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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 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, SerdarConventional 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.Öğe A distributed state and fault estimation scheme for state-saturated systems with measurements over sensor networks(Elsevier, 2024) Huang, Cong; Coskun, Serdar; Karimi, Hamid Reza; Ding, WeipingThis article explores a new framework of distributed state and fault estimation (DSFE) for the state -saturated systems over sensor networks. To this aim, the upper bound on estimation error covariance (EEC) is ensured and the explicit expression of the corresponding estimator gains is given with both quantization effects and state saturations. Further, a feasible upper bound is located on EEC and minimized by parameterizing the estimator gain. The matrix simplification technique is adopted to deal with the sensor network topology's sparseness problem. Additionally, the estimation performance is first analyzed and then ensured by conducting a sufficient condition. At last, experiments are carried out to verify the feasibility of the developed DSFE method.Öğe A Game Theoretic Model Predictive Controller With Aggressiveness Estimation for Mandatory Lane Change(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Zhang, Qingyu; Langari, Reza; Tseng, H. Eric; Filev, Dimitar; Szwabowski, Steven; Coskun, SerdarIn this article, we develop a game theoretic model predictive controller (GTMPC) with aggressiveness estimation to deal with the mandatory lane change (MLC) problem in presence of several surrounding vehicles. GTMPC is responsible for driving a subject vehicle (SV) to a desired longitudinal position and executing lane-changing at the optimal moment. Specifically, GTMPC constantly establishes and solves Stackelberg games corresponding to multiple game candidate vehicles (GCV) within the game scope when SV is able to interact with GCVs by signaling a lane change intention through turn signal. GTMPC first selects one target vehicle (TV) within multiple GCVs based on the Stackelberg equilibrium, followed by estimating TV's aggressiveness based on the interaction between SV and TV, then completes the maneuver through MPC. GTMPC performance is compared with level-k game theoretic controller. Human-in-the-Loop results showed that GTMPC is capable to safely complete the MLC by properly assessing the aggressiveness of surrounding vehicles, driven either by intelligent driver model or human drivers.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 A novel application for DC motor-generator cascade system by changing signal density of digital chaotic oscillator(Yildiz Technical Univ, 2023) Kose, Ercan; Muhurcu, Aydin; Coskun, SerdarPresented is a new method for the realization of a chaotic oscillator in a digital environment. First, a two-stroke sampling mathematical regulation is developed for discrete-time oscillator equations to change signal densities of chaotic signals. This proposed mathematical regulation is applied to Lorenz's chaotic oscillator, which presents a complex dynamical behavior. An application is shown with simulation through a Matlab-Simulink environment with time-de-pendent density changes of x, y and z 1 - D graphics and x, y 2 - D phase space graphics that are dependent on different density changes. Further to this, in an experimental study, Lorenz's chaotic oscillator's signals with variable density is applied to a DC motor as armature voltage via an 8-bit microcontroller based hardware environment. Chaotic supply voltage is applied to the motor rotor to generate a chaotic angular velocity. Time-dependent density change results of x, y and z 1 - D graphics are obtained and shown on an oscilloscope by converting chaotic rotor angular velocity to electrical signals, through a tacho-generator. The observed results re-vealed that chaotic signal production with variable density is achieved both in the simulation environment and the experimental environment. Also, it is shown that the proposed program and mathematical equations are feasible in terms of hardware and software implementations.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 Autonomous overtaking in highways: A receding horizon trajectory generator with embedded safety feature(Elsevier - Division Reed Elsevier India Pvt Ltd, 2021) Coskun, SerdarA primary task in autonomous driving is to design a control algorithm that presents an effective and yet human-compatible behavior. To this aim, present paper considers the problem of autonomous overtaking under both state and environment constraints with dynamic surrounding vehicles. The solution is formulated based on quadratic programming optimization and is solved in receding horizon fashion. The proposed method evaluates the traffic condition online and executes a safe trajectory of an overtaking maneuver with on-coming traffic. To better incorporate traffic participants' behaviors, a dynamic predictive model-based reachability analysis of the surrounding vehicles is utilized in the design. Reachable sets aim to ensure the drivability of the planned motions, as well as producing drivable collision-free trajectories. To this end, forward reachable sets are employed by predicting traffic vehicles' future actual and worst-case behaviors in the design, in which the autonomous vehicle determines its trajectory accordingly. A simulation scenario is tested via MATLAB/Simulink for autonomous overtaking and the effectiveness of the proposed method is shown, demonstrating the potential utility of the present approach for implementation as an advanced driver assistance system (ADAS) in next-generation vehicles. (C) 2021 Karabuk University. Publishing services by Elsevier B.V.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, JiachengElectrified 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.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, XiaosongEco-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.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, YahuiThis 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.