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The power management control system development and vehicle test results for a medium-duty hybrid electric truck are reported in this paper. The design procedure adopted is a model-based approach, and is based on the dynamic programming technique. A vehicle model is first developed, and the optimal control actions to maximize fuel economy are then obtained by the dynamic programming method. A near-optimal control strategy is subsequently extracted and implemented using a rapid-prototyping control development system, which provides a convenient environment to adjust the control algorithms and accommodate various I/O configurations. Dynamometer-testing results confirm that the proposed algorithm helps the prototype hybrid truck to achieve a 45% fuel economy improvement on the benchmark (non-hybrid) vehicle. It also compares favorably to a conventional rule-based control method, which only achieves a 31% fuel economy improvement on the same hybrid vehicle.

In this paper, a simulation-based dynamic rollover evaluation procedure is described. This work is based on the worst-case methodology developed at the University of Michigan, and is the result of a collaborated research project between the University of Michigan and TRW Inc. The target vehicle studied in this paper is a large production volume SUV. This vehicle is equipped with a production-intent TRW Vehicle Stability Control (VSC) system. The main goals of this paper are to (i) study the rollover propensity of this SUV, as influenced by vehicle and environment parameters such as vehicle speed, road condition, etc.; and (ii) investigate whether, and by how much, does the VSC system influence the rollover propensity of this SUV. The modeling, evaluation procedure, and preliminary evaluation results are reported.