Article
Energy & Fuels
Claudio Maino, Daniela Misul, Alessia Musa, Ezio Spessa
Summary: This paper introduces a self-adaptive statistical method based on the proper management of any acceptable battery energy variation to significantly improve computing times for HEV architectures while achieving the best possible accuracy in terms of CO2 emissions and total cost of ownership.
Article
Energy & Fuels
Zhengyu Yao, Hwan-Sik Yoon, Yang-Ki Hong
Summary: Hybrid electric vehicles can achieve better fuel economy by utilizing multiple power sources. Recent studies have shown that machine learning-based control algorithms, such as online Deep Reinforcement Learning (DRL), can effectively control these power sources. However, the optimization and training processes for the online DRL-based control strategy can be time and resource intensive. This paper presents a new offline-online hybrid DRL strategy that uses offline vehicle data to build an initial model and an online learning algorithm to improve fuel economy.
Article
Thermodynamics
Samuel Filgueira da Silva, Jony Javorski Eckert, Fabricio Leonardo Silva, Fernanda Cristina Correa, Ludmila C. A. Silva, Andre Valente Bueno, Franco Giuseppe Dedini
Summary: The study presents a comprehensive approach for optimizing the power distribution control and design of a Fuel Cell Hybrid Electric Vehicle (FCHEV) using a multi-objective evolutionary algorithm. The method aims to maximize the vehicle's driving range and the lifetimes of the fuel cell stack and battery while minimizing hydrogen fuel consumption and storage system size. The optimized FCHEV configuration achieved a driving range of 444 km and exhibited improved energy efficiency, driving autonomy, and power sources lifespan in real-world driving conditions.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Engineering, Electrical & Electronic
Petronilla Fragiacomo, Matteo Genovese, Francesco Piraino, Orlando Corigliano, Giuseppe De Lorenzo
Summary: Transportation is a major contributor to CO2 emissions, posing significant challenges for decarbonization. To achieve comprehensive decarbonization, transitioning to low-carbon fuels and developing necessary infrastructures is crucial. Renewable hydrogen is a promising option for sustainable transportation, applicable to fuel cell electric vehicles and synthetic fuels for ships and airplanes.
Article
Chemistry, Multidisciplinary
Yanzhao Su, Minghui Hu, Jin Huang, Datong Qin, Chunyun Fu, Yi Zhang
Summary: The proposed dynamic torque-coordinated control method effectively reduces jerks of a hybrid vehicle under engine starting conditions by including engine segment active control, feedforward and feedback control of engine starting conditions, as well as active damping feedback compensation control for system resonance.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Jie Li, Yonggang Liu, Abbas Fotouhi, Xiangyu Wang, Zheng Chen, Yuanjian Zhang, Liang Li
Summary: In this research, a learning-based method is used to achieve satisfactory fuel economy for connected plug-in hybrid electric vehicles (PHEVs) in car-following scenarios. By leveraging a data-driven energy consumption model and considering the nonlinear efficiency characteristics, an advanced ADP scheme is designed for connected PHEVs. The cooperative information is also incorporated to improve fuel economy and driving safety.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Green & Sustainable Science & Technology
Zhaowen Liang, Kai Liu, Jinjin Huang, Enfei Zhou, Chao Wang, Hui Wang, Qiong Huang, Zhenpo Wang
Summary: This manuscript addresses the continuous uphill requirements in the cold mountainous area of the 2022 Beijing Winter Olympics by adopting a dual-motor coupling technology and conducting structure design and parameter matching of the vehicle power system architecture. Furthermore, a fuzzy logic control-based energy management strategy (EMS) optimization method for the proton exchange membrane fuel cell (PEMFC) is proposed to enhance power stability and efficiency. Experimental results show that the proposed powertrain successfully reduces power fluctuation and improves energy efficiency by 20.7%.
