Article
Chemistry, Physical
Aidin Teimouri, Kaveh Zayer Kabeh, Sina Changizian, Pouria Ahmadi, Mehdi Mortazavi
Summary: This research study analyzes the role of personal vehicles in transportation and investigates the performance and emissions of different types of vehicles under different driving conditions. The results show that hydrogen fuel cell vehicles perform the best in terms of overall performance and emissions.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Thermodynamics
Hector Climent, Vicente Dolz, Benjamin Pla, David Gonzalez-Dominguez
Summary: The increased concern for environmental problems has led to the electrification of passenger cars, primarily through hybrid powertrains. Automotive manufacturers have chosen hybrids due to limitations and cost factors of battery electric vehicles and diesel vehicles. The exhaust gas recirculation (EGR) strategy reduces fuel consumption and CO2 emissions in gasoline engines. This research aimed to quantify the fuel saving achieved with EGR in a gasoline-electric hybrid powertrain under driving cycle conditions.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Energy & Fuels
Donghai Hu, Ren He, Jing Wang, Leli Hu, Chengkun He
Summary: This study investigated the stability issues of the initial hybrid drive mode for hybrid electric vehicles, by constructing a mathematical model and deriving stability and bifurcation conditions for analysis, and designing an instability control methodology to optimize the operating region, validated through vehicle tests on an existing platform. The results showed that different throttle openings can lead to changes in bifurcation characteristics, causing nonlinear behavior and random oscillations in the system. The proposed control methodology improved the stable operating region by 37% and restrained the oscillations of the hybrid powertrain.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Chemistry, Physical
Jie Li, Yonggang Liu, Yuanjian Zhang, Zhenzhen Lei, Zheng Chen, Guang Li
Summary: This paper proposes a data-driven eco-driving control strategy for plug-in hybrid electric vehicles, which can improve fuel economy and computational efficiency through neural network models.
JOURNAL OF POWER SOURCES
(2021)
Article
Engineering, Civil
Kyuhyun Sim, Sung-Ho Hwang
Summary: This paper proposes a control algorithm and stop prediction algorithm for hybrid electric vehicles, aiming to reduce fuel consumption by optimizing engine shutdown based on driving energy consumption. The stop prediction algorithm was validated using a driver-in-the-loop simulator, effectively predicting vehicle stops at traffic lights. The proposed ISG system with stop prediction function demonstrates fuel efficiency benefits in the transportation system.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xinyou Lin, Kuiliang Li, Liming Wang
Summary: This study proposes a driving-style-oriented adaptive control strategy for plug-in hybrid electric vehicles (PHEVs) using Particle Swarm Optimization (PSO) combined with Fuzzy expert algorithm. The strategy significantly decreases fuel consumption by 19.3% and has the best performance compared to other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Green & Sustainable Science & Technology
Teng Liu, Wenhao Tan, Xiaolin Tang, Jinwei Zhang, Yang Xing, Dongpu Cao
Summary: This paper summarizes driving cycle-driven energy management strategies for HEVs and emphasizes the importance of driving cycles in the field. It reviews relevant literature, studies different types of EMSs, and showcases the status quo of driving cycle databases. Finally, it discusses the future prospects of energy management technologies related to driving cycles.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Environmental Studies
Yiming Ye, Xuan Zhao, Jiangfeng Zhang
Summary: Due to the fundamental differences in motors and internal combustion engines, the real-time energy consumption profiles of ICEVs and EVs are different, which motivates the need to identify the driving cycle for different types of vehicles. This study proposes a systematic method to develop the driving cycle for EVs and ICEVs, and compares them in the same traffic environment to illustrate the impact of driving cycle electrification.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Energy & Fuels
Dehua Shi, Sheng Liu, Yingfeng Cai, Shaohua Wang, Haoran Li, Long Chen
Summary: This paper proposes a novel fuzzy adaptive method for the PMP-based optimal energy management strategy of hybrid electric vehicles, utilizing real-time traffic information and neural network prediction to improve fuel economy and battery charging sustainability. The proposed strategy outperforms traditional methods in various driving cycles.
Article
Transportation Science & Technology
Zhen Yang, Yiheng Feng, Henry X. Liu
Summary: A cooperative driving framework was proposed for urban arterials, combining centralized and distributed control concepts to optimize signal timing plans and improve traffic flow. By utilizing a hierarchical model design and implementation-ready traffic control solutions, the study demonstrated both mobility and fuel economy benefits of the cooperative driving framework.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Thermodynamics
Jony Javorski Eckert, Tarsis Prado Barbosa, Samuel Filgueira da Silva, Fabricio Leonardo Silva, Ludmila C. A. Silva, Franco Giuseppe Dedini
Summary: This paper presents a comprehensive optimization procedure for a series electric hydraulic hybrid vehicle powertrain and control, achieving maximum driving range and battery lifespan while minimizing onboard energy storage system mass. The results indicate that this powertrain architecture is attractive in terms of sustainability and economy, reducing battery aging effectively through the use of a high power density hydraulic accumulator.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Engineering, Civil
Sameh Abd-Elhaleem, Walaa Shoeib, Abdel Azim Sobaih
Summary: An improved power management strategy for plug-in hybrid electric vehicles is proposed, combining long-term power management with a short-term intelligent controller. The strategy optimizes the motor and engine torque using a chaotic improved generalized particle swarm optimization technique. A rule-based control system is employed to reduce computation time and estimate optimal values for the motor and engine torque in a hybrid mode. The performance of the strategy is compared to the current state of art, resulting in significant energy savings.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Environmental Studies
Junshi Xu, Ran Tu, Usman Ahmed, Glareh Amirjamshidi, Marianne Hatzopoulou, Matthew J. Roorda
Summary: This study uses two eco-scoring methods to evaluate driving behavior based on real-world GPS data, showing that representative of the eco-score system is improved after incorporating traffic conditions.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Thermodynamics
Pataparambath Noyalraj Shanu, Subramaniam Senthilkumar
Summary: The study focuses on the development of Dual Control Strategy (DCS) for a retrofitted 48 V P3 hybrid AMT vehicle to improve performance and fuel economy. By considering mode shift decision, optimal torque distribution, and global constraints, the vehicle achieves better driving performance and real-time fuel economy in Bangalore city Driving Cycle (BDC).
Article
Green & Sustainable Science & Technology
Shaohua Wang, Pengfei Yu, Dehua Shi, Chengquan Yu, Chunfang Yin
Summary: This paper investigates the eco-driving optimization of hybrid electric vehicles in urban road conditions considering driving characteristics. By providing the driver with economy-oriented velocity, the fuel economy can be improved. The driving style coefficients and driving feature parameters are introduced to enhance the adaptability of the driver to the advisory velocity.
JOURNAL OF CLEANER PRODUCTION
(2022)