New Control Methodology for the Next Generation of Engine Management Systems

下一代发动机管理系统的新控制方法

• 批准号: EP/J00930X/1
• 负责人: Hongming Xu
• 金额: $ 71.76万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ00930X%2F1
• 关键词: New; Control; Methodology; Next; Generation

Despite of the fact that electrical cars are under development and have the potential to provide alternatives for short distance light duty transport, the internal combustion engine will continue to be the main power unit in vehicles for several decades to come. Compared with extensive research on combustion and after-treatment systems, little work has been completed with respect to engine system control optimisation, leaving considerable room to improve fuel economy and lower emissions. Current engine calibration process relies on deriving static tabular relationships and the corresponding values between each calibrated engine operating point, with closed-loop feedback control to adjust the settings accordingly for air-fuel ratio control in real engine operation so as to meet the performance targets and emissions legislation. Such a widely adopted method, however, is not efficient in achieving the best fuel economy of the vehicle due to the constraints in the time duration and cost of engine-bed based calibration. Environmental conditions changes, the time required for the closed-loop control to respond, cycle-by-cycle variations, and cylinder-to-cylinder variations make the current engine control impossible to handle the the optimisation of the engine functionalities. The development trend for future engines is towards an on-board intelligence for control and calibration and some research activities for the development of model based control systems are reported in literature. However, feasible strategies to control the engine operation cycle-by-cycle and cylinder-by-cylinder are not yet available. Expanding the work of the applicants in the related areas for many years, the overall Goal of this project is to use a combination of joint efforts from 3 research groups with expertise of engine technology, control technology and computing algorithm in order to develop and test a new engine control and calibration methodology with on-line intelligence built in. This overall goal will be achieved through realising the following objectives:(1) To develop a full real-time multi-cylinder engine model for cylinder-resolved-control purpose(2) To develop a novel engine control strategy involving optimization of control points and control point locations, and multi-objective evaluation of test cycle performance(3) To develop dynamic multi-objective evolutionary algorithms for online engine control optimization(4) To demonstrate the implementation of the engine control models initially on Hardware-in-the-Loop (HIL) dSPACE system and then further rapid prototyping on a test engine.(5) To compare the engine performance using the new techniques with traditional calibration and control approaches, and demonstrate improvements in terms of engine output, fuel consumption, and emissions. The new engine control methodology will be evaluated on a new Jaguar gasoline direct injection (GDI) engine model.

尽管电动汽车正在开发中，并有可能为短距离轻型交通提供替代方案，但在未来几十年内，内燃机仍将是汽车的主要动力装置。与燃烧和后处理系统的广泛研究相比，在发动机系统控制优化方面完成的工作很少，在提高燃油经济性和降低排放方面留下了相当大的空间。当前的发动机标定过程依赖于推导出发动机各标定工作点之间的静态列表关系和对应值，并通过闭环反馈控制来相应地调整实际发动机运行中的空燃比控制设定值，以满足性能目标和排放法规。然而，由于基于发动机床的校准的时间和成本的限制，这种被广泛采用的方法在实现车辆的最佳燃油经济性方面并不有效。环境条件的变化、闭环控制响应所需的时间、循环间的变化以及气缸间的变化使得当前的发动机控制不可能处理发动机功能的优化。未来发动机的发展趋势是控制和标定的车载智能化，文献报道了基于模型的控制系统开发的一些研究活动。然而，目前还没有可行的策略来逐个循环和逐个气缸地控制发动机的运行。该项目的总体目标是利用3个具有发动机技术、控制技术和计算算法专业知识的研究小组的共同努力，开发和测试具有在线智能的新的发动机控制和标定方法。这一总体目标将通过实现以下目标来实现：(1)开发用于气缸解析控制目的的全实时多缸发动机模型(2)开发一种新的发动机控制策略，包括优化控制点和控制点位置，以及测试循环性能的多目标评估(3)开发在线发动机控制优化的动态多目标进化算法(4)演示发动机控制模型最初在硬件在环(HIL)dSPACE系统上的实现，然后在试验发动机上进行进一步的快速原型制作。(5)将新技术与传统的标定和控制方法进行发动机性能比较，并在发动机输出、燃油消耗和排放方面进行改进。新的发动机控制方法将在一款新的捷豹汽油直接喷射(GDI)发动机模型上进行评估。

项目成果

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

• DOI: 10.1016/j.apenergy.2019.113617
• 发表时间: 2019-11
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: Ji Li;Quan Zhou;Yinglong He;B. Shuai;Ziyang Li;Huw Williams;Hongming Xu

Space vector modulation of multilevel inverters with online vector identification and reduced switching losses for HEV/EV applications

多电平逆变器的空间矢量调制，具有在线矢量识别功能，可降低 HEV/EV 应用的开关损耗

• DOI: 10.1504/ijpelec.2021.117528
• 发表时间: 2021
• 期刊: International Journal of Power Electronics
• 作者: Cash S

Development of a Series Hybrid Electric Aircraft Pushback Vehicle: A Case Study

串联式混合动力电动飞机后推车的开发：案例研究

• DOI: 10.4236/eng.2019.111004
• 发表时间: 2019
• 期刊: Engineering
• 影响因子: 12.8
• 作者: Cash S

Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs

• DOI: 10.1109/tte.2020.2974588
• 发表时间: 2020-03-01
• 期刊: IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
• 影响因子: 7
• 作者: He, Yinglong;Zhou, Quan;Xu, Hongming
• 通讯作者: Xu, Hongming

Fuzzy Logic based Power-Split Hybrid Propulsion Control System using Digital Twin Assisted Parallel Learning

使用数字孪生辅助并行学习的基于模糊逻辑的功率分流混合动力推进控制系统

• DOI: 10.1504/ijpt.2022.10048469
• 发表时间: 2022
• 期刊: International Journal of Powertrains
• 作者: He X