Article
Automation & Control Systems
Iman Shafikhani, Jan Aslund
Summary: This study introduces a multi-objective energy management strategy for hybrid electric vehicles, aiming to reduce fuel consumption, minimize battery wear, and meet system constraints simultaneously. Short-term simulations show that the strategy works best for non-aggressive drive cycles.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Engineering, Multidisciplinary
Yigeng Huangfu, Peng Li, Shengzhao Pang, Chongyang Tian, Sheng Quan, Yonghui Zhang, Jiang Wei
Summary: This article proposes an improved real-time energy management strategy for finding a balance between fuel economy and durability of fuel cell hybrid power systems. It introduces the fuel cell output change rate with weighted coefficient to limit frequent power fluctuations and improve the durability of the fuel cell. Additionally, a real-time costate updating method is proposed to determine the optimal value of costate according to the real-time state of charge and control the SOC of the lithium battery.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Thermodynamics
Haowen Hu, Wei-Wei Yuan, Minghang Su, Kai Ou
Summary: This study proposes a power distribution optimization strategy for fuel cell hybrid electric vehicles (FCHEVs) using deep reinforcement learning (DRL) and Pontryagin's minimum principle (PMP). The algorithm effectively balances fuel economy, battery durability, and fuel cell durability objectives. In the simulation and hardware-in-the-loop testing, the proposed EMS framework demonstrates significant reduction in FC and battery degradation, as well as real-time application potential.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Automation & Control Systems
Feng Wang, Jiaqi Xia, Xiaoyuan Zhu, Xing Xu, Yi-Qing Ni
Summary: This article presents a real-time predictive energy management strategy with mode transition frequency constraints to improve the energy efficiency of plug-in hybrid electric vehicles. It ensures low mode transition frequency and high calculation efficiency.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Energy & Fuels
Kai Deng, Hujun Peng, Steffen Dirkes, Jonas Gottschalk, Cem Uenluebayir, Andreas Thul, Lars Lowenstein, Stefan Pischinger, Kay Hameyer
Summary: This paper proposes a new causal energy management strategy based on PMP for developing railway vehicles powered by fuel cell and battery systems, which was tested and validated for performance and real-time capability. The strategy achieves an optimal SoC trajectory without the need for complete rail track information and demand prediction.
Article
Engineering, Electrical & Electronic
Iman Shafikhani, Jan Aslund
Summary: The paper derives an analytical solution to the energy management problem for series hybrid electric vehicles by partitioning the positive power demand set into four subsets and deriving a solution for each case separately. The proposed solution includes effective equivalence factor bounds and an adaptive equivalent consumption minimization strategy, demonstrating effectiveness in real-world applications. Simulation results show that the proposed methodology is relatively fast and has satisfactory performance in the presence of drive cycle uncertainty, achieving fuel consumption figures close to optimal benchmarks.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Thermodynamics
Pierpaolo Polverino, Ennio Andrea Adinolfi, Cesare Pianese
Summary: This paper presents a vehicle speed management algorithm that suggests the speed to be followed by the driver in order to achieve fuel consumption reduction. The algorithm is applicable to any vehicle and powertrain configuration, and does not require complex mathematical optimization algorithms.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Engineering, Electrical & Electronic
Chao Yang, Mingjun Zha, Weida Wang, Liuquan Yang, Sixiong You, Changle Xiang
Summary: This article proposes a predictive EMS for PHEVs using rolling game optimization, considering motor temperature and conducting comparisons under different driving cycles. The results show that the strategy effectively improves fuel economy of the studied PHEV and limits motor temperature in a reasonable range.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Energy & Fuels
Aaron Rabinowitz, Farhang Motallebi Araghi, Tushar Gaikwad, Zachary D. Asher, Thomas H. Bradley
Summary: This study thoroughly evaluates the application of Predictive Optimal Energy Management Strategy (POEMS) in connected vehicles using 10 to 20 s predicted velocity, comparing different signal categories and models to analyze their effects on prediction fidelity. Results show that high-fidelity ego future speed prediction can significantly improve fuel economy, approaching the upper limit achievable with POEMS.
Article
Green & Sustainable Science & Technology
Raja Mazuir Raja Ahsan Shah, Mansour Al Qubeissi, Hazem Youssef, Hakan Serhad Soyhan
Summary: A new approach using fuel components as coolants for direct liquid-cooled battery thermal management systems was investigated. Numerical analysis and CFD modeling were conducted to examine the performance of the fuel components. Results show that the liquid-cooled system is more effective in maintaining the temperature of the battery module compared to the air-cooled system.
