Yazar "Li, Lin" seçeneğine göre listele

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    A comparative study of energy management systems under connected driving: cooperative car-following case
    (OAE Publishing Inc., 2022) Yazar, Ozan; Coskun, Serdar; Zhang, Fengqi; Li, Lin
    In this work, we propose connected energy management systems for a cooperative hybrid electric vehicle (HEV) platoon. To this end, cooperative driving scenarios are established under different car-following behavior models using connected and automated vehicles technology, leading to a cooperative cruise control system (CACC) that explores the energy-saving potentials of HEVs. As a real-time energy management control, an equivalent consumption minimization strategy (ECMS) is utilized, wherein global energy-saving is achieved to promote environment-friendly mobility. The HEVs cooperatively communicate and exchange state information and control decisions with each other by sixth-generation vehicle-to-everything (6G-V2X) communications. In this study, three different car-following behavior models are used: intelligent driver model (IDM), Gazis–Herman–Rothery (GHR) model, and optimal velocity model (OVM). Adopting cooperative driving of six Toyota Prius HEV platoon scenarios, simulations under New European Driving Cycle (NEDC), Worldwide Harmonized Light Vehicle Test Procedure (WLTP), and Highway Fuel Economy Test (HWFET), as well as human-in-the-loop (HIL) experiments, are carried out via MATLAB/Simulink/dSPACE for cooperative HEV platooning control via different car-following-linked-vehicle scenarios. The CACC-ECMS scheme is assessed for HEV energy management via 6G-V2X broadcasting, and it is found that the proposed strategy exhibits improvements in vehicular driving performance. The IDM-based CACC-ECMS is an energy-efficient strategy for the platoon that saves: (i) 8.29% fuel compared to the GHR-based CACC-ECMS and 10.47% fuel compared to the OVMbased CACC-ECMS under NEDC; (ii) 7.47% fuel compared to the GHR-based CACC-ECMS and 11% fuel compared to the OVM-based CACC-ECMS under WLTP; (iii) 3.62% fuel compared to the GHR-based CACC-ECMS and 4.22% fuel compared to the OVM-based CACC-ECMS under HWFET; and (iv) 11.05% fuel compared to the GHR-based CACC-ECMS and 18.26% fuel compared to the OVM-based CACC-ECMS under HIL. © The Author(s) 2022.
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    A multi-objective hierarchical deep reinforcement learning algorithm for connected and automated HEVs energy management
    (Pergamon-Elsevier Science Ltd, 2024) Coskun, Serdar; Yazar, Ozan; Zhang, Fengqi; Li, Lin; Huang, Cong; Karimi, Hamid Reza
    Connected and autonomous vehicles have offered unprecedented opportunities to improve fuel economy and reduce emissions of hybrid electric vehicle (HEV) in vehicular platoons. In this context, a hierarchical control strategy is put forward for connected HEVs. Firstly, we consider a deep deterministic policy gradient (DDPG) algorithm to compute the optimized vehicle speed using a trained optimal policy via vehicle-to-vehicle communication in the upper level. A multi-objective reward function is introduced, integrating vehicle fuel consumption, battery state-of-the-charge, emissions, and vehicle car-following objectives. Secondly, an adaptive equivalent consumption minimization strategy is devised to implement vehicle-level torque allocation in the platoon. Two drive cycles, HWFET and human-in-the-loop simulator driving cycles are utilized for realistic testing of the considered platoon energy management. It is shown that DDPG runs the engine more efficiently than the widely-implemented Q-learning and deep Q-network, thus showing enhanced fuel savings. Further, the contribution of this paper is to speed up the higher-level vehicular control application of deep learning algorithms in the connected and automated HEV platoon energy management applications.
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    Actor-Critic TD3-based Deep Reinforcement Learning for Energy Management Strategy of HEV
    (Institute of Electrical and Electronics Engineers Inc., 2023) Yazar, Ozan; Coskun, Serdar; Li, Lin; Zhang, Fengqi; Huang, Cong
    In the last decade, deep reinforcement learning (DRL) algorithms have been employed in the design of energy management strategy (EMS) for hybrid electric vehicles (HEVs). Investigation of the real-time applicability of DRL algorithms as an EMS is critical in terms of training time, fuel savings, and state-of-charge (SOC) sustainability. To this end, we propose a twin delayed deep deterministic policy gradient (TD3) algorithm that is an improved version of the deep deterministic policy gradient (DDPG) algorithm for HEV fuel savings. Compared to the existing Q-learning-based reinforcement learning and the deep Q-network-based and DDPG-based deep reinforcement algorithms, the proposed TD3 provides stable training efficiency, promising fuel economy, and a lower variation range of SOC charge sustainability under various drive cycles. © 2023 IEEE.
