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    Journal of Electrical Engineering    2022, 17 (4): 1-1.   DOI: 10.11985/2022.04.001
    Abstract136)   HTML22)    PDF (348KB)(172)      
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    State of Health Estimation of Lithium-ion Batteries Based on iCEEMDAN and Transfer Learning
    YANG Songyuan, TIAN Yong, TIAN Jindong
    Journal of Electrical Engineering    2022, 17 (4): 2-10.   DOI: 10.11985/2022.04.002
    Abstract501)   HTML44)    PDF (27029KB)(566)      

    The data-driven method for state of health(SOH) of lithium-ion batteries is currently a research hotspot. For electric vehicle applications, however, it has to face the challenge of small sample data, which leads to low accuracy and poor generalization. A SOH estimation method based on feature mode decomposition and transfer learning is proposed. Firstly, health features are extracted from a small section of the battery data set, and then they are divided into the intrinsic mode function(IMF) part and the residual signal(RES) part by using the improved complete ensemble empirical mode decomposition with adaptive noise(iCEEMDAN). Secondly, the IMF and RES parts are trained through a long short-term memory network and a back-propagation network, respectively, achieving a combined base model between the health features and the SOH. Finally, the base model is transferred to other data sets for SOH estimation. Validation results based on the NASA battery data set show that the proposed method performs high accuracy and generalization ability. The mean absolute error(MAE) and root mean square error(RMSE) are about 2.34% and 3.05%, respectively. With transfer learning, the MAE and RMSE are reduced to 1.13% and 1.68%, respectively.

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    Fast Estimating the State of Health of Lithium-ion Batteries Based on Improved Least Squares Support Vector Machine
    XU Binxiang, ZHENG Linfeng, HUANG Yiheng, XIAO Zhineng, WANG Xinyue
    Journal of Electrical Engineering    2022, 17 (4): 11-19.   DOI: 10.11985/2022.04.003
    Abstract301)   HTML8)    PDF (36463KB)(222)      

    State of health(SOH) is one of the core parameters for the safe management and operation of battery systems. Accurate and rapid estimation of SOH is of great significance to the safe utilization of batteries. To address the issue that conventional SOH estimation algorithms are difficult to involve both high running speed and high accuracy, a fast estimation method of battery SOH using an improved least squares support vector machine(ILS-SVM) is proposed. The robustness and running speed of the algorithm can be effectively improved by setting appropriate critical parameters, which discards some support vectors and weakens the influence of boundary samples on the algorithm. The ILS-SVM is then employed to fast estimate SOH of different battery data sets. By analyzing the operation data of batteries, a specific voltage data interval is used for data preprocessing to avoid the complete charging and discharging measurement of the battery, thus improving the efficiency of battery SOH estimation. The verified results show that accurate estimates can be achieved, and most of estimation errors of battery SOH are less than 1%. Compared with the original LS-SVM algorithm, the running speed of the ILS-SVM can be improved by up to 20%.

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    Research on Health Assessment Method of Lithium-ion Battery Based on Data-model Hybrid Drive
    FANG Deyu, CHU Xiao, LIU Tao, LI Junfu
    Journal of Electrical Engineering    2022, 17 (4): 20-31.   DOI: 10.11985/2022.04.004
    Abstract410)   HTML23)    PDF (29601KB)(226)      

    In this work, a battery’s state of health(SOH) estimation method is developed with capacity and energy as characterization parameters. Two methods are used to estimate the SOH. First, the metabolic grey algorithm(MGA) is used to predict the battery capacity and energy by insetting the original battery capacity and energy sequence directly. Second, the original model parameters are imput, the parameters of simplified electrochemical model(SEM) are predicted by using grey prediction algorithm, the predicted parameter values are brought back to the model, the battery terminal voltage curve is fit, and the battery capacity and energy are obtained by integration method. Aiming at the decay rate and estimation accuracy of the two characterization parameters, and a comprehensive battery health state estimation method based on data-model hybrid drive is developed to realize the accurate prediction of battery SOH.

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    Lithium-ion Battery RUL Prediction Method Based on Double Gaussian Model
    LI Yanmei, LIU Huihan, ZHANG Chaolong, LUO Laijing
    Journal of Electrical Engineering    2022, 17 (4): 32-40.   DOI: 10.11985/2022.04.005
    Abstract313)   HTML21)    PDF (32812KB)(272)      

    For the performance maximization and maintenance of lithium-ion batteries, accurate remaining useful life(RUL) predictions are essential. To accurately predict the RUL of lithium-ion batteries, a novel double Gaussian model is proposed to describe the aging process of lithium-ion batteries. Specifically, several popular empirical models for battery capacity degradation are analyzed and evaluated, and a double Gaussian model with better performance is proposed. Afterward, a double Gaussian aging model is established utilizing the particle filter(PF) technique, based on the historical capacity data. The fitted correlation coefficient and root mean square error are also introduced to assess the model. Finally, the RUL prediction experiments are conducted to verify the verification of the proposed aging model based on the battery aging data from the laboratory’s battery cells and the National Aeronautics and Space Administration(NASA) Ames Prognostics Center of Excellence. The experimental results demonstrate that the proposed aging model can predict the RUL accurately, and the prediction error is significantly improved compared to other models.

