Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption
Abstract
:1. Introduction
- Considering the dynamic time variation of the surrounding obstacles, a segmented trajectory planning model is developed to dynamically adjust the trajectory of lane changing;
- Considering the braking energy recovery characteristics of electric vehicles, a cost function that simultaneously considers economy, comfort, efficiency, and safety risks is constructed;
- The impact of various lane-changing factors on the energy usage of electric vehicle lane changes is analyzed.
2. System Architecture
3. Lang-Changing Feasibility Modeling
4. Lane-Changing Trajectory-Planning Model
4.1. Lane-Changing Energy Consumption Cost Function
4.2. Optimal Control Combined with Quintic Polynomial for Piecewise Trajectory Planning
4.2.1. First-Segment Trajectory Planning
4.2.2. Optimal Control Solves for the First Segment of the Lane-Change Trajectory
4.2.3. Second-Segment Trajectory Planning
4.2.4. Quintic Polynomial Programming to Solve for the Second Segment of Lane-Change Trajectories
5. Results and Discussion
5.1. Comparative Analysis of Different Lane-Changing Algorithms and Human Driver Lane-Changing Trajectories
5.2. Sensitivity Analysis
5.3. Comparative Analysis with Typical Automatic Lane-Change Models and Real Driver Lane-Change Trajectories
6. Conclusions
- The lane-change trajectory-planning method combining optimal control and quintic polynomials, compared with the double quintic polynomial real-time lane-change trajectory and the real-driver lane-change trajectory proposed by the previous scholars, can realize a better lane-changing economy under the premise of meeting the safety of the lane-changing process;
- Lane-change displacement and lane-change speed difference have a greater effect on the energy consumption of an electric vehicle. As the longitudinal displacement of the lane-change increases, the energy consumption of the lane-change firstly decreases and then increases, and the smaller the speed difference between the start point and the endpoint of the lane change, the lower the energy consumption;
- The smaller the speed difference between the start and completion of the lane change, the lower the energy consumption.
- Incorporating the effects of different vehicle dimensions and sensor measurement errors into the trajectory-planning process to enhance the generalizability and adaptability of the proposed method;
- Investigating trajectory-planning strategies under more complex traffic scenarios, such as urban intersections and multi-lane congested environments;
- Extending the current offline trajectory-planning algorithm to real-world vehicle applications and validating its real-time performance and robustness through on-road testing.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
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Parameters | Longitudinal Distance (m) | Lateral Distance (m) | Longitudinal Starting Velocity (m/s) | Longitudinal Terminal Velocity (m/s) | Lateral Starting Velocity (m/s) | Lateral Terminal Velocity (m/s) |
---|---|---|---|---|---|---|
Scene 1 | 203.06 | 3.64 | 36.27 | 36.52 | 0.00 | 0.00 |
Scene 2 | 114.16 | 3.63 | 19.36 | 21.93 | 0.00 | 0.00 |
Scene 3 | 201.61 | 3.72 | 25.4 | 25.01 | 0.08 | 0.08 |
Quintic Polynomial | Optimal Control | highD | ||
---|---|---|---|---|
Scene 1 | Energy consumption (kwh) | 4.09 × 10−3 | 3.87 × 10−3 | 7.36 × 10−3 |
Time(s) | 5.6 | 5.6 | 5.9 | |
Scene 2 | Energy consumption (kwh) | 4.98 × 10−3 | 4.75 × 10−3 | 8.37 × 10−3 |
Time(s) | 5.35 | 5.2 | 5.56 | |
Scene 3 | Energy consumption (kwh) | 2.89 × 10−3 | 1.97 × 10−3 | 6.60 × 10−3 |
Time (s) | 7.25 | 7.91 | 7.96 |
Parameters | Double Quintic Polynomial | Optimal Control + Quintic Polynomial | Real Driver | |
---|---|---|---|---|
Scene 1 | Energy consumption (kwh) | 3.22 × 10−3 | 2.76 × 10−3 | 6.12 × 10−3 |
Time (s) | 5.4 | 6 | 6 | |
Scene 2 | Energy consumption (kwh) | 3.16 × 10−3 | 2.83 × 10−3 | 3.91 × 10−3 |
Time (s) | 6.7 | 6.28 | 7.26 | |
Scene 3 | Energy consumption (kwh) | 3.097 × 10−3 | 2.646 × 10−3 | 4.439 × 10−3 |
Time (s) | 6.7 | 6.1 | 6.4 |
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Hu, L.; Wang, J.; Huang, J.; Wong, P.K.; Zhao, J. Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption. Sustainability 2025, 17, 4235. https://doi.org/10.3390/su17094235
Hu L, Wang J, Huang J, Wong PK, Zhao J. Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption. Sustainability. 2025; 17(9):4235. https://doi.org/10.3390/su17094235
Chicago/Turabian StyleHu, Lin, Jie Wang, Jing Huang, Pak Kin Wong, and Jing Zhao. 2025. "Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption" Sustainability 17, no. 9: 4235. https://doi.org/10.3390/su17094235
APA StyleHu, L., Wang, J., Huang, J., Wong, P. K., & Zhao, J. (2025). Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption. Sustainability, 17(9), 4235. https://doi.org/10.3390/su17094235