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    Incorporated vehicle lateral control strategy for stability and enhanced energy saving in distributed drive hybrid bus
    (Elsevier, 2021) Li, Lin; Coskun, Serdar; Langari, Reza; Xi, Junqiang
    Vehicle stability and energy efficiency are important considerations in vehicle engineering. In this context, the current paper presents an energy saving strategy for hybrid electric vehicles that incorporates vehicle lateral dynamic control in conjunction with energy efficiency. To this end, we first model the nonlinear vehicle lateral dynamics of a hybrid electric bus via a Takagi-Sugeno approach and combine this model with an H-infinity state-feedback controller via parallel distributed compensation. The controller matrices are obtained using linear matrix inequalities through an optimal energy-to-energy performance norm of the nonlinear vehicle model. Second, we propose a reference side-slip angle generating method and a set of tire force distribution rules, which under the premise of ensuring vehicle stability, minimize the overall energy consumption of the vehicle. Finally, we put forward a new speed prediction method based on vehicle lateral dynamics for hybrid electric vehicle energy saving. Human-in-the-loop simulated driving experiments are conducted where the bus performs lane-changing maneuvers with enhanced control properties under various driving conditions, demonstrating the reliability of the proposed energy-saving performance measures. (C) 2021 Published by Elsevier B.V.
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    Prediction of Traffic Flow Density and Velocity Based on Kalman Filter Fusion of the Non-Local Gas-Kinetic Model and Convolution Neural Networks-Long-Short Term Memory Model
    (Institute of Electrical and Electronics Engineers Inc., 2023) Li, Lin; Zhao, Jiahao; Coskun, Serdar; Langari, Reza
    The integrated perception, planning, and control of intelligent transportation system has become a research hotspot in academia. In this context, the safe running of an intelligent vehicle highly depends on the accurate prediction of its driving environment and working conditions. Traffic flow parameters represent the macro characteristics of an intelligent vehicle driving environment. Accurate prediction of traffic flow parameters is beneficial to improve the accuracy of trajectory prediction of vehicles in the surrounding environment. As two kinds of traffic flow parameter prediction models: the macroscopic traffic flow model and the data-based learning model have their own characteristics. To fully take advantage of these two models, our work proposes a study of the macroscopic traffic flow model based on a nonlocal gas-kinetic (GKT) and a deep learning model based on convolutional neural networks-long short term memory. Real data sets US-101 and PeMS are used respectively to predict traffic flow density and velocity. Finally, the Kalman filter is employed to fuse the results of the two models. The experiment shows that the prediction accuracy of traffic flow density and velocity can be improved as compared with the Macroscopic traffic flow model and the data-based learning model. © 2023 IEEE.
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    Predictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm
    (Gazi Univ, Fac Engineering Architecture, 2023) Gul, Merve Nur; Yazar, Ozan; Coskun, Serdar; Zhang, Fengqi; Li, Lin; Kaya, Irem Ersoz
    Purpose: The underlying research goal of this article is to put forward a reliable fuel saving performance based on the forecasted velocities of drive cycles for a power-split hybrid electric vehicle. Theory and Methods: The power distribution between energy sources is devised by utilizing the P-ECMS for the power-split hybrid electric vehicle using the uncertain drive cycle velocity estimation based on MC algorithm. Results: The effectiveness and accuracy of the method are evaluated under seven drive cycles. The MC provides good prediction results of the velocities. On the basis of it, the P-ECMS method decreases fuel consumption up to 6.01% under NEDC, up to 9.09% under WLTP, up to 6.33% under UDDS, up to 5.14% under HWFET, up to 1.96% under NYCC, up to 11.47% under LA-92, and up to 7.92% under ALL-CYC compared to a standard ECMS method. Conclusion: It is seen from the analysis results that battery SOC decreases slightly using the P-ECMS since the electric motor is actively used to meet power demand instead of the engine over the predicted speed profiles. In the end, the MC algorithm-based P-ECMS strategy can verify the optimal power distribution based on fuel-saving potentials as compared to the baseline ECMS strategy while keeping the battery SOC at a reasonable interval.
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    Vehicle lateral motion control via robust delay-dependent Takagi-Sugeno strategy
    (Sage Publications Ltd, 2021) Coskun, Serdar; Li, Lin
    Presented in this research paper is an integrated direct yaw moment control (DYC) and active front steering (AFS) for an uncertain vehicle lateral dynamics model considering network-induced communication delay, which is a time-varying continuous function with a known upper bound. Firstly, we consider tire cornering stiffness as a non-linear norm-bounded uncertain system that is modeled by fuzzy membership functions, and then vehicle lateral dynamics model is expressed by a set of linear Takagi-Sugeno (T-S) uncertain fuzzy models. Secondly, since the network-induced communication delay in vehicle control system is an inherent reason for stability and performance degradation, we derive a robust delay-dependent H-infinity control methodology via the Lyapunov-Krasovskii functional for stability and performance conditions of the closed-loop system. For the synthesis, the robust control method is employed within the T-S fuzzy-model-based analysis framework and formulations are performed based on the solution of delay-dependent linear matrix inequalities (LMIs). The simulation study is presented using MATLAB/Simulink to show the achieved improvements in tracking variables via the designed robust fuzzy H-infinity state-feedback controller. The proposed fuzzy robust delay-dependent controller is compared with a linear robust delay-dependent controller to clearly show the tracking improvements for different road conditions. Moreover, a performance-based analysis is carried out to demonstrate the advantage of the design with respect to different delay values. It is confirmed from the analysis results that the proposed fuzzy controller can successfully stabilize and possess improved tracking performance for vehicle lateral motion control.
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    Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples
    (Mdpi, 2021) Li, Lin; Coskun, Serdar; Wang, Jiaze; Fan, Youming; Zhang, Fengqi; Langari, Reza
    Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.