Analysis on the capacity degradation mechanism of a series lithium-ion power battery pack based on inconsistency of capacity

The lithium-ion battery has been widely used as an energy source. Charge rate, discharge rate, and operating tem- perature are very important factors for the capacity degradations of power batteries and battery packs. Firstly, in this paper we make use of an accelerated life test and a statistical analysis method to establish the capacity accelerated degradation model under three constant stress parameters according to the degradation data, which are charge rate, discharge rate, and operating temperature, and then we propose a capacity degradation model according to the current residual capacity of a Li-ion cell under dynamic stress parameters. Secondly, we analyze the charge and discharge process of a series power battery pack and interpret the correlation between the capacity degradations of the battery pack and its charge/discharge rate. According to this cycling condition, we establish a capacity degradation model of a series power battery pack under inconsistent capacity of cells, and analyze the degradation mechanism with capacity variance and operating temperature difference. The comparative analysis of test results shows that the inconsistent operating temperatures of cells in the series power battery pack are the main cause of its degradation; when the difference between inconsistent temperatures is narrowed by 5 ℃, the cycle life can be improved by more than 50%. Therefore, it effectively improves the cycle life of the series battery pack to reasonably assemble the batteries according to their capacities and to narrow the differences in operating temperature among cells.

Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17 W during charging were designed and simulated in the range of 0-12000 s using COMSOL Multiphysics software. This study efficiently employs the Taguchi optimization technique to develop lithium-ion batteries capable of storing a predetermined average electric power during the charging phase. Therefore, this method enables the battery to achieve complete charging within a specific timeframe tailored to a specificapplication. The implementation of this method can save costs, time, and materials compared with other alternative methods, such as the trial-and-error approach.

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

Strategies for Rational Design of High-Power Lithium-ion Batteries

Lithium-ion batteries(LIBs)have shown considerable promise as an energy storage system due to their high conversion efficiency,size options(from coin cell to grid storage),and free of gaseous exhaust.For LIBs,power density and energy density are two of the most important parameters for their practical use,and the power density is the key factor for applications such as fast-charging electric vehicles,high-power portable tools,and power grid stabilization.A high rate of performance is also required for devices that store electrical energy from seasonal or irregular energy sources,such as wind energy and wave energy.Significant efforts have been made over the last several years to improve the power density of LIBs through anodes,cathodes,and electrolytes,and much progress has been made.To provide a comprehensive picture of these recent achievements,this review discusses the progress made in high-power LIBs from 2013 to the present,including general and fundamental principles of high-power LIBs,challenges facing LIB development today,and an outlook for future LIB development.

Thermal Management of Air-Cooling Lithium-Ion Battery Pack

Lithium-ion battery packs are made by many batteries, and the difficulty in heat transfer can cause many safety issues. It is important to evaluate thermal performance of a battery pack in designing process. Here, a multiscale method combining a pseudo-two-dimensional model of individual battery and three-dimensional computational fluid dynamics is employed to describe heat generation and transfer in a battery pack. The effect of battery arrangement on the thermal performance of battery packs is investigated. We discuss the air-cooling effect of the pack with four battery arrangements which include one square arrangement, one stagger arrangement and two trapezoid arrangements. In addition, the air-cooling strategy is studied by observing temperature distribution of the battery pack. It is found that the square arrangement is the structure with the best air-cooling effect, and the cooling effect is best when the cold air inlet is at the top of the battery pack. We hope that this work can provide theoretical guidance for thermal management of lithium-ion battery packs.

Three-dimensionalization via control of laser-structuring parameters for high energy and high power lithium-ion battery under various operating con ditions

Laser-structuring is an effective method to promote ion diffusion and improve the performance of lithium-ion battery(LIB)electrodes.In this work,the effects of laser structuring parameters(groove pitch and depth)on the fundamental characteristics of LIB electrode,such as interfacial area,internal resistances,material loss and electrochemical performance,are investigated,LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) cathodes were structured by a femtosecond laser by varying groove depth and pitch,which resulted in a material loss of 5%-14%and an increase of 140%-260%in the in terfacial area between electrode surface and electrolyte.It is shown that the importance of groove depth and pitch on the electrochemical performance(specific capacity and areal discharge capacity)of laser-structured electrode varies with current rates.Groove pitch is more im porta nt at low current rate but groove depth is at high curre nt rate.From the mapping of lithium concentration within the electrodes of varying groove depth and pitch by laser-induced breakdown spectroscopy,it is verified that the groove functions as a diffusion path for lithium ions.The ionic,electronic,and charge transfer resistances measured with symmetric and half cells showed that these internal resistances are differently affected by laser structuring parameters and the changes in porosity,ionic diffusion and electronic pathways.It is demonstrated that the laser structuring parameters for maximum electrode performance and minimum capacity loss should be determined in consideration of the main operating conditions of LIBs.

