【摘要】：隨著全球經(jīng)濟(jì)和汽車(chē)電子技術(shù)的迅猛發(fā)展,汽車(chē)的產(chǎn)銷量急劇增加,但是這也帶來(lái)了交通擁堵、交通事故頻發(fā)、環(huán)境污染嚴(yán)重以及能源消耗量急劇增加等一系列社會(huì)問(wèn)題。為了解決以上社會(huì)問(wèn)題,電動(dòng)車(chē)以及車(chē)輛的主動(dòng)安全技術(shù)成為當(dāng)今汽車(chē)技術(shù)的發(fā)展方向。自適應(yīng)巡航控制(Adaptive Cruise Control,ACC)作為安全輔助駕駛技術(shù),是車(chē)輛主動(dòng)安全技術(shù)的一個(gè)重要組成部分,已成為國(guó)內(nèi)外研究的熱點(diǎn)。然而關(guān)于自適應(yīng)巡航系統(tǒng)的研究目前多集中于燃油車(chē),對(duì)電動(dòng)車(chē)的研究較少。又因?yàn)樽赃m應(yīng)巡航系統(tǒng)的研究方法也隨著車(chē)輛動(dòng)力系統(tǒng)的變化而變化,因此本文對(duì)基于電動(dòng)車(chē)的自適應(yīng)巡航系統(tǒng)的研究有較大的實(shí)際意義與價(jià)值。本文采用分工況、分層的策略對(duì)電動(dòng)車(chē)的自適應(yīng)巡航系統(tǒng)進(jìn)行控制算法研究。將控制系統(tǒng)分為決策層與執(zhí)行層:根據(jù)ACC車(chē)輛的行車(chē)環(huán)境,將決策層分為跟蹤控制、速度控制和勻速控制三種模式;執(zhí)行層分別設(shè)計(jì)驅(qū)動(dòng)控制器和制動(dòng)控制器,實(shí)現(xiàn)對(duì)決策層輸出的期望加速度的跟蹤控制。首先,本文建立了決策層跟蹤控制模式下的控制對(duì)象模型。跟蹤控制模式主要實(shí)現(xiàn)ACC車(chē)輛與目標(biāo)車(chē)輛的實(shí)際車(chē)間距對(duì)期望安全車(chē)間距的跟蹤。首先選擇兩車(chē)期望安全間距的規(guī)劃策略,完成ACC車(chē)輛與目標(biāo)車(chē)輛之間安全車(chē)間距的規(guī)劃;當(dāng)車(chē)輛進(jìn)入彎道行駛時(shí),需要將雷達(dá)獲得的兩車(chē)徑向相對(duì)運(yùn)動(dòng)狀態(tài)信息轉(zhuǎn)換為兩車(chē)的縱向相對(duì)運(yùn)動(dòng)信息,進(jìn)而對(duì)ACC車(chē)輛進(jìn)行縱向控制;最后僅考慮ACC系統(tǒng)的縱向控制,結(jié)合ACC車(chē)輛與目標(biāo)車(chē)輛之間的縱向運(yùn)動(dòng)學(xué)特性與規(guī)劃的期望安全車(chē)間距,建立兩車(chē)車(chē)間距誤差LPV模型。其次,設(shè)計(jì)了ACC系統(tǒng)決策層控制策略,包含ACC車(chē)輛在不同工況下的控制模式以及在各個(gè)控制模式下控制器的切換策略,實(shí)現(xiàn)控制器的平滑切換。在跟蹤控制模式下,由于已建立的車(chē)間距誤差LPV模型的參變量可測(cè)并且有界,故可使用H?控制算法設(shè)計(jì)間距控制器。針對(duì)速度控制模式,使用PID控制算法設(shè)計(jì)速度巡航控制器,實(shí)現(xiàn)對(duì)駕駛員設(shè)定速度的跟蹤。最后,建立了執(zhí)行層的驅(qū)動(dòng)控制器和制動(dòng)控制器,實(shí)現(xiàn)對(duì)決策層輸出的期望加速度的跟蹤。由于電動(dòng)車(chē)可以實(shí)現(xiàn)對(duì)車(chē)輪扭矩的精確控制,故可通過(guò)車(chē)輪扭矩來(lái)控制車(chē)輛的驅(qū)動(dòng)和制動(dòng)過(guò)程。在建立ACC車(chē)輛的縱向驅(qū)動(dòng)動(dòng)力學(xué)模型和制動(dòng)動(dòng)力學(xué)模型時(shí),考慮車(chē)輛滑移率以及前后軸載荷轉(zhuǎn)移的影響,并由于該模型的強(qiáng)非線性,可使用滑�？刂扑惴ㄔO(shè)計(jì)驅(qū)動(dòng)控制器和制動(dòng)控制器。在目標(biāo)車(chē)輛減速、目標(biāo)車(chē)輛先減速后加速、目標(biāo)車(chē)輛急剎車(chē)、相鄰車(chē)道車(chē)輛插入等工況下,用高精度仿真軟件veDYNA對(duì)由決策層與執(zhí)行層的控制器組成的ACC系統(tǒng)進(jìn)行仿真驗(yàn)證。仿真結(jié)果表明,設(shè)計(jì)的ACC算法控制效果良好,具有較強(qiáng)的魯棒性。
[Abstract]:With the rapid development of global economy and automobile electronic technology, the production and sales of cars have increased sharply, but this has also brought a series of social problems, such as traffic congestion, frequent traffic accidents, serious environmental pollution and a sharp increase in energy consumption. In order to solve the above social problems, electric vehicles and vehicle active safety technology has become the development direction of automobile technology. Adaptive cruise control (Adaptive Cruise Control,ACC), as a safety auxiliary driving technology, is an important part of vehicle active safety technology, and has become a hot research topic at home and abroad. However, the research on adaptive cruise system is mostly focused on fuel vehicles, but the research on electric vehicles is less. Because the research method of adaptive cruise system also changes with the change of vehicle power system, the research of adaptive cruise system based on electric vehicle has great practical significance and value in this paper. In this paper, the control algorithm of adaptive cruise system of electric vehicle is studied by using the strategy of dividing working