Closed-form solution to the dynamic programming for a heavy-duty parallel hybrid vehicle energy management

References

• Climent, H., Pla, B., Bares, P., & Pandey, V. (2021). Exploiting driving history for optimising the energy management in plug-in hybrid electric vehicles. Energy Conversion and Management, 234, 113919. https://doi.org/10.1016/j.enconman.2021.113919
   Web of Science ®Google Scholar
• Goos, J., Criens, C., & Witters, M. (2017). Automatic evaluation and optimization of generic hybrid vehicle topologies using dynamic programming. IFAC Papersonline, 50(1), 10065–10071. https://doi.org/10.1016/j.ifacol.2017.08.1778
   Google Scholar
• Guo, L., Gao, B., Liu, Q., Tang, J., & Chen, H. (2017). On-line optimal control of the gearshift command for multispeed electric vehicles. IEEE/ASME Transactions on Mechatronics, 22(4), 1519–1530. https://doi.org/10.1109/tmech.2017.2716340
   Web of Science ®Google Scholar
• Hao, L., Wang, Y., Bai, Y., & Zhou, Q. (2021). Energy management strategy on a parallel mild hybrid electric vehicle based on breadth first search algorithm. Energy Conversion and Management, 243, 114408. https://doi.org/10.1016/j.enconman.2021.114408
   Web of Science ®Google Scholar
• Larsson, V., Johannesson, L., & Egardt, B. (2014a). Cubic spline approximations of the Dynamic Programming cost-to-go in HEV energy management problems. 2014 European Control Conference (ECC).
   Google Scholar
• Larsson, V., Johannesson, L., & Egardt, B. (2015). Analytic solutions to the dynamic programming subproblem in hybrid vehicle energy management. IEEE Transactions on Vehicular Technology, 64(4), 1458–1467. https://doi.org/10.1109/tvt.2014
   Web of Science ®Google Scholar
• Larsson, V., Johannesson Mardh, L., Egardt, B., & Karlsson, S. (2014b). Commuter route optimized energy management of hybrid electric vehicles. IEEE Transactions on Intelligent Transportation Systems, 15(3), 1145–1154. https://doi.org/10.1109/tits.2013.2294723
   Web of Science ®Google Scholar
• Li, G., & Gorges, D. (2019). Ecological adaptive cruise control and energy management strategy for hybrid electric vehicles based on heuristic dynamic programming. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3526–3535. https://doi.org/10.1109/tits.2018.2877389
   Web of Science ®Google Scholar
• Li, P., Jiao, X., & Li, Y. (2021). Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles. Control Engineering Practice, 107, 104703. https://doi.org/10.1016/j.conengprac.2020.104703
   Web of Science ®Google Scholar
• Maamria, D., Gillet, K., Colin, G., Chamaillard, Y., & Nouillant, C. (2018). Computation of eco-driving cycles for hybrid electric vehicles: Comparative analysis. Control Engineering Practice, 71, 44–52. https://doi.org/10.1016/j.conengprac.2017.10.011
   Web of Science ®Google Scholar
• Maino, C., Misul, D., Musa, A., & Spessa, E. (2021). Optimal mesh discretization of the dynamic programming for hybrid electric vehicles. Applied Energy, 292, 116920. https://doi.org/10.1016/j.apenergy.2021.116920
   Web of Science ®Google Scholar
• Park, S., & Ahn, C. (2019). Power management controller for a hybrid electric vehicle With predicted future acceleration. IEEE Transactions on Vehicular Technology, 68(11), 10477–10488. https://doi.org/10.1109/tvt.2019.2939344
   Web of Science ®Google Scholar
• Peng, J., He, H., & Xiong, R. (2017). Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming. Applied Energy, 185, 1633–1643. https://doi.org/10.1016/j.apenergy.2015.12.031
   Web of Science ®Google Scholar
• Sabri, M. F., Danapalasingam, K. A., & & Rahmat, M. F. (2016). A review on hybrid electric vehicles architecture and energy management strategies. Renewable and Sustainable Energy Reviews, 53, 1433–1442. https://doi.org/10.1016/j.rser.2015.09.036
   Web of Science ®Google Scholar
• Silva, S. F. d., Eckert, J. J., Silva, F. L., Silva, L. C. A., & Dedini, F. G. (2021). Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization of energy consumption, exhaust emissions and battery degradation. Energy Conversion and Management, 234, 113909. https://doi.org/10.1016/j.enconman.2021.113909
   Web of Science ®Google Scholar
• Song, Z., Hofmann, H., Li, J., Han, X., & Ouyang, M. (2015). Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Applied Energy, 139, 151–162. https://doi.org/10.1016/j.apenergy.2014.11.020
   Web of Science ®Google Scholar
• Uebel, S., Murgovski, N., Tempelhahn, C., & Baker, B. (2018). Optimal energy management and velocity control of hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 67(1), 327–337. https://doi.org/10.1109/tvt.2017.2727680
   Web of Science ®Google Scholar
• Vinot, E., & Lahaye, D. (2017). Time reduction of the dynamic programming computation in the case of hybrid vehicle. International Journal of Applied Electromagnetics and Mechanics, 53, S213–S227. https://doi.org/10.3233/jae-140163
   Web of Science ®Google Scholar
• Xie, S., Hu, X., Xin, Z., & Brighton, J. (2019). Pontryagin’s minimum principle based model predictive control of energy management for a plug-in hybrid electric bus. Applied Energy, 236, 893–905. https://doi.org/10.1016/j.apenergy.2018.12.032
   Web of Science ®Google Scholar
• Yang, N., Han, L., Xiang, C., Liu, H., & Hou, X. (2021). Energy management for a hybrid electric vehicle based on blended reinforcement learning with backward focusing and prioritized sweeping. IEEE Transactions on Vehicular Technology, 70(4), 3136–3148. https://doi.org/10.1109/tvt.2021.3064407
   Web of Science ®Google Scholar
• Zhou, Q., & Du, C. (2021). A quantitative analysis of model predictive control as energy management strategy for hybrid electric vehicles: A review. Energy Reports, 7, 6733–6755. https://doi.org/10.1016/j.egyr.2021.09.119
   Web of Science ®Google Scholar