Article
Thermodynamics
Zhengyi Bao, Jiahao Nie, Huipin Lin, Jiahao Jiang, Zhiwei He, Mingyu Gao
Summary: This paper proposes a novel sequence-free framework for estimating the state of health (SOH) of lithium-ion batteries. By introducing a global-local context embedding module, both global and local-range information can be learned to establish the mapping relationship between battery charging/discharging curves and battery SOH.
Article
Energy & Fuels
Jan Kleiner, Magdalena Stuckenberger, Lidiya Komsiyska, Christian Endisch
Summary: An innovative NARX network was developed and compared to traditional feedforward networks for temperature prediction in Li-ion batteries, demonstrating higher accuracy and robustness. In terms of long-term prediction and dynamic applications, the NARX network showed superior performance.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Engineering, Multidisciplinary
Zhengyu Liu, Juan Xie, Huijuan He, Keqing Wang, Wei Huang
Summary: This study proposes a method for predicting the self-discharge voltage drop (SDV-drop) of lithium-ion batteries based on a pre-classifier. The method obtains features for predicting SDV-drop through direct extraction and generation from the charge-discharge curve, which are then input into an optimized neural network model to accurately predict SDV-drop.
Article
Chemistry, Multidisciplinary
Wei Liu, Kai Zong, Usman Ghani, Ali Saad, Dongqing Liu, Yonggui Deng, Waseem Raza, Ying Li, Arshad Hussain, Pengfei Ye, Zhaoqi Song, Xingke Cai
Summary: A new ternary alkali metal boride, Li1.2Ni2.5B2, has been studied as an anode material for fast-charging lithium-ion batteries. It exhibits high Li+ storage capacity, remarkable electrochemical stability, and excellent rate performance.
Article
Energy & Fuels
Wei-Jen Lin, Kuo-Ching Chen
Summary: Developing an accurate and high-efficiency parameter identification method is crucial for predicting the state of health of lithium ion batteries. This study proposes using the first-order derivative of the discharge curve as another fitting target and employs genetic algorithm and deep neural network for multi-objective optimization.
Article
Chemistry, Physical
Weihan Li, Jiawei Zhang, Florian Ringbeck, Dominik Joest, Lei Zhang, Zhongbao Wei, Dirk Uwe Sauer
Summary: The study introduces a hybrid state estimation method that combines physics-based and machine learning models to accurately estimate the internal states of lithium-ion batteries, demonstrating high reliability and generalization ability.
JOURNAL OF POWER SOURCES
(2021)
Article
Energy & Fuels
Jaewook Lee, Jay H. Lee
Summary: This study proposes a machine learning approach to predict the knees of lithium-ion batteries (LIBs) by extracting features from voltage-current-temperature (VIT) cycling datasets. The findings highlight the advantage of considering cycle-to-cycle behavior in conjunction with intracycle temporal behavior when constructing a data-driven prediction model. Additionally, the study demonstrates that the input size can be reduced to facilitate early knee prediction.
Article
Energy & Fuels
Yunjie Li, Stefanie Arnold, Samantha Husmann, Volker Presser
Summary: The rapid growth of electric vehicles and electronic devices has led to a significant increase in the number of spent batteries that have reached the end of their life. It is crucial to find a sustainable and efficient approach to battery recycling. A type of MXene material, AD-Ti(3)C(2)Tz electrode, has been developed and directly used as free-standing anodes for lithium-ion and sodium-ion batteries without the need for binder or carbon additives. The AD-Ti(3)C(2)Tz electrode exhibits excellent electrochemical performance and can be easily recycled, further converted into TiO2/C hybrids with adjustable carbon content, enabling its second life in various applications.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Hong Xu, Shunli Wang, Yongcun Fan, Jialu Qiao, Wenhua Xu
Summary: This study proposes an improved Drosophila algorithm combined with BP neural network to estimate the SOC of lithium-ion batteries. The improved algorithm shows better performance and estimation accuracy compared to traditional algorithms and other commonly used functions.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Ying Zhang, Zhongkai Zhou, Yongzhe Kang, Chenghui Zhang, Bin Duan
Summary: The study proposes a quick and accurate screening method for retired batteries based on an improved fuzzy c-means algorithm, achieving high efficiency and accuracy through feature extraction and optimization of partial charging curves. The approach outperforms support vector machine and neural network methods in generality and efficiency, with a screening accuracy of 90.9% and a potential accuracy of 95.5% with a permitted error of 1%, while the efficiency is about 7.6 times higher than supervised screening methods.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Thermodynamics
Zhikai Ma, Qian Huo, Wei Wang, Tao Zhang
Summary: This paper proposes a voltage-temperature aware thermal runaway alarming approach using advanced deep learning model, which improves the accuracy and robustness of the alarming system. Wavelet analysis is used to extract time-frequency features, deep learning with attention mechanism is adopted to map historical data to predicted data, and a voltage-temperature joint alarming method is proposed. Experiments show that the method has a combined relative error of only 0.28% for temperature and voltage prediction in a 7-minute time window and can achieve 8-13 minute ahead thermal runaway prediction in real-world scenarios.
