共查询到20条相似文献，搜索用时 15 毫秒
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Junnian Wang Jun Wang Qingnian Wang Xiaohua Zeng 《Journal of The Franklin Institute》2018,355(5):2283-2312
A rule-based energy management strategy, that the control rules are extracted from acknowledged optimal algorithms and its control parameters are optimized offline and corrected online, for a series-parallel hybrid powertrain with an automatic mechanical transmission (AMT) is proposed in this paper to achieve near optimal fuel economy and battery state-of-charge (SOC) balance. Firstly, the dynamic programming (DP) global optimization method is applied to extract driving-mode transition rules and gear shifting rules. Furthermore, an instantaneous equivalent fuel consumption minimizing optimization method (ECMS) is utilized to determinate the engine torque distribution rules during its parallel driving mode. Then selected control parameters of driving-mode switching rules and torque split distribution are optimized based on genetic algorithm (GA) for further fuel consumption improvement. And the adaptive correction of optimized control parameters based on online driving cycle recognition method is discussed also. The simulation results show that this real-time rule-based energy management control strategy associated with the series of optimization approaches comprehensively can achieve a relatively close fuel consumption results to global optimal results and sustain the battery SOC balance after the end of driving cycle without much cycle-depending care. 相似文献
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In parallel hybrid electric vehicles (HEVs), the power split between the engine and the electric motor as well as the gear shift in the gearbox determines the overall energy efficiency. In this paper an adaptive energy management strategy with velocity forecast is proposed to optimize the fuel consumption in parallel HEVs, which is formulated into a mixed-integer optimization problem. Approximate dynamic programming with a novel actor-gear-critic design is presented for simultaneously controlling the power split and gear shift online. The power split as a continuous variable is determined from an actor network to realize the energy distribution between two power sources. The gear shift as a discrete variable is obtained from a gear network to adjust the gear ratio in the gearbox. The concept enables an online learning of the energy management strategy for different driving behaviors without the requirement of a system model and the driving cycle. Simulation results indicate that the proposed strategy achieves close fuel economy compared with the optimal solutions resulting from dynamic programming. Furthermore, a multi-stage neural network is introduced for velocity forecast, providing a computationally efficient training framework with good prediction performance. The velocity prediction is finally combined with the energy management strategy for an effective application and fuel economy. 相似文献
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《Journal of The Franklin Institute》2022,359(16):8713-8741
In this paper, the optimal driving torques of four wheels in an electric vehicle (EV) are obtained by minimizing the losses of four in-wheel motors. In order to slightly change these optimal torques for vehicle stability recovery, a new constrained active front steering (AFS) system is analytically designed and integrated with the torque vectoring (TV) system in a novel decentralized structure. In the proposed structure, the required external yaw moment is applied by the TV controller only when the constrained AFS is filled to capacity determined by a stability index extracted from the nonlinear phase plane analysis. As a result of this integration strategy, the external yaw moment is used as low as possible. Consequently, the torques in electrical motors are used near the optimal values consistent with optimal energy consumption. Comparative simulation studies with the standalone TV are conducted in the CarSim software environment to show the efficiency of the proposed decentralized control structure in terms of energy consumption and stability. Moreover, the suitability of the constrained control method used in the integration structure is shown in comparison with the well-known nonlinear model predictive control method in terms of practical implementation. 相似文献
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为研究电动汽车的电池和整车的动力性能,建立了电动汽车的蓄电池、电机及整车的力学和数学模型,基于ADVISOR软件建立了车辆、电池、电机和整车的仿真模型。根据整车设计技术参数进行车辆行驶性能仿真,选取CYC_ECE循环工况,得出了速度和SOC值变化的仿真结果。与实车试验结果对比表明,建立的模型是合理,动力系统设计可行。 相似文献
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《Journal of The Franklin Institute》2022,359(6):2487-2513
