Abstract
Combination hybrid electric vehicle (HEV) mainly includes two configurations: series-parallel and power-split. It is necessary to consider a variety of metrics to comprehensively evaluate the configuration performance of HEV and compare the two configurations. In order to fully evaluate the HEVs’ potential in energy management, a practical and effective multi-objective method that can solve the global optimization-based energy management problem is needed. Based on the idea of dynamic programming (DP) and non-dominated sorting method, this paper proposes a global multi-objective optimization method of non-dominated sorting dynamic programming (NSDP) with multi-control variables. This algorithm can calculate a set of uniformly distributed Pareto solutions for the conflicting or coupling optimization objectives, and the performance of the solution set is improved due to the increase in the dimension of the control variables, which increases the strategy search space. NSDP is applied to two different configurations to fully evaluate the performance of fuel consumption and battery lifespan. The parameters of the configurations are optimized and comprehensively compared based on the implementation of NSDP. The above process can provide theoretical analysis for hybrid power system developers.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Abbreviations
- A :
-
frontal area, m2
- b e :
-
fuel consumption rate of engine, g/kWh
- Te :
-
output torque of engine, N·m
- T e_max :
-
maximum output torque of the engine, N·m
- Te_min :
-
minimum output torque of the engine, N·m
- n e :
-
output speed of the engine, rad/s
- n e_max :
-
maximum output speed of the engine, rad/s
- n e_min :
-
minimum output speed of the engine, rad/s
- SOC :
-
state of charge of the battery
- P bat :
-
output power of the power battery, kW
- U oc :
-
open circuit voltage of the power battery, V
- Q nom :
-
nominal capacity of the power battery, As
- R int :
-
internal resistance of the power battery, Ω
- ηt :
-
transmission working efficiency
- u a :
-
vehicle speed, m/s
- r v :
-
tire radius, m
- T d :
-
required torque at wheel end, N·m
- α :
-
planetary gear characteristic parameter
- i 0 :
-
gear ratio of main reducer
- i 1 :
-
gear ratio from Motor2 to main reducer
- T MG1 :
-
output torque of the Motor1, N·m
- T MG1_min :
-
minimum output torque of the Motor1, N·m
- T MG1_max :
-
maximum output torque of the Motor1, N·m
- n MG1 :
-
output speed of the Motor1, rad/s
- n MG1_min :
-
minimum output speed of the Motor1, rad/s
- n MG1_max :
-
maximum output speed of the Motor1, rad/s
- T MG2 :
-
output torque of the Motor2, N·m
- T MG2_min :
-
minimum output torque of the Motor2, N·m
- T MG2_max :
-
maximum output torque of the Motor2, N·m
- n MG2 :
-
output speed of the Motor2, rad/s
- n MG2_min :
-
minimum output speed of the Motor2, rad/s
- n MG2_max :
-
maximum output speed of the Motor2, rad/s
- T m :
-
output torque of the driving motor, N·m
- n m :
-
output speed of the driving motor, rad/s
- m :
-
curb weight of vehicle, kg
- g :
-
gravity constant, m/s2
- f :
-
rolling resistance coefficient
- i s :
-
slope of the road
- C D :
-
air resistance coefficient
- δ :
-
rotational inertial coefficient
- Ah :
-
accumulated Ah-throughput
- SOC max :
-
maximum allowable SOC values of the battery
- SOC min :
-
minimum allowable SOC values of the battery
- P bat_min :
-
minimum output power of the battery, kW
- P bat_max :
-
maximum output power of the battery, kW
References
Anselma, P. G., Biswas, A., Belingardi, G. and Emadi, A. (2020a). Rapid assessment of the fuel economy capability of parallel and series-parallel hybrid electric vehicles. Applied Energy, 275, 115319.
Anselma, P. G., Kollmeyer, P., Belingardi, G. and Emadi, A. (2020b). Multi-objective hybrid electric vehicle control for maximizing fuel economy and battery lifetime. IEEE Transportation Electrification Conf. and Expo (ITEC), Chicago, Illinois, USA.
Chen, Z. Hu, H., Wu, Y., Xiao, R., Shen, J. and Liu, Y. (2018). Energy management for a power-split plug-in hybrid electric vehicle based on reinforcement learning. Applied Sciences 8, 12, 2494.
Deng, T. Lin, C., Luo, J. and Chen, B. (2019). NSGA-II multi-objectives optimization algorithm for energy management control of hybrid electric vehicle. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 233, 4, 1023–1034.
