Article
Energy & Fuels
Zongxiang Li, Yan Yang, Liwei Li, Dongqing Wang
Summary: In this paper, a multi-fault online diagnosis approach combining a non-redundant measurement topology and weighted Pearson correlation coefficient (WPCC) is proposed to detect various circuit faults. The approach uses weighted measured data with different forgetting factors and can accurately distinguish and locate battery abuse faults, connection faults, sensor faults, adjacent homogeneous faults, and adjacent hybrid faults.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Jian Yang, Jaewook Jung, Samira Ghorbanpour, Sekyung Han
Summary: In this article, a method for diagnosing battery problems in electric vehicles is proposed, which uses the intraclass correlation coefficient method and the order of cell voltages to enhance EV performance. Experimental results indicate that this method can accurately detect battery cell faults.
Article
Engineering, Electrical & Electronic
Anurag Choudhary, Shahab Fatima, B. K. Panigrahi
Summary: Electric vehicle (EV) is essential for future transportation as it improves fuel economy and reduces emissions. To meet the increasing demands for performance, safety, and environmental impact reduction, EVs are becoming an integrated component of transportation. Therefore, an early fault diagnosis (FD) system is crucial to increase efficiency and reduce maintenance cost, enabling early detection of vehicle health deterioration and proactive solutions. This article comprehensively reviews the state-of-the-art condition monitoring (CM) and FD strategies for various EV components, and provides suggestions for future targeted research on emerging technology in EVs based on current needs.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Thermodynamics
Dongxu Shen, Chao Lyu, Dazhi Yang, Gareth Hinds, Lixin Wang
Summary: This work proposes a novel connection fault diagnosis method based on mechanical vibration signals rather than voltage and current measurements. The simulation of the vibration environment and optimal sensor placement are achieved, and a broad belief network (BBN) is proposed for detecting and locating connection faults in lithium-ion battery packs based on the vibration signals. Incremental-learning algorithms are paired with the BBN to adapt to new data in real-time. The empirical evidence shows a diagnostic accuracy of 93.25%, demonstrating the effectiveness and feasibility of the proposed method.
Article
Engineering, Electrical & Electronic
Yiming Xu, Xiaohua Ge, Weixiang Shen
Summary: This paper proposes a novel sensor fault diagnosis method that achieves simultaneous fault detection, fault source and type identification, and fault estimation in a comprehensive way. A set-valued observer is first constructed to guarantee the inclusion of the unavailable actual battery state. The proposed method is validated through experimental studies.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Energy & Fuels
Quanqing Yu, Jianming Li, Zeyu Chen, Michael Pecht
Summary: The continuous occurrence of lithium-ion battery system fires in recent years has made battery system fault diagnosis a current research hotspot. This paper proposes an improved method for online diagnosis of multi-fault in series connected battery packs and compares it with the correlation coefficient method. The feasibility of the three methods was verified through fault experiments.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Thermodynamics
Lulu Jiang, Zhongwei Deng, Xiaolin Tang, Lin Hu, Xianke Lin, Xiaosong Hu
Summary: This paper introduces a novel data-driven method for lithium-ion battery pack fault diagnosis and thermal runaway warning, achieving accurate identification of battery early faults and early warning of thermal runaway using normalized battery voltages. The proposed method is validated with real vehicle operation data to demonstrate its effectiveness.
Article
Chemistry, Physical
Xin Lai, Changyong Jin, Wei Yi, Xuebing Han, Xuning Feng, Yuejiu Zheng, Minggao Ouyang
Summary: This comprehensive review investigates the mechanism and evolutionary process of internal short circuit (ISC) within lithium-ion batteries (LIBs), covering types, inducing mechanisms, evolution stages, experimental methods, detection and diagnosis techniques, prevention methods, and future prospects. The study emphasizes the importance of improving safety in LIBs through advancements in modeling, simulation, detection, and prevention of ISC.