To improve the path tracking control performance of the intelligent vehicle under critical maneuvers, a novel control strategy is introduced in this work. Considering that the tire cornering characteristics show high nonlinearities and uncertainties under those special driving conditions, a three-dimensional piecewise affine (PWA) identification method is proposed to realize the nonlinear modeling of the tire cornering characteristics for the first time. On this basis, the PWA model of the vehicle lateral dynamics is established. To obtain the vehicle target yaw rate for path tracking, a driver direction control model with adaptive preview time is put forward. Then, the linear quadratic optimal control method is further adopted to design multiple path tracking controllers for different working areas of the affine subsystems, thus the optimal steering angles of the front wheels can be generated to guarantee the path tracking performance for the intelligent vehicle under a wide range of driving conditions. Finally, to evaluate the performance of the proposed path tracking control strategy which considers the tire nonlinear cornering characteristics in the PWA form, the CarSim-Simulink co-simulation work is conducted. The co-simulation results show that the proposed control strategy presents significant performance advantages over the other two methodologies and demonstrates satisfactory path tracking control performance. 相似文献
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《Journal of The Franklin Institute》2018,355(18):9321-9350
This paper simultaneously addresses the parameter/state uncertainties, external disturbances, input saturations, and actuator faults in the handling and stability control for four-wheel independently actuated (FWIA) electric ground vehicles (EGVs). Considering the high cost of the available sensors for vehicle lateral velocity measurement, a robust H∞ dynamic output-feedback controller is designed to control the vehicle motion without using the lateral velocity information. The investigated parameter/state uncertainties include the tire cornering stiffness, vehicle mass, and vehicle longitudinal velocity. The unmodeled terms in the vehicle lateral dynamics model are dealt as the external disturbances. Faults of the active steering system and in-wheel motors can cause dangerous consequences for driving, and are considered in the control design. Input saturation issues for the tire forces can deteriorate the control effects, and are handled by the proposed strategy. Integrated control with active front steering (AFS) and direct yaw moment (DYC) is adopted to control the vehicle yaw rate and sideslip angle simultaneously. Simulation results based on a high-fidelity and full-car model via CarSim-Simulink show the effectiveness of the proposed control approach. 相似文献
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《Journal of The Franklin Institute》2023,360(3):1835-1869
The optimal control strategy constructed in the form of a state feedback is effective for small state perturbations caused by changes in modeling uncertainty. In this paper, we investigate a robust suboptimal feedback control (RSPFC) problem governed by a nonlinear time-delayed switched system with uncertain time delay arising in a 1,3-propanediol (1,3-PD) microbial fed-batch process. The feedback control strategy is designed based on the radial basis function to balance the two (possibly competing) objectives: (i) the system performance (concentration of 1,3-PD at the terminal time of the fermentation) is to be optimal; and (ii) the system sensitivity (the system performance with respect to the uncertainty of the time-delay) is to be minimized. The RSPFC problem is subject to the continuous state inequality constraints. An exact penalty method and a novel time scaling transformation approach are used to transform the RSPFC problem into the one subject only to box constraints. The resulting problem is solved by a hybrid optimization algorithm based on a filled function method and a gradient-based algorithm. Numerical results are given to verify the effectiveness of the developed hybrid optimization algorithm. 相似文献
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《Journal of The Franklin Institute》2023,360(11):7269-7293
The interferences and drivers' maloperations are important factors affecting vehicle driving safety. This paper investigates the problem of authority allocation to weaken the impact of interferences and drivers’ maloperations on the shared steering control system. Based on the parallel framework of the shared steering control system, an extended framework including the upper level and the lower lever is proposed. The lower lever is used to realize the shared steering control, which includes the driver model, trajectory tracking controller and vehicle model. To improve the robustness of the system, the uncertainty of vehicle dynamics parameters is considered in the trajectory tracking controller, including tire cornering stiffness and longitudinal