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A Non-linear approach to dynamic torque modelling for expedited engine characterisation, control and calibration

机译：动态扭矩建模的非线性方法可加快发动机特性，控制和校准的速度

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Traditional approaches to engine torque mapping apply steady or quasi-steady-state approaches for data collection. Engine actuators are moved to some set-point and the engine is left to stabilise. Data is then recorded at fixed actuator settings over a period of time and averaged, the size of the averaging ‘window’ is often arbitrarily chosen. Such methods are time intensive and inefficient with the majority of the time available for data collection wasted. In this paper, a novel and fully transient characterisation methodology is introduced. Dynamic excitation signals are used in this work to significantly increase the rate of data collection and settling time (after an initial warm-up) is no longer required. Engine dynamic behaviour is described using first-order conditionally linear repeated measurement models, these reflect the inherent structure of the torque data that is a consequence of the experimental method employed. Model parameters are determined using Maximum Likelihood Estimation (MLE). To abstract steady-state data from the dynamic models (a requirement of legacy controllers) the models are extrapolated, in time, for fixed input settings. The efficacy of the extrapolation procedure is clearly demonstrated through direct comparison with actual steady-state sweeps utilised for validation of the method. The approach is shown to be seven times faster than conventional methods based on slow ramps or steady-state test.
机译：发动机扭矩映射的传统方法将稳态或准稳态方法应用于数据收集。将发动机执行器移至某个设定点，然后使发动机稳定下来。然后在固定的执行器设置下记录数据一段时间，然后取平均值，通常会随意选择平均“窗口”的大小。这种方法耗时且效率低下，浪费了大部分可用于数据收集的时间。本文介绍了一种新颖且完全瞬态的表征方法。在这项工作中使用动态激励信号来显着提高数据收集的速度，并且不再需要建立时间（在初始预热之后）。使用一阶条件线性重复测量模型描述发动机动态行为，这些模型反映了扭矩数据的固有结构，这是所采用的实验方法的结果。使用最大似然估计（MLE）确定模型参数。为了从动态模型中提取稳态数据（传统控制器的要求），可为固定输入设置及时推断模型。通过与用于验证该方法的实际稳态扫描进行直接比较，可以清楚地证明外推程序的有效性。结果表明，该方法比基于慢速斜坡或稳态测试的传统方法快7倍。

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