Article
Thermodynamics
Yaoyuan Zhang, Haoqing Wu, Shijie Mi, Wenbin Zhao, Zhuoyao He, Yong Qian, Xingcai Lu
Summary: This study investigates the optimal application of the dual-fuel intelligent charge compression ignition (ICCI) engine on hybrid powertrains, achieving global optimization in fuel consumption and emissions. The results indicate that power-split (PSHEV) hybrid electric vehicles achieve the best fuel economy and emission purification among the studied structures.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Energy & Fuels
Pier Giuseppe Anselma
Summary: An algorithm named SERCA is introduced in this study to rapidly identify near-optimal control trajectories for plug-in HEVs, showing good performance in real-world driving tasks. By optimizing control strategies, it accelerates the process of HEV powertrain design and controller development.
Article
Thermodynamics
Samuel Filgueira da Silva, Jony Javorski Eckert, Fabricio Leonardo Silva, Ludmila C. A. Silva, Franco Giuseppe Dedini
Summary: This paper presents a comprehensive study on the optimal powertrain design of plug-in hybrid electric vehicles (PHEV) through a multi-criteria analysis, aiming to minimize fuel consumption, emissions, electric powertrain size, battery health, charging time, and costs. The best configuration results in a significant reduction of vehicle travel cost by 39.57% and emissions of CO, HC, and NOx by 43.39%, 45.13%, and 72.64% respectively under the combined driving cycle.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Engineering, Civil
Saeid Bashash
Summary: This article proposes new improvements to control plug-in hybrid electric vehicles using macro-level trip forecast information. The proposed approach combines dynamic programming, instantaneous optimization, simplex search algorithm, adaptive pricing policy, extended-horizon optimization, and a revised speed forecast formula to achieve high optimality in energy management. The combination of these methods significantly enhances the energy economy of the system, demonstrating over 99% average optimality for adopted drive cycles.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Chinju Saju, Prawin Angel Michael, T. Jarin
Summary: This paper explores the modeling and control of a hybrid electric vehicle to optimize fuel efficiency, focusing on the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a controller. By combining the operation of an electric motor and an internal combustion engine, as well as utilizing regenerative braking, fuel efficiency can be improved. Additionally, replacing traditional driving cycles with the HWFET driving cycle provides a more accurate representation of real-world conditions.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Engineering, Electrical & Electronic
Wawrzyniec Golebiewski, Konrad Prajwowski, Krzysztof Danilecki, Maciej Lisowski, Karol Franciszek Abramek
Summary: The article discusses the operational aspects of energy management strategy (EMS) - model predictive control (MPC) and presents a mathematical model for hybrid electric vehicle (HEV) to demonstrate the synergy of working machines. The comparison of results between factory control and MPC with different linear quadratic tracking (LQT) curves shows that applying thirteen reference LQT trajectories can result in a 4% reduction in fuel consumption for the HEV.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Thermodynamics
Arivoli Anbarasu, Truong Quang Dinh, Somnath Sengupta
Summary: In this paper, an Advanced Dynamic Model Predictive Control (AMPC) based on a Nonlinear Model Predictive Control (NMPC) framework with dynamic weights is proposed to improve the energy performance and prolong the component lifetime of fuel cell hybrid electric vehicles. By utilizing dynamic weights and a Fuzzy Cognitive Map (FCM), the cost function of the AMPC is effectively formulated to adjust the importance of each cost component according to driving conditions. Simulation results using a FCHEV model demonstrate the efficacy of the proposed AMPC.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Energy & Fuels
Qingxiao Jia, Caihong Zhang, Hongxin Zhang, Zhen Zhang, Hao Chen
