【摘要】：混合動(dòng)力汽車兼顧了傳統(tǒng)汽車和純電動(dòng)汽車的優(yōu)點(diǎn),能夠?qū)崿F(xiàn)節(jié)能減排的目的,是當(dāng)今新能源汽車領(lǐng)域的研究熱點(diǎn)。檔位決策是汽車自動(dòng)變速系統(tǒng)中的重要研究?jī)?nèi)容,研究智能化、自適應(yīng)的檔位決策方法,對(duì)于提高混合動(dòng)力汽車的動(dòng)力性、經(jīng)濟(jì)性和乘坐舒適性等具有重要的意義。汽車行駛在復(fù)雜的人-車-路系統(tǒng)中,在不同的駕駛意圖和行駛環(huán)境下,如何確定出滿足人們對(duì)車輛性能需求的最佳換檔點(diǎn)存在困難�；诖�,本文以單軸并聯(lián)混合動(dòng)力汽車為研究對(duì)象,引入神經(jīng)網(wǎng)絡(luò)對(duì)包含駕駛員和環(huán)境信息的行車樣本數(shù)據(jù)進(jìn)行學(xué)習(xí)和泛化,建立車輛狀態(tài)參數(shù)與最佳檔位之間的非線性模型,并結(jié)合神經(jīng)網(wǎng)絡(luò)的不足,開展了如下研究:(1)分析人-車-路之間的關(guān)系,考慮駕駛意圖、行駛環(huán)境、車輛狀態(tài)對(duì)檔位決策的影響,制定了本文的檔位決策方案。(2)研究了駕駛意圖和行駛環(huán)境的識(shí)別方法。采集車輛運(yùn)行狀態(tài)參數(shù),根據(jù)加速踏板信號(hào)、制動(dòng)踏板信號(hào)等車輛參數(shù)利用模糊推理對(duì)駕駛員意圖進(jìn)行了識(shí)別,同時(shí)利用拉格朗日插值法、移動(dòng)平均數(shù)法等對(duì)行駛環(huán)境進(jìn)行了識(shí)別。(3)以油門開度、車速、加速度、變速箱輸入軸轉(zhuǎn)速為控制參數(shù),檔位值為輸出,建立神經(jīng)網(wǎng)絡(luò)模型。確定神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),包括網(wǎng)絡(luò)層數(shù)、各層節(jié)點(diǎn)個(gè)數(shù)等。為避免神經(jīng)網(wǎng)絡(luò)陷入局部極小,導(dǎo)致網(wǎng)絡(luò)局部收斂,采用遺傳算法對(duì)神經(jīng)網(wǎng)絡(luò)權(quán)值和閾值進(jìn)行了優(yōu)化。(4)在MATLAB下,基于識(shí)別結(jié)果,分別對(duì)急加速、上坡、顛簸工況建立神經(jīng)網(wǎng)絡(luò)模型,并進(jìn)行檔位值預(yù)測(cè)仿真與分析。仿真結(jié)果表明,訓(xùn)練好的遺傳神經(jīng)網(wǎng)絡(luò)模型,在特殊駕駛意圖和行駛環(huán)境下,能夠準(zhǔn)確地預(yù)測(cè)汽車檔位值;且經(jīng)遺傳算法優(yōu)化后的神經(jīng)網(wǎng)絡(luò)模型,精度有一定的提高。
[Abstract]:Hybrid electric vehicle (HEV), which takes into account the advantages of both traditional and pure electric vehicles, can achieve the goal of energy saving and emission reduction. It is a hot research topic in the field of new energy vehicles. Gear decision-making is an important research content in automatic transmission system of automobile. The research on intelligent and adaptive gear decision-making method is of great significance for improving the power performance, economy and ride comfort of hybrid electric vehicle. In the complicated man-vehicle-road system, it is difficult to determine the optimal shift point to meet people's demand for vehicle performance under different driving intention and driving environment. Based on this, this paper takes the single-axle parallel hybrid vehicle as the research object, introduces the neural network to study and generalize the driving sample data including driver and environment information, and establishes the nonlinear model between the vehicle state parameters and the optimal gear. Combined with the deficiency of neural network, the following research is carried out: (1) the relationship between man-car-road is analyzed, and the influence of driving intention, driving environment and vehicle state on the decision-making of stalls is considered. (2) the identification method of driving intention and driving environment is studied. According to the acceleration pedal signal, brake pedal signal and other vehicle parameters, fuzzy reasoning is used to identify the driver's intention, and Lagrangian interpolation method is used at the same time. The moving average method is used to identify the driving environment. (3) the neural network model is established by taking throttle opening, speed, acceleration, speed of transmission input shaft as control parameters and shift value as output. Determine the structure of neural network, including the number of network layers, the number of nodes in each layer and so on. In order to avoid the neural network falling into local minimum and lead to local convergence of the network, genetic algorithm is used to optimize the weights and thresholds of the neural network. (4) based on the recognition results, the neural networks are accelerated rapidly and up the slope respectively under MATLAB. The model of neural network is established under bumpy condition, and the prediction simulation and analysis of stalls are carried out. The simulation results show that the trained genetic neural network model can accurately predict the vehicle stall value under the special driving intention and driving environment, and the precision of the neural network model optimized by genetic algorithm is improved to a certain extent.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U469.7;TP183

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本文編號(hào)：2441913