(240d) State of Health Estimation Method Design for Energy Storage System of Lithium Ion Battery and Comparative Study : From Cell to Demonstration Sites

State of health Estimation method design
for Energy Storage System of Lithium ion battery and comparative study : from
cell to demonstration sites

Keonhee
Park, Ph.D. candidate

Seoul National University

Recently, Increasing
demands of renewable energy and necessity of stable electricity supply are
accelerating ESS(Energy storage system) market. ESS, which can stores the power
and supplies electricity when it is needed, is possible to increase efficiency
of electricity-usage and still has converted from production-consumption
paradigm of electricity system to production-storage-consumption paradigm of
that so far. ESS is comprehensive system including EMS(Energy Management
system), BMS(Battery management system), PCS(Power conversion system). BMS has
important role for sustainable ESS operation. Main state variables of BMS are
SOC(State of charge) and SOH(State of health). SOC means current residual
capacity based on present nominal capacity. And SOH is a prominent indicator to
determine degradation of battery. Performance of repeated cycled battery
decreased and it should be replaced at an appropriate time for usability.
Imprecise SOH can affected SOC accuracy which is important variable for on-line
monitoring. However, SOH accurate estimation is challenging due to few
obtainable output variables for the battery system and the lack of
understanding for electrochemical phenomena in cell. On-line SOH estimation
method is needed since a lot of ESS application sites are isolated. <_x0031_05_x0025__x0027_>This study proposes the modified online SOH estimation method, and
compares estimation results of related SOH estimation methods. The results are
validated with experimental cell data and demonstration operation data. This
method uses the equivalent circuit model parameters and recursive least square
method. The cell experimental data is given by Korea testing certification(KTC)
and demonstration data is obtained by 2 sites, which are interconnected with PV
power system. The one is installed in micro-grid system in the island and the
other is integrated with plant site. From the test results show maximum 2%
accuracy in cell data and 5% accuracy in demonstration data. The accuracy of
proposed method is dependent on input data quality highly. Also we can estimate
the life time of battery system and implement easily without complex change of
BMS algorithm.