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One of the challenges of analyzing vehicular electrical systems is the co-dependence of the electrical system and the propulsion system. Even in traditional vehicles where the electrical power budget is very low, the electrical system analysis for macro power utilization over a drive cycle requires knowledge of the generator shaft rpm profile during the drive cycle. This co-dependence increases as the electrical power budget increases, and the integration of the two systems becomes complete when hybridization is chosen. Last year at this conference, the authors presented a paper entitled “Dual Voltage Electrical System Simulations.” That paper established validation for a suite of electrical component models and demonstrated the ability to predict system performance both on a macro power flow (entire drive cycle) level and a detailed transient-event level. The techniques were applicable to 12V, 42V, dual voltage, and/or elevated voltage systems.

Hybrid electric vehicles (HEV's) offer additional flexibility to enhance the fuel economy and emissions of vehicles. The Real-Time Control Strategy (RTCS) presented here optimizes efficiency and emissions of a parallel configuration HEV. In order to determine the ideal operating point of the vehicle's engine and motor, the control strategy considers all possible engine-motor torque pairs. For a given operating point, the strategy predicts the possible energy consumption and the emissions emitted by the vehicle. The strategy calculates the “replacement energy” that would restore the battery's state of charge (SOC) to its initial level. This replacement energy accounts for inefficiencies in the energy storage system conversion process. User- and standards-based weightings of time-averaged fuel economy and emissions performance determine an overall impact function. The strategy continuously selects the operating point that is the minimum of this cost function.

The modeling of the energy storage system (ESS) of a Hybrid Electric Vehicle (HEV) poses a considerable challenge. The problem is not amenable to physical modeling without simplifying assumptions that compromise the accuracy of such models. An alternative is to build conventional empirical models. Such models, however, are time-consuming to build and are data-intensive. In this paper, we demonstrate the application of an artificial neural network (ANN) to modeling the ESS. The model maps the system's state-of-charge (SOC) and the vehicle's power requirement to the bus voltage and current. We show that ANN models can accurately capture the complex, non-linear correlations accurately. Further, we propose and deploy our new technique, Smart Select, for designing ANN training data.