模糊能耗及卡尔曼滤波的电动汽车剩余续驶里程估算
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摘要
为了提高电动汽车的剩余续驶里程估算精度,在工况识别基础上,提出了一种将模糊能耗与卡尔曼滤波相结合的剩余续驶里程估算模型。建立了整车能耗模型;在MATLAB/Simulink下建立特征参数与能耗之间的模糊规则库;基于卡尔曼滤波对输出剩余续驶里程进行优化。优化结果表明:采用该方法的行驶里程实际值与期望值平均误差为2.11%,相比传统平均能耗法,其剩余续驶里程估算精度提高了77%。
In order to improve the estimation accuracy of electric vehicle driving range,a new model of surplus driving range estimation by combining fuzzy energy consumption and Kalman filter was proposed based on condition identification. Firstly,vehicle energy consumption model was established. Then the fuzzy rule library about the characteristic parameters and energy consumption was established with the MATLAB / Simulink.Finally,the output of surplus driving range was optimized based on the Kalman filter. The experimental results show that by using this method the average error of actual mileage value to expectation is 2. 11%. The estimation accuracy of surplus driving range is improved by 77% compared with the traditional average energy consumption method.
In order to improve the estimation accuracy of electric vehicle driving range,a new model of surplus driving range estimation by combining fuzzy energy consumption and Kalman filter was proposed based on condition identification. Firstly,vehicle energy consumption model was established. Then the fuzzy rule library about the characteristic parameters and energy consumption was established with the MATLAB / Simulink.Finally,the output of surplus driving range was optimized based on the Kalman filter. The experimental results show that by using this method the average error of actual mileage value to expectation is 2. 11%. The estimation accuracy of surplus driving range is improved by 77% compared with the traditional average energy consumption method.
引文
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[3]伊安东,赵韩,周斌,等.基于行驶工况识别的纯电动汽车续驶里程估算[J].汽车工程,2014,36(11):1310-1315.
[4]SIY T,HERRMANN M A,LINDEMANN T P,et al.Electrical vehicle range prediction:US 8433455[P].2011-08-08.
[5]FRANKE T,KREMS J F.Interacting with limited mobility resources:psychological range levels in electric vehicle use[J].Transportation research part a(policy&practice),2013,48(2):109-122.
[6]MAZUROWSKI M A,HABAS P A,ZURADA J M,et al.Training neural network classifiers for medical decision making:the effects of imbalanced datasets on classification performance[J].Neural networks,2008,21(2/3):427-36.
[7]MEYER-EBELING J,ROTH M.Method for estimating the range of a motor vehicle:US 8594918 B2[P].2010-10-29.
[8]LU L,HAN X,LI J,et al.A review on the key issues for lithium-ion battery management in electric vehicles[J].Journal of power sources,2013,226(3):272-288.
[9]ZHANG C,VAHIDI A,PISU P,et al.Role of terrain preview in energy management of hybrid electric vehicles[J].IEEE transactions on vehicular technology,2010,59(3):1139-1147.
[10]PILLER S,PERRIN M,JOSSEN A.Methods for state-of-charge determination and their applications[J].Journal of power sources,2001,96(1):113-120.
[11]ZHANG X.Thermal analysis of a cylindrical lithium-ion battery[J].Electrochimica acta,2011,56(3):1246-1255.
[12]ZHU C,LI X,SONG L,et al.Development of a theoretically based thermal model for lithium ion battery pack[J].Journal of power sources,2013,223(1):155-164.
[13]张袅娜,丁海涛,于海芳,等.基于核主元约简与半监督核模糊聚类的车辆行驶工况判别[J].机械工程学报,2015,51(2):96-102.
[14]刘伟.电动汽车动力性与续驶里程分析[D].秦皇岛:燕山大学,2013.
[15]任密蜂.非高斯系统的控制及滤波方法研究[D].保定:华北电力大学,2014.