[1] 岳广照,刘兴华,仇滔.柴油机选择性催化还原系统氨泄漏及控制研究[J].兵工学报,2017,38(4):634-642.
YUE Guangzhao, LIU Xinghua, QIU Tao. Research on ammonia slip and control of diesel engine SCR system[J]. Acta Armamentarii,2017,38(4):634-642.
[2] 钱立军, 荆红娟, 邱利宏. 基于随机模型预测控制的四驱混合动力汽车能量管理[J]. 中国机械工程, 2018, 29(11):1342-1348.
QIAN Lijun, JING Hongjuan, QIU Lihong. Energy management of a 4WD HEV based on SMPC[J]. China Mechanical Engineering, 2018, 29(11):1342-1348.
[3] TORRES J L, GONZALEZ R, GIMENEZ A, et al. Energy manage-ment strategy for plug-in hybrid electric vehicles. A comparative study[J]. Applied Energy, 2014,
113:816-824.
[4] PENG J K, FAN H, HE H W, et al. A rule-based energy management strategy for a plug-in hybrid school bus based on a controller area network bus[J].
Energies, 2015, 8:5122-5142.
[5] 解少博, 陈欢, 刘通,等. 基于DP-ECMS的插电式混合动力城市客车能量管理策略研究[J]. 汽车工程, 2017,39(7):736-741.
XIE Shaobo, CHEN Huan, LIU Tong,et al. A research on energy management strategy for plug-in hybrid electric bus based on DP-ECMS strategy[J]. Automotive
Engineering, 2017,39(7): 736-741.
[6] ZOU Y, KONG Z H, LIU T, et al. A real-time Markov chain driver model for tracked vehicles and its validation: Its adaptability via stochastic dynamic
programming[J]. IEEE Transactions on Vehicular Technology, 2016, 66 (5):3571-3682.
[7] GUO L L, GAO B Z, Gao Y, et al. Optimal energy management for HEVs in eco-driving applications using bi-level MPC[J]. IEEE Transactions on Intelligent
Transportation Systems, 2017,18(8): 2153 - 2162.
[8] LI W M, XU G Q, XU Y S. Online learning control for hybrid electric vehicle[J]. Chinese Journal of Mechanical Engineering, 2012, 25(1):98-106.
[9] HU Y Q, YANG L, YAN B, et al. An online rolling optimal control strategy for commuter hybrid electric vehicles based on driving condition learning and
prediction[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6):4312-4327.
[10] LIU T, ZOU Y, LIU D X, et al. Reinforcement learning of adaptive energy management with transition probability for a hybrid electric tracked vehicle[J].
IEEE Transactions on Industrial Electronics, 2015, 62(12):7837-7846.
[11] LIU T, ZOU Y, LIU D X, et al. Reinforcement learning-based real-time energy management for a hybrid tracked vehicle[J]. Energies, 2015, 8(7), 7243-7260.
[12] HU Y, LI W M, XU H, et al. An online learning control strategy for hybrid electric vehicle based on fuzzy Q-Learning[J]. Energies, 2015, 8(10):11167-
11186.
[13] WU J D, HE H W, PENG J K, et al. Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus[J].
Applied Energy, 2018, 222:799-811.
[14] FENG T, LYU L. The characteristics of ammonia storage and the development of model-based control for diesel engine urea-SCR system[J]. Journal of
Industrial and Engineering Chemistry, 2015, 28:97-109.
[15] SCHAR, M, ONDER C H, GEERING H P. Control of an SCR catalytic converter system for a mobile heavy-duty application[J]. IEEE Transactions on Control
Systems Technology, 2006, 14(4):641-653.
[16] WILLI R, ROUDUIT B, KOEPPEL R A, et al. Selective reduction of NO by NH3, over vanadia-based commercial catalyst: Parametric sensitivity and kinetic
modelling[J]. Chemical Engineering Science,1996,51(11):2897-2902.
[17] 陈征, 刘亚辉, 杨芳. 基于进化-增强学习方法的插电式混合动力公交车能量管理策略[J]. 机械工程学报, 2017,53(16):86-93.
CHEN Zheng, LIU Yahui, YANG Fang. Energy management strategy for plug-in hybrid electric bus with evolutionary-reinforcement learning method[J]. Journal of
Mechanical Engineering, 2017,53(16): 86-93.
[18] 隗寒冰,朱宁.基于双状态动态规划算法的PHEV规则控制策略研究[J].机械传动,2018,42(2),6-13.
WEI Hanbing, ZHU Ning. Study on the rule-based control strategy for PHEV according to two-state dynamic programming algorithm[J]. Journal of Mechanical
Transmission,2018,42(2),6-13.
[19] HU Y, LI W M, XU K, et al. Energy management strategy for a hybrid electric vehicle based on deep reinforcement learning[J].Applied Sciences,2018, 8
(2):187-192.
[20] MNIH V, KAVUKCUOLU K, SILVER D, et al. Playing Atari with Deep Reinforcement Learning[R]. Deep Mind Technologies, 2013. |