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为了研究三(2,2,2-三氟乙基)亚磷酸酯(TTFP)作为锂离子蓄电池电解液阻燃剂的阻燃效果及其对电池电性能的影响,采用自熄时间(SET)法测试了TTFP对1mol/LLiPF6 EC/DMC(1∶1w/w)锂离子蓄电池电解液的阻燃性能;采用测电导率的方法研究了TTFP对电解液电导率的影响,并分别以其作为Li/LiCoO2、Li/MCMB和MCMB/LiCoO2电池的电解液阻燃添加剂分析了TTFP在MCMB负极、LiCoO2正极上的电化学稳定性及其对电池电性能的影响。研究结果表明,TTFP可显著降低电池电解液的可燃性,电解液的可燃性随着电解液中TTFP含量的增大而迅速降低,电解液的电导率也降低,当TTFP浓度为20%(质量百分数,以下同)时,电解液的SET几乎为零,电解液电导率的下降幅度与电解液中TTFP的浓度呈线性关系。TTFP在MCMB负极材料和LiCoO2正极材料上的电化学稳定性良好。TTFP不但对MCMB/LiCoO2电池的电化学性能的影响很小,而且能够明显提高其安全性能。研究结果对于开发锂离子蓄电池电解液新型阻燃剂和提高锂离子蓄电池的安全性能具有一定的理论意义和实用价值。 相似文献
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锂离子蓄电池正极材料锂钒氧化物研究进展 总被引:9,自引:1,他引:8
近年来 ,锂离子蓄电池因其优异的特性而受到化学电源界的极大重视。有关锂离子蓄电池正极材料的研究大部分集中在过渡金属嵌锂氧化合物上。本文对正极材料应具备的结构、性质及目前研究较多的层状化合物LiCoO2 、LiNiO2和尖晶石型化合物LiMn2 O4 类正极材料作了简单叙述 ,重点对嵌锂氧化钒系列化合物LixVO2 、LixV2 O4 、Li1 xV3 O8和LiNiVO4 等正极材料的制备方法、结构及电化学性能之间的关系及近期研究现状进行了阐述。随着新技术、新方法的出现 ,大容量的层状化合物Li1 xV3 O8及高电压反尖晶石型LiNiVO4 有望成为新一代性能优良的锂离子蓄电池正极材料 相似文献
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Investigation of Degradation and Voltage Response of Lithium‐Ion Battery Based on Impedance and Electromotive Force Measurement 下载免费PDF全文
Detailed information on degradation process of lithium‐ion batteries is essential to manage the batteries throughout their lifetime because degradation of the batteries associated with long‐term usage is inevitable. To evaluate degradation degree of the batteries in detail, it is necessary to separately make clear the decrease in output power due to the increase in AC impedance and the decrease in battery capacity caused by the decrease in electromotive force. In this study, therefore, the authors at first propose a new evaluation method of detailed degradation process of lithium‐ion batteries through measurements of AC impedance and electromotive force of degraded batteries. It is made clear that this proposed evaluation method can provide simple but detailed diagnosis of battery degradation degree, indicating usefulness of the proposed method. Next, as a first step for performance simulation of degraded lithium‐ion batteries, the authors make clear that the already developed simulation method of battery transient voltage response, validity of which has been confirmed only for new batteries, is also applicable to degraded batteries. 相似文献
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锂离子电池容量快速预测的新方法 总被引:2,自引:0,他引:2
提出了一种通过部分放电来快速预测锂离子电池放电容量的方法.在分析了锂离子电池开路电压和内阻与电池容量关系的基础上,将人工神经网络应用到锂离子电池的容量预测和模型建立中.实验结果表明这种方法能够满足电池容量预测的精度要求. 相似文献
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