Development of Energy Management of Hybrid Electric Vehicle for Improving Fuel Consumption via Sequential Approximate Optimization
Ryuhei Hagura* and Satoshi Kitayama**
*Suzuki Motor Corporation, 300 Takatsuka-cho, Minami-ku, Hamamatsu City 432-8611, Japan
**Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
This paper proposes a practical method for improving fuel consumption of hybrid electric vehicle (HEV) using a sequential approximate optimization. In particular, a new energy management is developed with four design variables. The numerical simulation of HEV is so expensive that a sequential approximate optimization using the radial basis function network is adopted. Numerical result showed that the proposed energy management significantly improves the fuel consumption of HEV.
-  C. Musardo, G. Rizzoni, and B. Staccia, “A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management,” Proc. of the 44th IEEE Conf. on Decision and Control, and the European Control Conf., pp. 1816-1823, December 2005.
-  F. Payri, C. Guardiola, B. Pla, and D. Blanco-Rodriguez, “On a Stochastic Approach of the ECMSMethod for Energy Management in Hybrid Electric Vehicles,” The IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling, pp. 341-348, October 2012.
-  A. Sciarretta and L. Guzzella, “Control of hybrid electric vehicles,” Control Systems, IEEE Control System, Vol.27, pp. 60-70, April 2007.
-  M. Suzuki, S. Yamaguchi, T. Araki, P. Raksincharoensak, M. Yoshizawa, and M. Nagai, “Fuel Economy Improvement Strategy for Light Duty Hybrid Truck Based on Fuel Consumption Computational Model Using Neural Network,” Proc. of the IFAC World Congress, July 2008.
-  D. V. Prokhorov, “Toyota Prius HEV neurocontrol and diagnostics,” Science Direct (Neural Networks), pp. 458-465, 2008.
-  K. Yu, M. Mukai, and T. Kawabe, “A Battery Management System using Nonlinear Model Predictive Control for a Hybrid Electric Vehicle,” Preprints of the IFAC Symposium on Advances in Automotive control, September 2013.
-  M. Debert, G. Colin, Y. Chamaillard, L. Guzzella, A. Ketfi-Cherif, and B. Bellicaud, “Predictive energy management for hybrid electric vehicles – Prediction horizon and battery capacity sensitivity,” 6th IFAC Symposium on Advances in Automotive control, July 2010.
-  H. Borhan, A. Vahidi, A. M. Phillips, M. L. Kuang, and I. V. Kolmanovsky, “Predictive Energy Management of a Power-Split Hybrid Electric Vehicle,” American Control Conf., June 2009.
-  Y. Yausi, “EV’s Energy Management Control Using Extremum Seeking Algorithm,” JSAE Annual Congress (Spring), May 2013.
-  C.-C. Lin, H. Peng, J. W. Grizzle, and J. Kang, “Power Management Strategy for a Parallel Hybrid Electric Truck,” IEEE Trans. on Control Systems Technology, Vol.11, No.6, November 2003.
-  T. Nuesch, M. Wang, C. Voser, and L. Guzzella, “Optimal Energy Management and Sizing for Hybrid Electric Vehicles Considering Transient Emissions,” The IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling, October 2012.
-  J. Dafeng, O. Yishi, L. Yugong, L. Keqiang, andW. Chuanyou, “Investigations on Both the optimal Control of a PHEV Power Assignment and Its Cost Function of the Dynamic Programming,” JSAE Annual Congress (Spring), May 2005.
-  M. Montazeri-Gh, A. Poursamad, and B. Ghalichi, “Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles,” J. of the Franklin Institute, Vol.343, pp. 420-435, August 2006.
-  Z. Wang, B. Huang, W. Li, and Y. Xu, “Particle Swarm Optimization for Operational Parameters of Series Hybrid Electric Vehicle,” IEEE Int. Conf. on Robotics and Biomimetics, December 2006.
-  J. Wu, C.-H. Zhang, and N.-X. Cui, “PSO Algorithm-Based Parameter Optimization for HEV Powertrain and its Control Strategy,” Int. J. of Automotive Technology, Vol.9, No.1, pp. 53-69, 2008.
-  W. Gao anb C. Mi, “Hybrid vehicle design using global optimisation algorithms,” Int. J. Electric and Hybrid Vehicles, Vol.1, No.1, 2007.
-  M. A. Ahmad, I. Baba, S. Azuma, and T. Sugie, “Model Free Tuning of Variable State of Charge Target of Hybrid Electric Vehicle,” Preprints of the IFAC Symposium on Advances in Automotive control, September 2013.
-  S. Kitayama, M. Arakawa, and K. Yamazaki, “Sequential approximate optimization using radial basis function network for engineering optimization” Optimization and Engineering, pp. 535-557, 2011.
-  S. Kitayama and K. Yamazaki, “Simple estimate of the width in Gaussian kernel with adaptive scaling technique,” Applied Soft Computing, pp. 4726-4737, 2011.