差分进化算法和蝙蝠算法在并联混合动力电动汽车速度控制优化的 PID 调节上的性能比较

Simson Simson
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引用次数: 0

摘要

在过去十年中,有许多交通工具使用燃油(内燃机)。由于污染物气体的排放,这对环境造成了严重影响。一种解决方案是使用混合动力电动汽车(HEV)来替代使用内燃机的车辆。混合动力汽车必须具备的性能之一是行驶时速度稳定。在本研究中,扰动观测器中元启发式算法所使用的几种方法具有无需建立数学模型即可描述工厂逆模型的优点。试验通过比较元启发式算法的两种方法,即差分进化和蝙蝠算法(Bat Algirthm)进行。仿真结果表明,该混合动力电动汽车采用的方法是保持速度,因此根据测试结果,差分进化法是控制并联混合动力电动汽车速度的最佳方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perbandingan Performansi Antara Differential Evolution dan Bat Algorithm pada Tuning PID Untuk Optimasi Kontrol Kecepatan Paralell Hybrid Electric Vehicle
In the last decade, there have been many means of transportation that use fuel oil (Internal Combustion Engine (ICE)). This has a serious impact on the environment due to the emission of pollutant gases. One solution is the use of a hybrid electric vehicle (HEV) as a substitute for vehicles that use ICE. One of the performance that must be owned by the HEV is to have a stable speed when driving. In this study, several methods used in the metaheuristic algorithm in the disturbance observer have the advantage of describing the inverse model of the plant without making a mathematical model. The test is carried out by comparing the two methods of the metaheuristic algorithm, namely Differential Evolution and Bat Algirthm. The simulation results show that the method used in this HEV is to maintain its speed, so according to the test results it shows that the Differential Evolution method is the best method for controlling the speed of the Parallel Hybrid Electric Vehicle.
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