A temperature monitoring method of supercapacitor module based on a small number of temperature sensors

doi:10.19799/j.cnki.2095-4239.2022.0234

Abstract

Abstract:

Supercapacitors' service performance and the aging process are closely related to temperature. Excessive temperature will cause thermal runaway and affect the operation safety of supercapacitors. Therefore, it is imperative to monitor the temperature of each unit in the supercapacitor system. However, the conventional sensor monitoring scheme has high costs and complex installation issues. With a commercial supercapacitor module as the research object, this paper suggests a method to estimate the temperature of residual cells in the module according to the temperature of a small number of cells, which can reduce the use of sensors. It is found that there is a strong correlation between cell temperatures by analyzing the temperature data of each cell in the module under various cooling wind speeds with multi-stage constant current charging and discharging. A module temperature estimation model according to BP neural network is established. The best model architecture is determined by comparing the pre-training effects of different cell combinations as input and removing current, voltage, wind speed, and other factors. By comparing the estimation impacts of various algorithms on the same data set, Levenberg Marquardt is chosen as the training algorithm of the model. The model can estimate the temperature of the remaining 9 cells through the temperature of 3 cells. The test data set's average absolute error is 0.06 ℃, and the maximum absolute error is within 0.3 ℃, which meets the energy storage system's requirements for temperature monitoring precision. The suggested method requires simple test conditions and can reduce the purchase cost of temperature sensors, which provides a new method for temperature monitoring of supercapacitor thermal management systems.

Key words: supercapacitor module, temperature estimation, neural network, temperature monitoring

CLC Number:

TM 911

Li WEI, Xuelin HUANG, Wanting ZHANG, Xintong BAI. A temperature monitoring method of supercapacitor module based on a small number of temperature sensors[J]. Energy Storage Science and Technology, 2022, 11(11): 3631-3640.

