元宇宙已在全球范围内引起各界的广泛关注,相关研究和应用不断涌现。为了深入了解国内外元宇宙研究和产业应用的现状、热点和趋势,本文采用了中国知网(CNKI)、Web of Science等权威数据库作为数据源,检索了元宇宙研究领域的重要文献,综...元宇宙已在全球范围内引起各界的广泛关注,相关研究和应用不断涌现。为了深入了解国内外元宇宙研究和产业应用的现状、热点和趋势,本文采用了中国知网(CNKI)、Web of Science等权威数据库作为数据源,检索了元宇宙研究领域的重要文献,综合运用VOSviewer、CiteSpace等可视化分析工具,对发文量、关键词共现网络、关键词聚类、关键词共现时区图谱、关键词突现时间线以及高被引文献进行了深度分析。结果表明,元宇宙是多种技术的系统集成,相关研究经历了较长的积累期,发文量和总被引频次在近两年呈现爆发式增长态势,其中中国的发文量领先。此外,该领域的研究内容涵盖面广泛,所属学科多元化,研究热点和前沿主要集中在虚拟现实、人工智能、区块链等方面。展开更多
The energy management strategy is an important part of a hybrid electrical vehicle design. It is used to improve fuel economy and to sustain a proper battery state of charge by controlling the power components while s...The energy management strategy is an important part of a hybrid electrical vehicle design. It is used to improve fuel economy and to sustain a proper battery state of charge by controlling the power components while satisfying various constraints and driving demands. However, achieving an optimal control performance is challenging due to the nonlinearities of the hybrid powertrain, the time varying constraints, and the dilemma in which controller complexity and real-time capability are generally conflicting objectives. In this paper, a real-time capable cascaded control strategy is proposed for a dual-mode hybrid electric vehicle that considers nonlinearities of the system and complies with all time-varying constraints. The strategy consists of a supervisory controller based on a non-linear model predictive control （MPC） with a long sampling time interval and a coordinating controller based on linear model predictive control with a short sampling time interval to deal with different dynamics of the system. Additionally, a novel data based methodology using adaptive Markov chains to predict future load demand is introduced. The predictive future information is used to improve controller performance. The proposed strategy is implemented on a real test-bed and experimental trials using unknown driving cycles are conducted. The results demonstrate the validity of the proposed approach and show that fuel economy is significantly improved compared with other methods.展开更多
The existing research into hybrid electric vehicle(HEV) control is mainly focussed on the optimisation of the power distribution between a conventional internal combustion engine(ICE) and an alternative power source(u...The existing research into hybrid electric vehicle(HEV) control is mainly focussed on the optimisation of the power distribution between a conventional internal combustion engine(ICE) and an alternative power source(usually a battery pack), however,transient control, which is a key technique that affects both fuel economy and the drivability of the HEV, has not been fully addressed. Especially in dual-mode power-split HE Vs, due to the different dynamic characteristics of the actuators in the transmission, and its complicated speed-torque relationship, transient control also affects the precision of power distribution and the speed of response of the electric output power. To improve the transient control performance, the design of an economic model predictive control(EMPC)-based transient controller for a dual-mode power-split HEV is developed. By incorporating an experimental identification model of a diesel ICE in a control-oriented transmission model, a better coordination among the actuators involved in HEV transmission can be achieved. Moreover, an ICE efficiency index is also added to the objective function to improve ICE fuel efficiency during this transient process. Then, a fast MPC method is applied to reduce the on-line computation effort required of the proposed control algorithm. By the flexible application of the EMPC and an innovative ICE model which is suited to the control-oriented model in EMPC, the transient control performance was improved. The effectiveness,and the real-time performance,of the control algorithm are validated by way of MATLABTM/Simulink-based simulations,as well as test-bed experiments combined with the use of the RapidECU platform.展开更多
文摘元宇宙已在全球范围内引起各界的广泛关注,相关研究和应用不断涌现。为了深入了解国内外元宇宙研究和产业应用的现状、热点和趋势,本文采用了中国知网(CNKI)、Web of Science等权威数据库作为数据源,检索了元宇宙研究领域的重要文献,综合运用VOSviewer、CiteSpace等可视化分析工具,对发文量、关键词共现网络、关键词聚类、关键词共现时区图谱、关键词突现时间线以及高被引文献进行了深度分析。结果表明,元宇宙是多种技术的系统集成,相关研究经历了较长的积累期,发文量和总被引频次在近两年呈现爆发式增长态势,其中中国的发文量领先。此外,该领域的研究内容涵盖面广泛,所属学科多元化,研究热点和前沿主要集中在虚拟现实、人工智能、区块链等方面。
基金supported by the National Natural Science Foundation of China(Grant Nos.51005017,51575043&U1564210)
文摘The energy management strategy is an important part of a hybrid electrical vehicle design. It is used to improve fuel economy and to sustain a proper battery state of charge by controlling the power components while satisfying various constraints and driving demands. However, achieving an optimal control performance is challenging due to the nonlinearities of the hybrid powertrain, the time varying constraints, and the dilemma in which controller complexity and real-time capability are generally conflicting objectives. In this paper, a real-time capable cascaded control strategy is proposed for a dual-mode hybrid electric vehicle that considers nonlinearities of the system and complies with all time-varying constraints. The strategy consists of a supervisory controller based on a non-linear model predictive control （MPC） with a long sampling time interval and a coordinating controller based on linear model predictive control with a short sampling time interval to deal with different dynamics of the system. Additionally, a novel data based methodology using adaptive Markov chains to predict future load demand is introduced. The predictive future information is used to improve controller performance. The proposed strategy is implemented on a real test-bed and experimental trials using unknown driving cycles are conducted. The results demonstrate the validity of the proposed approach and show that fuel economy is significantly improved compared with other methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.51005017,51575043&U1564210)
文摘The existing research into hybrid electric vehicle(HEV) control is mainly focussed on the optimisation of the power distribution between a conventional internal combustion engine(ICE) and an alternative power source(usually a battery pack), however,transient control, which is a key technique that affects both fuel economy and the drivability of the HEV, has not been fully addressed. Especially in dual-mode power-split HE Vs, due to the different dynamic characteristics of the actuators in the transmission, and its complicated speed-torque relationship, transient control also affects the precision of power distribution and the speed of response of the electric output power. To improve the transient control performance, the design of an economic model predictive control(EMPC)-based transient controller for a dual-mode power-split HEV is developed. By incorporating an experimental identification model of a diesel ICE in a control-oriented transmission model, a better coordination among the actuators involved in HEV transmission can be achieved. Moreover, an ICE efficiency index is also added to the objective function to improve ICE fuel efficiency during this transient process. Then, a fast MPC method is applied to reduce the on-line computation effort required of the proposed control algorithm. By the flexible application of the EMPC and an innovative ICE model which is suited to the control-oriented model in EMPC, the transient control performance was improved. The effectiveness,and the real-time performance,of the control algorithm are validated by way of MATLABTM/Simulink-based simulations,as well as test-bed experiments combined with the use of the RapidECU platform.