Öğe Computationally Efficient Energy Management in Hybrid Electric Vehicles Based on Approximate Pontryagin's Minimum Principle(Mdpi, 2020) Zhang, Fengqi; Wang, Lihua; Coskun, Serdar; Cui, Yahui; Pang, HuiThis article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin's Minimum Principle (A-PMP). The A-PMP optimizes gearshift commands and torque distribution for overall energy efficiency. As a practical numerical solution in PMP, the proposed methodology utilizes a piecewise linear approximation of the engine fuel rate and state of charge (SOC) derivative by considering drivability and fuel economy simultaneously. Moreover, battery aging is explicitly studied by introducing a control-oriented model, which aims to investigate the effect of battery aging on the optimization performance in the development of the HEVs. An approximate energy management strategy with piecewise linear models is then formulated by the A-PMP, which targets a better performance for the Hamiltonian optimization. The gearshift map is extracted from the optimal results in the standard PMP to hinder frequent gearshift by considering both drivability and fuel economy. Utilizing an approximated Hamilton function, the torque distribution, gearshift command, and the battery aging degradation are jointly optimized under a unified framework. Simulations are performed for dynamic programming (DP), PMP, and A-PMP to validate the effectiveness of the proposed approach. The results indicate that the proposed methodology achieves a close fuel economy compared with the DP-based optimal solution. Moreover, it improves the computation efficiency by 50% and energy saving by 3.5%, compared with the PMP, while ensuring good drivability and fuel efficiency.Öğe Dynamic output-feedback 𝑯∞ control design for ball and plate system(2020) Coskun, SerdarBall and plate system is a nonlinear and unstable system, thus introducing great challenges tocontrol scientists and it resembles many complicated real-time systems in several perspectives.There has been a good number of efforts to design a stabilizing controller for this system. Thispaper presents a dynamic output-feedback 𝐻∞ control strategy for the plate and ball systembased on the solution of linear matrix inequalities (LMIs). The discussion involves derivingthe equations of motion of the system by using the Lagrange method, linearizing the nonlinearequations, and designing an 𝐻∞ controller to achieve required tracking specifications on theposition of the ball. The intent is to show the specified trajectory tracking performanceoutcomes in time domain via simulation studies conducted using MATLAB/Simulink. Acircular and square trajectory following of the designed controller is compared with a baselinePID controller. It is revealed that the proposed controller exhibits an improved trackingperformance to following the reference trajectories.Öğe 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, XiaosongVehicle 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.Öğe Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook(Mdpi, 2020) Zhang, Fengqi; Wang, Lihua; Coskun, Serdar; Pang, Hui; Cui, Yahui; Xi, JunqiangHybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.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 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, JinsongConnected 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.Öğe Hybrid Fuzzy Control of Semi-Active Suspension System Using Magnetorheological Damper(Institute of Electrical and Electronics Engineers Inc., 2021) Coskun, Serdar; Ozgur, OmerThe semi-active suspension systems equipped with a magnetorheological (MR) damper are extensively used due to their improved performance in vibration reduction. Supplying proper voltage as an input, the MR fluid behavior will form a solid-state, controlling stiffness and damping in semi-active suspensions. Incorporating the benefits of active suspension and passive suspension, a semi-active suspension with an MR damper offers a mechanically simple, safe, and low power consumption solution. This study develops an extended fuzzy control strategy to improve ride control under road disturbances. To this aim, a quarter car model is adopted where the modified Bouc-Wen MR damper model is utilized to control damping force via a fuzzy control and a proportional-integral control. Several simulations are carried out with the designed controller under both bump and sinusoidal types of road disturbances. The merits of the proposed controller are compared with a classical fuzzy controller and uncontrolled case, wherein a significant improvement in reducing body displacement as well as suspension deflections is achieved. © 2021 IEEE.Öğe 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, JunqiangVehicle 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.Öğe Layered energy equalization structure for series battery pack based on multiple optimal matching(Elsevier, 2025) Jiao, Jianfang; Wang, Hongwei; Gao, Feng; Coskun, Serdar; Wang, Guang; Xie, Jiale; Feng, FeiThe equalization management system is an essential guarantee for the safe, stable, and efficient operation of the power battery pack, mainly composed of the topology of the equalization circuit and the corresponding control strategy. This article proposes a novel active balancing control strategy to address the issue of individual cell energy imbalance in battery packs. Firstly, to achieve energy equalization under complex conditions, a two-layer equalization circuit topology is designed, and the efficiency and loss of energy transfer in the equalization process are studied. Furthermore, a directed graph-based approach was proposed to represent the circuit topology equivalently as a multi-weighted network. Combined with a multi-weighted optimal matching algorithm, aims to determine the optimal energy transfer path and reduce equalization losses. In addition, a fuzzy controller that can dynamically adjust the equalization current with the state parameter of the cell as the input condition is designed to optimize the equalization efficiency. Matlab/Simulink software is used to build and simulate the model. The experimental results indicate that, under the same static state, the newly proposed control strategy improves
