人工智能在发动机控制开发中的应用及前景

doi:10.3969/j.issn.1674-8484.2021.02.002

汽车安全与节能学报 ›› 2021, Vol. 12 ›› Issue (2): 150-162.DOI: 10.3969/j.issn.1674-8484.2021.02.002

人工智能在发动机控制开发中的应用及前景

徐宏明(), 周泉

伯明翰大学先进汽车技术研究中心,伯明翰B15 2TT,英国

收稿日期:2021-05-17 出版日期:2021-06-30 发布日期:2021-06-30
作者简介:徐宏明（1959—）,男（汉）,安徽,教授。Email: h.m.xu@bham.ac.uk。徐宏明教授,英国伯明翰大学教授、先进汽车技术研究中心主任，国际汽车工程学会会士（SAE Fellow）,全英华人汽车工程师协会主席、全英华人教授协会副主席。1995年毕业于伦敦帝国理工学院，获得博士学位。先后担任伦敦帝国理工学院博士后研究员、高级研究员、捷豹路虎汽车公司项目工程师、团队负责人、技术专家。2005年加入伯明翰大学担任副教授。长期从事先进汽车动力系统设计、优化和控制相关的研究。
Prof. XU Hongming，He is Professor and Head of the Advanced Vehicle Research Centre at the University of Birmingham, UK. He is a SAE fellow, president of the UK-China Society of Automotive Engineers (UKCSAE), and vice- president of the Association of British Chinese Professors (ABCP). He obtained PhD from Imperial College London in 1995 and then worked there as a research fellow and a senior research fellow. He moved to Jaguar Cars Ford Premier Automotive Group in 2000, where he was project engineer (2000-2001), team leader (2002-2004), and principal technical specialist until he joined the University of Birmingham in 2005 as reader in Automotive Engineering. His research interests include design, optimization, and control of advanced powertrain system for automotive.

Artificial intelligence technologies for engine control development: State-of-the-art review and outlook

XU Hongming(), ZHOU Quan

Vehicle Research Centre, University of Birmingham, Birmingham B15 2TT, UK

Received:2021-05-17 Online:2021-06-30 Published:2021-06-30

摘要/Abstract

摘要：

电动化、智能化、网联化、共享化 (CASE) 是汽车技术发展的趋势。根据国际能源署 (IEA) 的预测,到2050年,以包括插电式混合动力在内的电动化汽车将占有市场产品97%的份额。越来越严苛并要求通过实际行驶条件下检测的排放法规也对先进发动机技术,特别是发动机控制技术提出了新挑战。该文围绕基于模型的发动机控制开发中的优化问题,从前馈控制优化、反馈控制优化和动力总成全局优化3个层面,分析了人工智能 (AI) 技术在上述开发场景中的应用案例,展望了人工智能在发动机控制开发中的前景。研究表明,人工智能技术能将推动发动机控制开发中的3个融合：一是以发动机数字孪生技术为代表的信息系统与物理系统融合;二是以发动机多场景智能优化技术为依托的机器学习系统与经典控制系统融合;三是以动力总成域控制技术为基础的多源系统信息融合。这些融合将推动发动机技术的进一步发展,助力实现近零碳排放。

关键词: 发动机控制, 基于模型的开发, 人工智能(AI), 系统优化, 控制器标定

Abstract:

Connection, automation, sharing and electrification (CASE) are the future of vehicle and mobility systems. International Energy Agency (IEA) predicts that electric vehicles including plug-in hybrid vehicles will count for 97% of the market by 2050. The increasingly stringent emission legislations for CO₂ reduction, especially when involving real driving emissions (RDE) testing are the main challenges to the control system of the vehicle powertrains. This paper focus on artificial intelligence (AI) technologies for engine control developments that follow the standard model-based routine. By reviewing state-of-the-art AI technologies for feedforward control, feedback control, and global optimization at system level, the advantage and disadvantage of the AI technologies are compared and summarized. An outlook is provided based on the literature survey. It indicates that AI will promote the fusion of technologies in three representative domains, 1) fusion of cyber systems and physical systems, e.g., digital twin of engine; 2) fusion of machine learning systems and classical control systems, e.g., AI-based calibration of engine controllers; and 3) fusion of information from multiple sources, e.g., powertrain domain control network. The technology fusions in these three domains are expected to promote the development of advanced engines which aims to achieve zero emissions.

Key words: engine control, model-based development, artificial intelligence (AI), system optimization, controller calibration

中图分类号:

TK421.5

徐宏明, 周泉. 人工智能在发动机控制开发中的应用及前景[J]. 汽车安全与节能学报, 2021, 12(2): 150-162.

XU Hongming, ZHOU Quan. Artificial intelligence technologies for engine control development: State-of-the-art review and outlook[J]. Journal of Automotive Safety and Energy, 2021, 12(2): 150-162.

图/表 7

图1 发动机控制系统简图

图2 发动机优化目标的进化过程：获得优化目标的时间代价[13]

图3 发动机工况点对比[26]

图4 智能多目标协同优化结果[50]

图5 混合动力发动机和电机的瞬态工况点[51]

图6 发动机控制器开发流程

图7 恩智浦提出的域控制电子构架[80]

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人工智能在发动机控制开发中的应用及前景

Artificial intelligence technologies for engine control development: State-of-the-art review and outlook

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