动力电池PACK智能制造复合型人才定向培养研究

分析了新能源汽车核心能量源——动力电池PACK制造的现状,根据当前动力电池PACK智能制造人才培养的现状,对动力电池PACK智能制造复合型人才定向培养路径进行了研究,为其他跨专业领域的复合型人才培养提供启示。

Applications of Lithium-Ion Batteries in Grid-Scale Energy Storage Systems

In the electrical energy transformation process,the grid-level energy storage system plays an essential role in balancing power generation and utilization.Batteries have considerable potential for application to grid-level energy storage systems because of their rapid response,modularization,and flexible installation.Among several battery technologies,lithium-ion batteries(LIBs)exhibit high energy efficiency,long cycle life,and relatively high energy density.In this perspective,the properties of LIBs,including their operation mechanism,battery design and construction,and advantages and disadvantages,have been analyzed in detail.Moreover,the performance of LIBs applied to grid-level energy storage systems is analyzed in terms of the following grid services:(1)frequency regulation;(2)peak shifting;(3)integration with renewable energy sources;and(4)power management.In addition,the challenges encountered in the application of LIBs are discussed and possible research directions aimed at overcoming these challenges are proposed to provide insight into the development of grid-level energy storage systems.

Unlock the potential of Li4Ti5O(12) for high-voltage/long-cycling-life and high-safety batteries: Dual-ion architecture superior to lithium-ion storage

Li4Ti5O(12)(LTO)has drawn great attention due to its safety and stability in lithium-ion batteries(LIBs).However,high potential plateau at 1.5 V vs.Li reduces the cell voltage,leading to a limited use of LTO.Dual-ion batteries(DIBs)can achieve high working voltage due to high intercalation potential of cathode.Herein,we propose a DIB configuration in which LTO is used as anode and the working voltage was 3.5 V.This DIB achieves a maximum specific energy of 140 Wh/kg at a specific power of 35 W/kg,and the specific power of 2933 W/kg can be obtained with a remaining specific energy of 11 Wh/kg.Traditional LIB material shows greatly improved properties in the DIB configuration.Thus,reversing its disadvantage leads to upgraded performance of batteries.Our configuration has also widened the horizon of materials for DIBs.

Enhanced Roles of Carbon Architectures in High-Performance Lithium-Ion Batteries

Lithium?ion batteries(LIBs), which are high?energy?density and low?safety?risk secondary batteries, are underpinned to the rise in electrochemical energy storage devices that satisfy the urgent demands of the global energy storage market. With the aim of achiev?ing high energy density and fast?charging performance, the exploitation of simple and low?cost approaches for the production of high capacity, high density, high mass loading, and kinetically ion?accessible electrodes that maximize charge storage and transport in LIBs, is a critical need. Toward the construction of high?performance electrodes, carbons are promisingly used in the enhanced roles of active materials, electrochemi?cal reaction frameworks for high?capacity noncarbons, and lightweight current collectors. Here, we review recent advances in the carbon engi?neering of electrodes for excellent electrochemical performance and structural stability, which is enabled by assembled carbon architectures that guarantee su cient charge delivery and volume fluctuation bu ering inside the electrode during cycling. Some specific feasible assem?bly methods, synergism between structural design components of carbon assemblies, and electrochemical performance enhancement are highlighted. The precise design of carbon cages by the assembly of graphene units is potentially useful for the controlled preparation of high?capacity carbon?caged noncarbon anodes with volumetric capacities over 2100 mAh cm^(-3). Finally, insights are given on the prospects and challenges for designing carbon architectures for practical LIBs that simultaneously provide high energy densities(both gravimetric and volumetric) and high rate performance.

New Composite Equalization Strategy for Lithium Battery Packs

In order to improve the working efficiency of the power battery pack and prolong the service life, there is a problem of inconsistency among the individual cells. Based on the centralized equalization structure of the multi-output winding transformer, a three-stage hybrid equalization control strategy is designed for equalization. The equalization scheme realizes that the high voltage single battery transfers the energy to the low voltage battery cell during the charging of the battery pack, improving not only charging efficiency and energy use loss, but also the high voltage battery transferring the power to the low voltage battery cell when the pressure difference is greater than 10 mv during the discharge. Between 5 mv and 10 mv, it performs passive equalization, reducing the output fluctuation of the power battery pack and achieving the balance purpose. During the standing time, the maximum active balancing operation within the battery pack is performed in order to achieve intra-group optimum consistency. It is proved by experiments that the equalization control method can realize the quick and effective equalization in the battery pack, and the energy balance of each single battery.