Du, A., Chen, C., Zhang, D. and Han, Y. (2021). Multi-objective energy management strategy based on PSO optimization for power-split hybrid electric vehicles. Energies 14, 9, 2438.
Fu, X., Zhang, Q., Tang, J. and Wang, C. (2019). Parameter matching optimization of a powertrain system of hybrid electric vehicles based on multi-objective optimization. Electronics 8, 8, 875.
Geng, W., Lou, D., Wang, C. and Zhang, T. (2020). A cascaded energy management optimization method of multimode power-split hybrid electric vehicles. Energy, 199, 117224.
Hannan, M. A., Azidin, F. A. and Mohamed, A. (2014). Hybrid electric vehicles and their challenges: A review. Renewable and Sustainable Energy Reviews, 29, 135–150.
Hu, D. and Zhang, Y. (2022). Deep reinforcement learning based on driver experience embedding for energy management strategies in hybrid electric vehicles. Energy Technology 10, 6, 2200123.
İnci, M., Büyük, M., Demir, M. H. and İlbey, G. (2021). A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects. Renewable and Sustainable Energy Reviews, 137, 110648.
Ju, F., Zhuang, W., Wang, L. and Zhang, Z. (2020). Comparison of four-wheel-drive hybrid powertrain configurations. Energy, 209, 118286.
Krithika, V. and Subramani, C. (2018). A comprehensive review on choice of hybrid vehicles and power converters, control strategies for hybrid electric vehicles. Int. J. Energy Research 42, 5, 1789–1812.
Mavrin, V., Magdin, K., Shepelev, V. and Danilov, I. (2020). Reduction of environmental impact from road transport using analysis and simulation methods. Transportation Research Procedia, 50, 451–457.
Serrao, L., Onori, S., Sciarretta, A., Guezennec, Y. and Rizzoni, G. (2011). Optimal energy management of hybrid electric vehicles including battery aging. Proc. American Control Conf. (ACC), San Francisco, California, USA.
Song, P., Lei, Y. and Fu, Y. (2020). Multi-objective optimization and matching of power source for PHEV based on genetic algorithm. Energies 13, 5, 1127.
Tang, X., Zhang, J., Cui, X., Lin, X., Grzesiak, L. M. and Hu, X. (2022a). Multi-objective design optimization of a novel dual-mode power-split hybrid powertrain. IEEE Trans. Vehicular Technology 71, 1, 282–296.
Tang, X., Zhang, J., Pi, D., Lin, X., Grzesiak, L. M. and Hu, X. (2022b). Battery health-aware and deep reinforcement learning-based energy management for naturalistic data-driven driving scenarios. IEEE Trans. Transportation Electrification 8, 1, 948–964.
Tie, S. F. and Tan, C. W. (2013). A review of energy sources and energy management system in electric vehicles. Renewable and Sustainable Energy Reviews, 20, 82–102.
Wei, C., Sun, X., Chen, Y., Zang, L. and Bai, S. (2021). Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle. Energy, 230, 120858.
Wu, J., He, H., Peng, J., Li, Y. and Li, Z. (2018). Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus. Applied Energy, 222, 799–811.
Xu, X., Zhao, J. L., Zhao, J. W., Shi, K., Dong, P., Wang, S., Liu, Y., Gou, W. and Liu, X. (2022). Comparative study on fuel saving potential of series-parallel hybrid transmission and series hybrid transmission. Energy Conversion and Management, 252, 114970.
Xue, Q., Zhang, X., Teng, T., Zhang, J., Feng, Z. and Lv, Q. (2020). A comprehensive review on classification, energy management strategy, and control algorithm for hybrid electric vehicles. Energies 13, 20, 5355.
Yang, Y., Pei, H., Hu, X. Liu, Y. Hou, C. and Cao D. (2019). Fuel economy optimization of power split hybrid vehicles: A rapid dynamic programming approach. Energy, 166, 929–938.
Zhang, Y., Zhao, H., Huang, K., Qiu, M. and Geng L. (2020). Hybrid optimization and its applications for multi-mode plug-in hybrid electric vehicle. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 234, 1, 228–244.
Acknowledgement
This work was supported by the Natural Science Foundation of Guangdong Province under Grant 2020A1515010773, and the Key-Area Research and Development Program of Guangdong Province under Grant 2019B090912001.
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
Xie, J., Liang, Z., Zhao, K. et al. Multi-objective Optimization Method with Multi Control Variables and Its Application in Configuration Comparison of Combination HEV. Int.J Automot. Technol. 24, 1493–1507 (2023). https://doi.org/10.1007/s12239-023-0120-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-023-0120-8