智能新能源汽车车载控制基础软硬件关键技术研究进展

doi:10.19562/j.chinasae.qcgc.2023.09.003

Abstract

Abstract:

The intelligence and networking of new energy vehicles have put forward higher requirements for the basic software and hardware of vehicle on-board control including core control chips， operating systems and networks， which must meet disruptive needs such as high-performance computing， high security control and big data communication. Focusing on the high reliability and safety design requirements of vehicle core control chips， vehicle control operating systems， and high-speed distributed fiber optic communication in vehicles， this paper introduces the latest research on key technologies of the architecture of vehicle control operating systems and vehicle core control chips that support intelligent control algorithms under complex driving conditions， the high reliability design technology and environmental adaptability enhancement technology of vehicle core control chips under harsh working conditions， functional safety design and guarantee technology for vehicle control operating system and onboard core control chip， control signal transmission tool based on high-speed distributed fiber optic communication technology， and communication protocol fault diagnosis and self testing technology. Based on the above research results， the independently developed vehicle core control chip and vehicle control operating system have all passed practical vehicle verification. The established research and development system for technology breakthrough， product development， standard formulation， and practical vehicle verification can provide necessary theoretical and technical support for the complete autonomy and controllability of the basic software and hardware of intelligent new energy vehicle on-board control in China.

Key words: vehicle core control chip, vehicle control operating system, vehicle network, reliable design, safety design

Zhaolin Li,Huawei Li,Sifa Zheng,Wenwei Wang,Guangcai Zou,Chuang Zhang,Ying Liu,Yongchang Zhang. Research Progress on Key Technologies of Basic Software and Hardware for Intelligent New Energy Vehicle Onboard Control[J].Automotive Engineering, 2023, 45(9): 1530-1542.

