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DUAL BATTERY BLOCK SYSTEM FOR ELECTRIC VEHICLES
Abstract
This work is aimed at the enlargement of the driving range in electric vehicles by improving the performance of the battery bench through a new configuration. The new design is based on a double battery bench, working in parallel, one of high discharge rate characteristics and the other of standard discharge rate. The operation of the double battery bench is regulated by a control system that drains current from the appropriate battery depending on the specific driving conditions and on the acceleration of the electric vehicle. Since acceleration requires a higher discharge rate than any other driving mode, the control system commutes from standard to high discharge rate battery when the acceleration exceeds a set up value or the driving conditions requirements are very demanding. The development of the project establishes the real driving conditions for urban routes based on the standard segments, acceleration, constant speed, deceleration, climbing and descent as well as on the specific driver’s mode, aggressive, moderate, and conservative. The design includes the layout of the system and how the control system “decides” which battery is being used. Specific algorithms for the operational mode are developed and verified using a scale prototype that reproduces the driving conditions of a real urban route
References
Wang, J., Besselink, I., & Nijmeijer, H. (2015). Electric vehicle energy consumption modelling
and prediction based on road information. World Electric Vehicle Journal, 7 3), pp 447–458
De Nunzio, G., & Thibault, L. (2017, June). Energy-optimal driving range prediction for electric
vehicles. In 2017 ieee intelligent vehicles symposium (iv) (pp. 1608–1613). IEEE
Vatanparvar, K., Faezi, S., Burago, I., Levorato, M., & Al Faruque, M.A. (2018). Extended range
electric vehicle with driving behavior estimation in energy management. IEEE transactions on
Smart Grid, 10 (3), pp 2959–2968
Varga, B.O., Sagoian, A., & Mariasiu, F. (2019). Prediction of electric vehicle range: A
comprehensive review of current issues and challenges. Energies, 12 (5), pp 946
Shankar, R., & Marco, J. (2013). Method for estimating the energy consumption of electric
vehicles and plug-in hybrid electric vehicles under real-world driving conditions. IET intelligent
transport systems, 7 (1), pp 138–150
Knowles, M., Scott, H., & Baglee, D. (2012). The effect of driving style on electric vehicle
performance, economy and perception. International Journal of Electric and Hybrid Vehicles, 4
(3), pp 228–247
Fiori, C., Ahn, K., & Rakha, H.A. (2016). Power-based electric vehicle energy consumption
model: Model development and validation. Applied Energy, 168, pp 257–268
Bozorgi, A.M., Farasat, M., & Mahmoud, A. (2017). A time and energy efficient routing
algorithm for electric vehicles based on historical driving data. IEEE Transactions on Intelligent
Vehicles, 2 (4), pp 308–320
Agrawal, S., Zheng, H., Peeta, S., & Kumar, A. (2016). Routing aspects of electric vehicle drivers
and their effects on network performance. Transportation Research Part D: Transport and
Environment, 46, pp246-266
Pi, J.M., Bak, Y.S., You, Y.K., Park, D.H., & Kim, H.S. (2016). Development of route
information based driving control algorithm for a range-extended electric vehicle. International
Journal of Automotive Technology, 17 (6), 1101–1111
Agrawal, S. K., Boyles, S.D., Jiang, N., Shahabi, M., & Unnikrishnan, A. (2015). Network route
choice model for battery electric vehicle drivers with different risk attitudes. Transportation
research record, 2498 (1), 75–83
Franke, T., & Krems, J.F. (2013). Interacting with limited mobility resources: Psychological
range levels in electric vehicle use. Transportation Research Part A: Policy and Practice, 48, pp
–122
Miri, I., Fotouhi, A., & Ewin, N. (2021). Electric vehicle energy consumption modelling and
estimation—A case study. International Journal of Energy Research, 45 (1), 501–520
Lee, C. H., & Wu, C.H. (2019). Learning to recognize driving patterns for collectively
characterizing electric vehicle driving behaviors. International journal of automotive technology,
(6), pp 1263–1276
Mao, L., Fotouhi, A., Shateri, N., & Ewin, N. (2021). A multi-mode electric vehicle range
estimator based on driving pattern recognition. Proceedings of the Institution of Mechanical