ENERGY STORAGE MATERIALS
(2021)
Article
Energy & Fuels
Zhang Fan, Xing Zi-xuan, Wu Ming-hu
Summary: This study proposes a method for accurately detecting early battery pack failures using a generalized dimensionless indicator (GDI) and the LOF algorithm based on real vehicle data.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Green & Sustainable Science & Technology
Zhifu Wang, Wei Luo, Song Xu, Yuan Yan, Limin Huang, Jingkai Wang, Wenmei Hao, Zhongyi Yang
Summary: Power batteries are crucial for electric vehicles, but even minor faults can lead to accidents, emphasizing the importance of battery fault diagnosis. To enhance the practicality of battery fault diagnosis methods, a multi-method fusion approach based on big data is proposed for lithium-ion batteries in electric vehicles. This method utilizes t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising for anomaly removal and early fault analysis. It identifies influential vehicle features through factor analysis and extracts faulty features using a two-way long and short-term memory network method with convolutional neural network. A self-learning Bayesian network is then employed for battery fault diagnosis. The results demonstrate a 12% improvement in fault diagnosis accuracy when tested with data from different vehicles. Additionally, this method exhibits higher accuracy and reduced response time compared to alternative approaches, aligning more effectively with practical engineering applications.
Article
Energy & Fuels
Na Yan, Yan-Bing Yao, Zeng-Dong Jia, Lei Liu, Cui-Ting Dai, Zhi-Gao Li, Zong-Hui Zhang, Wei Li, Lei Wang, Peng-Fei Wang, Shandong Luruan
Summary: With the rapid development of electric vehicles, electric vehicle battery health diagnosis has become a hot issue. In order to realize online battery health diagnosis, an online battery health diagnosis platform based on DTW-XGBoost was proposed. The platform adopts a feature extraction method of multi-source data fusion and performs data aggregation and feature extraction for real-time battery data during charging process using DTW clustering. It establishes an SOH prediction model using the XGBoost algorithm. The online battery health diagnosis platform, built using a cloud platform, aims to improve charging operation and maintenance management level.
Article
Engineering, Electrical & Electronic
Michael Schmid, Emanuel Gebauer, Christian Hanzl, Christian Endisch
Summary: This article develops and validates a model-based fault diagnosis algorithm that utilizes the switches of an RBS to improve fault isolability, utilizes a fuzzy clustering approach for fault isolation, and enhances sensitivity and robustness of the fault diagnosis method with a constrained sigma-point Kalman filter.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Energy & Fuels
Karan Shukla, Abu Raihan Mohammad Siddique, Kumar Venkateshwar, Mohammad Reza Mohaghegh, Syeda Humaira Tasnim, Shohel Mahmud
Summary: In the current era of sustainability, the effect of vibrations on the thermal behavior of lithium-ion batteries in electric and hybrid electric vehicles is experimentally analyzed. Different discharge rates, frequencies, and amplitudes of vibrations are considered, and it is observed that vibrations affect the transient temperature distribution of the batteries.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Quanqing Yu, Lei Dai, Rui Xiong, Zeyu Chen, Xin Zhang, Weixiang Shen
Summary: This paper proposes a fault diagnosis method based on an improved model, and uses this method to detect faults in current sensors. Experimental results show that the method can detect faults in current sensors more accurately and quickly, and has the ability to detect minor faults under different operating conditions and temperatures.
Article
Engineering, Electrical & Electronic
Naifeng Gan, Zhenyu Sun, Zhaosheng Zhang, Shiqi Xu, Peng Liu, Zian Qin
Summary: This article proposes a two-layer overdischarge fault diagnosis strategy based on machine learning for detecting and preventing overdischarge in lithium-ion batteries for electric vehicles. The method involves comparing battery voltage with cutoff voltage and utilizing a detection approach based on the eXtreme Gradient Boosting algorithm.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Automation & Control Systems
Chenghui Zhang, Yongzhe Kang, Bin Duan, Zhongkai Zhou, Qi Zhang, Yunlong Shang, Alian Chen
Summary: This article proposes an adaptive battery capacity estimation method suitable for arbitrary charging voltage range based on incremental capacity (IC) analysis and data-driven techniques. Experimental results show that the method can accurately estimate battery capacity under arbitrary charging conditions.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Green & Sustainable Science & Technology
Peng Huang, Pingwei Gu, Yongzhe Kang, Ying Zhang, Bin Duan, Chenghui Zhang
Summary: In this paper, a data-driven and model fusion method based on constant voltage charging process (CVCP) is proposed for the state of health (SOH) estimation of lithium-ion batteries. An improved equivalent circuit model (IECM) is established using current-time data, and the model parameters are used as health indicators. A SOH prediction model is then built using back propagation neural network and optimized with an improved particle swarm optimization algorithm. A new scheme based on incomplete CVCP is adopted to address the issue of time consumption. Comparative results show that the proposed IECM provides higher current estimation accuracy for different batteries.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Engineering, Electrical & Electronic
Nan Wang, Guangcai Zhao, Yongzhe Kang, Wei Wang, Alian Chen, Bin Duan, Chenghui Zhang
Summary: Temperature is a critical factor for the safety and reliability of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. This article proposes a method that combines long short-term memory (LSTM) with transfer learning (TL) to estimate the core temperature (CT) of LIBs. The experimental results demonstrate that the proposed LSTM-TL method has high accuracy in estimating LIB CT, with a maximum root-mean-square error (RMSE) of 0.3302 degrees C.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Thermodynamics
Guangcai Zhao, Yongzhe Kang, Peng Huang, Bin Duan, Chenghui Zhang
Summary: This paper proposes an intelligent battery health prognostic method using efficient and robust aging trajectory matching with ensemble deep transfer learning (EDTL). Experimental results show that this method can achieve accurate and reliable health prognostic at different aging stages or in the presence of incomplete data.
