## 2 Related Works  
This section covers EBP investigations, mainly regarding AI-based predictions in diesel engines and vehicular energy systems. Engine performance is negatively influenced by excessive EBP, thus requiring a higher pumping work to release exhaust gas. The literature has indicated slight reduction of power output and an increase of fuel consumption, exhaust temperature, and smoke emissions. In turbocharged engines, higher EBP means higher turbine outlet pressure, which decelerates the turbocharger, limits the air mass flowrate aspirated in the compressor, and enriches the air-fuel mixture [2,3]. Among engine after-treatment technologies, active DPF (Diesel Particulate Filter) is the most demanding device in terms of pressure drop exigence, and it is thus of paramount importance in the triggering of thermal regeneration process [4]. There are rather few published papers with experimental measures of EBP. For naturally aspirated single-cylinder diesel engines, EBP has been varied in the range of 1.1-1.5 bar, which explains the influence on residual gas fraction and emissions [5,6]; meanwhile, other work [7] has reported increased smoke and a drop of brake thermal efficiency at low loads. For turbocharged engines, a six-cylinder aftercooled 500 kW diesel engine has been investigated at a constant speed, and the influence of EBP on brake-specific fuel consumption (BSFC), exhaust gas temperature, and emissions was interpreted [8]. Similarly, the testing of a 63 kW turbocharged and intercooled diesel engine at a high EBP impeded engine power, torque, and smoke opacity, reporting, at full load, a drop of power of 9% at an externally applied EBP restriction of 35% out of exhaust duct free section [9]. Accurate EBP measurement is time-consuming and costly, being impeded by pulsing flow and high temperatures; that is why the influence of EBP increase has been frequently investigated with numerical simulation models [5,10]. The exhaust system has been considered a fixed-geometry restriction between the exhaust manifold and the outlet of the tailpipe being implemented a mean-value model to estimate the exhaust manifold pressure from a compressible flow equation. Simulations with Ricardo wave software on a 11-liter submarine diesel engine indicated the reduction of mass flow of air, increased brake-specific fuel consumption, and increased exhaust temperature [3]. Emission abatement techniques such as exhaust gas recirculation (EGR) can be controlled by the accurate estimation of EGR mass flowrate, which requires the engine exhaust pressure as input data. Instead of a physical sensor, virtual sensors are preferred, but their calibration needs real measurements; for example, the research work of [11] reported a virtual sensor, a model-based exhaust manifold pressure estimator that was calibrated with real measurements on a four-cylinder, 1.6 liter turbocharged diesel engine. Similarly, an exhaust pressure estimator was implemented on a 4.9 liter turbocharged and intercooled diesel engine fitted with DPF and DOC (Diesel Oxidation Catalyst) that was calibrated with a physical exhaust pressure sensor for DPF failure detection [12]. An extension of the numerical models for diesel engine study, in terms of number of parameters and processing capabilities, is related to the use of AI methodologies. The most spread AI technique applied to combustion engines is an artificial neural network (ANN). An ANN is a black-box identification technique that is able to automatically learn from examples and to derive models from experimental data. Initially, output and input variables are connected through unknown relations that can be predicted by ANN after training and testing stages [13; 14].