Electric Vehicle Lateral Stability Control With Torque Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing research on lateral stability control systems for electric vehicles focuses primarily on upper controllers, neglecting the optimization of torque distribution in four-wheel independent drive electric vehicles, which hinders the efficient coordination between active front steering and direct yaw moment control, and is constrained by issues like short cruising range.
Innovation Solution
An economical optimization strategy is developed, constructing a vehicle system dynamics model, determining a lateral stability control system model with active allocation optimization, and designing a lateral stability controller using model predictive control to optimize torque distribution in in-wheel motors, adjusting a coordinational variable to enhance overall efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cascade control structures based on AFS/DYC integrated control are used, then lateral stability control performance is improved, but coordination between effective working areas of AFS and DYC becomes difficult
Solution Approach 1:
The control system is segmented into upper and lower layers. The upper layer handles lateral stability control through AFS/DYC integrated control, while the lower layer independently manages torque distribution of in-wheel motors. This segmentation allows each layer to focus on specific functions, simplifying the coordination between AFS and DYC while maintaining overall control performance.
2Productivity
If torque distribution of in-wheel motor is optimized, then overall efficiency of motor is improved, but cruising range is constrained by other factors
Solution Approach 1:
The system dynamically adjusts the coordinational variable L that distributes torque between in-wheel motors, changing the operating parameters to optimize motor efficiency. By adjusting torque distribution based on real-time conditions, the system maximizes motor efficiency while considering overall energy consumption constraints, thereby improving productivity without excessively compromising cruising range.
3Reliability
If research focuses on upper controller design, then lateral stability control is improved, but torque distribution optimization in lower layer is neglected
Solution Approach 1:
The control architecture is divided into upper and lower layers with distinct functions. The upper layer focuses on lateral stability control through AFS/DYC integration, while the lower layer independently optimizes torque distribution of in-wheel motors. This segmentation allows both lateral stability control and torque distribution efficiency to be optimized simultaneously without compromising either function.
Solution Approach 2:
The lower layer uses feedback from motor operating conditions to dynamically adjust torque distribution through the coordinational variable L. This feedback mechanism ensures that torque distribution is continuously optimized based on actual motor performance, improving productivity while maintaining the lateral stability control achieved by the upper layer.
Data Source
AI summary
An economical optimization strategy construction method and system for lateral stability control of an electric vehicle, and a computer-readable storage medium are provided. The method includes: firstly constructing a lateral stability control system model with active allocation optimization to achieve the coordinational allocation between effective working areas of each part by means of a coordinational variable L; secondly, on the basis of the system model with active allocation optimization, designing a lateral stability controller under a model predictive control framework, where the designed objective function J makes the motion state of the vehicle track the expected value at steady state; and finally, considering the mapping relationship between the additional yaw moment and the coordinational variable L, constructing a three-dimensional surface, and analyzing the influence of the coordinational variable L, and determining the workflow of a regulator of the coordinational variable L.