Article
Automation & Control Systems
Guangcai Zhao, Chenghui Zhang, Bin Duan, Yunlong Shang, Yongzhe Kang, Rui Zhu
Summary: This article proposes a novel data-driven method using generative adversarial networks to estimate the health status of lithium-ion batteries. By generating auxiliary samples and detecting anomalous aging indicators, a general model is developed and a model building rule is proposed. The experimental results show that this method outperforms other models in prediction performance.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Zhongkai Zhou, Bin Duan, Yongzhe Kang, Yunlong Shang, Qi Zhang, Chenghui Zhang
Summary: In this article, a practical SoH estimation method for LiFePO4 batteries based on Gaussian mixture regression (GMR) and incremental capacity (IC) analysis is proposed. The method achieves high accuracy, high adaptability, and low complexity in estimating the SoH of the batteries.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Letter
Automation & Control Systems
Xin Gu, Yunlong Shang, Yongzhe Kang, Jinglun Li, Ziheng Mao, Chenghui Zhang
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Thermodynamics
Qifan Yang, Jinlei Sun, Yongzhe Kang, Hongzhong Ma, Dawei Duan
Summary: This paper proposes an ISC detection method based on the transformation matrix and an ISC resistance calculation method based on an improved state-space model. The transformation matrix is used to capture the skewed and downward voltage pattern of the ISC cell in the battery pack. An online detection flow is designed based on the opposite variation relationship between adjacent cells. Moreover, an improved state-space model is developed to directly estimate the ISC current, and a dual extended Kalman filter is deployed for accurate state estimations.
Article
Electrochemistry
Yuhao Zhu, Xin Gu, Ziheng Mao, Wenyuan Zhao, Yunlong Shang
Summary: This paper proposes a rapid cycle life test method based on intelligent prediction for lithium-ion batteries, decoupling original capacity data and utilizing LSTM model for prediction. The diversity of training data is improved by a data expansion technique, with the feasibility and effectiveness proven by NASA datasets. The results show a reduction of at least 90% in cycle life test time with an error of less than 3 cycles.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2023)
Article
Energy & Fuels
Haojie Yin, Yan Li, Yongzhe Kang, Chenghui Zhang
Summary: In order to ensure the comprehensive consistency of retired lithium-ion batteries, a two-stage sorting method is proposed based on static and dynamic characteristics. The method utilizes the DBSCAN algorithm and the K-means++ algorithm to classify the batteries based on discharge capacity, temperature rise, and voltage curves. The effectiveness of the method is verified using NASA public data set and evaluation indexes, showing its advantages in prolonging the service life of retired batteries.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Xin Gu, Jinglun Li, Yuhao Zhu, Yue Wang, Ziheng Mao, Yunlong Shang
Summary: In this study, a fast and intelligent screening approach for retired batteries is proposed, which achieves high efficiency and accuracy. By utilizing a cloud-edge collaborative framework and a Light Gradient Boosting Machine model, rapid classification and screening of large-scale retired batteries are achieved, while significantly improving the voltage consistency of battery modules.
Article
Automation & Control Systems
Xin Gu, Jinglun Li, Kailong Liu, Yuhao Zhu, Xuewen Tao, Yunlong Shang
Summary: This study presents a minor fault diagnosis approach for lithium-ion batteries based on phase plane sample entropy, which accurately detects minor faults and predicts the time of occurrence. Experimental results demonstrate the effectiveness, robustness, and generalizability of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
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.
