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Management of distributed power in hybrid vehicles based on D.P. or Fuzzy Logic

机译:基于D.P.的混合动力汽车分布式电源管理或模糊逻辑

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摘要

The application of optimization methods and algorithms to energy management is crucial when trying to find instantaneous compromises between various energy sources that can provide the power required by a powertrain. Because of the complexity of both the problem and the system structure, it is difficult to determine the optimal strategy in real time (on-line and using the onboard computer). This article tackles the problem of optimizing the power provided by various sources available to meet the power demand from the driver whilst minimizing the total hydrogen consumption during a journey. The real challenge is to find an energy management law applicable in real time on any power profile. This paper presents two new energy management methods: off-line "Dynamic Programming with Improved Constraints (DPIC)" and a real-time optimized decision-maker based on a two-levels optimized Fuzzy Logic (Fuzzy Switching of Fuzzy Rules-FSFR). DPIC produces better results than the classical discrete dynamic programming with state-of-the-art constraints, in terms of execution time and hydrogen consumption. FSFR is a real time energy management algorithm based on fuzzy rules learnt on specific profiles and real-time fuzzy switching of these fuzzy rules. Both methods are evaluated on different types of real world profiles (urban, road and highway profiles), to assess and confirm their effectiveness.
机译:当试图找到可以提供动力总成所需动力的各种能源之间的瞬时折衷时,将优化方法和算法应用于能源管理至关重要。由于问题和系统结构的复杂性,很难实时(在线和使用车载计算机)确定最佳策略。本文解决了优化各种可用动力源提供的功率以满足驾驶员对动力的需求,同时将旅途中的总氢消耗降至最低的问题。真正的挑战是找到适用于任何功率曲线的实时能源管理法则。本文介绍了两种新的能源管理方法:离线“具有改进约束的动态规划(DPIC)”和基于两级优化模糊逻辑(模糊规则的模糊切换-FSFR)的实时优化决策者。在执行时间和氢消耗方面,DPIC产生的结果要优于具有最新约束的经典离散动态编程。 FSFR是一种实时能源管理算法,它基于在特定配置文件中学习的模糊规则以及这些模糊规则的实时模糊切换。两种方法均根据不同类型的真实世界概况(城市,道路和高速公路概况)进行评估,以评估并确认其有效性。

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