智能网联电动汽车节能优化控制研究进展与展望

申永鹏; 袁小芳; 赵素娜; 孟步敏; 王耀南

doi:10.16383/j.aas.c220819

智能网联电动汽车节能优化控制研究进展与展望

doi: 10.16383/j.aas.c220819

1.
郑州轻工业大学电气信息工程学院郑州 450002
2.
湖南大学电气与信息工程学院长沙 410002
3.
湘潭大学自动化与电子信息学院湘潭 411105

基金项目: 国家自然科学基金(62273313, 62073127, 62003288, 62003312)资助

详细信息

作者简介:
申永鹏：郑州轻工业大学电气信息工程学院副教授. 2015年获湖南大学博士学位. 主要研究方向为智能网联电动汽车节能控制, 新能源汽车储能与电驱动系统控制与优化. 本文通信作者. E-mail: shenyongpeng@zzuli.edu.cn

袁小芳：湖南大学电气与信息工程学院教授. 2008年获湖南大学博士学位. 主要研究方向为智能网联电动汽车路径规划与控制. E-mail: yuanxiaofang@hnu.edu.cn

赵素娜：郑州轻工业大学电气信息工程学院讲师. 2017年获华南理工大学控制理论与控制工程专业博士学位. 主要研究方向为脑控机器人, 移动机器人运动控制. E-mail: snzhao1221@zzuli.edu.cn

孟步敏：湘潭大学自动化与电子信息学院副教授. 2018年获湖南大学博士学位. 主要研究方向为电动车辆智能化与网联化技术. E-mail: mengbm@163.com

王耀南：中国工程院院士, 湖南大学电气与信息工程学院教授. 1995 年获湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

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出版历程
- 收稿日期: 2022-10-19
- 录用日期: 2023-03-03
- 网络出版日期: 2023-05-04
- 刊出日期: 2023-12-27

Energy-saving Optimization Control for Connected Automated Electric Vehicles: State of the Art and Perspective

1.
College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002
2.
College of Electrical and Information Engineering, Hunan University, Changsha 410002
3.
College of Automation and Electronic Information, Xiangtan University, Xiangtan 411105

Funds: Supported by National Natural Science Foundation of China (62273313, 62073127, 62003288, 62003312)

More Information

Author Bio:
SHEN Yong-Peng　Associate professor at the College of Electrical and Information Engineering, Zhengzhou University of Light Industry. He received his Ph.D. degree from Hunan University in 2015. His research interest covers energy-saving control of connected automated electric vehicles, control and optimization of energy storage system and electric driving system in electric vehicle. Corresponding author of this paper

YUAN Xiao-Fang　Professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Hunan University in 2008. His research interest covers path plan and control of connected automated electric vehicles

ZHAO Su-Na　Lecturer at the College of Electric and Information Engineering, Zhengzhou University of Light Industry. She received her Ph.D. degree in control theory and control engineering from South China University of Technology in 2017. Her research interest covers brain controlled mobile robot and motion control of mobile robot

MENG Bu-Min　Associate professor at the College of Automation and Electronic Information, Xiangtan University. He received his Ph.D. degree from Hunan University in 2018. His research interest covers intelligent and connected technology of electric vehicle

WANG Yao-Nan　Academician at Chinese Academy of Engineering and professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers robotics, intelligent control, and image processing

摘要

摘要: 提升纯电动汽车整车能效、降低百公里耗电量, 是我国新能源汽车产业发展的重大需求. 智能网联背景下, V2X (Vehicle to everything)网联信息以及激光雷达、毫米波雷达、摄像头、定位及导航装置等各类车载传感器, 为智能网联电动汽车(Connected automated electric vehicle, CAEV)提供了全方位的信息交互、共享和状态感知能力, 赋予了其巨大的节能优化潜力. 针对CAEV节能优化控制问题, 首先从动力电池、电机控制器、驱动电机、传动机构、轮胎和驾驶决策六个环节分析电动汽车的典型损耗特性, 从决策、控制和执行三个层面分析CAEV的能量转换过程及耦合关系, 以及网联信息对CAEV 的节能影响; 然后, 从决策层车速优化、控制层驱动/制动转矩优化控制和执行层电流矢量优化控制三个方面, 对各层的节能优化问题进行阐述, 并重点对国内外研究现状进行归纳分析; 最后, 对决策层、控制层和执行层CAEV节能优化控制的难点以及现有研究工作进行总结, 并对下一步发展趋势进行展望.
- 智能网联 /
- 电动汽车 /
- 节能 /
- 优化控制
Abstract: Improving the energy efficiency of electric vehicles and reducing the power consumption are major demands for the development of China＇s new energy vehicle industry. With the development of CAEV (connected automated electric vehicle), V2X (vehicle to everything) network information and various on-board sensors such as lidar, millimeter-wave radar, camera, positioning and navigation devices which provide CAEVs with a comprehensive information interaction, sharing and state perception capabilities, and endowed it with a huge potential for energy-saving optimization. Aiming at the energy-saving optimization control problem of CAEV, the typical loss characteristics of electric vehicles are firstly analyzed from the six links of power battery, motor controller, drive motor, transmission mechanism, tires and driving decision-making, and the energy conversion process and coupling relationship of CAEV are analyzed from three levels of decision-making, control and execution, as well as the energy-saving impact of network connection information on CAEV; Then, from the three aspects of vehicle speed optimization at the decision-making level, driving/braking torque optimization control at the control level, and current vector optimization control at the action level, the energy-saving optimization problems at each level are expounded, and the research works at home and abroad are analyzed in detail; Finally, we summarized the difficulties of energy-saving optimal control of CAEV at the decision-making level, the control level and the action level, as well as the characteristics of the existing research works, and the future development trend is prospected.
- Intelligent connected /
- electric vehicle /
- energy-saving /
- optimization control

