基于小波和粒子群算法的HEV行驶状况辨识方法研究

郭海龙; 张永栋; 张胜宾

doi:10.3969/j.issn.10012222.2017.02.011

车用发动机 >

2017 , Vol. 0 >Issue 2: 62 - 66

DOI: https://doi.org/10.3969/j.issn.10012222.2017.02.011

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基于小波和粒子群算法的HEV行驶状况辨识方法研究

郭海龙 ,
张永栋 ,
张胜宾

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（1. 广东交通职业技术学院汽车与工程机械学院，广东广州 510650；2. 华南理工大学机械与汽车工程学院，广东广州 510641）

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Identification Method of HEV Driving Condition Based on Wavelet Filtering and PSO Algorithm

GUO Hailong ,
ZHANG Yongdong ,
ZHANG Shengbin

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(1. School of Automobile and construction machinery,Guangdong Communication Polytechnic, Guangzhou 510650, China;2.School of Mechanical & Automobile Engineering, South China University of Technology, Guangzhou 510641, China）

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摘要

针对混合动力汽车（HEV）行驶状况（道路坡度和整车载荷）变化难以有效识别，导致驱动系统控制策略不能有效满足驾驶员意图问题，以混联式HEV为研究对象，提出了基于小波滤波和粒子群算法的HEV行驶状况辨识方法。首先建立了汽车行驶状况辨识模型，采用最小二乘法确立了优化目标函数，其次研究了基于小波滤波和粒子群算法的HEV行驶状况辨识原理，最后进行了行驶状况粒子群智能算法辨识试验。在采集实车数据的基础上，对实车数据进行小波滤波，并运用行驶状况辨识方法对道路坡度和整车载荷进行了辨识，并对辨识结果进行小波滤波，结果表明，试验工况下整车载荷辨识的相对误差绝对平均值为2.71%，道路坡度辨识的相对误差绝对平均值为3.85%,验证了所提出方法的有效性。

关键词： 混合动力汽车; 最小二乘法; 粒子群算法; 小波滤波; 辨识

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郭海龙 , 张永栋 , 张胜宾 . 基于小波和粒子群算法的HEV行驶状况辨识方法研究[J]. 车用发动机, 2017 , 0(2) : 62 -66 . DOI: 10.3969/j.issn.10012222.2017.02.011

Abstract

The recognition method of driving condition for the parallel series HEV based on wavelet filtering and PSO algorithm was put forward to identify the realtime road slope and vehicle load changes effectively so that the driver could adjust his driving behavior in time through the control strategy of driving system. The identification model of vehicle driving condition was established and the optimization objective function was determined by the least square method. Then the recognition
principle of driving condition based on wavelet filtering and PSO algorithm was studied. Finally, the recognition test of driving condition with the method was
conducted. The wavelet filtering, the recognition of driving road slope and vehicle load and the wavelet refiltering of vehicle test data were further conducted. The results show that the absolute average value of relative error for vehicle load and road slope is 2.71% and 3.85% respectively. Therefore, the proposed method is feasible.

Key words： hybrid electric vehicle（HEV）; least square method; particle swarm optimization (PSO); wavelet filtering; identification

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