Supervisory control of the hybrid off-highway vehicle for fuel economy improvement using predictive double Q-learning with backup models

This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.

全球新能源汽车产业竞争格局研究

本文通过对影响新能源汽车产业竞争力的关键因素分析,得出结论如下:第一,全球新能源汽车替代率仍较低,仍然处于发展初期,但发展迅速、市场前景乐观。新能源汽车产业规模不断扩大,相对于传统汽车的竞争能力有所提升,纯电动汽车的市场竞争能力强于插电式混合动力电动车。第二,在新能源汽车产业链条中,中美欧在终端市场销售上拥有优势,中日美三国在生产制造环节具有竞争优势,中日韩在产业配套上具有竞争优势;中国公共基础设施方面具有竞争优势,挪威在新能源汽车替代传统汽车方面表现突出。第三,中美两国新能源汽车产业的发展路径不同,各自的竞争优势也不同,竞争优势差异主要表现在:美国在核心技术创新能力、新能源汽车市场对外开放程度和单一车型市场占有率等方面具有显著的优势;中国则在政策支持力度、产业规模、市场规模、产业配套、政策综合效果等方面优势显著。