TY - JOUR
T1 - Genetic optimization and experimental validation of a test cycle that maximizes parameter identifiability for a Li-ion equivalent-circuit battery model
AU - Rothenberger, Michael J.
AU - Docimo, Donald J.
AU - Ghanaatpishe, Mohammad
AU - Fathy, Hosam K.
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - This article presents an experimental study demonstrating the degree to which optimal experimental design can improve lithium-ion battery parameter estimation. The article is motivated by previous literature showing that lithium-ion batteries suffer from poor parameter identifiability. This makes it difficult to estimate battery parameters quickly and accurately from input-output cycling data. Previous research shows that optimizing the shape of a battery cycle for Fisher information - an identifiability metric - can improve parameter estimation speed and accuracy significantly. However, most studies demonstrating this improvement are simulation-based, rather than experimental. In contrast, the centermost goal in this article is to provide an experimental assessment of the degree to which trajectory optimization for Fisher identifiability can improve lithium-ion battery parameter estimation. We optimize battery cycling to maximize Fisher information for a nonlinear second-order model of a commercial lithium iron phosphate (LFP) cell. We implement this optimal cycle experimentally for 3 different battery cells, and compare it with two benchmark cycles representing automotive battery use. The results of this comparison are quite compelling: when parameterized using data from the optimal cycle, the cell voltage prediction signal-to-noise ratio improves significantly over the benchmarks. Moreover, only the optimized cycle produces reasonable estimates of battery parameters over the course of a 4-hour experiment.
AB - This article presents an experimental study demonstrating the degree to which optimal experimental design can improve lithium-ion battery parameter estimation. The article is motivated by previous literature showing that lithium-ion batteries suffer from poor parameter identifiability. This makes it difficult to estimate battery parameters quickly and accurately from input-output cycling data. Previous research shows that optimizing the shape of a battery cycle for Fisher information - an identifiability metric - can improve parameter estimation speed and accuracy significantly. However, most studies demonstrating this improvement are simulation-based, rather than experimental. In contrast, the centermost goal in this article is to provide an experimental assessment of the degree to which trajectory optimization for Fisher identifiability can improve lithium-ion battery parameter estimation. We optimize battery cycling to maximize Fisher information for a nonlinear second-order model of a commercial lithium iron phosphate (LFP) cell. We implement this optimal cycle experimentally for 3 different battery cells, and compare it with two benchmark cycles representing automotive battery use. The results of this comparison are quite compelling: when parameterized using data from the optimal cycle, the cell voltage prediction signal-to-noise ratio improves significantly over the benchmarks. Moreover, only the optimized cycle produces reasonable estimates of battery parameters over the course of a 4-hour experiment.
KW - Equivalent-circuit battery modeling
KW - Fisher identifiability
KW - Input shaping
KW - Parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=84964880134&partnerID=8YFLogxK
U2 - 10.1016/j.est.2015.10.004
DO - 10.1016/j.est.2015.10.004
M3 - Article
AN - SCOPUS:84964880134
SN - 2352-152X
VL - 4
SP - 156
EP - 166
JO - Journal of Energy Storage
JF - Journal of Energy Storage
ER -