Integrated, Feed-Forward Hybrid Electric Vehicle Simulation in SIMULINK and its Use for Power Management Studies

A hybrid electric vehicle simulation tool (HE-VESIM) has been developed at the Automotive Research Center of the University of Michigan to study the fuel economy potential of hybrid military/civilian trucks. In this paper, the fundamental architecture of the feed-forward parallel hybrid-electric vehicle system is described, together with dynamic equations and basic features of sub-system modules. Two vehicle-level power management control algorithms are assessed, a rule-based algorithm, which mainly explores engine efficiency in an intuitive manner, and a dynamic-programming optimization algorithm. Simulation results over the urban driving cycle demonstrate the potential of the selected hybrid system to significantly improve vehicle fuel economy, the improvement being greater when the dynamicprogramming power management algorithm is applied. INTRODUCTION Growing environmental concerns coupled with concerns about global crude oil supplies stimulate research aimed at new, fuel-efficient vehicle technologies. Hybrid-electric vehicles (HEV) appear to be one of the most viable technologies with significant potential to reduce fuel consumption within realistic economical, infrastructural and customer acceptance constraints. Dozens of prototype/concept hybrid vehicles have been developed. Toyota and Honda have already launched production vehicles and many other major automakers are expected to launch hybrid vehicles in the next 3-5 years. Due to the existence of dual power-sources, the additional design degrees of freedom of HEV offer unprecedented possibilities in fuel economy and exhaust emissions, particularly if parallel powertrain architectures are employed. At the same time, the complexity of the new vehicle system requires the application of simulations for accurate sizing and matching studies, as well as for development of control algorithms well ahead of the final design and physical prototyping. Most of the control strategies developed for parallel HEV fall into three categories. The first type applies intelligent control techniques such as rules/fuzzy logic/NN for estimation as well as control algorithm development [1 and 2]. The second type of approach is based on static optimization methods. Commonly, to calculate the cost of energy, the electric energy is translated into an equivalent amount of fuel [3 and 4]. The optimization scheme then figures out proper energy and/or power split between the two energy sources under steady-state operations. Due to its relatively simple point-wise optimization nature, it is possible to extend the optimization scheme to solve the simultaneous fuel economy and emission optimization problem [5]. The basic idea of the third type of HEV control algorithm is similar to that of static optimization; however, the optimization was performed for dynamic systems [6]. Further, the optimization is with respect to a time horizon, rather than for a fixed point in time. In general, the power split algorithm from the dynamic optimization will be more accurate under transient conditions. Usually, the dynamic optimization algorithms are not implementable due to their preview nature and heavy computation requirement. They are, however, a good benchmark based on which the first two types of algorithms can be improved or compared against. The objective of this work is to develop an integrated hybrid vehicle simulation tool and use it for the design of