Yazar "Yang, Yalian" seçeneğine göre listele

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    Co-optimized Analytical Solution of Speed Planning and Energy Management for Automated Hybrid Electric Vehicles under Multi-Signal Intersections Scenario
    (Institute of Electrical and Electronics Engineers Inc., 2025) Zhang, Fengqi; Xiao, Lehua; Xie, Shaobo; Coskun, Serdar; Guo, Yingshi; Yang, Yalian; Hu, Xiaosong
    Eco-driving is a viable technology with higher energy-saving potential at signalized intersections. The rapid development of connected and automated technology provides more opportunities for the eco-driving of hybrid electric vehicles (HEVs). However, it is more challenging to co-optimize speed planning and energy management due to their coupling and complex features. To this end, a co-optimization method of speed planning and energy management under multi-signal intersections scenario is proposed for automated HEV by obtaining an explicit optimal analytical solution. Firstly, considering the shifting behavior of a parallel HEV, a single-parameter gear-shifting model is adopted. Then, the co-optimization method is proposed, which consists of two steps. In the first step, the vehicle arrival time at signalized intersections is determined by calculating a vehicle reference speed. In the second step, the speed and powertrain energy management are co-optimized using the Pontryagin minimum principle by deriving an optimal analytical solution under multi-signal intersections. Finally, an iterative loop algorithm is utilized to compute the initial co-states, and the sensitivity analysis is conducted in this sequel. Simulation results demonstrate that the proposed co-optimization approach can greatly reduce the computational cost while maintaining satisfactory energy efficiency as compared with the widely-used dynamic programming method. © 2015 IEEE.
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    Dynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Zhao, Nan; Zhang, Fengqi; Yang, Yalian; Coskun, Serdar; Lin, Xianke; Hu, Xiaosong
    Vehicle electrification, automation, and connectivity in today's transportation require significant efforts in control design to meet conflicting goals of energy efficiency, traffic safety, as well as comfort. The rapid development of intelligent transportation systems (ITS) and the rapid growth of connectivity technologies enable vehicles to receive more information about traffic conditions, which provides a reliable solution for the energy management of plug-in hybrid electric vehicles (PHEVs). This article proposes a predictive energy management strategy (EMS) for connected PHEV based on real-time dynamic traffic prediction. First, the future traffic information is predicted by establishing a wavelet neural network (WNN). Thus, the global driving condition can be predicted. Then, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of WNN to plan a global battery state-of-charge (SOC) reference. Second, a long short-term memory-based velocity predictor is proposed for the predictive EMS, by planning SOC over a prediction horizon based on the global SOC reference. Finally, the performance of the proposed EMS with WNN and PSO-WNN is verified by the actual traffic data. The results show that it can improve the fuel economy by 17.57% and 28.19%, respectively.
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    Hierarchical Optimization of Speed Planning and Energy Management for Connected Hybrid Electric Vehicles Under Multi-Lane and Signal Lights Aware Scenarios
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Peng, Jinghui; Zhang, Fengqi; Coskun, Serdar; Hu, Xiaosong; Yang, Yalian; Langari, Reza; He, Jinsong
    Connected and automated vehicle technology via vehicle-to-everything communication, can assist in improving energy efficiency for hybrid electric vehicles (HEVs). In particular, information about the timing of traffic lights and surrounding vehicles can be exchanged between traffic vehicles and in conjunction with vehicle state information, to improve the fuel economy of HEVs significantly. To this end, we propose a multi-lane hierarchical optimization (MLHO) algorithm based on a predictive control framework. The dynamic behaviors of the surrounding vehicles are first predicted, and then the traffic light information (e.g., signal phasing and timing) and vehicles' state information are utilized in the design. MLHO is a two-level strategy wherein a multi-lane speed planning method for a host vehicle is formulated to plan the optimal speed and lane-change behaviors by considering vehicle power demand, driving comfort, and safety in the upper level. In the lower level, dynamic programming is adopted to devise energy management by tracking the optimal speed. Simulation results under real routes using the traffic simulation software Simulation of Urban Mobility show that the fuel economy of MLHO is improved by 32% on average compared to speed profile driven by a human driver model. In addition, traffic efficiency is enhanced significantly, i.e., different traffic occupancy results on the road indicate that the proposed MLHO is less affected by the traffic flow density. With different traffic densities, the maximum fuel consumption difference under the three considered scenarios is only 0.645L/100km.
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    Stochastic Velocity Prediction for Connected Vehicles Considering V2V Communication Interruption
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Wang, Lihua; Zhang, Fengqi; Cui, Yahui; Coskun, Serdar; Tang, Xiaolin; Yang, Yalian; Hu, Xiaosong
    Reliable and accurate velocity prediction can significantly contribute to the quality of connected vehicle control applications. Existing efforts focus on the velocity prediction without considering vehicle-to-vehicle (V2V) communication interruption. Hence, a stochastic velocity prediction method for connected vehicles considering V2V communication interruption is put forward for the first time. The missing V2V communication data are addressed by the piecewise cubic Hermite spline interpolation. Then, the processed data are used as the input variables of the best conditional linear Gaussian (CLG) prediction model. Specifically, the best CLG model is obtained by analyzing the influence of different input variables on the velocity prediction without V2V communication interruption. The results demonstrate that the prediction accuracy of CLG-based model is acceptable if the communication interruption time is less than 5 s compared to the non-interrupted V2V communication case. The sensitivity study of the best CLG model under multiple vehicles scenario indicates that choosing appropriate historical data substantially improve the prediction accuracy. Furthermore, the CLG-based predictor is proved to be an effective method to achieve higher prediction accuracy in two test road networks when compared with the Back-propagation and Long Short-Term Memory network.