Fuzzy Gain-Scheduling Proportional–Integral Control for Improving Engine Power and Speed Behavior in a Hybrid Electric Vehicle
| Autor: | Hao Ying, Matthew D. Smith, M.L. Kuang, Shunsuke Okubo, Fazal U. Syed |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Engine power
Engineering business.product_category Computer Networks and Communications business.industry Aerospace Engineering PID controller Fuzzy control system Automotive engineering Control theory Hybrid system Control system Automotive Engineering Electric vehicle Electrical and Electronic Engineering Hybrid vehicle business Power control |
| Zdroj: | IEEE Transactions on Vehicular Technology. 58:69-84 |
| ISSN: | 1939-9359 0018-9545 |
| DOI: | 10.1109/tvt.2008.923690 |
| Popis: | With the increased emphasis on improving fuel economy and reducing emissions, hybrid electric vehicles (HEVs) have emerged as very strong candidates to achieve these goals. The power-split hybrid system, which is a complex hybrid powertrain, exhibits great potential to improve fuel economy by determining the most efficient regions for engine operation and thereby high-voltage (HV) battery operation to achieve overall vehicle efficiency optimization. To control and maintain the actual HV battery power, a sophisticated control system is essential, which controls engine power and thereby engine speed to achieve the desired HV battery maintenance power. Conventional approaches use proportional-integral (PI) control systems to control the actual HV battery power in power-split HEV, which can sometimes result in either overshoots of engine speed and power or degraded response and settling times due to the nonlinearity of the power-split hybrid system. We have developed a novel approach to intelligently controlling engine power and speed behavior in a power-split HEV using the fuzzy control paradigm for better performances. To the best of our knowledge, this is the first reported use of the fuzzy control method to control engine power and speed of a power-split HEV in the applied automotive field. Our approach uses fuzzy gain scheduling to determine appropriate gains for the PI controller based on the system's operating conditions. The improvements include elimination of the overshoots as well as approximate 50% faster response and settling times in comparison with the conventional linear PI control approach. The improved performances are demonstrated through simulations and field experiments using a ford escape hybrid vehicle. |
| Databáze: | OpenAIRE |
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