A genetic-fuzzy control method for regenerative braking in electric vehicle

Zhiqiang Liu, Shan Lu, Rong-hua Du
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引用次数: 5

Abstract

In order to improve the recovery ratio of the regenerative braking energy in electric vehicles, the influence factors on braking energy feedback in electric vehicles were analysed. Then, a parallel braking force distribution model was established, and a fuzzy controller on braking force distribution was designed, in which the inputs were vehicle speed, braking strength, battery SOC, and output was regenerative braking ratio. On the other hand, the implementation of genetic algorithm in optimisation process was studied. Furthermore, the genetic algorithm was used to optimise the fuzzy control rules, and new fuzzy distribution rules of electro-hydraulic braking force were obtained. The experimental results showed that the recoverable energy ratio was increased by 2.7% with the comparison of the optimised distribution rules and the original rules. So, the genetic-fuzzy control method is effective for regenerative braking in electric vehicles.
电动汽车再生制动的遗传模糊控制方法
为了提高电动汽车制动再生能量的回收率,分析了电动汽车制动再生能量反馈的影响因素。然后,建立了并联制动力分配模型,设计了以车速、制动强度、电池荷电状态为输入,以再生制动比为输出的制动力分配模糊控制器。另一方面,研究了遗传算法在优化过程中的实现。利用遗传算法对模糊控制规则进行优化,得到了新的电液制动力模糊分配规则。实验结果表明,优化后的分配规则与原分配规则相比,可回收能量比提高2.7%。因此,遗传模糊控制方法对电动汽车再生制动是有效的。
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