© The Institution of Engineering and Technology
The fear of being stranded by a depleted electric vehicle (EV) battery is commonly referred to as ‘range anxiety’. This study explores a future vision for a comprehensive driver alerting algorithm to reduce the range anxiety by increasing the accuracy of the EV range estimation method. The key piece of information required to achieve this is an accurate battery state of charge (SoC) estimation. This study proposes an improved SoC estimation algorithm for implementation using low-cost microcontrollers. A method by which this improved algorithm can be implemented in a distributed battery management system is presented. The improved SoC estimation can be used to provide an enhanced range estimation method that can take into consideration a variety of environmental and behavioural factors. The proposed range estimate is more accurate than when only SoC is considered and can be implemented as part of a comprehensive driver alerting system to alert the EV driver of the expected energy required to reach the destination, the expected range with current SoC, an advisory if charging will be required prior to reaching the destination and the suggested duration of the charging required. By reducing the uncertainty surrounding EV range, it is hoped that the uptake of EVs can be improved.
References
-
-
1)
-
2)
-
7. 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).
-
3)
-
P. Sabine ,
P. Marion ,
A. Jossen
.
Methods for state-of-charge determination and their applications.
J. Power Sources
,
113 -
120
-
4)
-
5)
-
6)
-
15. 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).
-
7)
-
8. 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).
-
8)
-
3. Pop, V., Bergveld, H.J., Danilov, D., Regtien, P.P.L., Notten, P.H.L.: ‘Battery management systems: accurate state-of-charge indication for battery-powered applications’, , (2008).
-
9)
-
12. 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).
-
10)
-
21. 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, (11), pp. 1–9 (doi: 10.1016/j.compchemeng.2012.02.003).
-
11)
-
12)
-
10. Huria, T., Ceraolo, M., Gazzarri, J., Jackey, R.: ‘Simplified extended Kalman filter observer for SOB estimation of commercial power-oriented FLP lithium battery cells’ (The MathWorks, Inc., 2013).
-
13)
-
13. 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).
-
14)
-
S. Lee ,
J. Kim ,
J. Lee ,
B. Cho
.
State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge.
J. Power Sources
,
2 ,
1367 -
1373
-
15)
-
6. 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).
-
16)
-
17)
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