收稿日期: 2021-06-15
网络出版日期: 2022-06-28
基金资助
国家重点研发计划(2020YFB1711200)
Online Estimation of Supercapacitor State of Charge Based on Nonlinear Observer
Received date: 2021-06-15
Online published: 2022-06-28
超级电容器具有快速充放、高功率密度和长寿命等优点, 被广泛用于新能源汽车的储能系统. 系统可靠运行需获取其剩余电量, 即对其荷电状态(SOC)进行估算. 依托超级电容单体的等效模拟电路模型, 建立了以模型中多电容端电压为状态, 电容器输入电流为控制输入, 电容器输出电压为观测输出的电容器二阶非线性系统的状态空间模型, 包含了自放电现象产生的泄漏电流的因素. 为提高模拟精度, 辨识不同的模型参数,分别刻画充电和放电工况. 采用非线性观测器算法来获取模型内部状态从而实现对SOC的估计. 充放电实验的结果表明, 考虑泄漏因素和建立不同参数下的充放电模型, 能够更好地模拟超级电容器的动态特性, 同时验证了非线性观测器算法具有稳定的跟踪能力.
杜宇石, 琚长江, 杨根科 . 基于非线性观测器的超级电容器荷电状态在线估计[J]. 上海交通大学学报, 2022 , 56(12) : 1630 -1637 . DOI: 10.16183/j.cnki.jsjtu.2021.210
Supercapacitors have the advantages of fast charging and discharging, high power density, and long life, which are widely used in energy storage systems for new energy vehicles. Reliable operation of the system requires the acquisition of its remaining electricity, which is to estimate its state of charge (SOC). Relying on the equivalent analog circuit model of a single supercapacitor, this paper establishes a state-space model of the capacitor second-order nonlinear system with the multi-capacitor terminal voltage in the model as the state, the capacitor input current as the control input, and the capacitor output voltage as the observation output, and contains the leakage current caused by the self-discharge phenomenon. In order to improve the simulation accuracy, different model parameters were identified to characterize the charging and discharging conditions. In this paper, a nonlinear observer algorithm is used to obtain the internal state of the model to realize the estimation of SOC. The results of the charging and discharging experiment show that considering the leakage factor and establishing the charging and discharging model with different parameters, the dynamic characteristics of the supercapacitor can be better simulated, and the nonlinear observer algorithm has a stable tracking ability.
[1] | WANG J, CAO B, CHEN Q, et al. Combined state of charge estimator for electric vehicle battery pack[J]. Control Engineering Practice, 2007, 15(12): 1569-1576. |
[2] | ZHANG L, HU X, WANG Z, et al. A review of supercapacitor modeling, estimation, and applications: A control/management perspective[J]. Renewable and Sustainable Energy Reviews, 2018, 81: 1868-1878. |
[3] | ALLU S, ASOKAN B V, SHELTON W A, et al. A generalized multi-dimensional mathematical model for charging and discharging processes in a supercapacitor[J]. Journal of Power Sources, 2014, 256: 369-382. |
[4] | PARVINI Y, SIEGEL J B, STEFANOPOULOU A G, et al. Supercapacitor electrical and thermal modeling, identification, and validation for a wide range of temperature and power applications[J]. IEEE Transactions on Industrial Electronics, 2015, 63(3): 1574-1585. |
[5] | HU X, LI S E, YANG Y. Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles[J]. IEEE Transactions on Transportation electrification, 2015, 2(2): 140-149. |
[6] | WANG B, LI S E, PENG H, et al. Fractional-order modeling and parameter identification for lithium-ion batteries[J]. Journal of Power Sources, 2015, 293: 151-161. |
[7] | PAVKOVIC D, KOMLJENOVI A, HRGETI M, et al. Design of EKF-based SOC estimator for an ultracapacitor module[C]//IEEE EUROCON 2015—International Conference on Computer as a Tool. Salamanca, Spain:IEEE, 2015: 1-6. |
[8] | SAHA P, DEY S, KHANRA M. Accurate estimation of state-of-charge of supercapacitor under uncertain leakage and open circuit voltage map[J]. Journal of Power Sources, 2019, 434(9): 226696. |
[9] | CERAOLO M, LUTZEMBERGER G, POLI D. State-of-charge evaluation of supercapacitors[J]. Journal of Energy Storage, 2017, 11: 211-218. |
[10] | FAN S, DUAN J, SUN L, et al. State of charge estimate for super-capacitor based on sliding mode observer[C]//2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific. Harbin, China: IEEE, 2017: 1-5. |
[11] | 武国良. 电动汽车用镍氢电池剩余电量估计方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2010. |
[11] | WU Guoliang. Research on state of charge estimation of Ni-MH battery used in electric vehicles[D]. Harbin:Harbin Institute of Technology, 2010. |
[12] | FARANDA R. A new parameters identification procedure for simplified double layer capacitor two-branch model[J]. Electric Power Systems Research, 2010, 80(4): 363-371. |
[13] | XIA B, CHEN C, TIAN Y, et al. A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer[J]. Journal of Power Sources, 2014, 270: 359-366. |
[14] | ZUBIETA L, BONERT R. Characterization of double-layer capacitors for power electronics applications[J]. IEEE Transactions on Industry Applications, 2000, 36(1): 199-205. |
[15] | WANG C, HE H, ZHANG Y, et al. A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures[J]. Applied Energy, 2017, 196: 268-278. |
[16] | LIU C, WANG Y, CHEN Z, et al. A variable capacitance based modeling and power capability predicting method for ultracapacitor[J]. Journal of Power Sources, 2018, 374: 121-133. |
/
〈 |
|
〉 |