Abstract
Based on an analysis of the driving demand and system dynamics of heavy-duty vehicles equipped with electromechanical transmission (EMT), a double Markov model is put forward to represent drivers’ power demand for driving and electricity. Transfer probability matrices are calculated by utilizing the maximum likelihood estimation method. A power distribution control strategy based on stochastic dynamic programming (SDP) is proposed. With economy being the optimization goal, the model for power allocation control based on SDP is established while regarding the engine torque, motor speeds, vehicle speed and state of charge (SOC) as state variables’ engine speed and motor torques as control variables’ and power demands as interference variables. The SDP problem is solved by an improved policy iteration algorithm based on value iteration and policy iteration algorithms.
Similar content being viewed by others
References
Akca, S. and Ertugrul, S. (2014). eTS fuzzy driver model for simultaneous longitudinal and lateral vehicle control. Int. J. Automotive Technology 15, 5, 781–794.
Ali, I. B., Turki, M., Belhadj, J. and Roboam, X. (2018). Optimized fuzzy rule-based energy management for a battery-less PV/wind-BWRO desalination system. Energy, 159, 216–228.
Kang, M., Wu, Y. and Shen, T. (2017). Logical control approach to fuel efficiency optimization for commuting vehicles. Int. J. Automotive Technology 18, 3, 535–546.
Kim, N., Cha, S. and Peng, H. (2011). Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle. IEEE Trans. Control Systems Technology 19, 5, 1279–1287.
Kim, N. and Rousseau, A. P. (2011). Comparison between rule-based and instantaneous optimization for a single-mode, power-split HEV. SAE Technical Paper No. 2011-01-0873.
Lai, L. and Ehsani, M. (2012). Sensitivity analysis of vehicle performance to transmission parameters in parallel hevs with dynamic programming optimization. 2012 IEEE Transportation Electrification Conf. and Expo (ITEC). Dearborn, MI, USA.
Li, X., Han, L., Liu, H., Wang, W. and Xiang, C. (2019). Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm. Energy, 172, 1161–1178.
Lin, Y., Tang, P., Zhang, W. J. and Yu, Q. (2005). Artificial neural network modelling of driver handling behaviour in a driver-vehicle-environment system. Int. J. Vehicle Design 37, 1, 24–45.
Liu, G., Xu, Z., Xue, Y. and Tang, G. (2015). Optimized control strategy based on dynamic redundancy for the modular multilevel converter. IEEE Trans. Power Electron 30, 1, 339–348.
Liu, J. (2007). Modeling Configuration and Control Optimization of Power-Split Hybrid Vehicles. Ph.D. Dissertation. The University of Michigan. Ann Arbor, MI, USA.
Liu, J., Hagena, J., Peng, H. and Filipi, Z. S. (2008). Engine-in-the-loop study of the stochastic dynamic programming optimal control design for a hybrid electric HMMWV. Int. J. Heavy Vehicle Systems 15, 2–4, 309–326.
Martinez, C. M., Hu, X., Cao, D., Velenis, E., Gao B. and Wellers, M. (2017). Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective. IEEE Trans. Vehicular Technology 66, 6, 4534–1549.
Opila, D. F., Wang, X., McGee, R., Gillespie, R. B., Cook, J. A. and Grizzle, J. W. (2012). An energy management controller to optimally trade off fuel economy and drivability for hybrid vehicles. IEEE Trans. Control Systems Technology 20, 6, 1490–1505.
Opila, D. F., Wang, X., McGee, R. and Grizzle, J. W. (2013). Real-time implementation and hardware testing of a hybrid vehicle energy management controller based on stochastic dynamic programming. J. Dynamic Systems, Measurement, and Control 135, 2, 11.
Pippia, T., Sijs, J. and De Schutter, B. (2019). A single-level rule-based model predictive control approach for energy management of grid-connected microgrids. IEEE Trans. Control Systems Technology 28, 6, 2364–2376.
Qin, D. T., Peng, Z. Y., Duan, Z. H. and Yang, Y. (2014). Dynamic energy management strategy of HEV based on driving pattern recognition. China Mechanical Engineering 25, 11, 1550–1555.
Ramadan, H. S., Becherif, M. and Claude, F. (2017). Energy management improvement of hybrid electric vehicles via combined GPS/rule-based methodology. IEEE Trans. Automation Science and Engineering 14, 2, 586–597.
Smith, D., Douglas, R. and Naeem, W. (2018). Fuzzy rule-based energy management strategy for a parallel mild-hybrid electric bus. IEEE Int/.Conf. Electrical Systems or Aircraft, Railway, Ship Propulsion and Road Vehicles & Int. Transportation Electrification Conf. (ESARS-ITEC). Nottingham, UK.
Son, J., Park, M., Won, K., Kim, Y., Son, S., Mcgordon, A., Jennings, P. and Birrell, S. (2016). Comparative study between Korea and UK: relationship between driving style and real-world fuel consumption. Int. J. Automotive Technology 17, 1, 175–181.
Tate, E. D., Grizzle, J. W. and Peng, H. (2010). SP-SDP for fuel consumption and tailpipe emissions minimization in an EVT hybrid. IEEE Trans. Control Systems Technology 18, 3, 673–687.
Acknowledgement
This work was supported by Program for New Century Excellent Talents in University (NCET-12-0048) and National Natural Science Foundation of China under contract (NSFC PROGRAM, No. 51775040).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, H., Xu, R., Han, L. et al. Control Strategy for an Electromechanical Transmission Vehicle Based on a Double Markov Process. Int.J Automot. Technol. 22, 761–770 (2021). https://doi.org/10.1007/s12239-021-0069-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-021-0069-4