Yazar "Xie, Shaobo" seçeneğine göre listele

Listeleniyor 1 - 5 / 5
  • [ X ]
    Öğe
    A data-driven energy management strategy for plug-in hybrid electric buses considering vehicle mass uncertainty
    (Elsevier, 2024) Ma, Zheng; Luan, Yixuan; Zhang, Fengqi; Xie, Shaobo; Coskun, Serdar
    Conventional energy management strategies (EMSs) for hybrid electric vehicles are devised assuming the vehicle mass remains constant under dynamic driving conditions. However, the EMSs cannot adapt to different load conditions due to the dynamic change of vehicle mass. Investigating the characteristics of vehicle mass change plays significant roles in energy optimization, thereby improving overall efficiency and mobility. To this end, we propose an adaptive EMS for a plug-in hybrid electric bus (PHEB) based on artificial neutral network-Pontryagin minimum principle (ANN-PMP) by considering mass distribution characteristics. Firstly, the skew-normal distribution characteristics of PHEB mass are analyzed, and the distribution characteristic spectrum of vehicle mass is obtained based on the Monte Carlo method. Secondly, the influence of mass uncertainty on the PMP is analyzed, and then the ANN-PMP is devised by updating a dynamic co-state with ANN. Finally, an enhanced ANN-PMP (so-called ANN-PMP-CS) is proposed by combining the ANN-PMP strategy obtained by mass distri-bution training and CS (charge sustaining strategy) to include the no-load or full-load cases. The simulation results demonstrate that the ANN-PMP can adapt to mass change while ensuring that the final state-of-charge (SOC) convergence to the target value. We also observe that the fuel economy of ANN-PMP-CS is similar to that of the widely-used dynamic programming (DP) strategy. Compared with the charge depletion-charge sus-taining (CD-CS) strategy, the fuel economy can be improved by about 46.93 % on average.
  • [ X ]
    Öğe
    Co-optimization on ecological adaptive cruise control and energy management of automated hybrid electric vehicles
    (Pergamon-Elsevier Science Ltd, 2025) Zhang, Fengqi; Qi, Zhicheng; Xiao, Lehua; Coskun, Serdar; Xie, Shaobo; Liu, Yongtao; Li, Jiacheng
    Electrified drive systems and eco-driving technologies play a crucial role in promoting energy conservation. Ecodriving for hybrid electric vehicles(HEVs) is an intricate problem involving intertwined speed planning and energy management. In this context, an ecological adaptive cruise control (eco-ACC) and powertrain energy management strategy considering Signal Phase Timing Message (SPaT) can enhance both performance and realtime implementation. Specifically, this study develops a novel co-optimization method based on Pontryagin's minimum principle (PMP) that combines car-following control rules with the SPaT for a parallel HEV. The methodology involves the following steps: firstly, the parallel HEV model is established; secondly, the safe following distance model is constructed and the car-following control rules are devised to ensure safe driving. Subsequently, the co-optimization method based on PMP is then presented to simultaneously optimize the ecodriving problem of an ego-vehicle by converting the inter-vehicle distance constraint of the lead-vehicle into the limitation of the speed of the ego-vehicle. Finally, simulations are conducted under different scenarios for both fused SPaT and non-fused SPaT strategy. The simulation results demonstrate a reduction in fuel consumption by 6.27% and 5.69 % in two different scenarios, respectively, and a shorter driving time for the fused SPaT strategy compared to the non-fused SPaT strategy.
  • [ X ]
    Öğe
    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.
  • [ X ]
    Öğe
    Comparative study of energy management in parallel hybrid electric vehicles considering battery ageing
    (Pergamon-Elsevier Science Ltd, 2023) Zhang, Fengqi; Xiao, Lehua; Coskun, Serdar; Pang, Hui; Xie, Shaobo; Liu, Kailong; Cui, Yahui
    This article presents a thorough comparative study of energy management strategies (EMSs) for a par-allel hybrid electric vehicle (HEV), while the battery ageing is considered. The principle of dynamic programming (DP), Pontryagin's minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) considering battery ageing is elaborated. The gearshift map is obtained from the opti-mization results in DP to prevent frequent shifts by taking into account drivability and fuel economy, which is then applied in the PMP and ECMS. Comparison of different EMSs is conducted by means of fuel economy, battery state-of-charge charge-sustainability, and computational efficiency. Moreover, battery ageing is included in the optimization solution by utilizing a control-oriented model, aiming to fulfill one of the main cost-related design concerns in the development of HEVs. Through a unified framework, the torque split and battery degradation are simultaneously optimized in this study. Simulations are carried out for DP, PMP, and ECMS to analyze their features, wherein results indicate that DP obtains the best fuel economy compared with other methods. Additionally, the difference between DP and PMP is about 2% in terms of fuel economy. The observations from analysis results provide a good insight into the merits and demerits of each approach. (c) 2022 Published by Elsevier Ltd.
  • [ X ]
    Öğe
    Economic-social-oriented energy management of plug-in hybrid electric vehicles including social cost of carbon
    (Elsevier, 2024) Zhang, Tao; Peng, Guozhi; Zhang, Yanwei; Xie, Shaobo; Zhang, Fengqi; Serdar, Coskun
    For plug-in hybrid electric vehicles (PHEVs), conventional energy management strategies (EMSs) incorporate the output of the battery and power sources for energy-saving studies. The source of the battery charge and the social cost of carbon (SCC) associated with carbon emissions result in the economic-social cost of climate and environmental damage, particularly when the battery charge comes from thermal power. Consequently, conventional EMSs cannot improve the efficiency of PHEVs with respect to the SCC from an environment-friendly perspective. Also, overuse of battery charge can provide low-cost vehicular propulsion, but lead to battery aging, implying an associated cost. In this context, the battery discharge and aging are conflicted in the design of EMSs for PHEVs. This paper develops a novel EMS by explicitly considering the social cost of carbon emissions using model predictive control (MPC). The strategy devises the trade -off between the energy consumption cost, battery life loss cost, and social cost of carbon emissions by minimizing their total cost over a specific driving range. The driving cycle is developed based on real -world data in Xi ' an City, and the MPC is employed to assess the performance of EMS in a PHEV bus. Meanwhile, the widely used EMSs are compared in the simulation analysis, including the rule-based strategy, dynamic programming, and Pontryagin ' s minimum principle. Moreover, two sources of thermal power and wind power for the battery charge are conducted and compared. The results demonstrate that the proposed MPC-based EMS can generate the minimum total cost compared to the EMSs only considering the energy consumption and battery aging costs. Compared to thermal power as the source of battery charge, wind power can remarkably lower the total cost using the MPC, achieving a 19.2 % improvement. In addition, the results also demonstrate that the total cost in the case of wind power can be lowered by 24.6 %, 25.7 %, and 27.5 % compared to thermal power for battery charge under the methods of rule-based strategy, Pontryagin ' s minimum principle and dynamic programming, respectively.