融合交通信息與自適應(yīng)控制的插電式混合動力汽車能量管理策略研究主要方案作為現(xiàn)代汽車發(fā)展的重要方向之一,插電式混合動力汽車(PHEV)能夠兼顧純電動汽車及傳統(tǒng)燃油車的優(yōu)點,具有燃油經(jīng)濟(jì)性高、尾氣排放低及續(xù)航里程長等特點。PHEV能量管理將直接影響汽車動力、燃油經(jīng)濟(jì)以及排放等性能。另外，隨著全球定位系統(tǒng)、地理信息系統(tǒng)及智能交通系統(tǒng)等技術(shù)的快速發(fā)展,在設(shè)計PHEV能量管理步驟時充分考慮道路交通信息,可進(jìn)一步提升其燃油經(jīng)濟(jì)性。為優(yōu)化PHEV的節(jié)能效果,更好解決能量管理步驟的環(huán)境適應(yīng)性問題,

本文以一款裝備機(jī)械式自動變速器的并聯(lián)式PHEV為系統(tǒng)研究對象,實施融合交通信息與自適應(yīng)調(diào)節(jié)的能量管理策略研究,主要研究與創(chuàng)新如下:(1)對比各類PHEV動力傳動系統(tǒng)的構(gòu)型及優(yōu)缺點,深入分析確定了論文所采用的混合動力系統(tǒng)的基本結(jié)構(gòu)及工作原理;按照PHEV的動力性要求計算其理論需求功率;借助對國內(nèi)某城市出租車數(shù)據(jù)的統(tǒng)計與分析,獲取實際工況下動力需求功率分布;結(jié)合實際工況需求功率分布對PHEV動力傳動系統(tǒng)關(guān)鍵部件進(jìn)行參數(shù)匹配;在此基礎(chǔ)上,建立了PHEV動力系統(tǒng)模型,根據(jù)所建模型對PHEV的動力性實施仿真實驗,驗證所匹配參數(shù)的合理性。(2)應(yīng)用模擬退火優(yōu)化K均值聚類步驟,將實驗車采集的路況資料和標(biāo)準(zhǔn)行駛路況數(shù)據(jù)進(jìn)行聚類分析,最終劃分為四種工況類型。綜合電池荷電狀態(tài)以及實際行駛距離定義等效行駛距離系數(shù)用以描述PHEV的行駛距離特性,采用等效行駛距離及工況類型作為行駛工況的基本特征;根據(jù)所研究PHEV的各部件特征,采用動態(tài)規(guī)劃算法實施全局最優(yōu)能量管理求解,同時動態(tài)優(yōu)化四種行駛工況類型下的基本控制策略;分析行駛工況類型和等效行駛距離對PHEV最優(yōu)能量管理策略的影響,獲取不同行駛工況下發(fā)動機(jī)啟動功率、換擋及轉(zhuǎn)矩分配的變化規(guī)律。(3)研究全局交通流平均車速信息的獲取與處理方法,建立了路網(wǎng)模型模擬實際交通流的變化,計算路網(wǎng)模型的交通流平均車速信息,建立實時工況模型;針對資料采集過程中難以避免的缺失問題,基于K最近鄰算法進(jìn)行交通流平均車速缺失信息補(bǔ)齊;在此基礎(chǔ)上,采用小波變換和平滑濾波算法生成汽車行駛工況預(yù)期全局交通信息,仿真結(jié)果表明:所提出的資料補(bǔ)齊方法能夠有效補(bǔ)充缺失的交通信息,且可有效生成用于能量管理策略的預(yù)期全局交通信息,為后續(xù)PHEV自適應(yīng)等效燃油消耗最小能量管理策略研究奠定了基礎(chǔ)。(4)根據(jù)混合動力系統(tǒng)的能量平衡原理,提出了一種自適應(yīng)等效燃油消耗最小能量管理策略;根據(jù)全局交通信息預(yù)先確定一對邊界等效因子,基于動力系統(tǒng)實時能量變化獲得實時概率因子,通過概率因子與邊界等效因子實時計算等效因子,實現(xiàn)動力傳動系統(tǒng)自適應(yīng)能量控制;針對三種標(biāo)準(zhǔn)工況以及不同的電池老化特性的情況,通過仿真驗證所研究能量管理策略的可行性,結(jié)果表明所提出的策略與傳統(tǒng)的ECMS相比具有較強(qiáng)的適應(yīng)性和穩(wěn)定性,同時在不同電池老化情況下可進(jìn)一步提升燃油經(jīng)濟(jì)性5%-11%。(5)為驗證所研究的PHEV自適應(yīng)能量管理策略,以虛擬交通環(huán)境作為PHEV能量管理實驗平臺,提出了實驗平臺的搭建思路;實施實時仿真系統(tǒng)、整車控制單元、駕駛操作單元、虛擬場景系統(tǒng)和動態(tài)數(shù)據(jù)監(jiān)控系統(tǒng)集成;交通仿真環(huán)境由虛擬場景仿真軟件PreScan和交通流仿真軟件VISSIM共同搭建,整車調(diào)節(jié)器選用D2P對實驗平臺進(jìn)行控制;利用NI-PXI作為實時仿真系統(tǒng)模擬動力傳動系統(tǒng)各部件的狀態(tài)變化,采用NI-Veristand軟件對實驗平臺的數(shù)據(jù)進(jìn)行動態(tài)監(jiān)控;基于所搭建的仿真實驗平臺,制定了詳細(xì)的實驗方案并開展硬件在環(huán)仿真實驗研究,驗證了

