The reduction of fuel consumption in engines is always considered of vital importance.Along these lines,in this work,this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy.More ...The reduction of fuel consumption in engines is always considered of vital importance.Along these lines,in this work,this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy.More specifically,at first,a general first principles model for heavy-duty commercial vehicles and a transient fuel consumptionmodel for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data.The accuracy of the proposed modelwas demonstrated fromthe stage and the final results.Next,the control optimization problem resulting in low fuel consumption in heavy commercial vehicles was described,with minimal fuel usage as the optimization goal and throttle opening as the control variable.Then,a time-continuous engine management approach was assessed.Next,the factors that influence low fuel consumption in heavy-duty commercial vehicles were systematically examined.To reduce the computing complexity,the control strategies related to the time constraints of the engine were parametrized using three different methods.The most effective solution was obtained by applying a global optimization strategy because the constrained optimization problem was nonlinear.Finally,the effectiveness of the low-fuel consumption engine control strategy was demonstrated by comparing the simulated and field test results.展开更多
In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to ...In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.展开更多
利用车载全球定位系统(Global Position System,GPS)和地理信息系统(Geographic Information System,GIS)所提供的混合动力汽车未来一段预测路线上的道路交通信息、以及汽车当前运行状态模型,建立混合动力汽车在未来一段预测路线上的运...利用车载全球定位系统(Global Position System,GPS)和地理信息系统(Geographic Information System,GIS)所提供的混合动力汽车未来一段预测路线上的道路交通信息、以及汽车当前运行状态模型,建立混合动力汽车在未来一段预测路线上的运行状态模型;以蓄电池荷电状态为系统状态变量,电动机/发电机转矩为决策变量,混合动力汽车等效燃油消耗量最低为优化控制目标函数,运用动态规划逆序算法,建立了中度混合动力汽车预测控制数学模型;研究了混合动力汽车转矩分配的最优控制方法,并就如何减少动态规划法的计算量进行了研究。文中给出了某混合动力汽车的最优控制计算实例结果。展开更多
基金This work was supported in part by the Science and Technology Major Project of Guangxi under Grant AA22068001in part by the Key Research and Development Program of Guangxi AB21196029+3 种基金in part by the Project of National Natural Science Foundation of China 51965012in part by the Scientific Research and TechnologyDevelopment in Liuzhou 2022AAA0102,2021AAA0104 and 2021AAA0112in part by Agricultural Science and Technology Innovation and Extension Special Project of Jiangsu Province NJ2021-21,in part by the Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology,in part by the Guilin University of Electronic Technology 20-065-40-004Zin part by the Innovation Project of GUET Graduate Education 2022YCXS017.
文摘The reduction of fuel consumption in engines is always considered of vital importance.Along these lines,in this work,this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy.More specifically,at first,a general first principles model for heavy-duty commercial vehicles and a transient fuel consumptionmodel for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data.The accuracy of the proposed modelwas demonstrated fromthe stage and the final results.Next,the control optimization problem resulting in low fuel consumption in heavy commercial vehicles was described,with minimal fuel usage as the optimization goal and throttle opening as the control variable.Then,a time-continuous engine management approach was assessed.Next,the factors that influence low fuel consumption in heavy-duty commercial vehicles were systematically examined.To reduce the computing complexity,the control strategies related to the time constraints of the engine were parametrized using three different methods.The most effective solution was obtained by applying a global optimization strategy because the constrained optimization problem was nonlinear.Finally,the effectiveness of the low-fuel consumption engine control strategy was demonstrated by comparing the simulated and field test results.
文摘In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
文摘为改善四轮独立转向(4WIS)车辆的操纵稳定性,在设计了4WIS模型跟踪最优控制器的基础上,对最优控制参数对控制性能的影响以及4WIS车辆转向动力学特性进行了分析,提出了一种基于车辆转向状态的最优控制器参数调整策略,并设计了模糊逻辑控制参数调节器,实现最优控制器参数的自适应调整.结合4WIS车辆的八自由度动力学模型对提出的模糊最优控制系统进行仿真实验分析,结果表明:设计的4WIS模糊最优控制系统能够极大地改善车辆的稳定性与安全性;在高速低附着系数的极限工况下,该系统仍然够能保证车辆的理想转向状态.该系统对于强侧向风一类的侧向干扰具有很强的抑制能力;风速90 km/h的强侧风且无驾驶员干预情况下,车辆在320 m行驶距离内,侧向偏移量仅为0.78 m.
文摘利用车载全球定位系统(Global Position System,GPS)和地理信息系统(Geographic Information System,GIS)所提供的混合动力汽车未来一段预测路线上的道路交通信息、以及汽车当前运行状态模型,建立混合动力汽车在未来一段预测路线上的运行状态模型;以蓄电池荷电状态为系统状态变量,电动机/发电机转矩为决策变量,混合动力汽车等效燃油消耗量最低为优化控制目标函数,运用动态规划逆序算法,建立了中度混合动力汽车预测控制数学模型;研究了混合动力汽车转矩分配的最优控制方法,并就如何减少动态规划法的计算量进行了研究。文中给出了某混合动力汽车的最优控制计算实例结果。
