| 查看: 525 | 回复: 5 | |||
| 【有奖交流】积极回复本帖子，参与交流，就有机会分得作者 yaoshunbo 的 7 个金币 ，回帖就立即获得 1 个金币，每人有 1 次机会 | |||
[交流]
欢迎引用
|
|||
|
Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based con trol algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based pow ertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms. With the advancements of data science, machine learning has become a vital tool for improving decision making by using raw data and information as input. Significant results can be seen by using different machine learning techniques in various real-world domains, such as cybersecurity systems, engineering, healthcare, e-commerce, agriculture, etc. [1] [1] Yao, Z.; Yoon, H.-S.; Hong, Y.-K. Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning. Energies 2023, 16, 652. https://doi.org/10.3390/en16020652 |
回复此楼» 猜你喜欢
283求调剂，材料、化工皆可
已经有9人回复
-
081200-11408-276学硕求调剂
已经有4人回复
-
材料工程调剂
已经有4人回复
-
纺织、生物、化学、材料相关专业招生了
已经有7人回复
-
复试调剂
已经有4人回复
-
考研一志愿长安大学材料与化工309分请求调剂
已经有11人回复
-
2026年博士申请
已经有3人回复
-
26读博
已经有9人回复
-
伙伴们，祝我生日快乐吧
已经有11人回复
-
337一志愿华南理工0805材料求调剂
已经有8人回复
» 抢金币啦！回帖就可以得到:
-
招收苏州国家实验室和苏州大学联合培养博士（2026年9月入学）
+1/85
-
材料/化学相关专业2026级学术/专业型硕士研究生
+1/84
-
南开大学罗景山教授课题组招聘博士后（光/电催化、钙钛矿光伏方向）
+2/84
-
找工作经验求助
+1/43
-
纺织科学与工程、材料化工方向招收研究生
+1/41
-
arXiv 预印本求推荐
+2/40
-
sciencedirect 网页打不开了，怎么解决啊？
+1/39
-
广东工业大学管理科学与工程专业博士招生
+1/35
-
陕西理工大学急调考物理或天文方向的研究生
+1/32
-
深圳理工大学梁国进课题组（成会明院士团队）诚聘科研助理教授、博士后
+1/32
-
国自然的评审专家
+1/21
-
海南大学徐月山老师招生2026年第二批博士名额2~3个（高端设备开发方向）
+1/8
-
南方科技大学生命科学学院基础免疫与微生物学系招收博士后
+1/8
-
福建师范大学化学与材料学院杜克钊团队招生
+1/5
-
上海第二工业大学-朱大海课题组招生（过线就能调剂）
+1/5
-
浙江大学物理学院 汪银桥课题组 招收软凝聚态物理与统计物理方向 研究生和博士后
+1/4
-
国家杰青低维材料与器件力学团队2026年招收博士研究生
+1/4
-
哈尔滨工业大学（深圳）绿色化学团队招收2026年秋季入学博士生
+1/4
-
求调剂学校推荐，机械专硕342
+1/3
-
PD-1抗体如何拓展抗病毒治疗新 frontier？
+1/2
★
yaoshunbo(金币+1): 谢谢参与
yaoshunbo(金币+1): 谢谢参与
|
本帖内容被屏蔽 |
2楼2023-02-28 14:28:16
|
本帖内容被屏蔽 |
3楼2023-02-28 14:30:10
简单回复
2023-02-28 15:27
回复
yaoshunbo(金币+1): 谢谢参与
tzynew5楼
2023-02-28 16:21
回复
yaoshunbo(金币+1): 谢谢参与
o 发自小木虫Android客户端
2023-02-28 19:29
回复
