In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conven...In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conventional power sources (CPS) alone unable to control the frequency deviations caused by the aforementioned disturbances. Therefore, it is essential to modify the structure of LFC, to handle the disturbances caused by the RES and load. With regards to the above problem, this work proposes a novel coordinated LFC strategy with modified control signal to have Plug-in Hybrid Electric Vehicles (PHEVs) for frequency stability enhancement of the Japanese power system. Where, the coordinated control strategy is based on the PID controller, which is optimally tuned by the recently developed JAYA Algorithm (JA). Numerous simulations are performed with the proposed methodology and, the results have confirmed the effectiveness of a proposed approach over some recent and well-known techniques in literature. Furthermore, simulation results reveal that the proposed coordinated approach significantly minimizing the frequency deviations compared to the JAYA optimized LFC without PHEVs & with PHEVs but no coordination.展开更多
Plug-in hybrid electric vehicles(PHEVs) unite the advantages of the engine and electric motor which could provide great potential in saving energy. However, the fuel economy performance of the PHEVs is highly associat...Plug-in hybrid electric vehicles(PHEVs) unite the advantages of the engine and electric motor which could provide great potential in saving energy. However, the fuel economy performance of the PHEVs is highly associated with the driving condition, especially for parallel PHEVs because they could not decouple the engine work status from the driving condition. Meanwhile, fuel economy performance is not only a longitudinal issue but also related to lane selection. Lane selection is an important driving behavior and the algorithm of lane selection is necessary for the development progress of intelligent connected vehicles. Energy consumption cost is an important part of the vehicle’s using consumption cost. Therefore, lane selection strategies must consider this point.With the development of intelligent connected vehicle technology, such as V2X(Vehicle to Everything), the potential of energy consumption performance of intelligent connected PHEVs could be improved by taking environment information from V2X and smart sensors into lane selection. In this paper, a neural network(NN) based method is proposed to predict the future status of the local vehicle using the information from V2X, and then another network is used to estimate the future energy consumption of each lane. The lane selection is decided on energy consumption estimation. Lastly, the effectiveness of the method is validated by simulation using Matlab combined with SUMO(Simulation of Urban MObility).展开更多
The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term ...The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).展开更多
为改善某款混联插电式混合动力汽车(plug-in hybrid electric vehicle,PHEV)的燃油经济性,针对传统模糊控制中模糊隶属函数存在主观性过强、缺乏理论支撑等问题,提出了一种新的方法来优化PHEV模糊控制能量管理策略。首先,通过构建以整...为改善某款混联插电式混合动力汽车(plug-in hybrid electric vehicle,PHEV)的燃油经济性,针对传统模糊控制中模糊隶属函数存在主观性过强、缺乏理论支撑等问题,提出了一种新的方法来优化PHEV模糊控制能量管理策略。首先,通过构建以整车的需求转矩和电池荷电状态作为输入、发动机转矩作为输出的模糊控制器,选取21个隶属度函数变量,以优化燃油经济性为目标,利用混沌方式改进麻雀搜索优化算法进行优化;然后,通过Matlab/Simulink搭建控制策略,并联合AVL Cruise平台搭建的整车模型进行仿真,验证优化模糊控制能量管理策略的有效性。仿真结果表明,在全球轻型汽车测试循环(world light vehicle test cycle,WLTC)工况下,基于混沌方式改进麻雀搜索算法优化的模糊控制能量管理策略与原始模糊控制能量管理策略相比,百公里燃油消耗减少2.1%、NO_(x)气体排放减少13.3%、CO气体排放量下降1.3%、HC气体排放量减少2.9%,有效地提高整车燃油经济性,减少污染气体排放。展开更多
为了优化插电式混合动力汽车(Plug-in Hybrid Electric Vehicles,PHEV)能量管理策略,提高燃油经济性,提出基于深度强化学习的能量管理策略。通过对整车MATLAB/SimuLink建模,设计随动力电池SOC自适应奖励函数,使用NEDC和FTP-75工况进行...为了优化插电式混合动力汽车(Plug-in Hybrid Electric Vehicles,PHEV)能量管理策略,提高燃油经济性,提出基于深度强化学习的能量管理策略。通过对整车MATLAB/SimuLink建模,设计随动力电池SOC自适应奖励函数,使用NEDC和FTP-75工况进行智能体训练,在并联混动模式下,以WLTC-class3工况继续进行测试,相比于等效燃油消耗最小策略节省燃油8.63%,且实时性提高16.32倍,验证了该策略的可行性。展开更多
文摘In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conventional power sources (CPS) alone unable to control the frequency deviations caused by the aforementioned disturbances. Therefore, it is essential to modify the structure of LFC, to handle the disturbances caused by the RES and load. With regards to the above problem, this work proposes a novel coordinated LFC strategy with modified control signal to have Plug-in Hybrid Electric Vehicles (PHEVs) for frequency stability enhancement of the Japanese power system. Where, the coordinated control strategy is based on the PID controller, which is optimally tuned by the recently developed JAYA Algorithm (JA). Numerous simulations are performed with the proposed methodology and, the results have confirmed the effectiveness of a proposed approach over some recent and well-known techniques in literature. Furthermore, simulation results reveal that the proposed coordinated approach significantly minimizing the frequency deviations compared to the JAYA optimized LFC without PHEVs & with PHEVs but no coordination.
