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Today's energy economy holds efficiency at a premium. As engineers it is our job to find and eliminate inefficiencies no matter how big or small-whether a major change to an engine design or a small tweak to a shift schedule is required. Because of the ever-increasing need for these efficiency gains, system-level design is a crucial step in the hybrid vehicle development process. There exist several tools to simulate the behavior and performance of hybrid vehicles, but many of these are prohibitively expensive, too complex for engineers (particularly students) to learn from, or unable to support custom or unusual driveline configurations. This paper will discuss the development of a simple and extensible Simulink toolset which models hybrid vehicle systems and controls.

The development of a Powertrain Matching Analysis Tool (PMAT) addresses the challenge of matching the powertrain hardware and control strategy to specific vehicle attributes and driver applications for improved overall vehicle system efficiency. PMAT consists of a reverse tractive road load demand model and a dynamic optimization algorithm developed in MATLAB® and Simulink®. The reverse tractive road load demand model propagates the required wheel torque and speed derived from vehicle speed and road grade through the powertrain system to determine the required fuel flow for various states. The control strategy is treated as a multi-stage, multi-dimension decision process, where dynamic programming is applied to find an optimal control policy that minimizes the accumulated fuel flow over a drive cycle. PMAT is used to assess and develop transmission shift and lock-up control strategies, evaluate powertrain hardware configurations, and establish design criteria.

A greater understanding of where fuel energy is being demanded from a vehicle system standpoint is necessary for developing more fuel efficient vehicles. This paper presents an overview of the development and application of a vehicle energy analysis methodology and a MATLAB®/Simulink® based tool that uses empirical data and first principles to identify vehicle subsystem energy supply and demand. An accurate analysis requires the tool to be populated with chassis dynamometer drive cycle data as well as vehicle and component information. The tool can be used to investigate vehicle system energy requirements, prevailing fuel economy factors, and incremental hypothetical fuel saving scenarios that could not otherwise be measured due to inherent test-to-test variability.