| ECONOMIC VALUE AND UTILITY OF A POWERTRAIN SYSTEM FOR A PLUG-IN PARALLEL DIESEL HYBRID ELECTRIC BUS |
| B. SUH1, A. FRANK1, Y. J. CHUNG2, E. Y. LEE3, Y. H. CHANG4, S. B. HAN4 |
1University of California Davis
2Daegu Mirae College
3Kunsan National University
4Induk University |
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| ABSTRACT |
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This research is the first to develop a design for a powertain system of a plug-in parallel diesel hybrid electric bus equipped with a continuously variable transmission (CVT) and presents a new design paradigm of the plug-in hybrid electric bus (HEB). The criteria and method for selecting and sizing powertrain components equipped in the plug-in HEB are presented. The plug-in HEB is designed to overcome the vulnerable limitations of driving range and performance of a purely electric vehicle (EV) and to improve fuel economy and exhaust emissions of conventional bus and conventional HEBs. The control strategy of the complicated connected propulsion system in the plug-in parallel HEB is one of the most significant factors in achieving higher fuel economy and lower exhaust emissions of the HEV. In this research, a new optimal control strategy concept is proposed against existing rule-based control strategies. The optimal powertrain control strategy is obtained through two steps of optimizations: tradeoff optimization for emission control and energy flow optimization based on the instantaneous optimization technique. The proposed powertrain control strategy has the flexibility to adapt to battery SOC, exhaust emission amount, classified driving pattern, driving condition, and engine temperature. The objective of the optimal control strategy is to optimize the fuel consumption, electricity use, and exhaust emissions proper to the performance targets. The proposed control strategy was simulated to prove its validity by using analysis simulation tool ADVISOR (advanced vehicle simulator). |
| Key Words:
Plug-in parallel diesel hybrid electric bus, Powertrain system, State of charge (SOC), Exhaust emission, Thermal efficiency, Energy flow optimization |
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