【摘要】：隨著世界社會經(jīng)濟的高速發(fā)展，能源危機和環(huán)境污染問題日益凸顯。并聯(lián)式混合動力汽車因具有環(huán)保、節(jié)能和技術(shù)相對成熟等優(yōu)點而備受關(guān)注。并聯(lián)混合動力汽車的能量控制策略的優(yōu)劣是影響汽車能耗的重要因素，因此在滿足汽車動力性的前提下，優(yōu)化并聯(lián)式混合動力汽車能量控制策略對控制能源危機和實現(xiàn)環(huán)境的可持續(xù)發(fā)展具有非常重要的現(xiàn)實意義。 論文以并聯(lián)混合動力汽車能量控制系統(tǒng)為研究對象，對發(fā)動機轉(zhuǎn)矩和電機轉(zhuǎn)矩的分配問題進行了深入研究。在分析了并聯(lián)混合動力汽車的驅(qū)動系統(tǒng)結(jié)構(gòu)和結(jié)合方式的基礎(chǔ)上，建立了發(fā)動機數(shù)學模型、電機數(shù)學模型、蓄電池數(shù)學模型、車輪數(shù)學模型和傳動系動力學方程，并構(gòu)建了能量控制策略的優(yōu)化數(shù)學模型。因并聯(lián)混合動力汽車能量控制系統(tǒng)具有動態(tài)非線性的特點，論文采用模糊神經(jīng)網(wǎng)絡(luò)算法對發(fā)動機轉(zhuǎn)矩和電機轉(zhuǎn)矩進行優(yōu)化分配，分別設(shè)計了基于模糊邏輯算法的能量控制策略和基于模糊神經(jīng)網(wǎng)絡(luò)算法的能量控制策略，為仿真平臺提供了理論基礎(chǔ)。 在模糊神經(jīng)網(wǎng)絡(luò)能量控制策略中，根據(jù)并聯(lián)混合動力汽車控制策略的優(yōu)化數(shù)學模型，采用補償神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)，構(gòu)建由輸入層、隱含層和輸出層組成的前向神經(jīng)網(wǎng)絡(luò)，使網(wǎng)絡(luò)的輸入層與模糊邏輯算法的模糊化過程對應(yīng)，網(wǎng)絡(luò)的隱含層與模糊推理過程對應(yīng)，網(wǎng)絡(luò)的輸出層與解模糊過程對應(yīng)，為了解決神經(jīng)網(wǎng)絡(luò)節(jié)點與模糊邏輯輸入輸出接口統(tǒng)一的問題，通過量化公式對輸入輸出變量進行量化處理，進而利用神經(jīng)網(wǎng)絡(luò)自學習和自適應(yīng)的能力，自動生成模糊規(guī)則和隸屬函數(shù)，并不斷優(yōu)化神經(jīng)網(wǎng)絡(luò)輸入輸出隸屬函數(shù)的中心和寬度。為了提高系統(tǒng)精度，加快收斂速度，神經(jīng)網(wǎng)絡(luò)采用動態(tài)調(diào)整步長補償梯度下降的學習算法對能量控制策略進行優(yōu)化。 以豐田普銳斯轎車為例，在ADVISOR2002軟件環(huán)境下，建立了整車的后向仿真模型，其中包括發(fā)動機、電機、蓄電池、傳動系和汽車行駛動力學模型，為整車控制策略研究和開發(fā)提供了必要的仿真平臺，并基于此仿真平臺，在典型的NEDC循環(huán)工況下，對采用模糊邏輯能量控制策略和采用模糊神經(jīng)網(wǎng)絡(luò)能量控制策略的控制系統(tǒng)進行仿真，仿真結(jié)果驗證了模糊神經(jīng)網(wǎng)絡(luò)能量控制策略的有效性。采用模糊神經(jīng)網(wǎng)絡(luò)能量控制策略，，能夠同時保證發(fā)動機和電機工作在高效區(qū)域，從而提高了整車燃油經(jīng)濟性和排放性。論文對模糊神經(jīng)網(wǎng)絡(luò)能量控制策略的研究，對我國自主研發(fā)新型節(jié)能環(huán)保汽車，提高混合動力汽車能量控制系統(tǒng)設(shè)計水平，構(gòu)建自主知識產(chǎn)權(quán)的汽車電子開發(fā)平臺具有重要意義
[Abstract]:With the rapid development of world economy, energy crisis and environmental pollution have become increasingly prominent. Parallel hybrid vehicles (HEVs) have attracted much attention because of their advantages of environmental protection, energy saving and relatively mature technology. The energy control strategy of the parallel hybrid electric vehicle is an important factor that affects the energy consumption of the vehicle. Therefore, under the premise of satisfying the power performance of the vehicle, Optimizing the energy control strategy of parallel hybrid electric vehicle (HEV) is of great practical significance in controlling the energy crisis and realizing the sustainable development of the environment. In this paper, the energy control system of parallel hybrid electric vehicle is taken as the research object, and the distribution of engine torque and motor torque is studied deeply. On the basis of analyzing the driving system structure and combination mode of parallel hybrid electric vehicle, the mathematical model of engine, motor, battery, wheel and dynamic equation of transmission system are established. The optimal mathematical model of energy control strategy is constructed. Because the energy control system of parallel hybrid electric vehicle has the characteristics of dynamic nonlinearity, the fuzzy neural network algorithm is used to optimize the distribution of engine torque and motor torque. The energy control strategy based on fuzzy logic algorithm and the energy control strategy based on fuzzy neural network algorithm are designed respectively, which provides the theoretical basis for the simulation platform. In the energy control strategy of fuzzy neural network, according to the optimal mathematical model of the control strategy of parallel hybrid electric vehicle, a forward neural network composed of input layer, hidden layer and output layer is constructed by using compensatory neural network structure. The input layer of the network corresponds to the fuzzy process of the fuzzy logic algorithm, the hidden layer of the network corresponds to the fuzzy reasoning process, and the output layer of the network corresponds to the process of resolving the fuzzy logic. In order to solve the problem of the unity of the interface between the neural network node and the fuzzy logic input and output, the input and output variables are quantized by the quantization formula, and then the self-learning and adaptive ability of the neural network is utilized. The fuzzy rules and membership functions are generated automatically, and the center and width of the input and output membership functions of the neural network are optimized continuously. In order to improve the accuracy of the system and speed up the convergence, the neural network optimizes the energy control strategy by using the learning algorithm of dynamically adjusting the step size to compensate for the gradient descent. Taking the Toyota Prius car as an example, the backward simulation model of the whole vehicle is established under the environment of ADVISOR2002 software, which includes engine, motor, battery, transmission system and vehicle driving dynamics model. It provides a necessary simulation platform for the research and development of vehicle control strategy, and based on this simulation platform, under typical NEDC cycle conditions, The fuzzy logic energy control strategy and the fuzzy neural network energy control strategy are simulated. The simulation results verify the effectiveness of the fuzzy neural network energy control strategy. The fuzzy neural network (FNN) energy control strategy can ensure that the engine and motor can work in the high efficiency area at the same time, thus improving the fuel economy and emission performance of the whole vehicle. The research on the fuzzy neural network energy control strategy is of great significance to the independent research and development of new energy saving and environmental protection vehicles in China, to improve the design level of hybrid electric vehicle energy control system, and to construct the automobile electronic development platform with independent intellectual property rights.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：U469.7

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