Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions

Vincent R. Tannahill; Kashem M. Muttaqi; Danny Sutanto

Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions

View Fulltext

Author(s): Vincent R. Tannahill ¹ ; Kashem M. Muttaqi ¹ ; Danny Sutanto ¹
- Affiliations: 1: School of Electrical, Computer and Telecommunications Engineering, Australian Power Quality and Reliability Centre, University of Wollongong, Northfields Avenue, Gwynneville, 2522 Wollongong, NSW, Australia
Source: Volume 6, Issue 2, June 2016, p. 107 – 116
DOI: 10.1049/iet-est.2014.0067 , Print ISSN 2042-9738, Online ISSN 2042-9746

Received 16/12/2014, Accepted 23/04/2015, Revised 25/02/2015, Published 01/06/2016

As the technology supporting electric vehicles (EVs) is rapidly progressing and the cost of EV components is reducing, EVs are becoming more feasible for use in Australia and in many countries around the world. However, the public perception of EVs and their perceived limitations result in a slow uptake of the technology, partially because of the uncertainty regarding the ability of an EV to meet the driving needs of the general population. Range anxiety is a particular concern with drivers having fear of being stranded by a depleted EV battery. This study explores means of reducing range anxiety by taking into account a variety of environmental and behavioural factors. By considering such factors and implementing it in conjunction with a recently proposed improved state of charge (SoC) estimation method by the authors, a range estimate can be produced that is much more accurate than the conventional methods which consider the SoC and vehicle efficiency alone. This range estimate can be used to inform the driver of the capabilities of the EV and advise if a recharge is required to reach the intended destination.

References

1. 1)
  - 15. Tampere University of Technology: ‘Rolling Resistance Force’ [Online]. Available at https://www.tut.fi/ms/muo/tyreschool/moduulit/moduuli_8/hypertext_1/3/3_3.html, Accessed8 May 2014.
2. 2)
  - 18. Riener, A.: ‘Subliminal persuasion and its potential for driver behavior adaptation’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (1), pp. 71–80 (doi: 10.1109/TITS.2011.2178838).
3. 3)
  - 14. Chew, K.W., Ken, L.C., Goi, B.M.: ‘Contour data acquisition system for electric vehicle distance estimation method’, Appl. Mech. Mater., 2013, 479–480, pp. 503–507 (doi: 10.4028/www.scientific.net/AMM.479-480.503).
4. 4)
  - 11. Lee, S., Kim, J., Lee, J., Cho, B.H.: ‘State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge’, J. Power Sources, 2008, 185, pp. 1367–1373 (doi: 10.1016/j.jpowsour.2008.08.103).
5. 5)
  - 7. Cho, S., Jeong, H., Han, C., Jin, S., Lim, J.H., Oh, J.: ‘State-of-charge estimation for lithium-ion batteries under various operating conditions using an equivalent circuit model’, Comput. Chem. Eng., 2012, 41, pp. 1–9 (doi: 10.1016/j.compchemeng.2012.02.003).
6. 6)
  - 5. He, Y., Liu, X., Zhang, C., Chen, Z.: ‘A new model for state-of-charge (SOC) estimation for high-power li-ion batteries’, Appl. Energy, 2013, 101, pp. 080–814 (doi: 10.1016/j.apenergy.2012.08.031).
7. 7)
  - 13. Tannahill, V.R., Sutanto, D., Muttaqi, K.M., Masrur, M.A.: ‘A future vision for reduction of range anxiety by using an improved state of charge estimation algorithm for electric vehicle batteries implemented with low-cost microcontrollers’. Early Access, IET Electrical Systems in Transportation, September 2014.
8. 8)
  - 16. Marques, L., Pedreiras, P., Almeida, L.: ‘A flexible dashboard panel for a small electric vehicle’. Information Systems and Technologies (CISTI), Chaves, 2011.
9. 9)
  - 3. Piller, S., Perrin, M., Jossen, A.: ‘Methods for state-of-charge determination and their applications’, J. Power Sources, 2001, 96, (1), pp. 113–120 (doi: 10.1016/S0378-7753(01)00560-2).
10. 10)
  - 1. Australian Bureau of Statistics: ‘Australian Social Trends, July 2013’ [Online]. Available at http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/4102.0Main+Features40July+2013. Accessed29 August 2013.
11. 11)
  - 20. ‘2008 Hyundai Getz Phase-II Hatchback all Versions Specifications and Performance Data’, [Online]. Available at http://www.automobile-catalog.com/make/hyundai/click/getz_phase-ii_hatchback/2008.html, Accessed18 May 2014.
12. 12)
  - 12. He, H., Zhang, X., Xiong, R., Xu, Y., Guo, H.: ‘Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles’, Energy, 2012, 39, pp. 310–318 (doi: 10.1016/j.energy.2012.01.009).
13. 13)
  - 9. Hu, X., Sun, F., Zou, Y.: ‘Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles’, Simul. Model. Pract. Theory, 2013, 34, pp. 1–11 (doi: 10.1016/j.simpat.2013.01.001).
14. 14)
  - 21. Gillespie, T.D.: ‘Fundamentals of vehicle dynamics’ (SAE International, R-114, ISBN of 978-1-56091-199-9, 1992).
15. 15)
  - 6. He, Y., Liu, W., Koch, B.J.: ‘Battery algorithm verification and development using hardware-in-the-loop testing’, J. Power Sources, 2010, 195, pp. 2969–2974 (doi: 10.1016/j.jpowsour.2009.11.036).
16. 16)
  - 10. He, H., Xiong, R., Guo, H.: ‘Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles’, Appl. Energy, 2012, 89, pp. 413–420 (doi: 10.1016/j.apenergy.2011.08.005).
17. 17)
  - 4. Li, M., Jiang, Y., Zheng, J., Peng, X.: ‘Improved method for state of charge estimation of lithium iron phosphate power batteries’, Adv. Mater. Res., 2013, 712–715, pp. 1956–1959 (doi: 10.4028/www.scientific.net/AMR.712-715.1956).
18. 18)
  - 2. Australian Bureau of Statistics: ‘Survey of Motor Vehicle Use, Australia, 12 months ended 30 June 2012’ [Online]. Available at http://www.abs.gov.au/ausstats/[email protected]/mf/9208.0. Accessed29 August 2013.
19. 19)
  - 8. Li, J., Barillas, J.K., Guenther, C., Danzer, M.A.: ‘A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles’, J. Power Sources, 2013, 230, pp. 244–250 (doi: 10.1016/j.jpowsour.2012.12.057).
20. 20)
  - 19. Ferreira, J., Pereira, P., Filipe, P., Afonso, J.: ‘Recommender system for drivers of electric vehicles’. Electronics Computer Technology (ICECT), Kanyakumari, 2011.
21. 21)
  - 17. Syed, F.U., Filev, D., Tseng, F., Ying, H.: ‘Adaptive real-time advisory system for fuel economy improvement in a hybrid electric vehicle’. The 28th North American Fuzzy Information Processing Society Annual Conf., Cincinnati, 2009.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions

References

Related content