Engineers, Part C: Journal of Mechanical Engineering Science, 09544062211032994
Jalali, K., Lambert, S., & McPhee, J. (2012). Development of a path-following and a speed
control driver model for an electric vehicle
Alvarez, R., López, A., & De la Torre, N. (2015). Evaluating the effect of a driver’s behaviour on
the range of a battery electric vehicle. Proceedings of the Institution of Mechanical Engineers,
Part D: Journal of Automobile Engineering, 229 (10), 1379–1391
Marmaras, C., Xydas, E., & Cipcigan, L. (2017). Simulation of electric vehicle driver behaviour
in road transport and electric power networks. Transportation Research Part C: Emerging
Technologies, 80, 239–256
Al-Doori, M., Paluszczyszyn, D., Elizondo, D., Passow, B., & Goodyer, E. (2014). Range
extended for electric vehicle based on driver behaviour
https://dieselnet.com/standards/cycles/ftp75.php (Last online access: 10/01/2022)
https://en.wikipedia.org/wiki/Speed limits in the United States (Last online access: 10/01/2022)
https://dieselnet.com/standards/cycles/jp_jc08.php (Last online access: 10/01/2022)
https://en.wikipedia.org/wiki/Worldwide Harmonised Light Vehicles Test Procedure (Last online
access: 10/01/2022)
https://es.wikipedia.org/wiki/New_European_Driving_Cycle (Last online access: 10/01/2022)
Garche, J., Jossen, A., & Döring, H. (1997). The influence of different operating conditions,
especially over-discharge, on the lifetime and performance of lead/acid batteries for photovoltaic
systems. Journal of Power Sources, 67 (1–2),pp 201–212
Sauer, D. U., & Wenzl, H. (2008). Comparison of different approaches for lifetime prediction of
electrochemical systems—Using lead-acid batteries as example. Journal of Power sources, 176(2),
-546
Sulzer, V., Mohtat, P., Aitio, A., Lee, S., Yeh, Y.T., Steinbacher, F., ... & Howey, D. A. (2021).
The challenge and opportunity of battery lifetime prediction from field data. Joule, 5(8), pp1934-
Sarasketa-Zabala, E., Martinez-Laserna, E., Berecibar, M., Gandiaga, I., RodriguezMartinez, L.
M., & Villarreal, I. (2016). Realistic lifetime prediction approach for Li-ion batteries. Applied
energy, 162,pp 839-852
Hosen, M. S., Jaguemont, J., Van Mierlo, J., & Berecibar, M. (2021). Battery lifetime prediction
and performance assessment of different modeling approaches. Iscience, 24(2), 102060
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020). Battery lifetime prognostics. Joule, 4(2), 310-346
Yeap, Y. M., & Tran, D. (2019, October). Analysis of Driving Behavior’s Impact on Battery
Discharge Rate for Electric Vehicles. In 2019 IEEE Intelligent Transportation Systems
Conference (ITSC) (pp. 474–479). IEEE
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013). A review on the key issues for lithium-ion
battery management in electric vehicles. Journal of power sources, 226, 272- 288
Li, J., Ma, C., Chi, M., Liang, C., & Dudney, N.J. (2015). Solid electrolyte: the key for highvoltage lithium batteries. Advanced Energy Materials, 5 (4), 1401408
Yabuuchi, N., Takeuchi, M., Nakayama, M., Shiiba, H., Ogawa, M., Nakayama, K., Komaba, S.
(2015). High-capacity electrode materials for rechargeable lithium batteries: Li3NbO4-based
system with cation-disordered rocksalt structure. Proceedings of the National Academy of
Sciences, 112 (25), 7650–7655
Julien, C., Mauger, A., Vijh, A., & Zaghib, K. (2016). Lithium batteries. In Lithium Batteries (pp.
–68). Springer, Cham
Vincent, C.A. (2000). Lithium batteries: a 50-year perspective, 1959–2009. Solid State Ionics,
(1–2), 159–167
Linden’s Handbook of Batteries, Fifth Edition, Kirby W. Beard (ed.), Thomas B. Reddy (ed.
Emeritus), Ed. McGraw-Hill (2019)
Furukawa, N., & Nishio, K. (1993). Lithium batteries with polymer electrodes. In Applications of
electroactive polymers (pp. 150-181). Springer, Dordrecht
Wheelsatev.com (2020). Types of Lithium ion batteries that powers electric vehicles. [online]
Wheelsatev.com. Available from : https://www.wheelsatev.com/2020/10/types-of-lithium-ionbatteries-that.html
Wheelsatev.com (2020). Battery pack capacity calculation for EVs as per our required range.
[online] Wheelsatev.com. Available from: https://www.wheelsatev.com/2020/09/battery-packcapacity-calculation-for.html
Siliconlightworks.com. (2016). Silicon Lightworks. [online] Available from:https://siliconlightworks.com/li-ion-voltag
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