数据驱动的内燃机控制预校准参数优化

IF 10.1 1区 工程技术 Q1 ENERGY & FUELS
Matteo Meli , Zezhou Wang , Stefan Sterlepper , Mario Picerno , Stefan Pischinger
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引用次数: 0

摘要

提出了一种有效的内燃机控制预标定方法。它特别关注基于查找表的多输入单输出引擎控制系统中多个查找表的初始成形。该方法解决了发动机软件日益复杂、校准变量数量不断增加以及汽车开发中普遍存在的时间压力等问题。与传统的发动机校准技术相比,采用白盒循环模型(MiL)优化减少了对硬件依赖和优化时间的要求。白盒模型允许使用已知的系统输入、预期的系统输出和控制系统模型结构对lut进行预校准。为了优化白盒控制系统模型,lut通过Rational bzier Regression (RBR)进行参数化,促进了序列二次规划(Sequential Quadratic Programming, SQP)的优化。RBR,包括Rational b阴郁曲线回归(RBCR)和Rational b阴郁表面回归(RBSR),允许使用统一的和少量的参数灵活和平滑地塑造1D和2D lut。利用存储在关系数据库中的各种车型的历史校准数据,进一步改进了预校准过程。这确保了基于lut的MISO控制系统的最终输出与预期目标输出非常接近,具有很高的形状相似性。最后以某新型内燃机混合动力系统的油温控制模型为例进行了验证。结果表明,目标和预校准lut之间的Pearson相关系数(PCCs)超过0.8,表明形状相似度高。此外,预校准控制系统模型的系统输出与预期系统输出非常接近,R2值为0.9385。这强调了所提出的预校准方法在内燃机控制中的实际适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven parametric optimization for pre-calibration of internal combustion engine controls
This paper presents an efficient pre-calibration method for combustion engine controls. In particular, it focuses on the initial shaping of multiple Lookup Tables (LUTs) within LUT-based Multiple-Input Single-Output (MISO) engine control systems. The approach addresses the increasing complexity of engine software, the rising number of calibration variables, and the time pressures prevalent in automotive development. Employing a white-box Model-in-the-Loop (MiL) optimization reduces the demands on hardware reliance and optimization time compared to conventional engine calibration techniques. The white-box model enables the pre-calibration of LUTs using known system inputs, expected system outputs, and the control system model structure. To optimize the white-box control system model, LUTs are parametrized through Rational Bézier Regression (RBR), facilitating Sequential Quadratic Programming (SQP) for optimization. RBR, which includes both Rational Bézier Curve Regression (RBCR) and Rational Bézier Surface Regression (RBSR), allows for flexible and smooth shaping of 1D and 2D LUTs with a unified and few number of parameters. The pre-calibration process is further improved using historical calibration data from various vehicle variants stored in a relational database. This ensures that the final outputs of the LUT-based MISO control system closely approximate the expected target outputs with high shape similarity. The proposed method is exemplified using an oil temperature control model from a state-of-the-art hybrid powertrain with an internal combustion engine. The results demonstrate Pearson Correlation Coefficients (PCCs) exceeding 0.8 between target and pre-calibrated LUTs, indicative of high shape similarity. Additionally, the system outputs of pre-calibrated control system models closely match expected system outputs with an R2 value of 0.9385. This underscores the practical applicability of the proposed pre-calibration method for internal combustion engine controls.
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来源期刊
Applied Energy
Applied Energy 工程技术-工程:化工
CiteScore
21.20
自引率
10.70%
发文量
1830
审稿时长
41 days
期刊介绍: Applied Energy serves as a platform for sharing innovations, research, development, and demonstrations in energy conversion, conservation, and sustainable energy systems. The journal covers topics such as optimal energy resource use, environmental pollutant mitigation, and energy process analysis. It welcomes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gap between research, development, and implementation. The journal addresses a wide spectrum of topics, including fossil and renewable energy technologies, energy economics, and environmental impacts. Applied Energy also explores modeling and forecasting, conservation strategies, and the social and economic implications of energy policies, including climate change mitigation. It is complemented by the open-access journal Advances in Applied Energy.
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