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    Capacity Prediction of Lithium-ion Batteries Based on Multi-task LSTM with Attention Mechanism
    LU Nan, OUYANG Quan, HUANG Lianghui, WANG Zhisheng
    Journal of Electrical Engineering    2022, 17 (4): 41-50.   DOI: 10.11985/2022.04.006
    Abstract319)   HTML17)    PDF (85291KB)(184)      

    Accurate capacity prediction of lithium-ion batteries can effectively reduce the risk and loss caused by battery failure. The time series prediction model based on neural network is a very common method in the field of battery capacity prediction. However, most of the predictions of the past models only considered the future target points, but did not consider the auxiliary role of the information near the target points. A capacity prediction method of lithium-ion batteries based on multi-task LSTM with attention mechanism is proposed to realize the complementation of information at different times in the future and improve the prediction accuracy. The hard parameter sharing method is used to establish the connection among multiple tasks, and the convolutional neural network is used to extract features at different levels of abstraction. Comparing with the traditional neural network, and the experimental results show that the proposed MT-LSTM model has higher prediction accuracy. In addition, comparison experiments are designed for the multi-task learning and the attention mechanism to verify the positive effects of these two methods on the prediction accuracy of battery capacity.

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    Study on Impedance Characteristics of Lithium-ion Battery in Over Discharge State
    LIU Wangzeyu, LI Qing, YU Tiantian, XIONG Jinchen, ZHANG Hongyuan, DONG Ming, REN Ming
    Journal of Electrical Engineering    2022, 17 (4): 51-60.   DOI: 10.11985/2022.04.007
    Abstract389)   HTML32)    PDF (46883KB)(242)      

    At present, lithium-ion batteries are widely used in electrochemical energy storage systems. However, due to the differences between batteries, single batteries often have over discharge phenomenon, which brings safety risks to the application of battery modules and energy storage systems. Therefore, the detection and analysis of lithium-ion batteries in over discharge state are of great significance for their safety applications. Cycle tests of normal cycle and different degrees of over discharge are designed. The relaxation time distribution method, impedance difference analysis method and capacity increment method are used to analyze the impedance characteristics of lithium-ion batteries in the whole life cycle of over discharge state. The results show that over-discharge can accelerate battery aging and increase the temperature rise during charging and discharging. Compared with the normal cycle, the ohm internal resistance and charge transfer resistance increase in the over discharge cycle, the internal resistance of SEI film decreases, and the charge transfer resistance has no significant change. The solid-phase diffusion resistance of lithium-ion decreases first and then increases with the increase of over discharge degree. The results provide a theoretical basis for the study of the internal characteristics of lithium-ion battery over discharge and the detection of over discharge.

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    Thermal Runaway Modeling of Lithium-ion Batteries: A Review
    WANG Gongquan, KONG Depeng, PING Ping, LÜ Hongpeng
    Journal of Electrical Engineering    2022, 17 (4): 61-71.   DOI: 10.11985/2022.04.008
    Abstract604)   HTML48)    PDF (38545KB)(353)      

    With relatively high energy density, extended cycle lifespan and trivial environmental pollution, the lithium-ion batteries (LIBs) have been widely employed in the electric vehicles and energy storage systems. However, the recent proliferating fire and explosion accidents caused by thermal runaway(TR) has been the main shackles of restricting the large-scale application of LIBs. Therefore, the thermal safety issue of LIBs is research focus of energy storage field, where simulation is regarded as the critical technology to investigate the characteristics of TR and promote the safe application of LIBs considering its negligible cost in time and economics. The research progress of TR modelling works of LIBs at home and abroad is reviewed from the single cell scale to battery pack scale. The heat generation mechanism within batteries and corresponding thermodynamic modelling methods are expounded. Progress made in modelling the venting, combustion and explosion of gases generated by LIBs is summarized. The application of thermal resistance network model and computational fluid dynamics model in predicting the TR propagation behaviour of battery pack is analysed. Finally, the future research on TR thermal modelling of LIBs is discussed.

