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Comparison of comprehensive properties of Ni-MH (nickel-metal hydride) and Li-ion (lithium-ion) batteries in terms of energy efficiency

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  • Kang, Jianqiang
  • Yan, Fuwu
  • Zhang, Pei
  • Du, Changqing

Abstract

In this work, we successfully proposed a method to compare the comprehensive properties of different battery systems in terms of a parameter, energy efficiency. The quantitative relationship of OCV (open circuit voltage) and SOC (state of charge) for Ni-MH batteries is firstly established to calculate the energy efficiency. Then a comprehensive comparison of the energy efficiency for Ni-MH and Li-ion batteries is systemically analyzed under different operating conditions. The results suggest that the energy efficiency is larger for Li-ion batteries than for Ni-MH batteries under charge and charge–discharge cycles, but lesser under a large current rate discharge. The outcome indicates that Ni-MH batteries are more favorable in the case of large current rates discharge than Li-ion batteries. Under plus current rates, two factors, SOC and current rates are analyzed with respect to energy efficiency. For both the batteries, the energy efficiency is varied slightly with SOC, but declines greatly with increased current rates.

Suggested Citation

  • Kang, Jianqiang & Yan, Fuwu & Zhang, Pei & Du, Changqing, 2014. "Comparison of comprehensive properties of Ni-MH (nickel-metal hydride) and Li-ion (lithium-ion) batteries in terms of energy efficiency," Energy, Elsevier, vol. 70(C), pages 618-625.
    • Handle: RePEc:eee:energy:v:70:y:2014:i:c:p:618-625
    DOI: 10.1016/j.energy.2014.04.038
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    12. Xu, Meng & Zhang, Zhuqian & Wang, Xia & Jia, Li & Yang, Lixin, 2015. "A pseudo three-dimensional electrochemical–thermal model of a prismatic LiFePO4 battery during discharge process," Energy, Elsevier, vol. 80(C), pages 303-317.
    13. Eddahech, Akram & Briat, Olivier & Vinassa, Jean-Michel, 2015. "Performance comparison of four lithium–ion battery technologies under calendar aging," Energy, Elsevier, vol. 84(C), pages 542-550.
    14. Dong, Guangzhong & Zhang, Xu & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state of energy estimation of lithium-ion batteries based on neural network model," Energy, Elsevier, vol. 90(P1), pages 879-888.
    15. Julian Marius Müller & Raphael Kunderer, 2019. "Ex-Ante Prediction of Disruptive Innovation: The Case of Battery Technologies," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
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