A Mobility Performance Assessment on Plug-in EV Battery
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Published
Oct 18, 2020
Seyed Mohammad Rezvanizanian
Yixiang Huang
Jiang Chuan
Jay Lee
Abstract
This paper deals with mobility prediction of LiFeMnPO4 batteries for an emission-free Electric Vehicle. The data-driven model has been developed based on empirical data from two different road types –highway and local streets –and two different driving modes – aggressive and moderate. Battery State of Charge (SoC) can be predicted on any new roads based on the trained model by selecting the drving mode. In this paper, the performance of Adaptive Recurrent Neural Network (ARNN) and regression is evaluated using two benchmark data sets. The ARNN model at first estimates the speed profile of the new road based on slope and then both slope and speed is going to be used as the input to estimate battery current and SoC. Through comparison it is found that if ARNN system is appropriately trained, it performs with better accuracy than Regression in both two road types and driving modes. The results show that prediction SoC model follows the Columb-counting SoC according to the road slope.
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Keywords
recurrent neural networks, Battery SoC, Mobility, Road condition, driving behavior
References
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Montazeri-Gh, M., Fotouhi, A., & Naderpour, A. (2011). Driving patterns clustering based on driving feature analysis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 225(6), 1301-1317.
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Xu, L., Wang, J., & Chen, Q. (2012). Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model. Energy Conversion and Management, 53(1), 33-39.
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Zhu, C. B., Coleman, M., & Hurley, W. G. (2004). State of charge determination in a lead-acid battery: Combined EMF estimation and Ah-balance approach.
Adornato, B., Patil, R., Filipi, Z., Baraket, Z., & Gordon, T. (2009). Characterizing naturalistic driving patterns for plugin hybrid electric vehicle analysis.
Bo, C., Zhifeng, B., & Binggang, C. (2008). State of charge estimation based on evolutionary neural network. Energy Conversion and Management, 49(10), 2788-2794.
Charkhgard, M., & Farrokhi, M. (2010). State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE Transactions on Industrial Electronics, 57(12), 4178-4187.
Fodor, D., Enisz, K., Doman, R., & Toth, P. (2011). Tire road friction coefficient estimation methods comparison based on different vehicle dynamics models.
Gonder, J., Markel, T., Thornton, M., & Simpson, A. (2007) Using global positioning system travel data to assess real-world energy use of plug-in hybrid electric vehicles. (pp. 26-32).
He, H., Xiong, R., & Guo, H. (2012). Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles. Applied Energy, 89(1), 413-420.
He, H., Xiong, R., Zhang, X., Sun, F., & Fan, J. (2011). State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model. IEEE Transactions on Vehicular Technology, 60(4), 1461-1469.
Huang, X., Tan, Y., & He, X. (2011). An intelligent multifeature statistical approach for the discrimination of driving conditions of a hybrid electric vehicle. IEEE Transactions on Intelligent Transportation Systems, 12(2), 453-465.
Julka, N., Thirunavukkarasu, A., Lendermann, P., Gan, B. P., Schirrmann, A., Fromm, H., & Wong, E. (2011). Making use of prognostics health management information for aerospace spare components logistics network optimisation. Computers in Industry, 62(6), 613-622.
Khaled, M., Harambat, F., Yammine, A., & Peerhossaini, H. (2010). Aerodynamic forces on a simplified car body - Towards innovative designs for car drag reduction.
Lee, T. K., Baraket, Z., Gordon, T., & Filipi, Z. (2011). Characterizing One-day Missions of PHEVs Based on Representative Synthetic Driving Cycles. SAE International Journal of Engines, 4(1), 1088-1101.
Liaw, B. Y., & Dubarry, M. (2007). From driving cycle analysis to understanding battery performance in real-life electric hybrid vehicle operation. Journal of Power Sources, 174(1), 76-88.
MacLean, H. L., & Lave, L. B. (2003). Life Cycle Assessment of Automobile/Fuel Options. Environmental Science and Technology, 37(23), 5445-5452.
Marina de Queiroz Tavares, J. G., Flavio Perucchi, Franz Baumgartner, Maria Youssefzadeh. (2010). Understanding future customer needs by monitoring EV-drivers' behavior. Paper presented at the Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition, Shenzhen, China.
Meissner, E., & Richter, G. (2003). Battery Monitoring and Electrical Energy Management precondition for future vehicle electric power systems. Journal of Power Sources, 116(1-2), 79-98.
Montazeri-Gh, M., Fotouhi, A., & Naderpour, A. (2011). Driving patterns clustering based on driving feature analysis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 225(6), 1301-1317.
Piller, S., Perrin, M., & Jossen, A. (2001). Methods for state-of-charge determination and their applications. Journal of Power Sources, 96(1), 113-120.
Rodrigues, S., Munichandraiah, N., & Shukla, A. K. (2000). Review of state-of-charge indication of batteries by means of a.c. impedance measurements. Journal of Power Sources, 87(1), 12-20.
Shafiei, A., & Williamson, S. S. (2010). Plug-in hybrid electric vehicle charging: Current issues and future challenges.
Shukla, A. K., Aricò, A. S., & Antonucci, V. (2001). An appraisal of electric automobile power sources. Renewable and Sustainable Energy Reviews, 5(2), 137-155.
Sioshansi, R., & Denholm, P. (2009). Emissions impacts and benefits of plug-in hybrid electric vehicles and vehicle-to-grid services. Environmental Science and Technology, 43(4), 1199-1204.
Ulrich, L. (2012). State of charge. IEEE Spectrum, 49(1), 56-59.
Wang, W. Q., Golnaraghi, M. F., & Ismail, F. (2004). Prognosis of machine health condition using neuro-fuzzy systems. Mechanical Systems and Signal Processing, 18(4), 813-831.
Xu, L., Wang, J., & Chen, Q. (2012). Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model. Energy Conversion and Management, 53(1), 33-39.
Zhang, J., & Lee, J. (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 196(15), 6007-6014.
Zhu, C. B., Coleman, M., & Hurley, W. G. (2004). State of charge determination in a lead-acid battery: Combined EMF estimation and Ah-balance approach.
Section
Technical Briefs