Research on Longitudinal Control Algorithm of Adaptive Cruise Control System for Pure Electric Vehicles
Abstract
:1. Introduction
2. Materials and Methods
2.1. Vehicle Platform and Control Framework
2.1.1. Vehicle Platform
2.1.2. Control Framework
2.2. RBFNN-PID-Based Longitudinal Control Algorithm
2.2.1. Parameter Tuning of RBFNN-Based PID
2.2.2. Jacobian Information Recognition Based on RBFNN
2.2.3. Longitudinal Control Algorithm
3. Results and Discussion
3.1. Results and Discussion of the Driving Process
3.1.1. Step Response of the Driving Process
3.1.2. Response under Perturbation of the Driving Process
3.2. Results and Discussion of the Braking Process
3.2.1. Step Response of the Braking Process
3.2.2. Response under Perturbation of the Braking Process
4. Conclusions
- (1)
- In response to the step signal in the driving case, the control method in this paper reaches the steady state with no static difference faster than the traditional PID control in the steady state condition, and the time required is reduced by about two-thirds. In addition, the maximum overshoot of this control algorithm is smaller, only about one-seventh of the traditional PID control, so the system response process is smoother. When adding disturbances, the control method used in this paper takes about three-tenths of the time to restore the steady state than the traditional PID control, showing a better anti-jamming ability;
- (2)
- In response to the step signal during the braking process, the response speed of this control algorithm is doubled compared with the traditional PID control. Similarly, when adding disturbances, this control algorithm takes less time to restore the steady state, which is about three-tenths less than the traditional PID control. The control algorithm has a better anti-jamming ability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Para. | Value | Units |
---|---|---|
Vehicle weight | 1450 | kg |
Rolling resistance coefficient | 0.015 | |
Gravity acceleration | 9.8 | m/s2 |
Aerodynamic drag coefficient | 0.3 | |
Mass density of air | 1.29 | kg/m3 |
Vehicle frontal area | 1.2258 | m2 |
Vehicle transmission ratio | 8.28 | |
Transmission efficiency | 0.9 | |
Wheel radius. | 0.334 | m |
Height of mass center | 530 | mm |
Wheelbase | 2800 | mm |
Tread | 1500 | mm |
0.2, 0.0005, 0 | ||
3 | ||
6 | ||
0.25 | ||
0.05 | ||
in driving process | 0.4, 0.7, 0 | |
in braking process | 2.2, 1.5, 0 | |
Sampling time | 0.001 | s |
Para. | Value | Units |
---|---|---|
Settling time of PID in driving process | 1.14 | s |
Settling time of RBF-PID in driving process | 0.459 | s |
Maximum overshoot of PID in driving process | 39.9 | % |
Maximum overshoot of RBF-PID in driving process | 6 | % |
Time to steady of PID under disturbance in driving process | 1.56 | s |
Time to steady of RBF-PID under disturbance in driving process | 1.13 | s |
Settling time of the PID in braking process | 1.051 | s |
Settling time of the RBF-PID in braking process | 0.521 | s |
Maximum overshoot of the PID in braking process | 0 | % |
Maximum overshoot of the RBF-PID in braking process | 0 | % |
Time to steady of PID under disturbance in braking process | 0.67 | s |
Time to steady of RBF-PID under disturbance in braking process | 0.5 | s |
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Chu, L.; Li, H.; Xu, Y.; Zhao, D.; Sun, C. Research on Longitudinal Control Algorithm of Adaptive Cruise Control System for Pure Electric Vehicles. World Electr. Veh. J. 2023, 14, 32. https://doi.org/10.3390/wevj14020032
Chu L, Li H, Xu Y, Zhao D, Sun C. Research on Longitudinal Control Algorithm of Adaptive Cruise Control System for Pure Electric Vehicles. World Electric Vehicle Journal. 2023; 14(2):32. https://doi.org/10.3390/wevj14020032
Chicago/Turabian StyleChu, Liang, Huichao Li, Yanwu Xu, Di Zhao, and Chengwei Sun. 2023. "Research on Longitudinal Control Algorithm of Adaptive Cruise Control System for Pure Electric Vehicles" World Electric Vehicle Journal 14, no. 2: 32. https://doi.org/10.3390/wevj14020032