热变异性对SOC估计算法的影响
Impact of Thermal Variability on SOC Estimation Algorithms
AI总结：
本文旨在研究不同温度下锂离子电池的SOC估算问题，通过比较扩展卡尔曼滤波算法与库仑计数法，发现SOC与温度呈非线性相关，且石墨烯电池在最佳温度下SOC最高。
摘要：
尽管逆变器（inverters）和PV面板等可再生能源组件的效率处于历史最高水平，但电池方面仍存在研究空白。锂离子电池具有很大潜力，但仍有一些问题需要解决，例如热管理。正因如此，电池管理系统达成了其目标。为了使电池管理系统（BMS）正常运行，它必须对所有相关参数进行准确估计，包括健康状态（state of health）、荷电状态（state of charge, SOC）和温度；然而，就本文而言，我们仅讨论SOC。本文的目标是在不同温度下估算锂离子电池的SOC。通过在各种温度下将扩展卡尔曼滤波（Extended Kalam filter）算法与库仑计数（coulomb counting）进行比较，得出了这一详尽研究的结论。通过广泛的分析以及SOC与温度之间的相关性，发现在最佳温度下运行时，石墨烯电池具有最高的SOC，且该相关性并非线性的。
英文摘要：
While the efficiency of renewable energy components like inverters and PV panels is at an all-time high, there are still research gaps for batteries. Lithium-ion batteries have a lot of potential, but there are still some problems that need fixing, such as thermal management. Because of this, the battery management system accomplishes its goal. In order for a battery management system (BMS) to function properly, it must make accurate estimates of all relevant parameters, including state of health, state of charge, and temperature; however, for the purposes of this article, we will only discuss SOC. The goal of this article is to estimate the SOC of a lithium-ion battery at different temperatures. Comparing the Extended Kalam filter algorithm to coulomb counting at various temperatures concludes this exhaustive investigation. The graphene battery has the highest SOC when operated at the optimal temperature, as determined by extensive analysis and correlation between SOC and temperature is not linear
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
