TAILIEUCHUNG - Computational Intelligence in Automotive Applications Episode 2 Part 2

Tham khảo tài liệu 'computational intelligence in automotive applications episode 2 part 2', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Intelligent Vehicle Power Management An Overview Yi L. Murphey Department of Electrical and Computer Engineering University of Michigan-Dearborn Dearborn MI 48128 USA Summary. This chapter overviews the progress of vehicle power management technologies that shape the modern automobile. Some of these technologies are still in the research stage. Four in-depth case studies provide readers with different perspectives on the vehicle power management problem and the possibilities that intelligent systems research community can contribute towards this important and challenging problem. 1 Introduction Automotive industry is facing increased challenges of producing affordable vehicles with increased electri-cal electronic components in vehicles to satisfy consumers needs and at the same time with improved fuel economy and reduced emission without sacrificing vehicle performance safety and reliability. In order to meet these challenges it is very important to optimize the architecture and various devices and components of the vehicle system as well as the energy management strategy that is used to efficiently control the energy flow through a vehicle system 15 . Vehicle power management has been an active research area in the past two decades and more intensified by the emerging hybrid electric vehicle technologies. Most of these approaches were developed based on mathematical models or human expertise or knowledge derived from simulation data. The application of optimal control theory to power distribution and management has been the most popular approach which includes linear programming 47 optimal control 5 6 10 and especially dynamic programming DP have been widely studied and applied to a broad range of vehicle models 2 16 22 29 41 . In general these techniques do not offer an on-line solution because they assume that the future driving cycle is entirely known. However these results have been widely used as a benchmark for the performance of power control strategies. .

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