Article
Chemistry, Multidisciplinary
Matteo Spano, Pier Giuseppe Anselma, Daniela Anna Misul, Giovanni Belingardi
Summary: This research uses a multi-objective particle swarm optimization algorithm to find the optimal layout of a hybrid electric vehicle powertrain, aiming to maximize fuel economy capability and minimize production cost. The results show that different powertrain layouts may be suggested by assigning different weights to sizing targets related to fuel economy and production cost, respectively, to achieve higher fuel economy or lower production cost.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Mechanical
Hye Hyun Kang, In Chun Chung, Kwang Man An, Jin Il Park, Jong Hwa Lee
Summary: The demand for low-emission vehicles is increasing due to the global trend towards stricter regulations on fuel economy and emissions. This study aims to evaluate the strengths and weaknesses of parallel hybrid electric vehicles (HEVs) and power-split HEVs. Power-split HEVs have the disadvantage of power circulation, resulting in more energy generated by the engine. However, they have a simpler drivetrain structure and lower drivetrain losses compared to parallel HEVs. The fuel consumption of the two types of vehicles is similar in the UDDS 2 cycle, but the parallel HEV performs 17.1% better in HWFET.
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY
(2022)
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
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)
Review
Energy & Fuels
Alessandro Benevieri, Lorenzo Carbone, Simone Cosso, Krishneel Kumar, Mario Marchesoni, Massimiliano Passalacqua, Luis Vaccaro
Summary: The series architecture is mainly used on hybrid buses for higher efficiency, but introduces additional losses due to double energy conversion. New technologies like supercapacitors and silicon carbide devices may change this, making series architecture competitive on medium-size cars.
Article
Engineering, Electrical & Electronic
Xiaolin Tang, Jiaxin Chen, Huayan Pu, Teng Liu, Amir Khajepour
Summary: This article proposes an energy management strategy based on deep reinforcement learning to optimize the fuel economy of hybrid electric vehicles. By learning gear-shifting strategies and controlling engine throttle opening, the proposed strategy successfully reduces fuel consumption and improves computational efficiency.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
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
Thermodynamics
Fengqi Zhang, Lehua Xiao, Serdar Coskun, Hui Pang, Shaobo Xie, Kailong Liu, Yahui Cui
Summary: This article presents a comprehensive comparative study of energy management strategies (EMSs) for a parallel hybrid electric vehicle (HEV) considering battery ageing. The principles of dynamic programming (DP), Pontryagin's minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) with battery ageing are elaborated. A gearshift map is obtained from DP optimization results to optimize drivability and fuel economy, and it is applied in the PMP and ECMS. Fuel economy, battery state-of-charge charge-sustainability, and computational efficiency are compared for different EMSs. Battery ageing is also included in the optimization solution using a control-oriented model. DP achieves the best fuel economy compared to other methods, with about a 2% difference in fuel economy compared to PMP. The analysis results provide valuable insights into the advantages and disadvantages of each approach.
Article
Automation & Control Systems
J. Leon Bosco Raj, M. Marsaline Beno
Summary: This paper develops a proportional integral controller for a parallel hybrid electric vehicle with driving cycle. The controller improves fuel efficiency and energy efficiency by utilizing an electric motor to assist the engine and regenerative braking.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Hyun Woo Won
Summary: The performance of hybrid electric vehicles heavily relies on subsystem components and their architecture, necessitating comprehensive reviews before manufacturing. Designers can utilize simulations to develop virtual prototypes and quickly assess design modifications without the need for costly physical prototypes. Control strategies and tools such as computational modeling and optimization are essential for achieving emission and hardware cost targets. The author's rule-based hybrid simulation tool offers enhanced flexibility for users to select different control strategies and explore a variety of hybrid topologies, providing the ability to modify subsystems as needed.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Abhinav K. Gautam, Mohd Tariq, J. P. Pandey, K. S. Verma, Shabana Urooj
Summary: This paper introduces electric vehicles as an effective solution to address the environmental impact of transportation. It discusses key points such as powertrain strategy, fuel economy, and emission control, and systematically compares different energy management systems. It also provides directions for further development in powertrain and energy management systems.