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图 1 我国纯电动汽车能耗发展目标

Fig. 1 Energy consumption development goals for China＇s pure electric vehicles

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图 2 纯电动汽车百公里耗电量限值

Fig. 2 100 km power consumption limit for pure electric vehicles

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图 3 电动汽车典型损耗

Fig. 3 Typical energy loss of electric vehicles

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图 4 CAEV能量转换过程及耦合关系分析

Fig. 4 Analysis of energy conversion and coupling relationship of CAEV

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图 5 车速优化问题示意图

Fig. 5 Schematic diagram of vehicle speed optimization problem

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图 6 CAEV车速优化决策方法分类

Fig. 6 Classification of decision-making methods for speed optimization of CAEV

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图 7 动态规划求解过程示意图

Fig. 7 Schematic diagram of the dynamic programming

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图 8 模型预测控制求解车速优化问题示意图

Fig. 8 Schematic diagram of MPC for solving vehicle speed optimization problem

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图 9 强化学习原理

Fig. 9 Principle of reinforcement learning

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图 10 电机输出转矩至车轮牵引力环节的能量转换过程示意图

Fig. 10 Schematic diagram of the energy conversion process from motor output torque to the wheel traction

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图 11 直流电能至电机输出转矩环节的能量转换过程示意图

Fig. 11 Schematic diagram of the energy conversion process from direct current electric energy to motor output torque

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图 12 永磁同步电机电流矢量优化控制示意图

Fig. 12 Schematic diagram of current vector optimization control of PMSM

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表 1 车速优化决策方法优缺点总结

Table 1 Summary of advantages and disadvantages of vehicle speed optimal decision-making methods

方法	优点	缺点
变分法	有解析解、计算开销较小	只能应用于控制变量连续且不受限制, 而且状态变量连续可微的场景
Pontryagin极小值原理	可用于控制变量分段连续且受限情况	仅提供了最优性的必要条件
二次规划	收敛速度快, 求解效率高	目标函数必须是二次型函数, 并且约束条件是变量的线性不等式
动态规划	可求解不连续及有约束OPC问题, 适用性广泛	无法对连续空间进行精确表示和求解, 存在“维数灾难”
MPC	可有效地克服过程的不确定性、非线性, 鲁棒性强	求解精度取决于预测模型精度, 过于复杂的模型会降低运算速度
自适应神经模糊控制	兼具自适应性和逻辑推理能力	需要大量的数据样本对模糊神经网络进行离线训练
强化学习	与人类的学习过程类似, 可根据奖励机制实现不确定场景下的自主行为优化决策	奖励函数的设置需考虑多重因素, 价值网络和策略网络的训练过程需要多场景下的海量样本支撑

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表 2 三类离线MTPA方法的优缺点

Table 2 Advantages and disadvantages of three types of off-line MTPA methods

方法	优点	缺点
解析法	原理简单, 易于实现	控制精度易受电机参数变化影响
简化MTPA	控制复杂度低	控制精度随着简化而降低
查表法	避免了复杂运算, 降低了硬件负担	需要提前做大量实验以建立控制表格, 普适性差

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表 3 三类ME方法的优缺点

Table 3 Advantages and disadvantages of the three types of ME methods

方法	优点	缺点
基于模型的方法	考虑了铜耗和铁耗, 理论上可以实现最大效率控制	对电机参数十分敏感, 参数变化后将影响控制效果
基于搜索的方法	不受电机参数变化影响, 鲁棒性强	收敛速度较慢, 容易导致转矩、转速脉动, 甚至引起系统振荡
混合方法	理论上可实现全局最优, 并且可根据电机参数的变化动态调整	计算复杂度较高, 在实际电机控制中难以实现运行

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