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
Automation & Control Systems
Bo Hu, Yang Xiao, Sunan Zhang, Bocheng Liu
Summary: Energy management strategy (EMS) is crucial for improving fuel efficiency of hybrid electric vehicles (HEV). Recent advances in artificial intelligence have utilized reinforcement learning (RL) and deep neural networks for training EMS. However, RL-based policies typically require online trial-and-error training, making them inefficient and unsafe for industrial automation. This article proposes an algorithmic framework for model-based offline RL, which can mitigate the current issues and improve the use of RL methods by extracting a policy purely from historical datasets.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Energy & Fuels
Xinyou Lin, Jiajin Zhang
Summary: This paper proposes a battery aging-aware energy management strategy with dual-state feedback control based on multiple neural network learning algorithms to achieve real-time control in random driving cycles and prolong battery life for plug-in hybrid electric vehicles (PHEV). By combining offline optimization control results and neural network training, the strategy effectively reduces the life cycle cost by introducing dual-state feedback control.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Hongwen He, Zegong Niu, Yong Wang, Ruchen Huang, Yiwen Shou
Summary: Energy management strategy (EMS) is crucial for ensuring the long-term energy economy of hybrid electric vehicles. This paper proposes a novel EMS and a policy updating method based on the offline deep reinforcement learning algorithm to address the energy optimization problem. The proposed EMS can learn a superior policy from fixed data, and the proposed updating method can utilize real-time data to update the offline policy approaching the online deep reinforcement learning-based strategy in energy consumption.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Massimiliano Luna, Giuseppe La Tona, Angelo Accetta, Marcello Pucci, Andrea Pietra, Maria Carmela Di Piazza
Summary: This paper proposes an energy management system (EMS) for managing the microgrids of a hybrid DC/AC power system onboard cruise ships. The EMS aims to minimize the fuel cell (FC) operating point excursion by utilizing the battery's capability to manage load fluctuations and compensate for power demand forecasting errors. The effectiveness of the proposed approach was assessed through testing on a real-life case study of a cruise ship.
Article
Metallurgy & Metallurgical Engineering
Yubin Du, Xiaofeng Hu, Yuanyuan Song, Yangpeng Zhang, Lijian Rong
Summary: The yield strength of a tempered Cu-bearing high-strength low-alloy steel initially increases and then decreases with increasing tempering temperature, due to the precipitation and coarsening of Cu-riched clusters. The impact energy shows an inverse trend, reaching a low of 7 J for samples tempered at 450°C. The fracture mode is influenced by the competition between cleavage fracture strength and yield strength, with crack propagation along lath boundaries hindered by certain microstructural changes.
ACTA METALLURGICA SINICA-ENGLISH LETTERS
(2022)
Article
Engineering, Chemical
Xiaopeng Hu, Shuai Zhong, Gang Peng
Summary: The bond behavior between early-age concrete and deformed steel bars under unidirectional cyclic loading was investigated through pull-out tests. The results showed that unidirectional cyclic loading affected the bond performance, resulting in irreversible residual deformation. The bond energy increased with the strength grade and concrete age.
JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY
(2023)
Review
Engineering, Electrical & Electronic
Xiaosong Hu, Xinchen Deng, Feng Wang, Zhongwei Deng, Xianke Lin, Remus Teodorescu, Michael G. Pecht
Summary: This article reviews the research progress of second-life lithium-ion batteries for stationary energy storage applications, including battery aging mechanisms, repurposing, modeling, battery management, and optimal sizing. Energy management strategies and less-demanding applications for maximizing economic benefits of second-life batteries are discussed, and the technical challenges and future development trends of battery reusing technologies are explored.
PROCEEDINGS OF THE IEEE
(2022)
Article
Thermodynamics
Yuxuan Gu, Jianxiao Wang, Yuanbo Chen, Wei Xiao, Zhongwei Deng, Qixin Chen
Summary: The rapid increase in the penetration of lithium-ion batteries (LIBs) in transport, energy, and communication systems has prompted the search for a meticulous but simplified LIB model for non-uniform internal state monitoring and online control. A simplified electro-chemical model for LIBs based on the pseudo-two-dimensional (P2D) model is proposed, which includes a rigorous model of non-uniform reaction rates inside the battery and sub-models that capture the non-uniformity of current densities, potentials, and concentrations. The proposed model shows significant improvements in speed, estimation accuracy, and correction speed and accuracy compared to existing models.
Article
Automation & Control Systems
Kai Zhang, Lulu Jiang, Zhongwei Deng, Yi Xie, Jonathan Couture, Xianke Lin, Jingjing Zhou, Xiaosong Hu
Summary: This article proposes a fault diagnosis method for the early detection and assessment of soft internal short-circuit faults in lithium-ion battery packs, ensuring the safe operation of electric vehicles. Fault features are extracted from the data using the incremental capacity curve, making them easier to identify than small voltage differences. The local outlier factor method is then used to detect the early soft internal short-circuit fault by calculating the local outlier factor value of each cell within the battery pack.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Energy & Fuels
Lin Wang, Xiaowei Zhao, Zhongwei Deng, Lin Yang
Summary: This paper focuses on the accurate estimation of State of Charge (SoC) for electric vehicles and hybrid electric vehicles. A new model updating strategy based on electrochemical impedance spectroscopy (EIS) is proposed, which effectively enhances the SoC estimation accuracy by modifying model parameters and capacity according to the change rate of ohmic impedance.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Zhongwei Deng, Le Xu, Hongao Liu, Xiaosong Hu, Zhixuan Duan, Yu Xu
Summary: This paper proposes a battery capacity prognostic method based on charging data and data-driven algorithms. The statistical characteristics of battery charging data are extracted, and a Seq2Seq model is employed to predict the capacity trajectory. Two residual models based on Gaussian process regression are proposed to compensate for the prediction error caused by local capacity change. Experimental results show that the remaining capacity sequence can be accurately predicted with an error lower than 1.6% using the first 3 months of data as input.