Article
Energy & Fuels
Guodong Fan, Xi Zhang
Summary: In this work, a new battery capacity estimation approach using relaxation voltage data is proposed. A correlation is identified between the relaxation voltage and battery capacity, and a convolutional neural network model is developed to estimate capacity for batteries with different degradation paths. The model shows high predictive power with a low average test error of 1.8%. The method also has the potential to work for batteries with different chemistries.
Article
Energy & Fuels
Liping Chen, Xinyuan Bao, Antonio M. Lopes, Changcheng Xu, Xiaobo Wu, Huifang Kong, Suoliang Ge, Jie Huang
Summary: This paper proposes a new approach that integrates the equivalent circuit model (ECM) and data-driven methods for estimating the state of health (SOH) of lithium-ion batteries (LIBs). By identifying the internal resistance of the ECM using an optimization algorithm and using an optimized neural network to predict the SOH, the proposed method demonstrates fast convergence, high accuracy, and good generalization capability.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Chusheng Lu, Jian Hu, Yuanyi Zhai, Haibin Hu, Hangyu Zheng
Summary: This paper proposes a SOC estimation method based on the linearization of voltage hysteresis curve (EMBL) and establishes a novel equivalent model of lithium-ion battery based on a linear neural network (LEM). The superior performance of the LEM is verified through intermittent charging and discharging experiments. Furthermore, this paper constructs a SOC estimation method based on the linearization of voltage hysteresis curve and carries out equalization experiments with voltage equalization and SOC equalization. The results demonstrate that SOC equalization can significantly reduce energy consumption compared to voltage equalization, and the EMBL-based SOC equalization slightly outperforms the extended Kalman filter (EKF) algorithm-based SOC equalization in terms of energy consumption reduction.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Jungsoo Kim, Huiyong Chun, Jongchan Baek, Soohee Han
Summary: This paper proposes a novel parameter identification method for lithium-ion batteries using a neural network and genetic algorithm, resulting in more accurate and reliable identification of electrochemical model parameters with high data efficiency.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Chemical
Jianhui Zhou, Guohao Du, Jianfeng Hu, Xin Lai, Shan Liu, Zhengguo Zhang
Summary: In this study, composite phase change materials (PCMs) were developed using freeze-drying and vacuum impregnation methods. Polyethylene glycol (PEG) was used as the heat storage material, boron nitride (BN) was used as a filler to improve thermal conductivity, and sodium alginate (SA) was used as a supporting material to maintain the shape stability of the composite. The results showed that the BN@SA/PEG composite PCMs exhibited good chemical compatibility, stable morphology, and thermal stability. This study provides a strategy for manufacturing flexible, long-serving, and shape-stable PCMs, which have great potential in thermal management in the electronic field.
CHINESE JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Engineering, Environmental
Ying Xiang, Zhiyao Liu, Yu Gao, Lei Feng, Ting Zhou, Mengjiao Liu, Yan Zhao, Xin Lai, Jian Bi, Daojiang Gao
Summary: In this study, a novel W6+-doping double perovskite compound was designed, and a series of Ca2Gd0.5Nb1-xW5x/6O6:0.5Eu3+ red phosphors were synthesized. It was found that the concentration of W6+ ion had little effect on the microstructures of the phosphors, but significantly influenced their luminescence properties. The Ca2Gd0.5Nb1-xW5x/6O6:0.5Eu3+ (x = 0.05) phosphor exhibited the best luminescence properties and was used in a WLED device, resulting in bright warm-white light with a high CRI of 91 and a low CCT of 5386 K.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Chemistry, Physical
Lijun Quan, Ying Li, Wenwen Gu, Ting Su, Yunan Luo, Mengjiao Liu, Xin Lai, Jian Bi, Daojiang Gao, Yan Zhao
Summary: This study focuses on the preparation of porous hexagonal prism Zn-doped CoTiO3 as anode material for LIBs. The effects of Zn doping concentration on microstructure and electrochemical performance are investigated. The results show that Zn doping has little influence on microstructure but can enhance capacity, cyclic stability, and rate capability. The sample Co0.9Zn0.1TiO3 exhibits the best electrochemical performance with a reversible capacity of 430.6 mAh g(-1) after 280 cycles at 0.2 C. This work provides an effective strategy for improving the electrochemical performance of other Ti-based anode materials.
