Numerical Study on the Detailed Characterization of Ni-MH Battery Model for its Dynamic Behavior using Multi-Regression Analysis – MRA
M. Karthik1, S. Vijayachitra2
1Karthik M, Department of Electrical and Electronics Engineering, Kongu Engineering College, Erode, Affiliated to Anna University, Chennai, Tamilnadu, India.
2Vijayachitra S, Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Erode, Affiliated to Anna University, Chennai, Tamilnadu, India.

Manuscript received on January 02, 2014. | Revised Manuscript received on January 04, 2014. | Manuscript published on January  05, 2014. | PP: 34-44 | Volume-4 Issue-6, January 2014. | Retrieval Number: F2462014615/2015©BEIESP
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Abstract: A numerical study is presented in this paper to examine the dynamic behavior for the detailed characterization of commercially available Ni-MH battery. In the present study, a novel Multi-Regression Analysis (MRA) based model for the D-size HHR650D battery from panasonic is adopted to ascertain the charge and discharge characteristics along with its SoC estimation. Oxygen gas formation at the Ni electrode during charging and overcharging that affects the pressure variations inside the battery is essential to be analyzed for its characterization. Henceforth, the effect of battery charging conditions over the pressure and temperature variations are considered in the developed MRA model and the corresponding performance profiles subjected to recurrent load cycles are reported. Model validation of the steady state behavior is performed based on the benchmark data obtained from a 6.5Ah, 1.2V Nickel-Metal Hydride battery. The result obtained shows that the regression model responses fit well with the benchmark result. Moreover, the model can also predict pressure and temperature dynamics under a sudden change in charging and discharging states. The characterization results show that the proposed regression model of Ni-MH battery could be suited effectively for any kind of model based plug-in or hybrid electric vehicle technologies.
Keywords: Interpolation, Multi-Regression analysis, Ni-MH battery, SoC and voltage dynamics.