In this paper an air path model is presented for control system design. The model was developed for direct injected, turbocharged and intercooled commercial vehicle diesel engines which are equipped with compressed air booster system (PBS R -Pneumatic Booster System) [12], high pressure exhaust gas recirculation (EGR) with EGR-cooler and exhaust brake (EB). Current and next generation emission standards introduced significant limitations for NO x and soot. It is challenging to handle these components, especially at transient engine operations. Nitric oxide formation can be limited with an appropriate amount of exhaust gas recirculation. Soot formation is influenced mainly by the air-fuel ratio of the mixture which can be affected by the intake manifold pressure. Therefore with the targeted design of a suitable air path controller the modeled engine setup is able to handle both the NO x and soot formation in transient cases. The reported model is the first step of this work. KeywordsDiesel engine · Air path system · EGR · Compressed air booster · Turbo-lag · Model-based control AcknowledgementThe work is connected to the scientific program of the "Development of quality-oriented and harmonized R+D+I strategy and functional model at BME project. This project is supported by the New Széchenyi Plan (

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Electronic vehicle dynamics systems are expected to evolve in the future as more and more automobile manufacturers mark fully automated vehicles as their main path of development. State-of-the-art electronic stability control programs aim to limit the vehicle motion within the stable region of the vehicle dynamics, thereby preventing drifting. On the contrary, in this paper, the authors suggest its use as an optimal cornering technique in emergency situations and on certain road conditions. Achieving the automated initiation and stabilization of vehicle drift motion (also known as powerslide) on varying road surfaces means a high level of controllability over the vehicle. This article proposes a novel approach to realize automated vehicle drifting in multiple operation points on different road surfaces. A three-state nonlinear vehicle and tire model was selected for control-oriented purposes. Model predictive control (MPC) was chosen with an online updating strategy to initiate and maintain the drift even in changing conditions. Parameter identification was conducted on a test vehicle. Equilibrium analysis was a key tool to identify steady-state drift states, and successive linearization was used as an updating strategy. The authors show that the proposed controller is capable of initiating and maintaining steady-state drifting. In the first test scenario, the reaching of a single drifting equilibrium point with −27.5° sideslip angle and 10 m/s longitudinal speed is presented, which resulted in −20° roadwheel angle. In the second demonstration, the setpoints were altered across three different operating points with sideslip angles ranging from −27.5° to −35°. In the third test case, a wet to dry road transition is presented with 0.8 and 0.95 road grip values, respectively.