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Growing environmental concerns and stringent vehicle emissions regulations has created an urge in the automotive industry to move towards electrified propulsion systems. Reducing and eliminating the emission from public transportation vehicles plays a major role in contributing towards lowering the emission level. Battery electric buses are regarded as a type of promising green mass transportation as they provide the advantage of less greenhouse gas emissions per passenger. However, the electric bus faces a problem of limited range and is not able to drive throughout the day without being recharged. This research studies a public bus transit system example which servicing the city of Ann Arbor in Michigan and investigates the impact of different electrification levels on the final CO2 reduction. Utilizing models of a conventional diesel, hybrid electric, and battery electric bus, the CO2 emission for each type of transportation bus is estimated.

This paper presents a framework for modeling a modern diesel engine and its aftertreatment system which are intended to be used for real-time implementation as a virtual engine and in a model-based control architecture to predict critical variables such as fuel consumption and tailpipe emissions. The models are specifically able to capture the impact of critical control variables such as the Exhaust Gas Recirculation (EGR) valve position and fuel injection timing, as well as operating conditions of speed and torque, on the engine airpath variables and emissions during transient driving conditions. To enable real-time computation of the models, a minimal realization of the nonlinear airpath model is presented and it is coupled with a cycle averaged NOx emissions predictor to estimate feed gas NOx emissions. Then, the feedgas enthalpy is used to calculate the thermal behavior of the aftertreatment system required for prediction of tailpipe emissions.

Early exhaust valve opening (eEVO) increases the exhaust gas temperature by faster termination of the power stroke and is considered as a potential warm up strategy for diesel engines aftertreatment thermal management. In this study, first, it is shown that when eEVO is applied, the engine main variables such as the boost pressure, exhaust gas recirculation (EGR) and injection (timing and quantity) must be re-calibrated to develop the required torque, avoid exceeding the exhaust temperature limits and keep the air fuel ratio sufficiently high. Then, a two-step procedure is presented to optimize the engine operation after the eEVO system is introduced, using a validated diesel engine model. In the first step, the engine variables are optimized at a constant eEVO shift. In the second step, optimal eEVO trajectories are calculated using Dynamic Programming (DP) for a transient test cycle.

This paper numerically investigates the performance implications of the use of an electric supercharger in a heavy-duty DD13 diesel engine. Two electric supercharger configurations are examined. The first is a high-pressure (HP) configuration where the supercharger is placed after the turbocharger compressor, while the second is a low-pressure (LP) one, where the supercharger is placed before the turbocharger compressor. At steady state, high engine speed operation, the airflows of the HP and LP implementations can vary by as much as 20%. For transient operation under the Federal Test Procedure (FTP) heavy duty diesel (HDD) engine transient drive cycle, supercharging is required only at very low engine speeds to improve airflow and torque. Under the low speed transient conditions, both the LP and HP configurations show similar increases in torque response so that there are 44 fewer engine cycles at the smoke-limit relative to the baseline turbocharged engine.

This paper presents a novel model-based algorithm which is able to detect and isolate major faults assigned to the gas exchange path of a gasoline engine both in the intake and exhaust sides. The diagnostics system is developed for detection and isolation of these faults: air leakage fault between the compressor and the air throttle, exhaust manifold pressure sensor fault, wastegate stuck-closed fault and wastegate stuck-open fault. Sliding mode observers (SMOs) are the core detection algorithms utilized in this work. A first order SMO is designed to estimate the turbocharger rotational dynamics. The wastegate displacement dynamics coupled to the exhaust manifold pressure dynamics is estimated using a second order SMO. Verified with experimental data from a modern TC gasoline engine running in a test cell, the two sliding mode observers are then used in a strategy to detect the faults in the gas exchange path.

Proper operation of an internal combustion engine is required by demands of a vehicle driver and governmental legislations. Therefore it is necessary to monitor, within an online technique, the engine and detect any fault which disrupts its normal operation. In this paper, the air-charge path, as a key element in a turbocharged engine, is monitored for an air leakage fault. At first, a robust algorithm to estimate unmeasured turbocharger rotational speed is presented. The sliding mode methodology is used to design the estimator which is shown to be robust to the compressor modeling uncertainties. The estimation error from the sliding mode observer (SMO) is then used to detect abnormal behavior of the turbocharger along with the engine due to a leakage fault in the air-charge path. Experimental results from a modern turbocharged SI engine indicate the designed monitoring technique is able to detect a leakage fault, of 7 mm or higher sizes, in the air-charge path.

One of the main problems associated with design of fault detection (FD) strategy is availability of first generation engine. To solve this problem a methodology based on a virtual engine platform is proposed in this paper. This approach allows designing an FD algorithm in early stages of engine development. The application of this methodology is illustrated on a modern turbocharged gasoline engine by investigating the effect of a leakage in the exhaust manifold. Experimental results show good ability of the virtual engine platform to predict the effect of the leakage fault on the engine performance. Moreover unexpected results of exhaust manifold leakage effects are presented which are very useful for designing a leak detection strategy.