熔融盐法合成球形锂离子电池正极材料LiNi_(0.8)Co_(0.2)O_2

采用热分析法对不同组成的LiOH-LiNO3二元体系进行研究,绘制了具有最低共熔点的该二元体系T-x相图,该体系的最低共熔点为175.7℃.利用低共熔混合物LiNO3-LiOH为锂盐,与前驱体球形Ni0.8Co0.2(OH)2混合烧结制备出了球形锂离子电池正极材料LiNi0.8Co0.2O2.探讨了Li/(Ni+Co)摩尔比、合成温度、合成时间等因素对产品的影响.X射线衍射分析表明合成的材料具有规整的层状NaFeO2结构,SEM表明所得材料为球形.充放电测试表明在3.0~4.3的电压范围内,首次放电比容量可达170 mAh.g-1,充放电效率为95.5%.结果表明采用该工艺可以制备出电化学性能良好的LiNi0.8Co0.2O2正极材料.     

<Emphasis Type="Italic">Ab initio</Emphasis> study of the effects of Ag/Mn doping on the electronic structure of LiFePO<Subscript>4</Subscript>

Based on density functional theory （DFT） of the first-principle for the cathode materials of lithium ion battery, the electronic structures of Li（Fe1-x）PO4 （Me = Ag/Mn, x = 0-0.40） are calculated by plane wave pseudo-potential method using Cambridge serial total energy package （CASTEP） program. The calculated results show that the Fermi level of mixed atoms Fe1-xAgx moves into its conduction bands （CBs） due to the Ag doping. The Li（Fe1-xAgx）PO4 system displays the periodic direct semiconductor characteristic with the increase of Ag-doped concentration. However, for Fe1-xMnx mixed atoms, the Fermi level is pined at the bottom of conduction bands （CBs）, which is ascribed to the interaction between Mn（3d） electrons and Fe（4s） electrons. The intensity of the partial density of states （PDOS） near the bottom of CBs becomes stronger with the increase of Mn-doped concentration. The Fermi energy of the Li（Fe1-xMnx）PO4 reaches maximum at x = 0.25, which is consistent with the experimental value of x = 0.20. The whole conduction property of Mn-doped LiFePO4 is superior to that of Ag-doped LiFePO4 cathode material, but the structural stability is reverse.     

无粘结剂V2O5纳米线/CNT电极的制备及电化学性能

把羧化的碳纳米管与水热法合成的V2O5纳米线混合超声处理后,直接真空抽滤得到无粘结剂V2O5纳米线/CNT纸.对加入不同含量的碳纳米管的样品,综合考虑比容量和循环性能,其中m（V2O5）∶m（CNT）=1∶1样品的电化学性能最好.当电流密度为30 mA·g-1时,首次放电比容量能达到290.6 mAh·g-1,接近于V2O5的理论比容量,10次循环以后为265.4mAh·g-1,容量保持率为91.32％.当电流密度为600 mA·g-1,首次放电比容量71.2 mAh·g-1,第10次循环为62.5 mA·g-1,容量保持率可达87.8％.     

Three-dimensionally macroporous graphene-supported Fe3O4 composite as anode material for Li-ion batteries with long cycling life and ultrahigh rate capability

Fe3O4 is an attractive conversion reaction- based anode material with high theoretical capacity （928 mA h g^-1）. However, the poor cycling and rate per- formance hinder its applications in Li-ion batteries. In this work, we report an effective strategy to synthesize three- dimensionally macroporous graphene-supported Fe3O4 hybrid composite. Benefiting from advantage of the special structure, the hybrid composite exhibits excellent Li^＋ stor- age performance, delivering a high reversible capacity of 980 mA h g^-1at the current density of 4 A g^-1 even after 470 cycles and ultrahigh rate capability （293 mA h g^- 1 even at current density of 20 A g^-1）.     

Facile hydrothermal and sol-gel synthesis of novel Sn-Co/C composite as superior anodes for Li-ion batteries

As an anode material in lithium ion battery, the Sn-Co/C composite electrode materials have been successfully synthesized by hydrothermal and solgel methods, respectively. The resultant composites were mainly composed of Snbased oxides, nanometer Sn-Co alloy and carbon. Carbon and Co, acting as buffer materials, can accommodate to the large volume change of active Sn during the discharge-charge process, thus improving the cycling stability. Although charge/discharge curves revealed the excellent cycle performance for samples synthesized by both methods, composites obtained by the sol-gel showed a better dispersion effect of nanoparticles on the carbon matrix and possessed much more improved stable capacity with 624.9 mAh g-1 over 100 cycles and that by hydrothermal method only exhibited ～299.3 mAh g-1. Therefore, the Sn-Co/C composites obtained by solgel synthesis method could be a perfect candidate for anode material of Liion storage battery.     