Summary: This paper comprehensively researches the master-slave electric-hydraulic hybrid vehicle (MSEHHV), and proposes a cooperative optimization method of powertrain parameters and control strategy to improve the battery state of charge (SOC). The optimized MSEHHV shows lower energy consumption compared to the electric vehicle (EV) and initial MSEHHV in actual driving cycle.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2023)
Article
Automation & Control Systems
Yuxiao Chen, Huei Peng, Jessy W. Grizzle
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2019)
Article
Automation & Control Systems
Yuxiao Chen, Ayonga Hereid, Huei Peng, Jessy Grizzle
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2019)
Article
Robotics
Xingye Da, Jessy Grizzle
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2019)
Article
Robotics
Ross Hartley, Maani Ghaffari, Ryan M. Eustice, Jessy W. Grizzle
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2020)
Article
Robotics
Jiunn-Kai Huang, Shoutian Wang, Maani Ghaffari, Jessy W. Grizzle
Summary: LiDARTag is a novel fiducial tag design and detection algorithm suitable for light detection and ranging (LiDAR) point clouds, which can operate in a completely dark environment in real-time and process data quickly. By minimizing fitting errors between the point cloud and the marker's template, the method achieves millimeter translation error and few degrees rotation error, providing reliable pose estimation.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Jiunn-Kai Huang, William Clark, Jessy W. Grizzle
Summary: This letter introduces the concept of optimizing target shape for LiDAR point clouds to eliminate pose ambiguity and proposes a method to estimate target vertices using the target's geometry. By using the optimal shape and the global solver, high localization accuracy can be achieved even at a distance of 30 meters away.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Lu Gan, Jessy W. Grizzle, Ryan M. Eustice, Maani Ghaffari
Summary: This work presents a deep inverse reinforcement learning method for legged robots terrain traversability modeling using both exteroceptive and proprioceptive sensory data. By incorporating robot-specific inertial features, the model fidelity is improved and a reward dependent on the robot's state during deployment is provided. The proposed method utilizes the Maximum Entropy Deep Inverse Reinforcement Learning algorithm and trajectory ranking loss to optimize legged robot demonstrations. The evaluation is conducted using a dataset from an MIT Mini-Cheetah robot and a Mini-Cheetah simulator.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Lu Gan, Youngji Kim, Jessy W. Grizzle, Jeffrey M. Walls, Ayoung Kim, Ryan M. Eustice, Maani Ghaffari
Summary: In this article, a novel and flexible multitask multilayer Bayesian mapping framework is presented, providing richer environmental information for robots in a single mapping formalism while exploiting correlations between layers. The framework eliminates the need for accessing and processing information from separate maps, advancing the way robots interact with their environments. Experimental results demonstrate reliable mapping performance in different environments.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Computer Science, Information Systems
M. Eva Mungai, Jessy W. Grizzle
Summary: By using constrained optimization and two hybrid system descriptions, the study successfully achieves safe sit-to-stand motions for lower-limb exoskeletons, providing users with the opportunity to stand up independently.
Proceedings Paper
Robotics
Maani Ghaffari, William Clark, Anthony Bloch, Ryan M. Eustice, Jessy W. Grizzle
ROBOTICS: SCIENCE AND SYSTEMS XV
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Ayonga Hereid, Omar Harib, Ross Hartley, Yukai Gong, Jessy W. Grizzle
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2019)
Proceedings Paper
Automation & Control Systems
Thomas Gurriet, Sylvain Finet, Guilhem Boeris, Alexis Duburcq, Ayonga Hereid, Omar Harib, Matthieu Masselin, Jessy Grizzle, Aaron D. Ames
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2018)
Proceedings Paper
Automation & Control Systems
Ross Hartley, Josh Mangelson, Lu Gan, Maani Ghaffari Jadidi, Jeffrey M. Walls, Ryan M. Eustice, Jessy W. Grizzle
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2018)
Article
Computer Science, Information Systems
Jiunn-Kai Huang, Jessy W. Grizzle
Article
Robotics
Lu Gan, Ray Zhang, Jessy W. Grizzle, Ryan M. Eustice, Maani Ghaffari
IEEE ROBOTICS AND AUTOMATION LETTERS
(2020)