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Öğe 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, LinIn 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.Öğe 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 RezaConnected 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.Öğe 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, CongIn 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.Öğe Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicle with Emission Assessment(Institute of Electrical and Electronics Engineers Inc., 2022) Yazar, Ozan; Coskun, SerdarIn this study, fuel consumption and exhaust emissions are simultaneously analyzed using an equivalent consumption minimization strategy (ECMS) for power-split hybrid electric vehicles (HEVs). To fully consider emissions, we first extract the emission maps of harmful pollutants hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx). Then, the ECMS is properly designed for energy minimization and emissions are calculated using the emission data sets. Simulations under New European Driving Cycle (NEDC), Urban Dynamometer Driving Schedule (UDDS), and Worldwide Harmonized Light Vehicles Test Procedure (WLTP) are performed to evaluate the amounts of the fuel consumption and production of HC, CO, and NOx. We particularly assessed the influence of the driving conditions on emission production with the help of the designed ECMS. According to the obtained results, it is observed that the fuel consumption per kilometer is the lowest under the UDDS driving cycle. When the emission values per kilometer are examined, for HC, the highest emissions is produced under the UDDS driving cycle, and the lowest emissions is observed under the WLTP drive cycle. For CO, the highest amount is observed under the UDDS while the lowest is observed under the WLTP drive cycle. For NOx, the highest emissions is produced under the NEDC drive cycle and the lowest is under the WLTP drive cycle. © 2022 IEEE.Öğe 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 ErsozPurpose: 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.
