# Vehicle Control Strategy Evaluation Based on the Driving Stability Region
## 5 Conclusions  
A hybrid algorithm based on GA + SQP was used to solve the driving stability region of a 5 DOF vehicle dynamics model with DYC and 4 WS control strategies. The control effectiveness of DYC and 4 WS is evaluated by driving stability regions. The main procedures and conclusions are as follows:  
1. Taking DYC and 4 WS as examples, the 5 DOF vehicle system models with different control strategies were established. The influence of different control strategies on the system dynamics' characteristics was analyzed by simulation. The correctness of the dynamic model and the effectiveness of the control strategies were verified.
2. The hybrid algorithm of the Genetic Algorithm and Sequential Quadratic Programming methods was applied to solve the system equilibrium points under different control strategies. Subsequently, the bifurcation characteristics of the equilibrium points are used to determine the driving stability region of the vehicle. The simulation results indicate that both control strategies can expand the original driving stability region of the vehicle system. The expansion of stable driving areas under low-speed conditions is larger than that under high-speed conditions. The driving stability region with the introduction of DYC is bigger than the driving stability region with 4 WS, indirectly indicating that the control effect of DYC is better than that of 4 WS.
3. A simulation model was established using Simulink (R2022b)and CarSim (2019.1). The driving stability regions of the vehicle with DYC and 4 WS control strategies were analyzed by simulation. The test point was selected for verification. The simulation results showed that the driving stability region under different control strategies was correct. The boundary of stable driving under different control strategies can be described by the solved driving stability region; therefore, the driving stability regions are able to be used for evaluating vehicle handling and stability control strategies.  
The number of simulated control strategies was limited in this study. In the process of solving the equilibrium point, there was a strong dependence on the model and a large amount of computation. The dynamic models used in this paper ignored the influence of pitch and roll, which deviates to a certain extent from the actual driving situation. Considering the danger of vehicle instability and the inadequacy of current experimental conditions, the actual vehicle verification was not carried out. In future research, we will improve the accuracy of the model to make it more consistent with the actual vehicle. The plan is to first use model vehicles to validate the research results, then conduct actual vehicle experiments based on the model experiments, and finally, analyze the control effects of more control strategies.  
Conceptualization, X.W. and Z.L.; methodology, X.W.; software and data analysis, Z.L. and F.Z.; validation, F.Z., W.L. and W.B.; writing--original draft preparation, Z.L.; writing--review and editing, X.W., Z.L., W.L., F.Z. and W.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Plan of Heilongjiang Province, grant number JD22A014, National Natural Science Foundation of China, grant number 52175497.  
Not applicable.  
The authors declare no conflict of interest.