# Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods

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## Abstract

**:**

## 1. Instruction

## 2. Battery Model Description

## 3. Online Resistance

#### 3.1. Resistance calculation

_{before}is the shelved voltage. When V

_{d}takes the average value of peak voltage of pulse, R represents the pulse’s resistance. The advantage of this method is that it is precise in testing the battery resistance in the level of mΩ.

_{oc}and the resistance R. For all analyses presented in this work, the time step is 1 s and n = 100. These equations will fail when the variance S2 − S1 × S1 = 0, or when this quantity is neatly zero [9]. In addition, the equations will fail to provide a reasonable result when the many recorded currents used in the statistical analysis are similar. Thus in practical engineering application, it is important to allow for a correction to the extraction of the resistance R. This paper uses the previous value to replace the failure resistance. This statistical method can recognize the online resistance examination but don’t need extra testing mode and expensive testing equipment.

#### 3.2. Experimental preparation

#### 3.3. The result and analysis of resistance calculation

simulative mode serial number | charge mean value (A) | discharge mean value (A) | current dispersion |
---|---|---|---|

a | 8.6 (62) | –5.7 (31) | 1.2 |

b | 12.6 (98) | –5.1 (2) | 1.5 |

c | 15.8 (63) | –20.7 (37) | 6.4 |

d | 19.4 (68) | –18.4 (32) | 6.1 |

e | 10.9 (62) | –11.0 (38) | 2.8 |

## 4. Open-Circuit Voltage

_{i}can be ignored. Advantage of direct measurement method is simple. The drawback is that has big error and can’t monitor in real-time.

driving patten | error |
---|---|

driving patten 1 | 1.03% |

driving patten 2 | 3.4% |

## 5. SOC

U(v) | 96 | 120 | 140 | 198 |

SOC(%) | 0 | 10 | 30 | 100 |

the error before amendment | the error after amendment | |
---|---|---|

test sample 1 | 5.2% | 4.9% |

test sample 2 | 4.8% | 4.6% |

## 6. Conclusions

## Acknowledgements

## References and Notes

- Hou, X.; Xue, J.; Nan, J. The cycle life performance of MH-Ni batteries for high power applications. Acta Sci. Natur. Univ. Sunyats.
**2005**, 44, 77–80. [Google Scholar] - Verbrugge, M. Adaptive multi-parameter battery state estimator with optimized time-weighting factors. J. Appl. Electrochem.
**2007**, 37, 605–616. [Google Scholar] [CrossRef] - Wei, X.; Xu, W.; Shen, D. Internal resistance identification of Li-ion battery and its application in battery life estimation. Chin. J. Power Sources
**2009**, 33, 217–220. [Google Scholar] - Buchmann, I. Batteries in a Portable World, 2nd ed.; Cadex Electronics Inc Press: Richmond, BC, Canada, 2001; pp. 81–84. [Google Scholar]
- Piller, S.; Perrin, M.; Jossen, A. Methods for state-of-charge determination and their applications. J. Power Sources
**2001**, 96, 113–120. [Google Scholar] [CrossRef] - Johnson, V.H. Battery performance models in advisor. J. Power Sources
**2002**, 110, 321–329. [Google Scholar] [CrossRef] - Lin, C.; Qiu, B.; Chen, Q. A comparative study on power input equivalent circuit model for electric vehicle battery. Automot. Eng.
**2006**, 28, 229–234. [Google Scholar] - Verbrugge, M.; Tate, E. Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena. J. Power Sources
**2004**, 126, 236–249. [Google Scholar] [CrossRef] - Felder, R.M.; Rousseau, R.W. Elementary Principles of Chemical Processes, 3rd ed.; John Wiley & Sons press: New York, NY, USA, 1978; pp. 501–503. [Google Scholar]
- QC/T 744-2006, Nickel Metal Hydride Power Battery of EV; Development & Reform Commision of China press: Beijing, China, 2006.
- Hu, J.; Shui, J.; Guo, C. MH/Ni battery discharge performance test research of hybrid electric vehicle. J. Chongqing Univ.
**2007**, 30, 1–6. [Google Scholar] - Bian, Z. Pattern Recognition, 2nd Ed. ed; Tsinghua University Press: Beijing, China, 2002; pp. 88–89. [Google Scholar]
- Cai, C.; Du, D.; Liu, Z.; Ge, J. State of charge estimation of high power NI-MH rechargeable battery with artificial neural network. In Proceedings of the 9th International Conference on Neural Information, Singapore, November 2002; pp. 824–828.

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## Share and Cite

**MDPI and ACS Style**

Piao, C.-h.; Fu, W.-l.; Lei, G.-h.; Cho, C.-d.
Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods. *Energies* **2010**, *3*, 206-215.
https://doi.org/10.3390/en3020206

**AMA Style**

Piao C-h, Fu W-l, Lei G-h, Cho C-d.
Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods. *Energies*. 2010; 3(2):206-215.
https://doi.org/10.3390/en3020206

**Chicago/Turabian Style**

Piao, Chang-hao, Wen-li Fu, Gai-hui Lei, and Chong-du Cho.
2010. "Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods" *Energies* 3, no. 2: 206-215.
https://doi.org/10.3390/en3020206