Article
Automation & Control Systems
Xiaoyan Hu, Yuan-Xin Li, Shaocheng Tong, Zhongsheng Hou
Summary: This article addresses the fixed-time prescribed event-triggered adaptive asymptotic tracking control problem for nonlinear pure-feedback systems with uncertain disturbances. By introducing the fuzzy-logic system (FLS), it deals with the unknown nonlinear functions in the system. It proposes a new type of Lyapunov function and fixed-time performance function (FPF), which achieve the fixed-time prescribed performance (FPP) and bounded signals of the system while avoiding Zeno behavior.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Yi Xie, Xingyu Mu, Zhongwei Deng, Kaiqing Zhang, Bin Chen, Yining Fan
Summary: This paper studies the unbalanced discharge of a lithium-ion battery module due to heat dissipation. A three-dimensional electrochemical-thermal model of a single battery and a battery pack is established and verified. The non-uniform temperature distribution and the coupling relationship between electrical parameters and electrochemical parameters under inhomogeneous heat dissipation are studied. The paper also explores the mechanism of how the temperature difference affects the distribution of current and state of charge (SOC). The research shows that controlling the average temperature and temperature difference of the battery pack is a trade-off, and the suitable temperature difference for SOC uniformity increases with ambient temperature and cooling medium temperature.
Article
Engineering, Electrical & Electronic
Le Xu, Zhongwei Deng, Yi Xie, Xianke Lin, Xiaosong Hu
Summary: This study proposes a novel hybrid method that combines physics-based and data-driven approaches to achieve early prediction of battery capacity degradation trajectory. The method extracts a hybrid feature using an electrochemical model and measured voltage data, and utilizes clustering and data augmentation techniques to train a deep neural network for prediction. The method provides accurate predictions using only 20% of training data and is robust under noisy input, with mean absolute percentage errors below 2.5% and 6.5% for capacity degradation trajectory and remaining useable cycle life, respectively.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Engineering, Electrical & Electronic
Xianglong You, Jiacheng Li, Zhongwei Deng, Kai Zhang, Hang Yuan
Summary: This paper proposes a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. The triaxial vibration signals are compressed using hybrid and joint measurement matrices, and the fused spectra are employed for sparse-representation-based classification with the batch matching pursuit algorithm. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis and provide evidence for professional vibration analysis by reconstructing the compressed vibration data. The method is validated with high accuracies in two practical case studies, 99.73% and 96.70% respectively.
Article
Thermodynamics
Wenchao Guo, Lin Yang, Zhongwei Deng, Jilin Li, Xiaolei Bian
Summary: This paper proposes a rapid online health estimation method for lithium-ion batteries based on partial constant-voltage (CV) charging segment. By extracting charging behavior features and using health indicators to construct data-driven models, the accurate evaluation of battery health is achieved.
Article
Energy & Fuels
Hongao Liu, Zhongwei Deng, Yalian Yang, Chen Lu, Bin Li, Chuan Liu, Duanqian Cheng
Summary: Accurately calculating the capacity of battery packs is essential for various aspects in electric vehicles. This paper proposes a specialized method for EVs, using an OCV correction strategy to ensure the credibility of battery SOC. The method is validated with a mean absolute error of 2.6 Ah and applied to 707 on-road electric vehicles, resulting in degradation models with mean absolute errors of 3.138 Ah and 3.137 Ah. The analysis also reveals the correlation between capacity degradation and user behaviors, suggesting that starting the charging at a SOC between 30% and 40% can effectively alleviate degradation.
JOURNAL OF ENERGY STORAGE
(2023)
Proceedings Paper
Engineering, Manufacturing
Jiarui Yu, Weiping Wang, Jun Cao, Heyang Guoyu, Xiaoyan Hu
Summary: The optical on-chip integration technology has great potential in spectrum analysis. The on-chip spectrometer offers chip-scaled, low-cost, and suitable detection in complicated environments. This paper demonstrates a digital Fourier transform spectrometer based on an interferometer with different arm lengths connected by optical switches. The combination of optical switches and MZIs allows for variations in optical path difference, achieving a high spectral resolution.
SEVENTH ASIA PACIFIC CONFERENCE ON OPTICS MANUFACTURE (APCOM 2021)
(2022)
Article
Chemistry, Multidisciplinary
Shuai Qiu, Zhao Gao, Xin Song, Xiao Hu, Hongxing Yuan, Wei Tian
Summary: Novel CPL-active supramolecular helical nanowires (SHNWs) were constructed through hierarchical self-assembly of supramolecular coordination polymers. The CPL colors of the resulting SHNWs are drastically regulated from blue to red, passing through white.
CHEMICAL COMMUNICATIONS
(2022)
