation mode reference models are established. E matching degree between the actual operation state and three operation mode reference models is computed by the fuzzy method. The control output of MPC-OSRCS is computed by the weighted output of each model to achieve optimal slip ratio control under various operation states and different road surfaces. (1) e state of EV is divided into fifteen kinds of typical modes, and the reference model is established for these fifteen modes, which separately represent the link of five road surfaces and three operation modes. E main contributions of this paper cover the following points. (1) e state of EV is divided into fifteen kinds of typical modes, and the reference model is established for these fifteen modes, which separately represent the link of five road surfaces and three operation modes. (2) e identifier is designed for recognizing road surface adhesion coefficient and operation mode, respectively. e output of the identifier presents the matching degree between the state of actual EV and each typical model. (3) Aiming at obtaining the control output under each state of EV, each type of matching coefficient is substituted into the controller and the weighted output of each state of EV makes up the output of MPC-OSRCS. (4) e optimal design of MPC is achieved by PCOA because of the global optimization with fast and accurate performance can be achieved by it
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