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    Research Review on Early Warning and Suppression Technology of Lithium-ion Battery Fire in Energy Storage Power Station
    CHEN Yin, XIAO Ru, CUI Yilin, CHEN Mingyi
    Journal of Electrical Engineering    2022, 17 (4): 72-87.   DOI: 10.11985/2022.04.009
    Abstract641)   HTML25)    PDF (56366KB)(319)      

    The excellent performance of lithium-ion batteries makes them widely used, and it is also one of the core components of electrochemical energy storage power stations. However, accidents such as fires and explosions of energy storage power stations not only bring great economic losses to enterprises, but also have great impact on the development of the entire industry. Therefore, the safety of energy storage power stations cannot be ignored. The mechanism of lithium-ion battery thermal runaway and fire, and focuses on summarizing the runaway and fire early warning technology, such as current domestic and foreign research on battery surface defect detection, voltage, current-ultrasonic early warning system, sound early warning system and gas-acoustic signal early warning system fusion are expounded. The common technical means and advantages and disadvantages of existing lithium-ion battery fire extinguishing are also studied. On this basis, a fire early warning and fire control technology suitable for lithium-ion battery energy storage power stations is proposed, which can effectively improve the safety protection level of energy storage systems, reduce the probability of fire occurrence and property damage after fire occurrence.

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    Application Prospect Analysis of Solid-state Lithium Battery in Vehicle
    LI Weicong, MU Hao, SHEN Henglong, YU Quanqing
    Journal of Electrical Engineering    2022, 17 (4): 88-102.   DOI: 10.11985/2022.04.010
    Abstract540)   HTML60)    PDF (23432KB)(613)      

    Currently, liquid lithium-ion batteries are the most common type of power battery used in new energy vehicles, however, liquid lithium-ion batteries have the problems that electrolyte is prone to leakage and is flammable and explosive. Due to the use of lithium metal as the negative electrode material, the solid-state lithium batteries have a high energy density, and substitute the flammable and explosive liquid electrolyte with solid electrolyte that is neither flammable nor explosive. For these reasons, the solid-state lithium batteries will have wide range application prospects in new energy vehicles and other carriers. The research status of secondary chemical batteries is reviewed, including lead-acid batteries, nickel-based batteries and lithium-ion batteries that are currently widely used. The electrode and electrolyte technology of solid-state lithium-ion batteries are emphatically analyzed. The main electrolyte types of solid-state lithium batteries are oxides, sulfides and polymers. From the perspective of electrolyte types, the relevant technical development level and industrialization progress of solid-state lithium batteries are analyzed. Finally, the characteristics of various carriers are summarized and the application prospects of solid-state lithium batteries in various carriers are reasonably analyzed.

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    Design and Analysis of Lithium-ion Battery Management System Based on Digital Twin
    ZHANG Yuxin, WU Jianhua, ZHENG Linfeng, YE Tao
    Journal of Electrical Engineering    2022, 17 (4): 103-112.   DOI: 10.11985/2022.04.011
    Abstract406)   HTML20)    PDF (55470KB)(256)      

    Scientific and reliable battery management system(BMS) is the key to the safe and efficient application of lithium-ion battery energy storage system. Traditional BMSs have few computing resources and weak data processing ability, which limit the application of intelligent management and control algorithms and high-fidelity models. Characterized by the integration of information and physics, the digital twin brings a new opportunity for the development of BMSs. By establishing a digital twin mapping with the battery physical entity, the development of intelligent battery management system can be realized through the interactive feedback of virtuality and reality and the fusion of mechanism and data. Technical system and functions of digital twin, including data support layer, modeling and computing layer, functional application layer, and human-computer interaction layer are systematically introduced. The key technologies such as model modeling, data and mechanism model fusion in battery digital twin construction are analyzed. On this basis, the design framework of lithium-ion BMS based on the digital twin technology is clarified, aiming to provide the guidance and reference for the research of building intelligent management system.

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    Hybrid Battery Thermal Management System with New Fins Added to Immersion Cooling
    LIU Jiahao, MA Qingwen
    Journal of Electrical Engineering    2022, 17 (4): 113-121.   DOI: 10.11985/2022.04.012
    Abstract294)   HTML22)    PDF (81371KB)(191)      

    In order to effectively control the maximum temperature and temperature difference of the battery, a cooling system is designed, which adopts an innovative spiral fin and combines it with an immersion cooling system. The effects of fin width, spiral number and flow rate of cooling medium on battery heat dissipation are studied. The results show that the temperature of the battery can be effectively reduced by increasing the fin width and spiral number. When the fin width increases from 2 mm to 8 mm, the maximum temperature of the battery decreases by 1.33 ℃. On this basis, the influence of the number of helical turns is further discussed. When the number of helical turns increases from 2 Q to 8 Q, the maximum temperature of the battery decreases by 0.84 ℃. When the fin width is 8 mm and the spiral number is 6 Q, the influence of the flow rate of cooling medium on the temperature of the battery is further studied. When the flow rate is 0.064 m/s, the maximum temperature of the battery is only 28.64 ℃. When the flow rate is more than 0.024 m/s, the temperature difference of the battery increases first and then decreases, both of which are less than 5 ℃. The pressure drop range is 10.96-93.73 Pa. The above findings provide more insights into the cooling system of oil-immersed batteries, and prove that the addition of fins in the system can effectively reduce the maximum temperature difference between the battery and the temperature difference, which provides a reference for enhancing the heat transfer design of the system.