Article
Chemistry, Physical
Binghan Cui, Han Wang, Renlong Li, Lizhi Xiang, Jiannan Du, Huaian Zhao, Sai Li, Xinyue Zhao, Geping Yin, Xinqun Cheng, Yulin Ma, Hua Huo, Pengjian Zuo, Chunyu Du
Summary: Researchers developed an accurate and fast method for detecting the early internal short circuit (ISC) of Lithium-ion batteries by combining electrochemical impedance spectroscopy (EIS) with a deep neural network (DNN). The ISC detection accuracy reached 97.5% for the normal battery with zero false positives, and the proposed method was also proven to be applicable to other batteries. By selecting the most sensitive EIS spectrum to ISC based on the distribution of relaxation times and sensitivity methods, the required EIS measurement time was further reduced and computational efficiency was improved. The results demonstrate the value of EIS spectrum in battery management systems.
JOURNAL OF POWER SOURCES
(2023)
Article
Biochemistry & Molecular Biology
Yuhan Gao, Lei Feng, Linglin Wang, Jun Zheng, Feiyao Ren, Siyu Liu, Zhanglei Ning, Ting Zhou, Xiaochun Wu, Xin Lai, Daojiang Gao
Summary: Nowadays, Mn4+-activated fluoride red phosphors have gained tremendous attention for enhancing the performance of WLEDs. However, their poor moisture resistance hinders commercialization. In this study, we proposed the dual strategies of solid solution design and charge compensation to synthesize Mn4+-activated K2Nb1-xMoxF7 red phosphors with improved moisture resistance, luminescence properties, and thermal stability. The K2Nb1-xMoxF7: Mn4+ (x = 0.05) phosphor showed a quantum yield of 47.22% and retained 69.95% of its initial emission intensity at 353 K after immersion for 1440 min. Furthermore, a high-performance WLED with high CRI of 88 and low CCT of 3979 K was fabricated by incorporating the K2Nb1-xMoxF7: Mn4+ (x = 0.05) red phosphor.
Article
Engineering, Chemical
Quanwei Chen, Yukun Hou, Xin Lai, Kai Shen, Huanghui Gu, Yiyu Wang, Yi Guo, Languang Lu, Xuebing Han, Yuejiu Zheng
Summary: With the increasing usage of electric vehicles, the environmental recycling of retired lithium-ion batteries has become a pressing issue. A process-based life cycle model was established to assess the environmental impacts of different recycling routes and investigate the potential of battery remanufacturing. The results indicate that the environmental burden during recycling is mainly caused by high electricity consumption and auxiliary materials, and using recycled materials for battery remanufacturing can significantly reduce the carbon footprint and cumulative energy demand.
SEPARATION AND PURIFICATION TECHNOLOGY
(2023)
Article
Engineering, Chemical
Quanwei Chen, Xin Lai, Yukun Hou, Huanghui Gu, Languang Lu, Xiang Liu, Dongsheng Ren, Yi Guo, Yuejiu Zheng
Summary: Recycling lithium-ion batteries from electric vehicles is a meaningful way to alleviate the global resource crisis and supply chain risks. This study examines the environmental impacts of different advanced recycling technologies for NCM and LFP batteries. The results show that direct material recycling methods can significantly reduce the environmental burden compared to hydrometallurgy, and the environmental benefits of battery remanufacturing increase with the amount of materials obtained through recycling.
SEPARATION AND PURIFICATION TECHNOLOGY
(2023)
Article
Energy & Fuels
Jianfeng Hu, Sixing Zhang, Guohao Du, Xin Lai, Ye Wang, Jinqing Qu, Zhengguo Zhang
Summary: A novel composite phase change material (PCMs) was prepared by assembling boron nitride-chitosan (BNCA) porous structure with lyophilization and embedded sodium acetate trihydrate (SAT). The supercooling of samples was adjusted by adding disodium hydrogen phosphate dodecahydrate (DHPD), and the melting enthalpy was adjusted by adding gum arabic powder (GA). The resulting BNCA/SDG composite PCMs showed great potential for application in electrical power systems due to their low supercooling and high thermal conductivity.
SOLAR ENERGY MATERIALS AND SOLAR CELLS
(2023)
Article
Electrochemistry
Fang Zhang, Tao Sun, Bowen Xu, Yuejiu Zheng, Xin Lai, Long Zhou
Summary: The label-less characteristics of real vehicle data pose great challenges for engineering modeling and capacity identification of lithium-ion batteries. The randomness and unpredictability of vehicle driving conditions, sampling frequency, and other factors in the raw data collected from driving cycles have adverse effects on effective modeling and capacity identification. Therefore, data cleaning and optimization are necessary, and the capacity of a battery pack is identified using the improved two-point method. By utilizing the charging and discharging data segments and a Fuzzy Kalman filter optimization capacity estimation curve, the current available capacity is obtained. This algorithm is integrated into a new energy big data cloud platform. The results demonstrate the successful application of the capacity identification algorithm in academic and engineering fields through charge and discharge mutual verification, meeting the engineering requirements for life expectancy.
