Abstract
Driving cycles greatly influence the fuel economy and exhaust of the vehicle, especially in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle with better accuracy and less sampling time than other driving cycle recognition algorithms. A driving cycle recognition algorithm based on Learning Vector Quantization neural network is first created to analyze four selected representative standard driving cycles. Micro-trip extraction and box-and-whisker plots are then applied to ensure the diversity and magnitude of training samples. Finally, a sample training simulation is conducted to determine the minimum neuron number of learning vector quantization network, using the simulation platform of Matlab/Simulink. Afterwards, we simplify the structure of the recognition model to reduce data convergence time. Simulation results show the feasibility and efficiency of the proposed algorithm, which decreases the time window length from 120 s to 60 s with acceptable accuracy. Furthermore, the driving cycle recognition algorithm is used in a series-parallel hybrid vehicle model to improve the fuel economy by about 6.29%.
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Abbreviations
- g batt :
-
electricity equivalent fuel consumption [g/h]
- H LHV :
-
diesel combustion characteristic parameters [J/g]
- n m :
-
motor speed [1/min]
- N :
-
the total number of data [—]
- η batt :
-
battery operating efficiency [100%]
- η m :
-
motor operating efficiency [100%]
- SOC :
-
battery state of charge [100%]
- SOC obj :
-
battery SOC [100%]
- s chg :
-
charging equivalent coefficient [—]
- s dis :
-
discharging equivalent coefficient [—]
- T m :
-
motor torque [Nm]
- t max :
-
the maximum time length for the specific drive cycle [s]
- t i :
-
target output [—]
- t 0 :
-
end point of a single sample [s]
- Δ t :
-
forecast period based on the current conditions [s]
- t cur :
-
current time [s]
- ΔT :
-
sample length [s]
- Δω :
-
identification cycle [s]
- y i :
-
model prediction output [—]
- β 0 :
-
random parameter [—]
References
Bogosyan, S., Gokasan, M. and Goering, D. (2007). A novel model-based validation and estimation approach for hybrid serial electric vehicles. IEEE Trans. Veh. Technol. 56, 4, 1485–1497.
Boukehili, A., Zhang, Y. T., Zhao, Q., Ni, C. Q., Su, H. F. and Huang, G. J. (2012). Hybrid vehicle power management modeling and refinement. Int. J. Automotive Technology 13, 6, 987–998.
Chan, K. T. and Wong, Y. S. (2002). Overview of power management in hybrid electric vehicles. Energy Conversion and Management, 43, 1953–1968.
Deb, K., Jain, P., Gupta, N. K. and Maji, H. K. (2004). Multi-objective placement of electronic components using evolutionary algorithms[J]. IEEE Trans. Components and Packaging Technologies 27, 3, 480–492.
Gao, W. and Mi, C. (2007) Hybrid vehicle design using global optimisation algorithms[J]. Int. J. Electric and Hybrid Vehicles 1, 1, 57–70.
Gong, Q., Li, Y. and Peng, Z. R. (2008). Trip-based optimal power management of plug-in hybrid electric vehicles. Vehicular Technology, IEEE Trans., 57, 3393–3401.
He, H., Sun, C. and Zhang, X. (2012). A method for identification of driving patterns in hybrid electric vehicles based on a LVQ neural network. Energies, 5, 3363–3380.
Langari, R. and Won, J. S. (2003). A driving situation awareness-based energy management strategy for parallel hybrid vehicles. SAE Trans., 112, 1938–1947.
Langari, R. and Won, J. S. (2005). Intelligent energy management agent for a parallel hybrid vehicle-part I: System architecture and design of the driving situation identification process[J]. IEEE Trans. Vehicular Technology 54, 3, 925–934.
Lin, C. C., Jeon, S., Peng, H. and Lee, J. M. (2004). Driving pattern recognition for control of hybrid electric trucks. Veh. Syst. Dyn.: Int. J. Veh. Mech. Mobil., 42, 41–58.
Lin, C. C., Peng, H., Grizzle, J. W. and Kang, J. M. (2003). Power management strategy for a parallel hybrid electric truck[J]. IEEE Trans. Control System Technology 11, 6, 839–849.
Mansour, C. and Clodic, D. (2012). Optimized energy management control for the Toyota hybrid system using dynamic programming on a predicted route with short computation time. Int. J. Automotive Technology 13, 2, 309–324.
Mehrotra, K., Mohan, C. K. and Ranka, S. (1997). Elements of Artificial Neural Networks. MIT Press. Cambridge, MA, USA. 173–180.
Montazeri, M., Fotouhi, A. and Naderpour, A. (2012). Driving segment simulation for determination of the most effective driving features for HEV intelligent control. Veh. Syst. Dyn.: Int. J. Veh. Mech. Mobil, 50, 229–246.
Park, J., Chen, Z. and Murphey, Y. (2010). Intelligent vehicle power management through neural learning. Proc. Int. Joint Conf. Neural Netw., 1–7.
Park, J., Chen, Z. H., Kiliaris, L., Kuang, M. L., Masrur, M. A., Phillips, A. M. and Murphey, Y. L. (2009). Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion. IEEE Trans., 58, 4741–4756.
Schouten, N. J., Salman, M. A. and Kheir, N. A. (2003). Energy management strategies for parallel hybrid vehicles using fuzzy logic. Contr. Eng. Pract. 11, 2, 171–177.
Suh, B., Frank, A., Chung, Y. J., Lee, E. Y., Chang, Y. H. and Han, S. B. (2010). Economic value and utility of a powertrain system for a plug-in parallel diesel hybrid electric bus. Int. J. Automotive Technology 11, 4, 555–563.
Won, J. S. and Langari, R. (2005). Intelligent energy management agent for a parallel hybrid vehicle-Part I: System architecture and design of the driving situation identification process. IEEE Trans. Veh. Technol., 54, 925–934.
Wu, J., Zhang, C. H. and Cui, N. X. (2012). Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition. Int. J. Automotive Technology 13, 7, 1159–1167.
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Wang, J., Wang, Q.N., Zeng, X.H. et al. Driving cycle recognition neural network algorithm based on the sliding time window for hybrid electric vehicles. Int.J Automot. Technol. 16, 685–695 (2015). https://doi.org/10.1007/s12239-015-0069-3
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DOI: https://doi.org/10.1007/s12239-015-0069-3