| Summary: | 碩士 === 國立成功大學 === 機械工程學系 === 104 === An innovative Energy Management Strategy (EMS) for mild-parallel-hybrid electric vehicles equipped with Belt-Driven Starter Generators (BSGs) is proposed by this thesis. On the basis of Equivalent Consumption Minimization Strategy (ECMS), the approaches by Genetic Algorithm (GA), Learning Vector Quantization (LVQ) Neural Network and Fuzzy Logic Control (FLC) are integrated to adjust/tune the power split ratio between ICE (Internal Combustion Engine) and BSG. HEV (Hybrid Electric Vehicle) model and its corresponding power allocation strategy are developed and verified by using vehicle simulator- ADVISOR (ADvanced VehIcle SimulatOR) and Simulink at the preliminary research stage. To be more practical, the proposed control strategy is converted into “C code” and burned onto the embedded micro-processor to conduct the necessary Hardware-In-the-Loop (HIL) experiments. According to the simulation results, the improvement degree of fuel economy is up to 40.39 % in terms of “MANHATTAN” drive cycle, a typical type of metropolitan road pattern, with respect to conventional pure ICE vehicles, in addition to the State Of Charge (SOC) can be retained within an appropriate range. On the other hand, the improvement degree of mean engine operation efficiency is up to 45.97 % in terms of “NYCC (New York City Cycle)” drive cycle with respect to conventional pure ICE vehicles. Finally and significantly, the experimental results of HIL are pretty close to the simulations undertaken earlier by Simulink. It implies that the proposed control strategy can be potentially applied to the real-world HEVs directly in the future.
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