conditions and layering. The control system is divided into decision layer and execution layer: according to the driving environment of ACC vehicle, the decision layer is divided into three modes: tracking control, speed control and uniform speed control, and the driving controller and braking controller are designed respectively to realize the tracking control of the expected acceleration of the output of the decision layer. Firstly, the control object model in decision layer tracking control mode is established in this paper. The tracking control mode mainly realizes the tracking of the actual workshop distance between the ACC vehicle and the target vehicle to the expected safety workshop distance. Firstly, the planning strategy of the expected safety distance between the two vehicles is selected to complete the planning of the safety workshop distance between the ACC vehicle and the target vehicle. When the vehicle enters the bend, the radial relative motion state information obtained by the radar needs to be transformed into the longitudinal relative motion information of the two vehicles, and then the longitudinal control of the ACC vehicle is carried out. Finally, considering only the longitudinal control of ACC system, combining the longitudinal kinematic characteristics between ACC vehicle and target vehicle and the planned expected safety workshop distance, the LPV model of two-car workshop distance error is established. Secondly, the decision layer control strategy of ACC system is designed, which includes the control mode of ACC vehicle under different working conditions and the switching strategy of controller under each control mode, so as to realize the smooth switching of the controller. In the tracking control mode, the parameters of the established workshop distance error LPV model can be measured and bounded, so H? The distance controller is designed by the control algorithm. Aiming at the speed control mode, the speed cruise controller is designed by using PID control algorithm to track the speed set by the driver. Finally, the driving controller and braking controller of the executive layer are established to track the expected acceleration of the output of the decision layer. Because the electric vehicle can realize the accurate control of the wheel torque, the driving and braking process of the vehicle can be controlled by the wheel torque. When establishing the longitudinal drive dynamics model and braking dynamics model of ACC vehicle, the influence of vehicle slip rate and front and rear axle load transfer is taken into account. Because of the strong nonlinear of the model, the sliding mode control algorithm can be used to design the drive controller and braking controller. Under the conditions of deceleration of the target vehicle, deceleration and acceleration of the target vehicle, brake of the target vehicle and insertion of the adjacent lane vehicle, the ACC system composed of the controller of the decision layer and the executive layer is simulated and verified by using the high precision simulation software veDYNA. The simulation results show that the designed ACC algorithm has good control effect and strong robustness.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U469.72;TP273

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