Abstract
One of the fundamental advantages of hybrid and electric vehicles compared to conventional vehicles is the regenerative braking mechanism. Some portion of the kinetic energy of the vehicle can be recovered during regenerative braking by using the electric drive system as a generator with the appropriate control strategy. The control requires distribution of the brake forces between front and rear axles of the vehicle and also between regenerative braking and frictional braking. In this paper, we propose solving the optimal brake force distribution problem using an Artificial Neural Network based methodology in order to maximize the available energy for recovery while following the rules for stability. Using the proposed approach, we find that for urban driving pattern, UDDS, up to 37 % of the total energy demand can be recovered. Then we compare the amount of recovered energy for different driving cycles and show that aggressive driving reduces recoverable energy up to 7%. An increase in the energy recovery rate directly translates into improvements in fuel economy and reductions in emissions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Shakouri, P., Ordys, A., Askari, M., Laila, D.S.: Longitudinal vehicle dynamics using Simulink/Matlab. In: UKACC International Conference on Control 2010, September 7-10, pp. 1–6 (2010), doi:10.1049/ic.2010.0410
Lintern, M.A., Chen, R., Carroll, S., Walsh, C.: Simulation study on the measured difference in fuel consumption between real-world driving and ECE-15 of a hybrid electric vehicle. In: Hybrid and Electric Vehicles Conference 2013 (HEVC 2013), November 6-7, pp. 1–6. IET (2013)
Cao, B., Bai, Z., Zhang, W.: Research on control for regenerative braking of electric vehicle. In: IEEE International Conference on Vehicular Electronics and Safety, October 14-16, pp. 92–97 (2005), doi:10.1109/ICVES.2005.1563620
Ortmeyer, T.H., Pillay, P.: Transportation sector technology energy use and GHG emissions. In: 2002 IEEE Power Engineering Society Summer Meeting, July 25-25, vol. 1, pp. 34–35 (2002), doi:10.1109/PESS.2002.1043173
Mutoh, N., Hayano, Y., Yahagi, H., Takita, K.: Electric Braking Control Methods for Electric Vehicles With Independently Driven Front and Rear Wheels. IEEE Transactions on Industrial Electronics 54(2), 1168–1176 (2007), doi:10.1109/TIE.2007.892731
Gao, Y., Chu, L., Ehsani, M.: Design and Control Principles of Hybrid Braking System for EV, HEV and FCV. In: IEEE Vehicle Power and Propulsion Conference, VPPC 2007, September 9-12, pp. 384–391 (2007)
Xu, G., Li, W., Xu, K., Song, Z.: An Intelligent Regenerative Braking Strategy for Electric Vehicles. . Energies 2011 4, 1461–1477 (2011)
Jing-Ming, Z., Bao-Yu, S., Shu-Mei, C., Dian-Bo, R.: Fuzzy Logic Approach to Regenerative Braking System. In: International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009, August 26-27, vol. 1, pp. 451–454 (2009)
Bathaee, S.M.T., Gastaj, A.H., Emami, S.R., Mohammadian, M.: A fuzzy-based supervisory robust control for parallel hybrid electric vehicles. In: 2005 IEEE Conference on Vehicle Power and Propulsion, September 7-9, p. 7 (2005)
Zeng, X., Ba, T., Wang, Q., Qu, X., Song, D.: A kind of accurately Optimized braking energy distribution strategy applied to switched Series-parallel Hybrid Electric Bus. In: 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), August 8-10, pp. 3634–3637 (2011)
Li, G., Zeng, Z.: A Neural-Network Algorithm for Solving Nonlinear Equation Systems. In: International Conference on Computational Intelligence and Security, CIS 2008, December 13-17, vol. 1, pp. 20–23 (2008), doi:10.1109/CIS.2008.65
National Renewable Energy Laboratory. ADVISOR Documentation, [EB/OL] (2001-01-19) [2005-04-15], http://www.ctts.nrel.gov/analysis/
Neural Network Toolbox [MathWorks MATLAB], http://www.mathworks.com/products/neural-network/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Shetty, S.S., Karabasoglu, O. (2014). Regenerative Braking Control Strategy for Hybrid and Electric Vehicles Using Artificial Neural Networks. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-11071-4_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11070-7
Online ISBN: 978-3-319-11071-4
eBook Packages: Computer ScienceComputer Science (R0)