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    Research on an Improved Balanced Topology of Two-circuit Interleaved Parallel Architecture Based on Inductance
    WU Chunling, LIU Zhixuan, MENG Jinhao, XU Xianfeng, ZHENG Kejun
    Journal of Electrical Engineering    2022, 17 (4): 122-132.   DOI: 10.11985/2022.04.013
    Abstract200)   HTML12)    PDF (24713KB)(110)      

    Battery balancing has become a research hotspot in the field of new energy vehicles, in which active balancing is the main research direction. In order to speed up the equalization speed of the battery pack, based on the two-circuit interleaved parallel architecture for battery charge equalization, the equalization unit is equaled through grading control strategy, so as to keep the equalization current at a high level. The equalization model of 8 batteries is built and simulated in Simulink, and the initial SOC value is set to three different cases respectively. The experimental results show that under the first SOC initial value condition, the two-circuit interleaved parallel architecture for battery charge equalization takes 992.57 s to complete the equalization, while the improved equalization topology takes 655.01 s to complete the equalization, and the equalization time is reduced by 34%. Under the second SOC initial value condition, the two-circuit interleaved parallel architecture for battery charge equalization takes 226.52 s to complete the equalization, while the improved equalization topology takes 121.54 s to complete the equalization, and the equalization time is reduced by 46%. Under the third SOC initial value condition, the two-circuit interleaved parallel architecture for battery charge equalization takes 197.24 s to complete the equalization, while the improved equalization topology takes 82.34 s to complete the equalization, and the equalization time is reduced by 58%. It shows that the improved equalization topology can effectively improve the equalization speed.

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    Development of V2G Optimal Frequency Regulation Strategy for Actively Suppressing Battery Aging
    LUO Guoqing, ZHANG Yongzhi, JIA Yuanwei
    Journal of Electrical Engineering    2022, 17 (4): 133-144.   DOI: 10.11985/2022.04.014
    Abstract222)   HTML6)    PDF (69224KB)(187)      

    The continuous expansion of the penetration scale of renewable energy into the power grid has brought great challenges to the frequency stability of the power system. The participation of electric vehicles in V2G(vehicle-to-grid) frequency regulation as a mobile power source can effectively solve this problem. However, the implementation of V2G frequency regulation will aggravate battery aging, which greatly discourages EVs(electric vehicles) owners from participating in V2G services. Therefore, in order to suppress the negative impact of V2G frequency regulation on the battery aging of EVs, an optimization model aiming at suppressing battery aging by introducing a mechanism-based battery aging model is established, and then a new model is developed based on the model predictive control theory. The optimized controller realizes the real-time and efficient control of charging and discharging power of EVs. The effect of different look-ahead time lengths on the performance of the optimized controller is studied, and on this basis, the degree of influence of the developed controller on the aging of the battery under different state of health conditions is discussed. The simulation results show that deterioration of battery performance will exacerbate battery degradation during V2G frequency regulation. In addition, while ensuring a good tracking effect on the frequency regulation power signal, compared with the reference optimization control strategy, the proposed V2G frequency regulation strategy can reduce battery aging by up to 22.34%.

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    Research Progress of Power Battery Cooling Technology
    YU Zhongan, CHEN Keyi, ZHANG Junling, HU Zezhou
    Journal of Electrical Engineering    2022, 17 (4): 145-162.   DOI: 10.11985/2022.04.015
    Abstract513)   HTML18)    PDF (130942KB)(237)      

    The technology of lithium battery has gradually been matured to be applied in various industries, and its products are widely used in grid energy storage, smart home appliances, communication energy storage, new energy vehicles and other fields. The thermal management technology of lithium batteries is an important guarantee for extending the life of the battery pack and operating safety. The thermal management system of lithium battery plays a crucial role in the safety and stability of the battery. An extensive introduction and elaboration of the existing heat dissipation technology are provided. Firstly, the generation, transfer and distribution of battery heat are summarized. Secondly, the working principles and characteristics of four modes of battery heat dissipation system, such as air cooling, liquid cooling, heat pipe and phase change material, are discussed. Finally, the development direction and feasible technology of battery heat dissipation system are proposed based on the development needs of battery heat dissipation system.

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