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The energy storage system (ESS) is the key enabler to hybrid electric vehicles (HEVs) that offer improved fuel economy and reduced vehicle emissions. The power capability of a battery has significant impact on the fuel economy of HEVs. This paper presents the power capability testing of a lithium-ion battery with a conventional metal oxide cathode using the hardware in the loop (HIL) at a wide range of charge/discharge conditions and at different temperatures. The achieved test results provide critical data of battery power characteristics and effectively accelerate the development of battery power prediction algorithm.

Artificial neural networks (ANN) have been recognized as universal approximators for nonlinear continuous functions and actively applied in engine research in recent years [1, 2, 3, 4, 5, 6, 7 and 8]. This paper describes the methodology and results of using the ANN to model a turbocharged DI diesel engine. The engine was simulated using the CFD code (KIVA-ERC) over a wide range of operating conditions, and numerical simulation results were used to train the ANN. An efficient data collection methodology using the Design of Experiments (DOE) techniques was developed to select the most characteristic engine operating conditions and hence the most informative data to train the ANN. This approach minimizes the time and cost of collecting training data from either computational or experimental resources. The trained ANN was then used to predict engine parameters such as cylinder pressure, cylinder temperature, NOx and soot emissions, and cylinder heat transfer.

A large proportion (about 33%) of the fuel energy is lost through exhaust gas in a gasoline engine. Electric turbo compounding (ETC) is a promising technology for gasoline engine exhaust energy recovery. In this paper, optimization of an ETC system for turbocharged gasoline engines is carried out. The ETC system has a turbo-generator that is in parallel with the turbocharger, the flow distribution between the turbocharger and the turbo-generator is controlled. The engine exhaust energy is recovered by the turbo-generator with fixed geometry turbine (FGT) or variable nozzle turbine (VNT). The design and control of the ETC system are optimized for best recovery of engine exhaust energy at engine full load and part load operating conditions. The system performance is studied by 1D simulation methods. The gasoline engine is modeled with the GT-POWER software and the turbochargers and turbo-generators are modeled with turbo through-flow models.

Over the last decade significant efforts have been made in the automotive industry to move into a math-based control development approach where much of the development could be done off-line using computer simulations. High-fidelity simulation of an engine and control system helps to shorten controller development time with reduced risk. This requires the integration of a detailed engine model with a representative controller model. This paper describes the development and validation of an integrated engine and controller model of a turbocharged diesel engine. The integrated model incorporates a detailed engine model in GT-Power and a comprehensive controller model in Simulink with functionalities like the production ECM. The focus of this study is a non-real time simulation and analysis of the control of EGR, turbocharger, and fueling with engine performance.

An integrated system model containing sub-models for a diesel engine, NOx and soot emissions, and a diesel particulate filter (DPF) has been used to simulate stead-state engine operating conditions. The simulation results have been used to investigate the effect of DPF loading and passive regeneration on engine performance and emissions. This work is the continuation of previous work done to create an overall diesel engine/exhaust system integrated model. As in the previous work, a diesel engine, exhaust system, engine soot emissions, and diesel particulate filter (DPF) sub-models have been integrated into an overall model using Matlab Simulink. For the current work new sub-models have been added for engine-out NOx emissions and an engine feedback controller. The integrated model is intended for use in simulating the interaction of the engine and exhaust aftertreatment components.

An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude.

In a parallel hybrid electric vehicle, higher fuel economy gains are typically achieved if significant electric drive (or engine-off) operation is possible, shifting the engine operating schedule so that it only runs at medium to high load for best efficiency. To enable efficient engine-off driving, a typical configuration will have a disconnect clutch between the engine and the rest of the driveline. In some configurations, when engine-on operation is requested the disconnect clutch is applied in conjunction with the traction motor/generator to crank the engine (i.e., a flying engine start). In this paper we describe the development of a control system for a flying engine start using an engine disconnect clutch. The clutch is located between the engine and electric motor, which is connected to the input of a multispeed transmission. We first describe an initial control algorithm evaluation using a driveline model.