Figures/Tables 13

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References 28

1	曹广华, 高佶, 高洁, 等. 超级电容的原理及应用[J]. 自动化技术与应用, 2016, 35(5): 131-135.
	CAO G H, GAO J, GAO J, et al. Principle and application of super capacitor[J]. Techniques of Automation and Applications, 2016, 35(5): 131-135.
2	韩冬林, 徐琤颖, 陈愚. 基于超级电容的后备式UPS系统设计[J]. 电源技术, 2016, 40(10): 2036-2039.
	HAN D L, XU Z Y, CHEN Y. Design of backup UPS system based on super capacitors[J]. Chinese Journal of Power Sources, 2016, 40(10): 2036-2039.
3	王民华, 李凤霞. 混合储能平抑微电网功率波动控制策略研究[J]. 电力电容器与无功补偿, 2020, 41(4): 215-220.
	WANG M H, LI F X. Study on control strategy of hybrid energy storage used in stabilization power fluctuation of microgrid[J]. Power Capacitor ＆ Reactive Power Compensation, 2020, 41(4): 215-220.
4	华黎, 杨恩东, 梁全顺, 等. 车用超级电容器动力系统在世博公交的应用及推广[C]. 中国智能交通协会, 2011: 9.
	HUA L, YANG E D, LIANG Q S, et al. Application and promotion of automotive supercapacitor power system in expo bus[C]. China Intelligent Transportation Association, 2011: 9.
5	芮学宝, 朱刚阳. 电容储能型再生电能吸收装置在地铁系统中的应用[J]. 电气化铁道, 2020, 31(S1): 195-198.
	RUI X B, ZHU G Y. Application of capacitive energy storage regenerative energy absorption device in subway system[J]. Electric Railway, 2020, 31(S1): 195-198.
6	陈朝晖, 张伟先, 黄钰强. 48V超级电容模组的研究[J]. 技术与市场, 2015, 22(5): 68-69.
	CHEN C H, ZHANG W X, HUANG Y Q. Research on 48V supercapacitor module[J]. Technology and Market, 2015, 22(5): 68-69.
7	陈书礼, 韩金磊, 荣常如, 等. 温度和电压对超级电容器单体内阻影响的研究[J]. 汽车技术, 2016(3): 40-44.
	CHEN S L, HAN J L, RONG C R, et al. Influence of temperature and voltage on internal resistance of supercapacitor[J]. Automobile Technology, 2016(3): 40-44.
8	KÖTZ R, HAHN M, GALLAY R. Temperature behavior and impedance fundamentals of supercapacitors[J]. Journal of Power Sources, 2006, 154(2): 550-555.
9	ZHOU Y T, WANG Y N, WANG K, et al. Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors[J]. Applied Energy, 2020, 260: doi: 10.1016/j.apenergy.2019.114169.
10	FLETCHER S I, SILLARS F B, CARTER R C, et al. The effects of temperature on the performance of electrochemical double layer capacitors[J]. Journal of Power Sources, 2010, 195(21): 7484-7488.
11	顾帅, 韦莉, 张逸成, 等. 超级电容器不一致性研究现状及展望[J]. 中国电机工程学报, 2015, 35(11): 2862-2869.
	GU S, WEI L, ZHANG Y C, et al. Review of nonuniformity research and analysis on supercapacitor[J]. Proceedings of the CSEE, 2015, 35(11): 2862-2869.
12	Q31/0115000918C005—2018, 船用超级电容器系统[S]. 上海: 上海奥威科技开发有限公司, 2018.
	Q31/0115000918C005—2018, Marine supercapacitor system[S]. Shanghai: Shanghai Aowei Technology Development Co., LTD, 2018.
13	VOICU I, LOUAHLIA H, GUALOUS H, et al. Thermal management and forced air-cooling of supercapacitors stack[J]. Applied Thermal Engineering, 2015, 85: 89-99.
14	FRIVALDSKY M, CUNTALA J, SPANIK P. Simple and accurate thermal simulation model of supercapacitor suitable for development of module solutions[J]. International Journal of Thermal Sciences, 2014, 84: 34-47.
15	戴朝华, 傅雪婷, 杜云, 等. 不同空间结构的有轨电车超级电容热行为[J]. 西南交通大学学报, 2020, 55(5): 920-927, 900.
	DAI C H, FU X T, DU Y, et al. Supercapacitor thermal behavior of trams with different spatial structures[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 920-927, 900.
16	郭文强, 董瑶, 李清华, 等. PSO-BP神经网络在开关柜设备温度预测中的应用[J]. 陕西科技大学学报, 2020, 38(1): 149-153.
	GUO W Q, DONG Y, LI Q H, et al. Application of PSO-BP neural network in temperature prediction for switchgear equipment[J]. Journal of Shaanxi University of Science ＆ Technology, 2020, 38(1): 149-153.
17	赵明珠, 王丹, 方杰, 等. 基于LSTM神经网络的地铁车站温度预测[J]. 北京交通大学学报, 2020, 44(4): 94-101.
	ZHAO M Z, WANG D, FANG J, et al. Prediction of subway station temperature based on LSTM neural network[J]. Journal of Beijing Jiaotong University, 2020, 44(4): 94-101.
18	贾永会, 杜建桥, 汪潮洋, 等. 基于BP神经网络的燃煤锅炉温度分布预测[J]. 热能动力工程, 2020, 35(7): 130-138.
	JIA Y H, DU J Q, WANG C Y, et al. Prediction model of temperature distribution in combustion zone of coal-fired boiler based on BP neural network[J]. Journal of Engineering for Thermal Energy and Power, 2020, 35(7): 130-138.
19	FANG K Z, MU D B, CHEN S, et al. A prediction model based on artificial neural network for surface temperature simulation of nickel-metal hydride battery during charging[J]. Journal of Power Sources, 2012, 208: 378-382.
20	LIU K L, LI K, DENG J. A novel hybrid data-driven method for li-ion battery internal temperature estimation[C]//2016 UKACC 11th International Conference on Control (CONTROL). Belfast, UK. IEEE : 1-6.
21	HASAN M M, POURMOUSAVI S A, JAHANBANI ARDAKANI A, et al. A data-driven approach to estimate battery cell temperature using a nonlinear autoregressive exogenous neural network model[J]. Journal of Energy Storage, 2020, 32: doi:10.1016/j.est.2020.101879.
22	KIM T J, YOUN B D, KIM H J. Online-applicable temperature prediction model for EV battery pack thermal management[C]. ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. 2013.
23	BO Z, LI H W, YANG H C, et al. Combinatorial atomistic-to-AI prediction and experimental validation of heating effects in 350 F supercapacitor modules[J]. International Journal of Heat and Mass Transfer, 2021, 171: doi:10.1016/j.ijheatmasstransfer.2021.121075.
24	赵崇文. 人工神经网络综述[J]. 山西电子技术, 2020(3): 94-96.
	ZHAO C W. A survey on artificial neural networks[J]. Shanxi Electronic Technology, 2020(3): 94-96.
25	RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.
26	HORNIK K, STINCHCOMBE M, WHITE H. Multilayer feedforward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359-366.
27	SUTSKEVER I, MARTENS J, DAHL G, HINTON G. On the importance of initialization and momentum in deep learning[C]. 30th International Conference on Machine Learning, ICML 2013, 1139-1147.
28	YADAV S, SHUKLA S. Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification[C]//2016 IEEE 6th International Conference on Advanced Computing. Bhimavaram, India. IEEE : 78-83.

技术指标	数值
额定工作电压区间/V	2.5～4.0
截止电压/V	2.5
额定充放电电流/A	50
最大充放电电流/A	100
标称容值/F	34000
静电容量/Ah	15
储存能量/Wh	54
直流内阻/mΩ	0.7
工作温度/℃	-25～55

模型代号	相较于NNv1的操作	MAE/℃
NNv1	无	0.0651
NNv2	移除电流	0.1290
NNv3	移除电压	0.0601
NNv4	移除风速	0.1169
NNv5	移除Tin_k-1	0.0851
NNv6	移除全部温度	0.8366
NNv7	移除环境温度	0.1091

算法	MAE/℃
Levenberg-Marquardt	0.0601
动量法	1.7444
拟牛顿法	0.0848
学习率可变的BP算法	0.5318

单体	最大绝对误差/℃	平均绝对误差/℃
#1	0.2496	0.0639
#2	0.2396	0.0650
#3	0.1682	0.0520
#5	0.1671	0.0575
#7	0.2225	0.0529
#9	0.1703	0.0442
#10	0.2807	0.0693
#11	0.2908	0.0749
#12	0.2234	0.0604

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