Energy management controllers for hybrid electric vehicles typically contain numerous parameters that must be tuned in order to arrive at a desired compromise among competing attributes, such as fuel economy and driving quality. This paper estimates the Pareto tradeoff curve of fuel economy versus driving quality for a baseline industrial controller, and compares it to the Pareto tradeoff curve of an energy management controller based on Shortest Path Stochastic Dynamic Programming (SPSDP). Previous work demonstrated important performance advantages of the SPSDP controller in comparison to the baseline industrial controller. Because the baseline industrial controller relies on manual tuning, there was always the possibility that better calibration of the algorithm could significantly improve its performance. To investigate this, a numerical search of possible controller calibrations is conducted to determine the best possible performance of the baseline industrial controller and estimate its Pareto tradeoff curve. The SPSDP and baseline controllers are causal; they do not rely on future drive cycle information. The SPSDP controllers achieve better performance (i.e., better fuel economy with equal or better driving quality) over a wide range of driving cycles due to fundamental structural limitations of the baseline controller that cannot be overcome by tuning. The message here is that any decisions that specify or restrict controller structure may limit attainable performance, even when many tunable parameters are made available to calibration engineers. The structure of the baseline algorithm and possible sources of its limitations are discussed.

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