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基于多因素输入模糊控制的再生制动策略
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Regenerative Braking Strategy Research Based onMulti-factor Input Fuzzy Control
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    摘要:

    大部分再生制动策略研究仅考虑制动方向稳定性,忽略制动效能恒定性,在理想制动前提下的研究存在缺陷.以良好制动性和能量回收率最大化为目标,对前驱型纯电动汽车进行研究,提出了基于多因素输入模糊控制的再生制动策略.在某整车模型的基础上,先以制动方向稳定性和ECE法规完成前、后轴制动力分配,同时保证前轴制动力最大化;再采用摩擦副动态摩擦因数预估机械制动效能因数,然后将电池荷电状态、制动强度和预估的机械制动效能因数引入模糊控制器,得到再生制动力分配份额,完成能量回收.研究结果表明:在频繁且强度较恒定的制动工况下,制动效能恒定性表现较好,同时制动能量回收率提升了18.5%;城市道路工况蓄电池满电到零电的整个测试中,能量回收率提升了5.3%.

    Abstract:

    Most of the regenerative braking strategies only considered the stability on the braking direction and ignored the braking efficiency constancy,thus these researches may have defect under the ideal braking condition. A front driving electric vehicle was studied with the object to obtaining good braking and maximizing energy recovery rate. A regenerative braking strategy based on the multi-factor input fuzzy control was also proposed. Using the passage vehicle model,the front and rear axle braking force distribution was first set up according to the braking stability and ECE regulations. The front axle braking force was tried to be kept at the maximum at the same time. Second,the dynamic friction coefficient was used to predict the mechanical braking performance factor. Third,the battery state of charge,the braking strength and the estimated mechanical brake efficiency factor were introduced to the fuzzy controller. Finally,the distribution of regenerative braking force was obtained,and thus energy recovery was finished. The results show that with the new method,the braking performance constancy is improved in the frequent and constant intensity braking conditions. The braking energy recovery rate is increased by 18.5%. In the urban road condition with battery full power to zero,the energy recovery rate is increased by 5.3%.

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杨小龙,杨功正,张泽坪.基于多因素输入模糊控制的再生制动策略[J].湖南大学学报:自然科学版,2017,44(10):17~24

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  • 在线发布日期: 2017-10-30
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