Experimental validation of the recovery effect in batteries for wearable sensors and healthcare devices discovering the existence of hidden time constants

Harneet Arora; Robert Simon Sherratt; Balazs Janko; William Harwin

Experimental validation of the recovery effect in batteries for wearable sensors and healthcare devices discovering the existence of hidden time constants

View Fulltext

Author(s): Harneet Arora¹ ; Robert Simon Sherratt¹ ; Balazs Janko¹ ; William Harwin¹
- Affiliations: 1: Department of Biomedical Engineering , University of Reading , Reading RG6 6AY , UK
Source: Volume 2017, Issue 10, October 2017, p. 548 – 556
DOI: 10.1049/joe.2017.0303 , Online ISSN 2051-3305

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 17/07/2017, Accepted 17/09/2017, Revised 12/09/2017, Published 22/09/2017

Wearable sensors and healthcare devices use small lightweight batteries to power their operations of monitoring and tracking. It becomes absolutely vital to effectively utilise all the available battery charge for device longevity between charges. The electrochemical recovery effect enables the extraction of more power from the battery when implementing idle times in between use cycles, and has been used to develop various power management techniques. However, there is no evidence concerning the actual increase in available power that can be attained using the recovery effect. Also, this property cannot be generalised on all the battery chemistries since it is an innate phenomenon, relying on the anode/cathode material. Indeed recent developments suggest that recovery effect does not exist at all. Experimental results to verify the presence and level of the recovery effect in commonly used battery chemistries in wearable sensors and healthcare devices are presented. The results have revealed that the recovery effect significantly does exist in certain batteries, and importantly the authors show that it is also comprised of two different time constants. This novel finding has important implications for the development of power management techniques that utilise the recovery effect with application in a large range of battery devices.