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
Engineering, Electrical & Electronic
Maryam Razi, Nikolce Murgovski, Tomas McKelvey, Torsten Wik
Summary: This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of a combustion engine and an electric machine in a hybrid electric vehicle (HEV). The study models SOC constraints and proposes sub-optimal analytic solutions with convex objective functions. Additionally, the controllers' robustness to measurement noise is considered, with simulation results comparing the effectiveness of the two controllers.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Yu He, Kyoung Hyun Kwak, Youngki Kim, Dewey Jung, Jason Hoon Lee, Jinho Ha
Summary: In this study, a real-time torque-split strategy for a 48-V P0+P4 mild hybrid electric vehicle (MHEV) is proposed. The strategy considers realistic operational constraints and is optimized using dynamic programming. Simulation results show that the proposed strategy achieves close to global optimality in terms of fuel economy.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Automation & Control Systems
Boli Chen, Xiao Pan, Simos A. Evangelou
Summary: This article presents an energy management (EM) control strategy for series hybrid electric vehicles (HEVs) with an engine start-stop system (SSS), aiming to optimize energy distribution and minimize fuel consumption. The control strategy incorporates a fuel penalty mechanism and proposes the hysteresis power threshold strategy () to realistically model engine restarts. The article demonstrates the effectiveness and robustness of the control strategy, suggesting its potential as a benchmark strategy for high-fidelity HEV models.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Engineering, Mechanical
Pier Giuseppe Anselma, Giovanni Belingardi
Summary: This study proposes a multi-objective optimal computer-aided engineering (CAE) methodology for designing hydraulic brake systems in electric vehicles. The particle swarm optimization (PSO) algorithm efficiently explores the design space. The front-wheel drive (FWD) powertrain layout appears to be more favorable in terms of electrical energy recovery during braking compared to the rear-wheel drive (RWD) option.
VEHICLE SYSTEM DYNAMICS
(2022)
Article
Engineering, Mechanical
Pier Giuseppe Anselma
Summary: This paper introduces a novel Formula-E race controller based on the A-ECMS method, optimizing battery energy consumption and thermal management mode to minimize overall race time. Simulation results demonstrate that the performance of this controller is near-optimal.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2022)
Article
Energy & Fuels
Pier Giuseppe Anselma
Summary: This paper introduces a dynamic programming approach, named Slope-weighted Rapid Dynamic Programming (SRDP), for optimal energy management in hybrid electric vehicles (HEVs). SRDP achieves computational advantage by considering only the most efficient HEV powertrain operating points, while complying with control constraints.
Article
Energy & Fuels
Alessandro Falai, Tiziano Alberto Giuliacci, Daniela Misul, Giacomo Paolieri, Pier Giuseppe Anselma
Summary: This study aims to fill the knowledge gap in performance and electric range estimation of electric scooters by developing a numerical model to evaluate the performance and electric range of a two-wheeler electric scooter in real-world driving conditions.
Article
Mechanics
Alberto Ciampaglia, Raffaele Ciardiello, Federico Cesano, Giovanni Belingardi, Valentina Brunella
Summary: This study investigates the effect of carbon black dispersion on the mechanical and electrical properties of polyamide 6 and 6.6 matrices. By increasing the carbon black concentration, the elastic modulus increases by 12%. Results show that carbon black can functionalize thermoplastic polymers by activating conductive networks, with a percolation threshold of 13% wt. The sensitivity of conductivity to mechanical strain is analyzed in both direct and alternate current, and a novel model for estimating the material's gauge factor variation with applied electric frequency is proposed.
COMPOSITE STRUCTURES
(2023)
Article
Energy & Fuels
Alberto Ponso, Angelo Bonfitto, Giovanni Belingardi
Summary: The increasing popularity of electric vehicles (EVs) can be attributed to the growing environmental awareness and government incentives. However, the limited range and extended charging time of EVs, along with the scarcity of charging stations, pose challenges to widespread adoption. To address this, EV manufacturers are developing route planners that consider range and charging station availability. This article introduces an innovative route planning method that takes into account battery health, temperature, and driving style, and selects charging stations along the planned route that can be reached with the available battery energy. Simulations were conducted to verify its effectiveness, considering factors such as declared range, battery health, external temperature, and driving style, and highlighting the risk of running out of battery before reaching the destination.