Figures/Tables 16

References 29

1	王会杰，杨燕红，李志强. 我国智能网联汽车发展现状及策略分析［J］. 汽车实用技术，2023，48（6）：53-57.
	WANG H J， YANG Y H， LI Z Q. Analysis on the development status and strategy of intelligent connected vehicles in China［J］. Automobile Technology， 2023， 48（6）：53-57.
2	李寒洋. 浅谈智能网联汽车发展现状及趋势［J］. 汽车工业研究，2020（1）：2-9.
	LI H Y. Discussion on the development status and trends of intelligent connected vehicles ［J］. Auto Industry Research， 2020（1）：2-9.
3	崔明阳，黄荷叶，许庆，等.智能网联汽车架构、功能与应用关键技术［J］.清华大学学报（自然科学版）， 2022，62（3）：493-508.
	CUI M Y，HUANG H Y， XU Q， et al. Survey of intelligent and connected vehicle technologies： architectures， functions and applications ［J］. Journal of Tsinghua University（Science and Technology）， 2022，62（3）：493-508.
4	英飞凌科技股份公司. TC397器件数据手册［G］. 2020.
	Infineon Technologies Co.， Ltd. TC397 device data manual［G］. 2020.
5	朱敏慧.瑞萨R-Car系列助力自动驾驶研发［J］. 汽车与配件，2021（8）：56.
	ZHU H M. Reza R-Car series assists in the development of autonomous driving ［J］. Automobile & Parts， 2021（8）：56.
6	XU Dawen， HE Meng， LIU Cheng， et al. R2F： a remote retraining framework for AIoT processors with computing errors［J］. IEEE Transactions on Very Scale Integration（VLSI） System， 2021， 29（11）：1955-1966.
7	WANG Mingyu， LI Zhaolin. A spatial and temporal locality-aware adaptive cache design with network optimization for tiled many-core architectures［J］. IEEE Transactions on Very Large Scale Integration Systems，2017， 25（9）： 2419-2433.
8	潘妍，张也，周瑞坤，等.我国智能网联汽车操作系统研究［J］. 电子元器件与信息技术，2022（5）：142-146.
	PAN Y， ZHANG Y， ZHOU R K， et al. Research on the intelligent connected vehicle operating system in China ［J］. Electronic Components and Information Technology， 2022（5）：142-146.
9	李鲁苗，周玮.全球车用操作系统发展现状［J］.汽车纵横， 2022（1）：39-42.
	LI L M， ZHOU W. Current situation of global automotive operating system development ［J］. Auto Review， 2022（1）：39-42.
10	盛炜杰，陈锦云，王雅思，等. 车载网络发展趋势和车载光纤传输研究进展［J］. 激光与光电子学进展，2023， 60（5）：1-11.
	SHENG W J， CHEN J Y， WANG Y S， et al. Development trend of in-vehicle networks and research progress of in-vehicle optical fiber transmission ［J］. Laser & Optoelectronics Progress， 2023， 60（5）：1-11.
11	郑志超，南金瑞，南江峰.车载网络 CAN FD 总线的应用前景和技术研究［J］. 现代电子技术， 2021， 44（1）：5-9.
	ZHENG Z C， NAN J R， NAN J F. Research on application prospect and technology of CAN FD bus of vehicle network ［J］. Modern Electronics Technique， 2021， 44（1）：5-9.
12	HE Yintao， WANG Ying， LIU Cheng， et al. TARe： task-adaptive in-situ ReRAM computing for graph learning［C］. ACM/IEEE Design Automation Conference （DAC）， 2021： 577-582.
13	HE Lei， LIU Cheng， WANG Ying， et al. GCiM： a near-data processing accelerator for graph construction［C］. ACM/IEEE Design Automation Conference， 2021：205-210.
14	HAN Jianhui， FEI Xiang， LI Zhaolin， et al. Polyhedral-based compilation framework for in-memory neural network accelerators［J］. ACM Journal on Emerging Technology in Computing Systems， 2021， 18（1）：1-23.
15	LIU Cheng， CHU Cheng， XU Dawen， et al. HyCA： a hybrid computing architecture for fault tolerant deep learning［J］. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems， 2021， 41（10）： 3400-3413.
16	ZHANG Ying， DING Yi， PENG Zebo， et al. BMC-based temperature-aware SBST for worst-case delay fault testing under high temperature［J］. IEEE Trans. Very Large Scale Integration Systems， 2022， 30（11）： 1677-1690.
17	LI Huawei， LI Xiaowei. Selection of crosstalk-induced faults in enhanced delay test［J］. Journal of Electronic Testing： Theory and Applications， 2005， 21（2）： 181-195.
18	KRSTIC A， LIOU Jingjia， JIANG Yimin， et al. Delay testing considering crosstalk-induced effects［C］. Proceedings of IEEE International Test Conference， 2001： 558-567.
19	LI Wen， WANG Ying， LIU Cheng， et al. On-line fault protection for reram-based neural networks［J］. IEEE Transactions on Computers， 2023，72（2）： 423-437.
20	MA Haitong， LIU Changliu， LI Shengbo Eben， et al. Joint synthesis of safety certificate and safe control policy using constrained reinforcement learning［J］. PMLR， 2022， 144： 97-109.
21	ZHENG Haotian， CHEN Chaoyi， LI Shuai， et al. Learning-based safe control for robot and autonomous vehicle using efficient safety certificate［J］. IEEE Open Journal of Intelligent Transportation Systems， 2023，4： 419-430.
22	GU Ziqing， GAO Lingping， MA Haitong， et al. Safe-state enhancement method for autonomous driving via direct hierarchical reinforcement learning［C］. IEEE Transactions on Intelligent Transportation Systems， 2023.
23	GU Ziqing， YIN Yuming， LI Shengbo Eben， et al. Integrated eco-driving automation of intelligent vehicles in multi-lane scenario via model-accelerated reinforcement learning［J］. Transportation Research Part C，2022，144：1-14.
24	王遵彤，李彩，吴启迪.多处理器系统动态调度负载均衡节约算法［J］.控制与决策， 2011， 26（11）：5.
	WANG Z T， LI C， WU Q D. Load-balancing thrift algorithm for dynamic scheduling of multiprocessor systems ［J］. Control and Decision， 2011， 26（11）：5.
25	CAO Wanke， WANG Lecheng， LI Jianwei，et al. Analysis and design of drivetrain control for the AEV with network-induced compounding-construction loop delays［J］. IEEE Transactions on Vehicular Technology， 2021， 70（6）：5578-5591.
26	CAO Wanke， LU Jizhi， LI Jianwei， et al. Networked motion control for smart EV with multiple-package transmissions and time-varying［J］. IEEE Transactions on Industrial Electronics， 2022， 69（4）：4076-4086.
27	CAO Wanke， LIU Shao， LI Jianwei， et al. Analysis and design of adaptive cruise control for smart electric vehicle with domain-based poly-service loop delay［J］. IEEE Transactions on Industrial Electronics， 2023， 70（1）：866-877.
28	WANG Wenwei， YU Shiyao， CAO Wanke. Review of in-vehicle optical fiber communication technology［J］. Automotive Innovation， 2022，5（3）：272-284.
29	CAO Wanke， YANG Mengchao， WEI Zhongbao， et al. Autonomous emergency braking of electric vehicles with high robustness to cyber-physical uncertainties for enhanced braking stability［J］. IEEE Transactions on Vehicular Technology， 2022， 72（4）： 4426-4441.

