Applications to Modelling and Predictive Control: Development and Validation of a Semi-physical 1D Model for Virtual Engine Strategy Optimization

在机器学习中，回归通常被认为是一种黑盒方法，通常用于从假设中识别合适的函数。它的主要目的是估计一个预测的功能，导致未来数据的预期误差最小，并不一定有利于整体理解的系统的物理解释所支配的导出的输出-输入关系。因此，这种建模应用程序往往受到高度限制，只允许训练稀疏的线性模型。到今天为止，通过物理系统观察形成能够进行非线性解释的智能算法的任务很少得到考虑，但它构成了系统识别领域的基础。典型地，用于非线性系统识别的方法包括自回归地对时间演化建模或利用沃尔泰拉系列多维卷积积分。该项目将解决与传统的地图为基础的控制器设计和校准的动力系统开发中使用的做法相关的缺点。它将为系统识别提供新颖的，未来的，非线性物理因果关系预测建模和实验方法，通过基于监督神经网络的机器学习算法进行系统优化，从而得出一个实时控制系统。该项目将与Koenigsegg Automotive AB和Freevalve AB合作，开发新型无凸轮发动机技术Freevalve，为未来的动力系统提供重大的效率和动力改进。它将充分利用Freevalve的潜力，减少有害气体和颗粒物的排放，使该技术处于市场领先地位，为大规模实施做好准备。

In machine learning, regression is often considered a black box methodology used commonly for identification of suitable functions from a hypothesis. Its primary aim is to estimate a predicted function which leads to minimal expected error on future data, and not necessarily to benefit the overall understanding of the derived output-input relationship governed by the physical interpretation of the system. Consequently, such modelling applications are often highly constrained, allowing training solely for thinly dispersed linear models. To this day, the task of forming intelligent algorithms capable of nonlinear interpretations through physical system observation has received very little consideration, it nevertheless forms the foundation of the field of system identification. Typically, approaches for nonlinear system identification include autoregressively modelling time evolution or utilising Volterra series multidimensional convolution integrals. This project will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system through supervised neural network-based machine learning algorithms for system optimization. The project will be undertaken in collaboration with Koenigsegg Automotive AB and Freevalve AB on the novel cam-less engine technology, Freevalve, enabling major efficiency and power improvements for future powertrains. It will enable the full utilization of Freevalve's potential and the reduction of harmful gaseous and particulate matter emissions, putting the technology in a market-leading position ready for large-scale implementation.