DocumentCode :
231081
Title :
Fuzzy logic-controlled online state-of-health (SOH) prediction in large format LiMn2O4 cell for energy storage system (ESS) applications
Author :
Jonghoon Kim ; Nikitenkov, Dmitry
Author_Institution :
Dept. of Electr. Eng., Chosun Univ., Gwangju, South Korea
fYear :
2014
fDate :
Feb. 26 2014-March 1 2014
Firstpage :
474
Lastpage :
479
Abstract :
This paper investigates a new approach based on the fuzzy logic-controlled methodology that is suitable for analyzing and evaluating large format LiMn2O4 cell performance via online state-of-health (SOH) prediction for energy storage system (ESS) applications. The proposed method for online SOH prediction is comprised of two parts. First of all, the values of the cell resistance R and maximum cell capacity Qmax are calculated from four factors such as voltage, current, time, and temperature that were measured during charge-discharge sequence at unknown temperature. Specifically, in order to minimize of SOH jump and drops with temperature variation, temperature compensation for R normalization is efficiently implemented. Then, using two values R and Qmax previously calculated at unknown temperature, present SOH of an arbitrary LiMn2O4 cell can be predicted using the defined fuzzy-logic inference system. The main advantage of this approach is wide parameters tuning possibility for good correspondence of SOH decay with time, and the possibility to perform suitable online SOH estimation. The proposed model used as part of either a Matlab/Simulink model or an integral part of the battery management system (BMS)-micro controller unit (MCU) of STM32F105VC, was verified by the comparison with experimental data of Samsung SDI 60Ah LiMn2O4 cell and a method for SOH prediction by measuring cell capacity during fully charging/ discharging sequence.
Keywords :
battery management systems; condition monitoring; electrical maintenance; fuzzy control; lithium compounds; secondary cells; LiMn2O4; STM32F105VC microcontroller unit; battery management system; cell resistance; energy storage system; fuzzy logic controlled online state-of-health prediction; large format cell; maximum cell capacity; temperature variation; Batteries; Discharges (electric); Estimation; Resistance; System-on-chip; Temperature measurement; LiMn2O4; State-of-health (SOH); battery management system (BMS); energy storage system (ESS); fuzzy-logic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Technology (ICIT), 2014 IEEE International Conference on
Conference_Location :
Busan
Type :
conf
DOI :
10.1109/ICIT.2014.6894986
Filename :
6894986
Link To Document :
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