Analysis of BASNs Battery Performance at Different Temperature Conditions Using Artificial Neural Networks (ANN)

  • B. BanuselvasaraswathyEmail author
  • R. Vimalathithan
  • T. Chinnadurai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)


In Body Area Sensor Network (BASN), battery power management is an important issue to be addressed to extend the lifetime of the sensor with increased performance. The lifetime of the battery relies on several factors like charging and discharging cycles, Voltage rating, current ratings, and temperature. The temperature variation in the battery leads to increased performance but shortens its lifetime due to the internal chemical reaction that occurs inside the battery. Therefore, it is essential to analyze the temperature variations to enhance the battery lifetime as well as to improve the lifetime of entire sensor network. In this paper, BASNs battery efficiency is analyzed at different temperature profile, charging, and discharging cycles. The voltage is measured from the obtained results. As rise in temperature influence the battery discharging capacity. Thus, maintaining optimal temperature is very essential in BASNs battery to increase the lifetime of battery. Further, Artificial Neural Network (ANN) is developed to examine the experimental results to obtain the optimum battery operating temperature.


Body area sensor networks Battery performance Temperature variations Artificial neural networks 


  1. 1.
    Hooshmand, M., Zordan, D.Del, Testa, D., Grisan, E., Rossi, M.: Boosting the battery life of wearables for health monitoring through the compression of biosignals. IEEE Internet Things J. 4(5), 1647–1662 (2017)CrossRefGoogle Scholar
  2. 2.
    Wood, A.D., Stankovic, J.A., Virone, G., Selavo, L., He, Z., Cao, Q., Stoleru, R.: Context-aware wireless sensor networks for assisted living and residential monitoring. IEEE Netw. 22(4), 26–33 (2008)CrossRefGoogle Scholar
  3. 3.
    Akyildiz, I.F., Vuran, M.C., Akan, O.B.: On exploiting spatial and temporal correlation in wireless sensor networks. Proc. WiOpt 4, 71–80 (2004)zbMATHGoogle Scholar
  4. 4.
    Galzarano, S., Liotta, A., Fortino, G. (2013) QL-MAC: A Q-learning based MAC for wireless sensor networks. In: International Conference on Algorithms and Architectures for Parallel Processing, pp. 267–275. Springer, ChamCrossRefGoogle Scholar
  5. 5.
    Carrano, R.C., Passos, D.G., Magalhães, L.C.S., Célio Vinicius, N. (2014) Survey and taxonomy of duty cycling mechanisms in wireless sensor networks. IEEE Commun. Surv. Tutor. 16(1), 181–194CrossRefGoogle Scholar
  6. 6.
    Liu, C., Wu, K., Pei, J.: An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Trans. Parallel Distrib. Syst. 18(7), 1010–1023 (2007)CrossRefGoogle Scholar
  7. 7.
    Rong, P., Pedram, M. (2006) An analytical model for predicting the remaining battery capacity of lithium-ion batteries. IEEE Trans. Very Large Scale Integration (VLSI) Syst. 14(5), 441–451Google Scholar
  8. 8.
    Newman, J., FORTRAN Programs for the Simulation of Electrochemical Systems. Accessed 12 Dec 2016
  9. 9.
    Hausmann, A., Depcik, C.: Expanding the Peukert equation for battery capacity modeling through inclusion of a temperature dependency. J. Power Sour. 235, 148–158 (2013)CrossRefGoogle Scholar
  10. 10.
    Urbina, A., Paez, T.L., Jungst, R.G., Liaw, B.Y.: Inductive modeling of lithium-ion cells. J. Power Sour. 110(2), 430–436 (2002)CrossRefGoogle Scholar
  11. 11.
    Karami, H., Mousavi, M.F., Shamsipur, M., Riahi, S.: New dry and wet Zn-polyaniline bipolar batteries and prediction of voltage and capacity by ANN. J. Power Sour. 154(1), 298–307 (2006)CrossRefGoogle Scholar
  12. 12.
    Stroe, D.I., Swierczynski, M., Kær, S.K., Teodorescu, R.: Degradation behavior of lithium-ion batteries during calendar ageing—the case of the internal resistance increase. IEEE Trans. Ind. Appl. 54(1), 517–525 (2018)CrossRefGoogle Scholar
  13. 13.
    Vetter, J., Novák, P., Wagner, M.R., Veit, C., Möller, K.C., Besenhard, J.O., Hammouche, A.: Ageing mechanisms in lithium-ion batteries. J. Power Sour. 147(1–2), 269–281 (2005)CrossRefGoogle Scholar
  14. 14.
    Waag, W., Käbitz, S., Sauer, D.U.: Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. Appl. Energy 102, 885–897 (2013)CrossRefGoogle Scholar
  15. 15.
    Matsushima, T., Takagi, S., Muroyama, S., Horie, T. (2005) Lifetime and residual capacity estimate for Lithium-ion secondary cells for stationary use in telecommunications systems. In: Telecommunications Conference, INTELEC’05. Twenty-Seventh International, pp. 199–204. IEEEGoogle Scholar
  16. 16.
    Szente-Varga, D., Horvath, G., Rencz, M. (2006) Creating temperature dependent Ni-MH battery models for low power mobile devices. In: Proceedings of the 12th International Workshop on Thermal investigations of ICs (THERMINIC), pp. 27–29. Nice, FranceGoogle Scholar
  17. 17.
    Valle, O.T., Milack, A., Montez, C., Portugal, P., Vasques, F. (2013) Polynomial approximation of the battery discharge function in IEEE 802.15. 4 nodes: case study of MicaZ. In: Advances in Information Systems and Technologies, pp. 901–910. Springer, Berlin, HeidelbergGoogle Scholar
  18. 18.
    Erdinc, O., Vural, B., Uzunoglu, M. (2009) A dynamic lithium-ion battery model considering the effects of temperature and capacity fading. In: Proceedings of the 2009 International Conference on Clean Electrical Power (ICCEP), pp. 9–11. Capri, ItalyGoogle Scholar
  19. 19.
    Ye, Y., Shi, Y., Cai, N., Lee, J., He, X.: Electro-thermal modeling and experimental validation for lithium ion battery. J. Power Sour. 199, 227–238 (2012)CrossRefGoogle Scholar
  20. 20.
    Manwell, J.F., McGowan, J.G.: Lead acid battery storage model for hybrid energy systems. Sol. Energy 50(5), 399–405 (1993)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • B. Banuselvasaraswathy
    • 1
    Email author
  • R. Vimalathithan
    • 2
  • T. Chinnadurai
    • 3
  1. 1.Department of ECESri Krishna College of TechnologyCoimbatoreIndia
  2. 2.Department of ECEKarpagam College of EngineeringCoimbatoreIndia
  3. 3.Department of ICESri Krishna College of TechnologyCoimbatoreIndia

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