Battery Management System Algorithm for Energy Storage Systems Considering Battery Efficiency

被引:27
作者
Lee, Jeong [1 ]
Kim, Jun-Mo [2 ]
Yi, Junsin [1 ]
Won, Chung-Yuen [1 ]
机构
[1] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, South Korea
[2] Sungkyunkwan Univ, Interdisciplinary Program Photovolta Syst Engn, Suwon 16419, South Korea
关键词
energy storage system (ESS); battery management system (BMS); battery efficiency; state of charge (SoC); state of health (SoH); LITHIUM-ION BATTERY; OF-HEALTH ESTIMATION; CHARGE ESTIMATION; STATE; TEMPERATURE; PERFORMANCE; ISSUES;
D O I
10.3390/electronics10151859
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aging increases the internal resistance of a battery and reduces its capacity; therefore, energy storage systems (ESSs) require a battery management system (BMS) algorithm that can manage the state of the battery. This paper proposes a battery efficiency calculation formula to manage the battery state. The proposed battery efficiency calculation formula uses the charging time, charging current, and battery capacity. An algorithm that can accurately determine the battery state is proposed by applying the proposed state of charge (SoC) and state of health (SoH) calculations. To reduce the initial error of the Coulomb counting method (CCM), the SoC can be calculated accurately by applying the battery efficiency to the open circuit voltage (OCV). During the charging and discharging process, the internal resistance of a battery increase and the constant current (CC) charging time decrease. The SoH can be predicted from the CC charging time of the battery and the battery efficiency, as proposed in this paper. Furthermore, a safe system is implemented during charging and discharging by applying a fault diagnosis algorithm to reduce the battery efficiency. The validity of the proposed BMS algorithm is demonstrated by applying it in a 3-kW ESS.
引用
收藏
页数:19
相关论文
共 76 条
[1]   Energy efficiency of Li-ion battery packs re-used in stationary power applications [J].
Ahmadi, Leila ;
Fowler, Michael ;
Young, Steven B. ;
Fraser, Roydon A. ;
Gaffney, Ben ;
Walker, Sean B. .
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2014, 8 :9-17
[2]   Electromagnetic Susceptibility of Battery Management Systems' ICs for Electric Vehicles: Experimental Study [J].
Aiello, Orazio .
ELECTRONICS, 2020, 9 (03)
[3]  
Andrea D., 2010, Battery Management Systems for Large Lithium-ion Battery Packs
[4]   Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter [J].
Baccouche, Ines ;
Jemmali, Sabeur ;
Manai, Bilal ;
Omar, Noshin ;
Ben Amara, Najoua Essoukri .
ENERGIES, 2017, 10 (06)
[5]   Design of an Optimized Thermal Management System for Li-Ion Batteries under Different Discharging Conditions [J].
Bhattacharjee, Ankur ;
Mohanty, Rakesh K. ;
Ghosh, Aritra .
ENERGIES, 2020, 13 (21)
[6]   A Two-Step Parameter Optimization Method for Low-Order Model-Based State-of-Charge Estimation [J].
Bian, Xiaolei ;
Wei, Zhongbao ;
He, Jiangtao ;
Yan, Fengjun ;
Liu, Longcheng .
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2021, 7 (02) :399-409
[7]   SOC and SOH Identification Method of Li-Ion Battery Based on SWPSO-DRNN [J].
Che, Yanbo ;
Liu, Yushu ;
Cheng, Ze ;
Zhang, Ji'ang .
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, 2021, 9 (04) :4050-4061
[8]   Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach [J].
Chen, Zewang ;
Yang, Liwen ;
Zhao, Xiaobing ;
Wang, Youren ;
He, Zhijia .
APPLIED MATHEMATICAL MODELLING, 2019, 70 :532-544
[9]  
Copper Development Association, 2012, MARK EV EN STOR US
[10]   Potential of lithium-ion batteries in renewable energy [J].
Diouf, Boucar ;
Pode, Ramchandra .
RENEWABLE ENERGY, 2015, 76 :375-380