本文所提出的融合交通信息與自適應(yīng)調(diào)節(jié)的PHEV能源管理策略的顯著性。?簡介：擅長數(shù)據(jù)搜集與處理、建模仿真、程序設(shè)計、仿真代碼、論文寫作與指導(dǎo)，畢業(yè)論文、期刊論文經(jīng)驗交流。

?具體問題可以聯(lián)系QQ或者微信：30040983。仿真代碼importnumpyasnp

importmatplotlib.pyplotasplt

fromscipy.optimizeimportminimize

classVariableSpeedLimit:

def__init__(self,num_segments=10,segment_length=500):

self.num_segments=num_segments

self.segment_length=segment_length

self.density=np.zeros(num_segments)

self.speed=np.zeros(num_segments)

self.flow=np.zeros(num_segments)

self.speed_limits=np.ones(num_segments)*120

deffundamental_diagram(self,density,vf=120,kj=180,km=45):

ifdensity<km:

speed=vf

elifdensity<kj:

speed=vf*(1-(density-km)/(kj-km))

else:

speed=0

returnmax(speed,0)

defcalculate_flow(self,density,speed):

returndensity*speed

defupdate_traffic_state(self,density,inflow,outflow,dt=30):

new_density=np.zeros(self.num_segments)

new_density[0]=density[0]+(dt/self.segment_length)*(inflow-self.flow[0])

foriinrange(1,self.num_segments):

new_density[i]=density[i]+(dt/self.segment_length)*(self.flow[i-1]-self.flow[i])

new_density=np.clip(new_density,0,180)

returnnew_density

defoptimize_speed_limits(self,density,min_speed=60,max_speed=120):

defobjective(speed_limits):

total_flow=0

safety_penalty=0

foriinrange(self.num_segments):

speed=min(speed_limits[i],self.fundamental_diagram(density[i]))

flow=density[i]*speed

total_flow+=flow

ifi>0:

speed_diff=abs(speed_limits[i]-speed_limits[i-1])

ifspeed_diff>20:

safety_penalty+=speed_diff**2

return-total_flow+0.1*safety_penalty

bounds=[(min_speed,max_speed)for_inrange(self.num_segments)]

constraints=[]

foriinrange(self.num_segments-1):

constraints.append({

'type':'ineq',

'fun':lambdax,idx=i:30-abs(x[idx]-x[idx+1])

