With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid elec...With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.展开更多
How to reduce the energy consumption powered mainly by battery to prolong the standby time is one of the crucial issues for IEEE 802.16e wireless MANs.By predicting the next downlink inter-packet arrival time,three tr...How to reduce the energy consumption powered mainly by battery to prolong the standby time is one of the crucial issues for IEEE 802.16e wireless MANs.By predicting the next downlink inter-packet arrival time,three traffic-prediction-assisted power saving mechanisms based on P-PSCI,i.e.,PSCI-PFD,PSCI-ED and PSCI-LD,were proposed.In addition,the corresponding adjustment strategies for P-PSCI were also presented when there were uplink packets to be transmitted during sleep mode.Simulation results reveal that compared with the sleep mode algorithm recommended by IEEE 802.16e,the proposed mechanism P-PSCI can improve both energy efficiency and packet delay for IEEE 802.16e due to the consideration of the traffic characteristics and rate changes.Moreover,the results also demonstrate that PSCI-PFD (a=-2) significantly outperforms PSCI-ED,PSCI-LD,and the standard sleep mode in IEEE 802.16e is in terms of energy efficiency and packet delay.展开更多
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanc...Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.展开更多
文摘With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.
基金Projects(60873265,61070194)supported by the National Natural Science Foundation of ChinaProject(2009AA112205)supported by the National High Technology Research and Development Program of China+1 种基金Project(2011FJ2003)supported by Science and Technology Key Projects of Hunan Province,ChinaProject(531107040201)supported by Chinese Universities Scientific Fund
文摘How to reduce the energy consumption powered mainly by battery to prolong the standby time is one of the crucial issues for IEEE 802.16e wireless MANs.By predicting the next downlink inter-packet arrival time,three traffic-prediction-assisted power saving mechanisms based on P-PSCI,i.e.,PSCI-PFD,PSCI-ED and PSCI-LD,were proposed.In addition,the corresponding adjustment strategies for P-PSCI were also presented when there were uplink packets to be transmitted during sleep mode.Simulation results reveal that compared with the sleep mode algorithm recommended by IEEE 802.16e,the proposed mechanism P-PSCI can improve both energy efficiency and packet delay for IEEE 802.16e due to the consideration of the traffic characteristics and rate changes.Moreover,the results also demonstrate that PSCI-PFD (a=-2) significantly outperforms PSCI-ED,PSCI-LD,and the standard sleep mode in IEEE 802.16e is in terms of energy efficiency and packet delay.
基金Project(2012CB725403)supported by the National Basic Research Program of ChinaProjects(71210001,51338008)supported by the National Natural Science Foundation of ChinaProject supported by World Capital Cities Smooth Traffic Collaborative Innovation Center and Singapore National Research Foundation Under Its Campus for Research Excellence and Technology Enterprise(CREATE)Programme
文摘Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.
