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Downsizing the ICE in Commercial Series Hybrids with Known Routes Using a Modular Energy Storage Approach
Technical Paper
2021-01-0794
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
Language:
English
Abstract
An efficient and low cost approach to managing the internal combustion engine of commercial series hybrids with a priori known routes is introduced. The concept put forward explores the boundaries of minimizing the internal combustion engine using a modular approach to storage capacity. Engine size is chosen such that brake-specific fuel consumption (bsfc) optimal operating point is approximately at (or slightly above) the maximum average power level of all encountered routes. The energy storage device has a modular structure, so energy storage capacity can be adapted to the route in steps of 1 KWh. The engine can operate at a high power operating point in case of unexpectedly high power draws, which the engine management algorithm tries to avoid due to the higher bsfc values associated with the corresponding operating points. The paper introduces the state diagram for power management of the ICE and provides state transition information based on route and energy storage conditions. Two simple but effective engine management algorithms are analyzed, and simulation results regarding engine and energy storage state are shown for the Manhattan and Orange County Bus Cycles. Some critical benchmarks are introduced, and the obtained results are evaluated with respect to these benchmarks. It is demonstrated that superior fuel efficiency can be obtained relative to typical diesel and hybrid buses if route and future power demand knowledge is available. The efficiency improvements are consistently above 35%, and if compared to the corresponding conventional diesel-powered bus, efficiency gains of up to 78% can be reached.
Citation
Farr, K. and Bauer, P., "Downsizing the ICE in Commercial Series Hybrids with Known Routes Using a Modular Energy Storage Approach," SAE Technical Paper 2021-01-0794, 2021, https://doi.org/10.4271/2021-01-0794.Data Sets - Support Documents
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