基金the National Key Research and Development Program of China(Grant No.2017YFB0103502)the National Natural Science Fundation of China(Grant No.51805290)+1 种基金the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY18E050015)the Ningbo Natural Science Foundation(Grant No.2018A610124)。
文摘Plug-in hybrid electric vehicles(PHEVs) unite the advantages of the engine and electric motor which could provide great potential in saving energy. However, the fuel economy performance of the PHEVs is highly associated with the driving condition, especially for parallel PHEVs because they could not decouple the engine work status from the driving condition. Meanwhile, fuel economy performance is not only a longitudinal issue but also related to lane selection. Lane selection is an important driving behavior and the algorithm of lane selection is necessary for the development progress of intelligent connected vehicles. Energy consumption cost is an important part of the vehicle’s using consumption cost. Therefore, lane selection strategies must consider this point.With the development of intelligent connected vehicle technology, such as V2X(Vehicle to Everything), the potential of energy consumption performance of intelligent connected PHEVs could be improved by taking environment information from V2X and smart sensors into lane selection. In this paper, a neural network(NN) based method is proposed to predict the future status of the local vehicle using the information from V2X, and then another network is used to estimate the future energy consumption of each lane. The lane selection is decided on energy consumption estimation. Lastly, the effectiveness of the method is validated by simulation using Matlab combined with SUMO(Simulation of Urban MObility).
文摘The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).
文摘为改善某款混联插电式混合动力汽车(plug-in hybrid electric vehicle,PHEV)的燃油经济性,针对传统模糊控制中模糊隶属函数存在主观性过强、缺乏理论支撑等问题,提出了一种新的方法来优化PHEV模糊控制能量管理策略。首先,通过构建以整车的需求转矩和电池荷电状态作为输入、发动机转矩作为输出的模糊控制器,选取21个隶属度函数变量,以优化燃油经济性为目标,利用混沌方式改进麻雀搜索优化算法进行优化;然后,通过Matlab/Simulink搭建控制策略,并联合AVL Cruise平台搭建的整车模型进行仿真,验证优化模糊控制能量管理策略的有效性。仿真结果表明,在全球轻型汽车测试循环(world light vehicle test cycle,WLTC)工况下,基于混沌方式改进麻雀搜索算法优化的模糊控制能量管理策略与原始模糊控制能量管理策略相比,百公里燃油消耗减少2.1%、NO_(x)气体排放减少13.3%、CO气体排放量下降1.3%、HC气体排放量减少2.9%,有效地提高整车燃油经济性,减少污染气体排放。
文摘为了优化插电式混合动力汽车(Plug-in Hybrid Electric Vehicles,PHEV)能量管理策略,提高燃油经济性,提出基于深度强化学习的能量管理策略。通过对整车MATLAB/SimuLink建模,设计随动力电池SOC自适应奖励函数,使用NEDC和FTP-75工况进行智能体训练,在并联混动模式下,以WLTC-class3工况继续进行测试,相比于等效燃油消耗最小策略节省燃油8.63%,且实时性提高16.32倍,验证了该策略的可行性。