References

1. 1)
  - 18. Jongerden, M.R., Haverkort, B.R.: ‘Which battery model to use?’, IET Softw., 2009, 3, (6), pp. 445–457 (doi: 10.1049/iet-sen.2009.0001).
2. 2)
  - 14. Reichert, M., Andre, D., Rösmann, A., et al: ‘Influence of relaxation time on the lifetime of commercial lithium-ion cells’, J. Power Sources, 2013, 239, pp. 45–53 (doi: 10.1016/j.jpowsour.2013.03.053).
3. 3)
  - 15. Pei, L., Wang, T., Lu, R., et al: ‘Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries’, J. Power Sources, 2014, 253, pp. 412–418 (doi: 10.1016/j.jpowsour.2013.12.083).
4. 4)
  - 7. http://chem.libretexts.org/Core/Analytical_Chemistry/Electrochemistry/Case_Studies/Rechargeable_Batteries, accessed January 2017.
5. 5)
  - 21. Coughanowr, D.R., LeBlanc, S.E.: ‘Process systems analysis control’ (McGraw-Hill, 1994, 3rd edn).
6. 6)
  - 20. Randles, J.E.B.: ‘Kinetics of rapid electrode reactions’, Discuss. Faraday Soc., 1947, 1, p. 11 (doi: 10.1039/df9470100011).
7. 7)
  - 22. Manwell, J.F., McGowan, J.G.: ‘Extension of the kinetic battery model for wind/hybrid power systems’. Proc. Fifth European Wind Energy Association Conf., 1994, pp. 284–289.
8. 8)
  - 10. Castillo, S., Samala, N.K., Manwaring, K., et al: ‘Experimental analysis of batteries under continuous and intermittent operations’. Proc. Int. Conf. Embedded Systems Applications, June 2004, pp. 18–24.
9. 9)
  - 11. Lafollette, R.M.: ‘Design and performance of high specific power, pulsed discharge, bipolar lead acid batteries’. Proc. Tenth IEEE Battery Conf. Applications and Advances, CA, USA, January 1995, pp. 43–47, doi: 10.1109/BCAA.1995.398511.
10. 10)
  - 13. Lee, Y.S., Sun, Y.K., Nahm, K.S.: ‘Synthesis and characterization of LiNiO2 cathode material prepared by an adiphic acid-assisted sol–gel method for lithium secondary batteries’, Solid State Ion., 1999, 118, (1–2), pp. 159–168 (doi: 10.1016/S0167-2738(98)00438-X).
11. 11)
  - 9. Nelson, R.F., Rinehart, R., Varley, S.: ‘Ultrafast pulse discharge film and recharge battery capabilities of thin-metal film battery technology’. Proc. 11th IEEE Int. Pulsed Power Conf., Baltimore, USA, June–July 1997, pp. 636–641, doi: 10.1109/PPC.1997.679411.
12. 12)
  - 8. Rakhmatov, D., Vrudhula, S.B.K.: ‘Time-to-failure estimation for batteries in portable electronic systems’. Proc. IEEE Int. Symp. Low Power Electronics and Design, CA, USA, August 2001, pp. 88–91, doi: 10.1109/LPE.2001.945380.
13. 13)
  - 7. Ng, K.S., Moo, C.-S., Chen, Y.-P., Hsieh, Y.-C.: ‘Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries’, Appl. Energy, 2009, 86, pp. 1506–1511 (doi: 10.1016/j.apenergy.2008.11.021).
14. 14)
  - 2. Chiasserini, C.F., Rao, R.R.: ‘Improving battery performance by using traffic shaping techniques’, IEEE J. Sel. Areas Commun., 2001, 19, (7), pp. 1385–1394 (doi: 10.1109/49.932705).
15. 15)
  - 18. Milenković, A., Otto, C., Jovanov, E.: ‘Wireless sensor networks for personal health monitoring: issues and an implementation’, J. Comput. Commun., 2006, 29, (13–14), pp. 2521–2533 (doi: 10.1016/j.comcom.2006.02.011).
16. 16)
  - 6. http://www.batmax.com/technology-degeneration.php, accessed January 2017.
17. 17)
  - 17. Mikhaylov, K., Tervonen, J.: ‘Optimization of microcontroller hardware parameters for wireless sensor network node power consumption and lifetime improvement’. Proc. IEEE Int. Congress Ultra Modern Telecommunications and Control Systems and Workshops, Moscow, Russia, October 2010, pp. 1150–1156, doi: 10.1109/ICUMT.2010.5676525.
18. 18)
  - 12. Fuller, T.F., Doyle, M., Newman, J.: ‘Relaxation phenomena in lithium-ion-insertion cells’, J. Electrochem. Soc., 1994, 141, (4), pp. 982–990 (doi: 10.1149/1.2054868).
19. 19)
  - 4. Narayanaswamy, S., Schlueter, S., Steinhorst, S., et al: ‘On battery recovery effect in wireless sensor nodes’, ACM Trans. Des. Autom. Electron. Syst., 2016, 21, (4), pp. 1–28 (doi: 10.1145/2890501).
20. 20)
  - 5. Crompton, T.R.: ‘Battery reference book’ (Newnes, 2000, 3rd edn.).
21. 21)
  - 1. Barakah, D.M., Ammad-Uddin, M.: ‘A survey of challenges and applications of wireless body area network (WBAN) and role of a virtual doctor server in existing architecture’. Proc. Third IEEE Int. Conf. Intelligent Systems Modeling and Simulation, Kota Kinabalu, Malaysia, February 2012, pp. 214–219, doi: 10.1109/ISMS.2012.108.
22. 22)
  - 16. Devarakonda, L., Hu, T.: ‘Effects of rest time on discharge response and equivalent circuit model for a lead-acid battery’, J. Power Sources, 2015, 282, pp. 19–27 (doi: 10.1016/j.jpowsour.2015.02.030).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Experimental validation of the recovery effect in batteries for wearable sensors and healthcare devices discovering the existence of hidden time constants

References

Related content