废旧锂离子电池正极材料钴酸锂层状结构的活化研究

为了实现废旧锂离子电池材料的再利用,本文采用热重-差热分析法对废旧锂离子电池正板材料钴酸锂做了分析研究.根据它的热稳定,通过在400℃和600℃下保温,分别将粘结剂PVDF和碳粉除去,使其活化再生,并对其活化机理进行了讨论.通过XRD表征及分析,结果表明:失效的钴酸锂正极材料经过焙烧活化后,纯度较高,结晶度提高,晶体结构恢复成为规整层状结构,离子排列有序,有利于锂离子在晶体中有效嵌入和脱出.     

基于CDPSO-EBRB的锂离子电池健康状态估计

锂离子电池在武器系统中广泛应用,对其健康状态的评估对于保证武器系统作战效能具有重要意义.但构建锂离子电池退化过程的物理模型难度较大,同时由于数据的不确定性与不完整性影响,使用纯数据驱动的方式也不能准确描述此过程.扩展置信规则库模型结合了知识结构和证据推理的特性,可对数据的不确定性与不完整性进行定量描述,但原始模型的参数选择对其性能影响较大,针对以上问题,提出了一种中心离散粒子群算法(CDPSO)优化的扩展置信规则库(EBRB)模型,并将该模型应用于锂离子电池的健康状态评估中,使用数据驱动方式将规则集转化为规则,采用CDPSO对初始参数进行训练,最后使用测试数据集来测试模型的有效性,通过与传统的方法进行比对,验证了所提出方法的有效性.     

基于树莓派的房车电池管理系统研制

为解决目前房车使用中存在的电池、 用电器的管理问题, 设计了一种以 Raspberry Pi 3B+为主控制器的房车电源管理系统, 该系统包括车载蓄电池监测模块和用电器监测模块。 电池监测模块利用电池专用监测芯片DS2438, 对电池组温度、 电压等车载蓄电池信息进行检测并统一管理, 完成单体蓄电池状态显示和故障报警提示; 用电器监测模块利用 RN8209 芯片检测房车用电器的电功率并及时通过主控制器对电器进行智能化管理。通过测试表明, 系统能准确测定电池和用电器的相关信息, 具有一定的实用性。 同时针对传统的充放电状态(SOC: State Of Charge)预测困难的问题, 提出了一种修正安时积分法,充分考虑了电池在实际使用中存在容量差的问题, 经 Matlab 仿真结果表明该方法有较高的估算精度, 可用于 SOC 估算策略。     

电化学阻抗技术在高容量锂离子电池中的研究进展

The world’s energy system is changing dramatically. Li-ion battery, as a powerful and highly effective energy storage technique, is crucial to the new energy revolution for its continuously expanding application in electric vehicles and grids. Over the entire lifetime of these power batteries, it is essential to monitor their state of health not only for the predicted mileage and safety management of the running electric vehicles, but also for an “end-of-life” evaluation for their repurpose. Electrochemical impedance spectroscopy (EIS) has been widely used to diagnose the health state of batteries quickly and nondestructively. In this review, we have outlined the working principles of several electrochemical impedance techniques and further evaluated their application prospects to achieve the goal of nondestructive testing of battery health. EIS can scientifically and reasonably perform real-time monitoring and evaluation of electric vehicle power batteries in the future and play an important role in vehicle safety and battery gradient utilization.     

动力锂电池荷电状态的联合估计与实验研究

针对动力锂电池常用的荷电状态(SOC)估计算法存在的扩展卡尔曼滤波法精度低、无迹卡尔曼滤波法收敛速度慢等问题,在动力锂电池的Randles等效模型的基础上,通过脉冲放电实验对模型参数进行辨识;并设计了一种基于迭代扩展卡尔曼滤波(IEKF)与无迹卡尔曼滤波(UKF)联合估计的SOC估计法。在电池实验平台上设计模拟工况实验,实验分析表明:该算法的SOC初值修正速度快于EKF和UKF,计算量比UKF小,且稳态误差不超过1.5%,相对扩展卡尔曼滤波(EKF)提高了40%,是一个收敛快、计算量少、静差小的迭代估计算法。