Diesel engine parameter prediction became a topic of interest in recent years, along with the development of condition-based maintenance, and is now considered a key instrument for engine diagnosis research. This contribution compares two different approaches for diesel engine performance prediction: thermodynamic modelling and artificial neural networks (ANNs). The thermodynamic modelling was developed using AVL Boost™ software simulating a single-cylinder diesel engine with different engine loads and operating conditions. The ANN modelling was conducted by comparing two efficient training algorithms to achieve the best prediction performance, with the ANN structure parameters determined by network error analysis. Both models’ prediction accuracy was verified by a single-cylinder engine test bench operating under real conditions. The adaptability and robustness of the two approaches was studied for the whole engine load spectrum, comparing predicted values to experimental measurements. Both prediction tools, ANN and thermodynamic modelling, proved to be reliable for engine performance and emissions prediction. In both models brake-specific fuel consumption (BSFC), exhaust gas temperature (T_exh), carbon monoxide (CO) and nitrogen oxides (NO_x) were predicted using brake mean effective pressure (BMEP) and engine speed as inputs. ANN show higher accuracy for BSFC prediction in all engine loads, and T_exh prediction accuracy is better for ANN when dealing with medium to high loads, while the thermodynamic model shows better results when dealing with medium to low loads. CO is better predicted by the thermodynamic model except for the highest engine loads, and NO_x predictions present high accuracy in both models, except for the lowest loads. Calculation time is lower for ANN, but the thermodynamic model provides additional performance results (i.e. combustion pressure tracing and associated values).

随着基于状态的维护的发展，近年来，柴油机参数预测已成为人们关注的话题，并且现在被认为是用于发动机诊断研究的关键工具。该贡献比较了柴油机性能预测的两种不同方法：热力学建模和人工神经网络（ANN）。热力学模型是使用AVL Boost™软件开发的，用于模拟具有不同发动机负载和工况的单缸柴油发动机。通过比较两种有效的训练算法以实现最佳的预测性能，以及通过网络误差分析确定的ANN结构参数，进行ANN建模。两种模型的预测精度均通过在实际条件下运行的单缸发动机试验台进行了验证。研究了两种方法在整个发动机负荷谱中的适应性和鲁棒性，将预测值与实验测量值进行了比较。人工神经网络和热力学建模这两种预测工具均被证明对发动机性能和排放预测是可靠的。在两种型号中，特定于制动器的燃油消耗量（BSFC），排气温度（T_例如，使用制动平均有效压力（BMEP）和发动机转速作为输入来预测一氧化碳（CO）和氮氧化物（NO _x）。ANN在所有发动机负载下都显示出更高的BSFC预测准确度，而在处理中到高负载时，ANN的T _exh预测准确性更好，而热力学模型在处理中到低负载时显示出更好的结果。CO是更好地除了最高的发动机负荷的热力学模型预测，和NO _X预测目前的高精确度在两个模型中，除了最低负荷。ANN的计算时间较短，但是热力学模型提供了其他性能结果（即燃烧压力跟踪和相关值）。