<p>The
increasing amounts of vehicle emissions and vehicle energy consumption are major
problems for the environment and energy conservation. Hybrid vehicles, which
have less emissions and energy consumption, play more and more important roles in
energy efficiency and sustainable development.</p>
<p> </p>
<p>The
power management strategies of a parallel-through-the-road hybrid architecture
vehicle are different from traditional hybrid electric vehicles since one
additional dimension is added. To study power management strategies, a
simplified model of the vehicle is developed. Four types of power management
strategies have been discovered previously based on the simplified model,
including dynamic programming model, equivalent consumption minimization
strategy, proportional state-of-charge algorithm, and regression model. A new
power management strategy, which is artificial neural network model, is
developed. All these five power management strategies are compared, and the
artificial neural network model is proven to have the best results among the
implementable strategies.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7428026 |
| Date | 16 January 2019 |
| Creators | Mingyu Sun (5930885) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/ARTIFICIAL_NEURAL_NETWORKS_CONTROL_STRATEGY_OF_A_PARALLEL_THROUGH-THE-ROAD_PLUG-IN_HYBRID_VEHICLE/7428026 |
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