Abstract
Mission planning is the upper management module of the automatic driving system, which needs to plan the optimal global path and provide the relevant information of each mission quickly and autonomously with consideration of the user mission, dynamic traffic information and vehicle status. Pure electric vehicle has made great progress in the field of automatic driving because of its environmental friendliness and excellent control characteristics, and it is of great significance to properly estimate and optimize the energy consumption for its use and promotion. Aiming at the mission planning requirement of the autonomous electric vehicle, this paper is mainly focuses on the global path planning and the expected speed planning in order to optimize the travel time and energy consumption.
First of all, the energy consumption model is established with the speed as independent variable based on the vehicle driving resistance formula, and the unknown parameter is identified by the recursive least square method with forgetting factor. Then, the time series neural network is used to estimate the battery SOC, and the capacity of the mission point can be predicted by combining the energy consumption model and the SOC estimation. The validity and accuracy of the algorithm are verified based on the data of real working condition.
Secondly, considering the time dependence of traffic speed, the historical data and dynamic data are weighted by using the Sigmoid function to improve the reliability of the speed prediction. The Dijkstra algorithm is used to calculate the time and energy consumption weight matrices, so that the driving cost between any two road segments can be obtained. Then, the mission point sequence is optimized based on the dynamic programming algorithm, and the local searching strategy is designed to avoid the shortage of power. Also, A* algorithm is applied to realize the global path planning between adjacent mission points. The proposed methods are validated via sufficient simulations.
Next, the path breakpoint model is introduced to calculate the road travel time and energy consumption, and the energy consumption model is simplified and analyzed based on the ratio of energy to mileage. The optimization objectives and constraints of speed planning are designed according to the mission requirements and traffic flow characteristics, and a non-dominated sorting genetic algorithm with elite strategy is proposed to solve the multi-objective optimization problem. Then, the objective function is normalized and the design principle of the weighting coefficient is given, so that the optimal solution is obtained by weighting multiple objective functions. The simulation results verify the stability and consistency of the algorithm.
Finally, the mission planning function is tested based on the small scale road network and the non-parametric estimation is used to obtain the probability distribution of estimation error. An estimated value correction strategy based on the mean of error is proposed, which can not only improve the accuracy and reliability of the estimation information but also guarantee the effectiveness and practicability.
Keywords: Autonomous Electric Vehicle, Mission Planning, Global Path Planning, Expected Speed Planning, Battery SOC Estimation
目录
摘要 ........................................................................................................................... I Abstract ............................................................................................................................. I I 第1 章绪论 (1)
1.1 课题研究背景与意义 (1)
1.2 国内外发展现状 (2)
1.2.1 国内外自动驾驶汽车任务规划研究现状 (2)
1.2.2 国内外纯电动汽车电池能耗估计研究现状 (4)
1.2.3 国内外车辆路径问题研究现状 (5)
1.3 论文主要内容及章节安排 (6)
第2 章纯电动汽车行驶能耗建模及SOC估算方法设计 (7)
2.1 引言 (7)
2.2 纯电动汽车行程能耗建模及参数辨识 (7)
2.2.1 行驶能耗模型建立 (7)
2.2.2 遗忘因子递推最小二乘法原理 (8)
2.2.3 基于遗忘因子最小二乘法的参数辨识 (9)
2.3 基于神经网络的锂电池SOC估算方法设计 (9)
2.3.1 锂电池特性分析 (10)
2.3.2 神经网络模型原理 (12)
2.3.3 基于NARX神经网络的锂电池SOC估计 (14)
2.4 仿真结果及分析 (16)
2.4.1 能耗模型参数辨识仿真结果 (16)
2.4.2 锂电池SOC估算仿真结果 (18)
2.5 本章小结 (21)
第3 章纯电动自动驾驶汽车全局路径规划算法设计 (22)
3.1 引言 (22)
3.2 全局路径规划问题描述 (22)
3.3 路网权值矩阵计算 (23)
3.3.1 路网有向图建立 (23)
3.3.2 车辆动态行驶速度估计 (23)
3.3.3 基于Dijkstra算法的权值矩阵计算 (24)
3.4 任务点序列优化 (26)
3.4.1 基于动态规划算法的旅行商问题求解 (26)
3.4.2 任务点剩余电量检测算法设计 (27)
3.5 全局路径规划算法设计 (29)
3.5.1 A*算法原理 (29)
3.5.2 基于A*算法的全局路径规划 (29)
3.6 仿真结果 (31)
3.7 本章小结 (34)
第4 章纯电动自动驾驶汽车指导速度规划算法设计 (35)
4.1 引言 (35)
4.2 路段断点模型建立 (35)
4.2.1 路径断点速度规划策略设计 (35)
4.2.2 路段能耗模型简化与分析 (36)
4.3 指导速度规划问题数学描述 (38)
4.3.1 优化目标函数设计 (38)
4.3.2 约束条件模型建立 (39)
4.4 指导速度规划问题求解 (40)
4.4.1 遗传算法原理 (40)
4.4.2 多目标优化算法设计 (42)
4.4.3 基于遗传算法的指导速度规划问题求解 (43)
4.5 算法验证与分析 (44)
4.6 本章小结 (47)
第5 章纯电动自动驾驶汽车任务规划算法验证 (48)
5.1 引言 (48)
5.2 实验系统搭建 (48)
5.2.1 自动驾驶操作系统组成 (49)
5.2.2 任务规划程序框架设计 (49)
5.3 任务规划模块功能测试 (50)
5.4 估计误差分析 (52)
5.4.1 非参数估计算法原理 (53)
5.4.2 速度误差来源分析 (53)
5.4.3 时间估计误差分析 (54)
5.4.4 能耗估计误差分析 (55)
5.5 本章小结 (56)
结论 (57)
参考文献 (59)
哈尔滨工业大学学位论文原创性声明和使用权限 (63)
致谢 (64)