Article
Chemistry, Multidisciplinary
Clint Yoannes Angundjaja, Yu Wang, Wenying Jiang
Summary: This paper proposes a power management strategy for a hybrid energy storage system (HESS) with the support of the internet of things (IoT). A two-level control structure utilizing fuzzy logic controller (FLC) and artificial neural network (ANN) is developed to optimize the power distribution. Simulation results show significant reduction in discharging battery power and power variation compared to the battery-only case.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Adel Oubelaid, Nima Khosravi, Youcef Belkhier, Nabil Taib, Toufik Rekioua
Summary: This paper proposes a new multi-stage power management strategy for hybrid electric vehicles (HEVs) to enhance security and performance. The strategy utilizes a model-based coordinated switching approach tailored to the transient dynamics of power sources, protecting sensitive power sources like fuel cells (FCs) from potential damages caused by abrupt load variations. A fuzzy energy management method is employed to manage power flow in HEVs, allowing safe and defined operation of FCs at multiple operating points. Fault detection algorithms are integrated to detect and correct any power source failures. Simulation results demonstrate that the proposed strategy provides efficient usage and precise control of FC power, compensating for FC sluggishness and reducing transient power ripples and voltage fluctuations. The effectiveness of the strategy is validated through simulations on the RT LAB platform with satisfactory results.
JOURNAL OF ENERGY STORAGE
(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
Energy & Fuels
Alexander Fill, Tobias Schmidt, Tobias Mader, Raphael Llorente, Arber Avdyli, Kai Peter Birke
Summary: This article presents a model for improving battery performance in electric vehicles, which can be implemented in the Battery Management System. The accuracy and effectiveness of the model are demonstrated through experimental data validation.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Automation & Control Systems
Tiong Teck Teo, Thillainathan Logenthiran, Wai Lok Woo, Khalid Abidi
Summary: This paper introduces a fuzzy logic-based controller for a grid-connected microgrid with renewable and energy storage capability, aiming to reduce end-user operating cost through arbitrage operation and minimizing power exchange between the main grid and microgrid.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ashish Kumar Karmaker, Md. Alamgir Hossain, Hemanshu Roy Pota, Ahmet Onen, Jaesung Jung
Summary: This paper presents an energy management algorithm that considers techno-economic and environmental factors for a hybrid solar and biogas-based electric vehicle charging station. The proposed algorithm, designed for a 20-kW charging station, utilizes a fuzzy inference system in MATLAB SIMULINK to optimize real-time charging costs and renewable energy utilization by managing power generation, EV power demand, charging periods, and existing charging rates. The results demonstrate a 74.67% reduction in energy costs compared to existing flat rate tariffs, with lower charging costs on weekdays and weekends. The integration of hybrid renewables also leads to a significant decrease in greenhouse gas emissions, making the project profitable with short payback periods for charging station owners.
Article
Computer Science, Hardware & Architecture
Naila Ben Halima, Naourez Ben Hadj, Mohamed Chaieb, Rafik Neji
Summary: This study proposes a fuzzy logic control strategy (FLCS-em) to optimize emissions of hybrid electric vehicles (HEVs). Compared with other commonly used control strategies, FLCS-em shows significant advantages in terms of performance efficiency, emissions, and fuel consumption.
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
(2023)
Article
Energy & Fuels
Alankrita, A. Pati, N. Adhikary, S. K. Mishra, B. Appasani, Taha Selim Ustun
Summary: This paper presents an optimal energy management system for a hybrid PV-battery storage system, utilizing fuzzy logic to control battery and grid-connected inverter for optimal resource utilization and power fluctuation handling.
Article
Engineering, Electrical & Electronic
Ji Li, Quan Zhou, Yinglong He, Huw Williams, Hongming Xu, Guoxiang Lu
Summary: This article develops a distributed cooperative energy management system for plug-in hybrid electric vehicles, which can adaptively adjust power-split control parameters based on personalized driving behavior and vehicle state information. The proposed system demonstrates strong robustness in experimentation with offline optimization of personalized control parameters and real-time driving style recognition.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Energy & Fuels
Mingliang Bai, Wenjiang Yang, Ruopu Zhang, Marek Kosuda, Peter Korba, Michal Hovanec
Summary: Hybrid-electric propulsion system (HEPS) is gaining attention in UAVs for its potential to reduce fuel consumption and emissions. This study introduces a fuzzy logic control-equivalent consumption minimum strategy (FLC-ECMS) that improves energy management in HEPS. Simulation tests show that HEPS-equipped hybrid UAVs can significantly decrease fuel consumption and emissions, while maintaining battery state of charge (SOC) and reducing SOC difference. This research provides insights into optimal energy management for HEPS in UAVs, highlighting the importance of UAVs in reducing environmental impacts.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Electrical & Electronic
Mawloud Omar, Abdullah Baz, Hosam Alhakami, Wajdi Alhakami
Summary: This paper proposes a framework of energy trading based on blockchain and smart contracts, which uses unmanned aerial vehicles to transfer energy from sellers to requester vehicles. The selection mechanism and trust management approach are designed to optimize service availability and reliability. Simulation results show significant improvements in charging latency, service availability, and robustness.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Green & Sustainable Science & Technology
Zhen Zhang, Tiezhu Zhang, Jichao Hong, Hongxin Zhang, Jian Yang
Summary: This paper proposes a novel parallel electric-hydraulic hybrid electric vehicle (PEHHEV) with multiple working modes and power sources. It applies the long short-term memory (LSTM) neural network to the proximal policy optimization (PPO) algorithm to establish an energy management strategy for optimal working mode switching. Through offline training, online testing, and entropy evaluation, the PEHHEV achieved lower energy consumption and maintained dynamic performance under the PPO-LSTM-based strategy.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Duong Phan, Alizera Bab-Hadiashar, Mojgan Fayyazi, Reza Hoseinnezhad, Reza N. Jazar, Hamid Khayyam
Summary: This paper investigates an intelligent energy management system for a hybrid electric autonomous vehicle under uncertain and ambiguous road conditions, demonstrating the advantages of Interval Type 2 fuzzy logic control in reducing fuel usage and extending battery life.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
Article
Energy & Fuels
Chaofeng Pan, Yuanxue Tao, Qian Liu, Zhigang He, Jun Liang, Weiqi Zhou, Limei Wang
Summary: This paper researched grey wolf fuzzy optimal energy management strategy optimization for electric vehicles based on driving condition prediction, proposing a combined prediction method to improve accuracy. The fuzzy logic control strategy based on driving condition prediction, optimized by the grey wolf optimizer algorithm, showed significant improvement in energy consumption rate. HIL experiments validated the feasibility and effectiveness of the proposed control strategy in real-time environment.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Automation & Control Systems
Peng Mei, Hamid Reza Karimi, Hehui Xie, Fei Chen, Cong Huang, Shichun Yang
Summary: Considering the importance of energy management strategy for hybrid electric vehicles, this paper addresses the energy optimization control issue using reinforcement learning algorithms. It establishes a hybrid electric vehicle power system model and designs a hierarchical energy optimization control architecture based on networked information. Three learning-based energy optimization control strategies, namely Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) algorithms, are introduced. The superiority of the DDPG algorithm over Q-learning and DQN algorithms in terms of robustness and faster convergence for better energy management purposes is illustrated through simulation.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
