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首页> 外文期刊>International Journal of Mechanical Engineering and Applications >An Intelligent Regenerative Braking Strategy for Power-split Hybrid Electric Vehicle
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An Intelligent Regenerative Braking Strategy for Power-split Hybrid Electric Vehicle

机译:电力分流混合动力电动汽车智能再生制动策略

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Nowadays, hybrid electric vehicle (HEV) is a popularly vehicle due to its advances such as reducing fossil fuel consumption and emissions that affect on environment. Brake energy regeneration system is essential part in HEV and electric vehicle. It assists HEV in reducing fuel consumption and pollution emission. Regenerative braking system aims to discard heat energy from mechanical braking as vehicle decelerated. Therefore, design and develop a suitable regenerative braking system were always intended. The braking control strategies were variation and improvement. The mechanical - electric braking system was utilized. This braking system must achieve the criteria such as safety, stability, maximum energy recovery and the shortest the braking distance. This paper proposed a control strategy for this hybrid braking system. Firstly, braking performances were satisfied by braking torque distribution strategy between front and rear axles. Secondly, maximum energy recovery was computed by compromising between mechanical and electric braking torque. Two issues were implemented by applying fuzzy logic and rule-based to design the braking torque controllers. Two controllers were estimated through the results of simulation in power-split HEV. The controller, applied fuzzy-based, had significant improvements in fuel consumption compare with another one. In addition, this controller was more flexible in various driving conditions.
机译:如今,混合动力电动车(HEV)是一种普遍的车辆,因为其进步,例如减少对环境影响的化石燃料消耗和排放。制动能量再生系统是HEV和电动车辆的重要组成部分。它有助于HEV降低燃料消耗和污染排放。再生制动系统旨在将热能从机械制动中丢弃,因为载体减速。因此,始终打算设计和开发合适的再生制动系统。制动控制策略是变化和改善。使用机械制动系统。该制动系统必须达到诸如安全性,稳定性,最大​​能量回收和最短制动距离的标准。本文提出了这种混合动力制动系统的控制策略。首先,通过前后轴和后轴之间的制动扭矩分布策略满足制动性能。其次,通过在机械和电动制动扭矩之间损害来计算最大能量回收。通过应用模糊逻辑和规则基于设计制动扭矩控制器来实现两个问题。通过Power-Split HEV的模拟结果估计了两个控制器。控制器应用模糊基于燃料消耗的显着改善与另一个相比。此外,该控制器在各种驾驶条件下更灵活。

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