[1]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[2]	LI Ling, MA Li, MOU Yu, XU Chao, LI Wen-Ru, SHI Shu-Ming. [J]. , 2016, 38(12): 1508 -1514 .
[3]	DONG Zhu-Rong, ZHANG Xin, HU Song-Hua, QIU Hao. [J]. , 2017, 39(1): 79 -85 .
[4]	CUI Jun-Jia, YUAN Wei, LI Guang-Yao. [J]. , 2017, 39(1): 113 -120 .
[5]	GUO Peng-Cheng, XU Cong-Chang, LIU Zhi-Wen, FANG Xiang-Dong, LI Luo-Xing. [J]. , 2017, 39(2): 138 -144 .
[6]	Chen-Xin, FENG Xiao, SHEN Chuan-Liang, WANG Shuo, HU Cui-Song, LI Yan-Yang, YANG Chang-Hai-　. [J]. , 2017, 39(2): 206 -213 .
[7]	CAO Li-Bo, CHEN Zheng, YAN Ling-Bo, QIN Qin, ZHANG Rui-Feng. [J]. , 2017, 39(2): 225 -231 .
[8]	YU Zhuo-Ping, LENG Bo. [J]. , 2017, 39(3): 243 -248 .
[9]	ZHANG Yong, GU Zheng-Qi, LIU Shui-Chang, MI Cheng-Ji. [J]. , 2017, 39(3): 275 -280 .
[10]	. Design and Implementation of Electric Vehicle Drive Motor Control Based on AUTOSAR[J]. , 0, (): 0 .

类别	模块	数据字段区分	分析说明
芯片运行数据	功能安全	电源	极值
		CPU	极值
		总线	极值
	温度模块	传感器温度	极值、均值、标准差、时间序列
	接口（CAN）	错误计数器	极值
	接口（CAN）	错误计数器	极值
环境数据	温度	芯片表面温度	极值、均值、标准差、时间序列
	振动	VCU纵向振动	极值、均值、标准差、时间序列
		VCU横向振动	极值、均值、标准差、时间序列
		VCU竖向振动	极值、均值、标准差、时间序列
	湿度	环境湿度	极值、均值、标准差、时间序列

Research Progress on Key Technologies of Basic Software and Hardware for Intelligent New Energy Vehicle Onboard Control

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 29

Related Articles 1

Metrics

Comments

Recommended 10