})

result=minimize(objective,self.speed_limits,method='SLSQP',

bounds=bounds,constraints=constraints)

returnresult.x

defincident_detection(self,density,speed,threshold_density=100,threshold_speed=40):

incidents=[]

foriinrange(self.num_segments):

ifdensity[i]>threshold_densityandspeed[i]<threshold_speed:

incidents.append(i)

returnincidents

defapply_incident_control(self,speed_limits,incident_location,upstream_range=3):

controlled_limits=speed_limits.copy()

foriinrange(max(0,incident_location-upstream_range),incident_location):

reduction_factor=1-(0.2*(upstream_range-(incident_location-i)))

controlled_limits[i]=controlled_limits[i]*reduction_factor

controlled_limits[incident_location]=40

returncontrolled_limits

defsimulate_fog_scenario(vsl_controller,duration=3600,dt=30):

time_steps=int(duration/dt)

visibility=np.ones(time_steps)

visibility[20:40]=0.3

visibility[40:60]=0.5

visibility[60:80]=0.8

density_history=[]

speed_history=[]

flow_history=[]

limit_history=[]

density=np.random.uniform(20,40,vsl_controller.num_segments)

fortinrange(time_steps):

vis=visibility[t]

max_safe_speed=120*vis

optimal_limits=vsl_controller.optimize_speed_limits(density)

optimal_limits=np.minimum(optimal_limits,max_safe_speed)

foriinrange(vsl_controller.num_segments):

free_flow_speed=vsl_controller.fundamental_diagram(density[i])

vsl_controller.speed[i]=min(optimal_limits[i],free_flow_speed)

vsl_controller.flow[i]=vsl_controller.calculate_flow(density[i],vsl_controller.speed[i])

inflow=2000+500*np.sin(2*np.pi*t/time_steps)

outflow=vsl_controller.flow[-1]

density=vsl_controller.update_traffic_state(density,inflow,outflow,dt)

density_history.append(density.copy())

speed_history.append(vsl_controller.speed.copy())

flow_history.append(vsl_controller.flow.copy())

limit_history.append(optimal_limits.copy())

return{

'density':np.array(density_history),

'speed':np.array(speed_history),

'flow':np.array(flow_history),

'limits':np.array(limit_history),

'visibility':visibility

}

defevaluate_vsl_performance(results_vsl,results_no_vsl):

total_travel_time_vsl=np.sum(results_vsl['density'])*30/3600

total_travel_time_no_vsl=np.sum(results_no_vsl['density'])*30/3600

avg_speed_vsl=np.mean(results_vsl['speed'])

avg_speed_no_vsl=np.mean(results_no_vsl['speed'])

speed_variance_vsl=np.var(results_vsl['speed'])

speed_variance_no_vsl=np.var(results_no_vsl['speed'])

metrics={

'travel_time_reduction':(total_travel_time_no_vsl-total_travel_time_vsl)/total_travel_time_no_vsl*100,

'avg_speed_vsl':avg_speed_vsl,

'avg_speed_no_vsl':avg_speed_no_vsl,

'speed_variance_reduction':(speed_variance_no_vsl-speed_variance_vsl)/speed_variance_no_vsl*100

}

returnmetrics

defcrash_risk_calculation(speed,density):

risk=np.zeros_like(speed)

foriinrange(len(speed)-1):

speed_diff=abs(speed[i+1]-speed[i])

ifspeed_diff>30:

risk[i]=0.8

elifspeed_diff>20:

risk[i]=0.5

elifspeed_diff>10:

risk[i]=0.2

ifdensity[i]>100:

risk[i]+=0.3

returnrisk

defmain():

vsl=VariableSpeedLimit(num_segments=10,segment_length=500)

results_vsl=simulate_fog_scenario(vsl)

vsl_no_control=VariableSpeedLimit(num_segments=10,segment_length=500)

vsl_no_control.speed_limits=np.ones(10)*120

density=np.random.uniform(20,40,10)

density_history_no_vsl=[]

speed_history_no_vsl=[]

flow_history_no_vsl=[]

fortinrange(120):

foriinrange(10):

vsl_no_control.speed[i]=vsl_no_control.fundamental_diagram(density[i])

vsl_no_control.flow[i]=vsl_no_control.calculate_flow(density[i],vsl_no_control.speed[i])

density=vsl_no_control.update_traffic_state(density,2000,vsl_no_control.flow[-1])

density_history_no_vsl.append(density.copy())

speed_history_no_vsl.append(vsl_no_control.speed.copy())

flow_history_no_vsl.append(vsl_no_control.flow.copy())

results_no_vsl={

'density':np