Author:
Su Qicong,Huang Ruchen,He Hongwen,Han Xuefeng
Publisher
Springer Nature Singapore
Reference17 articles.
1. Han, R., Lian, R., He, H., et al.: Continuous reinforcement learning based energy management strategy for hybrid electric-tracked vehicles. IEEE J. Emerg. Sel. Topics Power Electron. 11(1), 19–31 (2023)
2. Huang, R., He, H., Zhao, X., et al.: Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm. Appl. Energy 321, 119353 (2022)
3. Sun, F., Zhang, C.: Technologies for the Hybrid Electric Drive System of Armored Vehicles, pp. III-X+1–29+284–326. National Defense Industry Press, Beijing (2016)
4. Huang, R., He, H., Zhao, X., et al.: Longevity-aware energy management for fuel cell hybrid electric bus based on a novel proximal policy optimization deep reinforcement learning framework. J. Power Sources 561, 232717 (2023)
5. He, H., Meng, X.: A review on energy management technology of hybrid electric vehicles. Beijing Ligong Daxue Xuebao/Trans. Beijing Inst. Technol. 42(08), 773–783 (2022)