There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric ...There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.展开更多
This paper proposes a cruise control system(CCS)to improve an electric vehicle's range,which is a significant hurdle in market penetration of electric vehicles.A typical driver or a conventional adaptive cruise co...This paper proposes a cruise control system(CCS)to improve an electric vehicle's range,which is a significant hurdle in market penetration of electric vehicles.A typical driver or a conventional adaptive cruise control(ACC)controls an electric vehicle(EV)such that it follows a lead vehicle or drives close to the speed limit.This driving behaviour may cause the EV to cruise significantly above the average traffic speed.It may later require the EV to slow down due to the traffic ripples,wasting a part of the EV's kinetic energy.In addition,the EV will also waste higher speed dependent dissipative energies,which are spent to overcome the aerodynamic drag force and rolling resistance.This paper proposes a CCS to address this issue.The proposed CCS controls an EV's speed such that it prevents the vehicle from speeding significantly above the average traffic speed.In addition,it maintains a safe inter-vehicular distance from the lead vehicle.The design and simulation analysis of the proposed CCS were in a MATLAB simulation environment.The simulation environment includes an energy consumption model of an EV,which was developed using data collected from an electric bus operation in London.In the simulation analysis,the proposed system reduced the EV's energy consumption by approximately 36.6%in urban drive cycles and 15.4%in motorway drive cycles.Finally,the experimental analysis using a Nissan e-NV200on two urban routes showed approximately 30.8%energy savings.展开更多
In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming...In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming that travelers only focus on their past travel experience,a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality.In the proposed model,the Bush-Mosteller model,a reinforcement learning model,is modified to calculate path choice probability according to bounded rationality.The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold.Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles.The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows.The adaptation of battery electric vehicles leads to the poor performance of the traffic system.展开更多
传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概...传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概率计算的电动汽车充电负荷概率分布计算方法。首先,分析电动汽车的交通特性和充电特性,并提出一种可行路径集构建方法;然后,引入交通均衡理论进行电动汽车空间分布建模,建立考虑随机效用的半动态交通均衡模型,实现宏观交通流均衡分配。进一步地,从理论层面分析电动汽车群的荷电状态变化,建立基于CSOC的充电负荷概率分布计算模型。最后,分别在13节点路网和实际大路网中验证所提方法的有效性,并分析了电动汽车渗透率和路网结构对充电负荷概率分布的影响。展开更多
文摘There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.
基金partly supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(EP/R035199/1)
文摘This paper proposes a cruise control system(CCS)to improve an electric vehicle's range,which is a significant hurdle in market penetration of electric vehicles.A typical driver or a conventional adaptive cruise control(ACC)controls an electric vehicle(EV)such that it follows a lead vehicle or drives close to the speed limit.This driving behaviour may cause the EV to cruise significantly above the average traffic speed.It may later require the EV to slow down due to the traffic ripples,wasting a part of the EV's kinetic energy.In addition,the EV will also waste higher speed dependent dissipative energies,which are spent to overcome the aerodynamic drag force and rolling resistance.This paper proposes a CCS to address this issue.The proposed CCS controls an EV's speed such that it prevents the vehicle from speeding significantly above the average traffic speed.In addition,it maintains a safe inter-vehicular distance from the lead vehicle.The design and simulation analysis of the proposed CCS were in a MATLAB simulation environment.The simulation environment includes an energy consumption model of an EV,which was developed using data collected from an electric bus operation in London.In the simulation analysis,the proposed system reduced the EV's energy consumption by approximately 36.6%in urban drive cycles and 15.4%in motorway drive cycles.Finally,the experimental analysis using a Nissan e-NV200on two urban routes showed approximately 30.8%energy savings.
基金The National Natural Science Foundation of China(No.51478110)Postgraduate Research & Practice Innovation Program of Jiangsu Province(No.KYCX18_0139)
文摘In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming that travelers only focus on their past travel experience,a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality.In the proposed model,the Bush-Mosteller model,a reinforcement learning model,is modified to calculate path choice probability according to bounded rationality.The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold.Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles.The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows.The adaptation of battery electric vehicles leads to the poor performance of the traffic system.
文摘传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概率计算的电动汽车充电负荷概率分布计算方法。首先,分析电动汽车的交通特性和充电特性,并提出一种可行路径集构建方法;然后,引入交通均衡理论进行电动汽车空间分布建模,建立考虑随机效用的半动态交通均衡模型,实现宏观交通流均衡分配。进一步地,从理论层面分析电动汽车群的荷电状态变化,建立基于CSOC的充电负荷概率分布计算模型。最后,分别在13节点路网和实际大路网中验证所提方法的有效性,并分析了电动汽车渗透率和路网结构对充电负荷概率分布的影响。