A Passive Circuit of Battery Management Without Power Supply Designed for Commercial Space

Recently for lithium-ion batteries in satellites and spaceships,the Battery Management System(BMS)has become essential to enhance life and guarantee safety.However,most of the balance management circuits need complicated cell sensing and duty control modules.Such circuits are too costly to commercialize.In order to reduce the cost,weight,volume and energy consumption of BMS,this paper proposes a new battery management circuit.The designed circuit is passive and small in size.The charge voltage is regulated by increasing the bypass current through an adjustable reference chip.Experimental results show that the bypass current increases linearly if the charging voltage is in the range of 4.05 V to 4.2 V Also after several charge-discharge cycles,the differences between batteries visibly decrease.The proposed circuit is small in size,low in power con sumption and econo mical,making it ideal for commercial space.

动力电池模组和PACK装配线的工艺技术应用研究

随着电动自行车共享换电的市场占有率越来越大,各大新能源电池生产企业纷纷建设了动力电池模组线和PACK装配线。为了提高共享换电的动力电池模组和PACK装配组装的工艺水平和生产效率,笔者以某新能源电池包模组和PACK装配线项目为例,介绍了模组和装配线的结构组成,阐述了模组和装配线工艺技术的应用,并对各模块应用做了简要的概述,为相似项目的解决方案提供了参考和依据。

方形动力电池组多风道热管理研究

针对传统风冷式电池热管理系统的散热能力不足、电池组整体均温性较差的问题,提出了一种分层多风道风冷式冷却方法,采用挡风板将单风道系统分割为多风道系统,建立了多风道动力电池热管理系统。通过实验方法测量了钴酸锂电池的放电温升以及电阻与放电深度的关系,并针对多风道电池热管理模型进行了数值计算研究。结果表明,三风道U型热管理系统的散热效果优于其他热管理系统。当冷却空气流速为0.5 m/s时,三风道U型电池热管理系统的最高温度降低8.4 K,最大温差减小12.4 K,电池组均温性提高。当冷却空气流速为5 m/s时,三风道U型电池热管理系统的最高温度及温差均可以降低5.3 K。使电池组的最高温度和最大温差保持在合理范围内。

橡胶黏弹性对电池包密封件密封性能的影响

动力电池包橡胶密封件密封性是关系到新能源汽车动力系统安全的关键因素之一。橡胶材料是一种典型的黏弹性材料,决定了橡胶密封件在使用过程中会发生应力松弛,进而有可能会导致密封失效。基于橡胶材料单轴压缩以及应力松弛实验,与ABAQUS数值模拟相结合,确定橡胶材料超弹-黏弹本构模型及相关参数,对电池包密封件进行有限元分析。分析结果表明,接触压力和Von-Mises应力随着时间增加出现明显的下降;橡胶材料的黏弹特性对密封性能有显著的影响。

无模组动力电池包振动特性研究

为探讨某车用无模组动力电池包的振动特性,建立了不同电芯尺寸和电芯布置的两种电池包有限元模型;基于所构建的电池包有限元模型,在3种极限工况下,对两种电池包进行静力学分析与模态分析;基于分析结果对电池包进行改进设计。研究表明,动力电池包中的电芯尺寸及电芯布置方式对电池包的振动特性具有显著影响;电池包1.0的上盖刚度不足,其最大应力和上盖最大变形量都较大,1阶约束模态固有频率低于平整路面的最大激振频率,易导致动力电池包共振;动力电池包2.0具有较好的静力学特性,且其1阶固有频率高于平整路面的最大激振频率,可避免路面激励导致的电池包共振;上盖改进后动力电池包1.0的静力学特性和振动特性均能满足要求;增加电池包的约束范围可以提高其1阶固有频率。

基于机器视觉的电池PACK装配线装配工艺研究

为了提高动力电池PACK自动化行业的产品质量和生产效率,通过机器视觉识别模块和壳体组件来组装螺栓孔的位置和状态,机器人执行模块抓取和螺栓拧紧,并在指定的姿势下实现模块壳体组装和螺栓紧固工作。