Review
Electrochemistry
Long Zhou, Xin Lai, Bin Li, Yi Yao, Ming Yuan, Jiahui Weng, Yuejiu Zheng
Summary: This paper comprehensively reviews the research status, technical challenges, and development trends of state estimation of lithium-ion batteries, which is a core function in the battery management system. It summarizes the key issues and technical challenges in battery state estimation and provides a deep analysis of these challenges. The paper also reviews the joint estimation methods for four typical battery states and proposes feasible estimation frameworks. Furthermore, it discusses the prospect of state estimation development and the influence of advanced technologies like artificial intelligence and cloud networking.
Article
Chemistry, Multidisciplinary
Changyong Jin, Yuedong Sun, Yuejiu Zheng, Jian Yao, Yu Wang, Xin Lai, Chengshan Xu, Huaibin Wang, Fangshu Zhang, Huafeng Li, Jianfeng Hua, Xuning Feng, Minggao Ouyang
Summary: In this study, an in situ observation method for thermal runaway (TR) and its propagation (TRP) in lithium-ion battery electrodes was proposed using high-frequency induction heating as the TR triggering mechanism. The non-contact, rapid heating technique enabled direct observation of TRP. It was found that venting occurred in all samples and dendritic burn patterns appeared on separators during TRP. The heat-induced shrinkage of separators was more pronounced on the anode side. Phenomena observed during tests included gas flow paths, drifting sparks, short-circuit propagation, and electrolyte boiling. This method contributes to a better understanding of LIB TRP behavior and facilitates strategies to enhance safety and TR characteristics.
CELL REPORTS PHYSICAL SCIENCE
(2023)
Article
Chemistry, Physical
Xin Lai, Bin Li, Xiaopeng Tang, Yuanqiang Zhou, Yuejiu Zheng, Furong Gao
Summary: Detecting the internal short circuit (ISC) of Lithium-ion batteries is crucial for preventing thermal runaway. Conventional approaches focus on ISC detection for dynamic load profiles, neglecting the high-risk float-charging scenarios. We propose a simple and accurate method to identify the leakage current of the battery with ISC by examining the battery equalization system's behavior, which can be transplanted into low-cost embedded systems for wider applications.
JOURNAL OF POWER SOURCES
(2023)
Article
Engineering, Chemical
Quanwei Chen, Xin Lai, Junjie Chen, Yi Yao, Yi Guo, Mengjie Zhai, Xuebing Han, Languang Lu, Yuejiu Zheng
Summary: Although recycling retired lithium-ion batteries can help address global warming and the energy crisis, the environmental impacts of different recycling routes need further evaluation. This study quantifies and compares the environmental indicators of three hydrometallurgical recycling and remanufacturing routes in China, and assesses the potential of reducing environmental impacts through battery remanufacturing with recycled materials. The results show that different chemical reagents, energy consumption, and processes result in variations in environmental indicators, and battery manufacturing with recycled materials can significantly reduce environmental impacts compared to using raw materials.
SEPARATION AND PURIFICATION TECHNOLOGY
(2023)
Article
Chemistry, Applied
Xin Lai, Zheng Meng, Fangnan Zhang, Yong Peng, Weifeng Zhang, Lei Sun, Li Wang, Fei Gao, Jie Sheng, Shufa Su, Yuejiu Zheng, Xuning Feng
Summary: This study presents a new approach to mitigate the thermal runaway hazard in lithium-ion batteries by using poison agents. A self-destructive cell with an embedded poison layer was constructed and the poisoning mechanism and paths were experimentally investigated at different levels. The proposed route was validated through thermal runaway tests, showing a significantly reduced maximum temperature and improved battery safety.
JOURNAL OF ENERGY CHEMISTRY
(2023)
Article
Thermodynamics
Kai Shen, Jin Dai, Yuejiu Zheng, Chengshan Xu, Rongbiao Zhang, Huaibin Wang, Changyong Jin, Xuebing Han, Xin Lai, Xinzhe Qian, Xuning Feng
Summary: The safe fast-charging of lithium-ion batteries is crucial for the rapid development of electric vehicles. This paper proposes a new charging method that balances lithium plating prevention and maximum temperature control, saving charging time and ensuring battery safety.
THERMAL SCIENCE AND ENGINEERING PROGRESS
(2023)
Article
Green & Sustainable Science & Technology
Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang
Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu
Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang
Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He
Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.
JOURNAL OF CLEANER PRODUCTION
(2024)