Article
Energy & Fuels
Edoardo Lelli, Alessia Musa, Emilio Batista, Daniela Anna Misul, Giovanni Belingardi
Summary: The present study investigates the use of machine learning algorithms to estimate the state of health (SOH) of high-voltage batteries in electric vehicles. The analysis is based on open-circuit voltage (OCV) measurements from 12 vehicles with different mileage conditions and focuses on establishing a correlation between the OCV values, the energy stored in the battery, and the battery SOH. Among the evaluated algorithms, random forest (RF) exhibits the best performance in predicting the state of health of high-voltage batteries. The findings of the study will contribute to the development of efficient maintenance strategies, thus reducing the risk of unexpected battery failures.
Article
Engineering, Mechanical
Giovanni Belingardi, Alessandro Scattina
Summary: This paper focuses on the necessary architectural modifications to the car body in order to accommodate the battery pack for electric vehicles. The positioning of the battery pack is crucial due to its size, weight, and cost. Various solutions and sketches are presented, with a preference for locating the battery housing below the passenger compartment floor for safety, maintenance, and performance reasons. The integration of the battery pack's housing structure with the vehicle floor creates a sandwich structure that can improve the body's stiffness and impact protection.
Article
Mechanics
Valentina Giammaria, Giulia Del Bianco, Elena Raponi, Dario Fiumarella, Raffaele Ciardiello, Simonetta Boria, Fabian Duddeck, Giovanni Belingardi
Summary: In this study, finite element analysis was used to optimize and predict the behavior of flax/epoxy composite laminates under low-velocity impact. The optimization process aimed to find an optimal parameter configuration that is less sensitive to variations in impact energy, using surrogate modeling techniques. The results demonstrated the potential of surrogate-based optimization in identifying material parameters and provided a characterization of the studied composite configuration for future applications.
COMPOSITE STRUCTURES
(2023)
Article
Polymer Science
Raffaele Ciardiello, Dario Fiumarella, Giovanni Belingardi
Summary: The mechanical properties of glass-fibre-reinforced composite (GFRP) plates made of twill fabric and a thermoplastic recyclable infusion resin were evaluated through mechanical testing. The results showed that the considered thermoplastic resin, ELIUM®, can be infused using properly tuned vacuum techniques. X-ray microtomography analysis revealed that the produced laminates were defect-free, different from what was reported in the literature. The mechanical properties of the GFRP laminates were enhanced with higher vacuum pressures and were not influenced by degassing the resin before infusion.
Proceedings Paper
Engineering, Industrial
Pier Giuseppe Anselma, Waiyuntian Lou, Ali Emadi, Giovanni Belingardi
Summary: This paper proposes an approach to solve the overtaking planning problem by developing an energy-saving ACC algorithm, achieving significant improvements in terms of passenger comfort in different overtaking scenarios.
2022 IEEE 31ST INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE)
(2022)
Review
Engineering, Mechanical
Filippo Germanetti, Dario Fiumarella, Giovanni Belingardi, Alessandro Scattina
Summary: This paper provides an overview of the most commonly used injury criteria and metrics in the study of vehicle passive safety, focusing on those applicable to finite element simulations and Human Body Models. It helps in choosing the appropriate injury criteria for assessing vehicle passive safety.
Proceedings Paper
Engineering, Aerospace
Alessia Musa, Pier Giuseppe Anselma, Matteo Spano, Daniela Anna Misul, Giovanni Belingardi
Summary: This study proposes a deep learning-based approach combined with vehicle communication technology for real-time cooperative adaptive cruise control (CACC) and trains a gated recurrent unit (GRU) for control. Experimental results demonstrate that the trained GRU can achieve ecofriendly driving in CACC without compromising passenger comfort and safety requirements.
2022 IEEE/AIAA TRANSPORTATION ELECTRIFICATION CONFERENCE AND ELECTRIC AIRCRAFT TECHNOLOGIES SYMPOSIUM (ITEC+EATS 2022)
(2022)
Article
Materials Science, Multidisciplinary
Giulio Piazza, Matthew Burczyk, Marco Gerini-Romagnoli, Giovanni Belingardi, Sayed A. Nassar
Summary: This paper experimentally investigates the influence of Thermally Expandable Particle (TEP) adhesive additive on the mechanical and reversibility performance of epoxy-bonded load single lap joints (SLJs). The study finds that TEP can reduce adhesive strength and lead to joint debonding, while also displaying reversible performance under certain conditions.
JOURNAL OF ADVANCED JOINING PROCESSES
(2022)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.