Data-driven analysis on thermal effects and temperature changes of lithium-ion battery
Data-driven analysis on thermal effects and temperature changes of lithium-ion battery
复制标题
锂离子电池热效应和温度变化的数据驱动分析
DOI：
10.1016/j.jpowsour.2020.228983
复制
发表时间：
2021-01
影响因子：
9.2
通讯作者：
Sha Junwei
中科院分区：
文献类型：
--
作者：
Zhu Shan;He Chunnian;Zhao Naiqin;Sha Junwei
热效应引起的温度变化对锂离子电池的性能影响很大。为了辅助电池热管理，需要弄清热源，并预测电池温度，以便对异常情况进行预警。在此，本工作展示了一系列数据驱动的方法来分析电池热效应。我们从时间序列数据的角度，对电池运行过程中的温度变化进行分解，区分出可逆热量和不可逆热量。验证了可逆热和充放电电流之间的强相关性。同时，发现不可逆热对电池寿命后期的整体温度有严重的影响。此外，采用长短期记忆（LSTM）模型预测电池温度变化。依靠这种机器学习方法，我们可以准确地计算出电池在某一时刻的温度值。此外，利用每个周期的平均温度作为训练数据，可以有效地预测电池在很长一段时间内的温度波动，作为电池温度预测。
Temperature changes caused by thermal effects greatly impact the performance of lithium-ion batteries. It is necessary to figure out the source of heat to assist battery thermal management, and to predict the battery temperature in order to warn the abnormal situation. Herein, this work demonstrate a series of data-driven approaches to analyze the battery thermal effects. From the perspective of time series data, we decompose the temperature change during the battery operation to distinguish the reversible heat and the irreversible heat. The strong correlation between reversible heat and charge/discharge current is verified. Meanwhile, it is found that the irreversible heat have a severe effect on the overall temperature in the later period of battery lifespan. Besides, the long short-term memory (LSTM) model is applied to predict the battery temperature changes. Relying on this machine learning method, we can accurately compute the temperature value of the battery at a certain time. Moreover, using the average temperature of each cycle as the training data, the temperature fluctuation of the battery can be efficiently predicted in a long period, which can serve as the battery temperature prognostic.
登录
查看更多内容
电动汽车锂离子电池的数据驱动安全范围
影响因子：
39.8
作者：
Li, Wei;Zhu, Juner;Wierzbicki, Tomasz
通讯作者：
Wierzbicki, Tomasz
轻微过充循环引起老化商用18650锂离子电池的老化机制和热稳定性
影响因子：
9.2
作者：
Liu, Jialong;Duan, Qiangling;Wang, Qingsong
通讯作者：
Wang, Qingsong
影响因子：
4.4
作者：
Jianbo Zhang;Jun Huang;Zhe Li;Bin Wu;Zhihua Nie;Ying Sun;Fuqiang An;Ningning Wu
通讯作者：
Jianbo Zhang;Jun Huang;Zhe Li;Bin Wu;Zhihua Nie;Ying Sun;Fuqiang An;Ningning Wu
使用 LSTM 和 UKF 估计锂离子电池的充电状态
影响因子：
9
作者：
Fangfang Yang;Shaohui Zhang;Weihua Li;Qiang Miao
通讯作者：
Qiang Miao
影响因子：
9.2
作者：
Johannes Sturm;Alexander Rheinfeld;I. Zilberman;F. Spingler;Stephan Kosch;Fabian Frie;A. Jossen
通讯作者：
Johannes Sturm;Alexander Rheinfeld;I. Zilberman;F. Spingler;Stephan Kosch;Fabian Frie;A. Jossen