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A dynamic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towed vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point. Input parameters of this model include initial conditions of the system, real-time locations of a reference point (e.g. front center of the towing vehicle) that can be determined from a beacon and radar system, and instantaneous accelerations of this system, which come from driver maneuvers (accelerating, braking, steering, etc.) can be read from a data acquisition system installed on the towing vehicle.

The Powertrain Analysis and Computational Environment (PACE) is a powertrain simulation tool that provides an advanced behavioral modeling capability for the powertrain subsystems of conventional or hybrid-electric vehicles. Due to its origins in Argonne National Lab’s Autonomie, PACE benefits from the reputation of Autonomie as a validated modeling tool capable of simulating the advanced hardware and control features of modern vehicle powertrains. However, unlike Autonomie that is developed and executed in Mathwork’s MATLAB/Simulink environment, PACE is developed in C++ and is targeted for High-Performance Computing (HPC) platforms. Indeed, PACE is used as one of several actors within a comprehensive ground vehicle co-simulation system (CRES-GV MERCURY): during a single MERCURY run, thousands of concurrent PACE instances interact with other high-performance, distributed MERCURY components.

Although the cost-saving and good environmental impacts are the benefits that make Electric Vehicles (EVs) popular, these advantages are significantly influenced by the cost of battery replacement over the vehicle lifetime. After several charging and discharging cycles, the battery is subjected to energy and power degradation which affects the performance and efficiency of the vehicle. In addition to battery replacement cost, the electricity cost being paid by drivers is another key factor in selecting the EVs. An Energy Management System (EMS) with Model Predictive Control-based (MPC) algorithm is presented for a specific case of heavy-duty EV. Such EV draws its energy from the grid via catenary in addition to the on-board battery. Dynamic model of the vehicle will be defined by State Space Equations (SSE).

The Powertrain Analysis and Computational Environment (PACE) is a forward-looking powertrain simulation tool that is ready for a High-Performance Computing (HPC) environment. The code, written in C++, is one actor in a comprehensive ground vehicle co-simulation architecture being developed by the CREATE-GV program. PACE provides an advanced behavioral modeling capability for the powertrain subsystem of a conventional or hybrid-electric vehicle that exploits the idea of reusable vehicle modeling that underpins the Autonomie modeling environment developed by the Argonne National Laboratory. PACE permits the user to define a powertrain in Autonomie, which requires a single desktop license for MATLAB/Simulink, and port it to a cluster computer where PACE runs with an open-source BSD-3 license so that it can be distributed to as many nodes as needed.

An approach is being pursued for a series hybrid electric vehicle (SHEV). The twin goals of maximizing Fuel Economy (FE) and improving consumer acceptance has led to a SHEV powertrain using energy storage as a means for filtering drive cycle power demands on the engine, rather than an energy source for supplying all-electric mode. The concept is intended to minimize, if not eliminate, the battery in the SHEV without resorting to full range proportional control of the engine and generator. An initial optimization study reported for a mid-size SHEV showed a 4.5 kWh Li-ion battery pack was still required. In a new research, a sports car class SHEV was studied, which inspires this manuscript. The challenge with this vehicle is to reduce the ESS size even more because the available space allocation is only one fourth of the battery size in the mid-size. In this manuscript, a controller is developed that allows a hybridized SHEV to be realized with a light ESS.

The SiC Junction Field Effect Transistor (JFET) technology has continued to mature, allowing for a wider range of product offerings that are expected to play an important role in the future aerospace and hybrid vehicle system designs. This paper will give an overview of vertical trench SiC JFET technology detailing the high-temperature dc characteristics of the discrete devices also show power module switching behavior up to 100A. Additional characterization of the all-SiC power modules used as solid-state circuit breakers will be given.

A 1200-V, 600-A silicon carbide (SiC) JFET half-bridge module has been developed for drop-in replacement of a 600-V, 600-A IGBT intelligent power module (IPM). Advances in the development of SiC field effect transistors have resulted in reliable high yield devices that can be paralleled and packaged to produce high-voltage and high-current power modules not only competitive with existing IGBT technology but the modules have expanded capabilities. A SiC vertical junction field effect transistor VJFET has been produced with the properties of lower conduction loss, zero tail current, higher thermal conductivity, and higher power density when compared to a similarly rated silicon IGBT or any practical SiC MOSFETs previously reported. Three prototype SiC JFET half-bridge modules with gate drivers have been successfully integrated into a three-phase 30-kW (continuous), 100-kW (intermittent) AC synchronous motor drive designed to control a traction motor in an electric vehicle.