velocity. The upper level is used to calculate the authority level of the driver and controller required by the lower lever, which consists of an authority dynamic allocation model and an authority allocation decision strategy. The role of the authority dynamic allocation model is to calculate the reference allocation level of the driver and controller dynamically. When the driver's operation and vehicle working states are trustworthy, the reference allocation levels of the driver and controller will be followed. Conversely, a decision result will be gained by the authority allocation decision strategy to replace the reference allocation levels, and the sum of the authority levels of the driver and the automation will not be fixed as 1. The simulation results show that the proposed approach can effectively improve vehicle driving safety, anti-interference and reliability, and can effectively reduce the impact of crosswind and driver's maloperation on vehicle safety, and alleviate the driver's operation load. 相似文献
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提出建设项目决策中的工期—成本—碳排放平衡问题,并建立多目标决策模型,提出求解算法。提出的改进自适应性混和遗传算法可求解该多目标优化问题,设计单点交叉和变异的修复式策略来避免不可行解的产生。通过锦屏二级水电建设项目的案例说明模型和算法的有效性和合理性,通过灵敏度分析以及与其他算法的比较说明该优化方法的高效性、灵活性和适应性。结果表明,降低待工时间、提高使用效率是降低碳排放的关键因素,揭示碳排放和成本、进度间的变化机理;结果可产生多个帕累托最优解;决策者可根据三个目标的偏好选择最终方案。 相似文献
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《Journal of The Franklin Institute》2022,359(10):4776-4803
Stability and energy consumption have always been important issues in electric vehicle research. Excessive slip energy not only aggravates tire wear, but also consumes energy of electric vehicle. In order to ensure the lateral stability and to reduce the slip energy dissipation of the distributed drive electric vehicle (DDEV) equipped with Mechanical Elastic Wheel (MEW), an integrated framework considering both tire slip energy dissipation and lateral stability control is proposed. The SESC (Slip Energy and Stability Control) is a hierarchical control framework for DDEV with MEW. A PID speed tracking controller and an (Integral Terminal Slide Mode) ITSM controller are designed at the upper-level controller. The ITSM controller can improve the lateral stability of the vehicle by obtaining the desired yaw moment. Speed tracking controller can stabilize the speed of the vehicle and obtain the desired longitudinal force. At the lower-level controller, the brush model of the MEW is proposed to express tire slip energy. In order to reduce the error of the vehicle dynamics and the slip energy dissipation, a mixed objective function including a holistic corner controller (HCC) and a minimum tire slip energy characterization is proposed. The proposed control framework is verified by Carsim and Matlab/Simulink under emergency simulation conditions. The simulation results show that the SESC based method can improve the lateral stability of DDEV with MEW effectively, and has better performance compared with fuzzyPID+AD based method. Meanwhile, the SESC achieves less slip energy than conventional torque distribution method. 相似文献
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Yogendra Arya 《Journal of The Franklin Institute》2018,355(5):2662-2688
The interconnected large-scale power systems are liable to performance degradation under the presence of sudden small load demands, parameter ambiguity and structural changes. Due to this, to supply reliable electric power with good quality, robust and intelligent control strategies are extremely requisite in automatic generation control (AGC) of power systems. Hence, this paper presents an output scaling factor (SF) based fuzzy classical controller to enrich AGC conduct of two-area electrical power systems. An implementation of imperialist competitive algorithm (ICA) is made to optimize the output SF of fuzzy proportional integral (FPI) controller employing integral of squared error criterion. Initially the study is conducted on a well accepted two-area non-reheat thermal system with and without considering the appropriate generation rate constraint (GRC). The advantage of the proposed controller is illustrated by comparing the results with fuzzy controller and bacterial foraging optimization algorithm (BFOA)/genetic algorithm (GA)/particle swarm optimization (PSO)/hybrid BFOA-PSO algorithm/firefly algorithm (FA)/hybrid FA-pattern search (hFA-PS) optimized PI/PID controller prevalent in the literature. The proposed approach is further extended to a newly emerged two-area reheat thermal-PV system. The superiority of the method is depicted by contrasting the results of GA/FA tuned PI controller. The proposed control approach is also implemented on a multi-unit multi-source hydrothermal power system and its advantage is established by Correlating its results with GA/hFA-PS tuned PI, hFA-PS/grey wolf optimization (GWO) tuned PID and BFOA tuned FPI controllers. Finally, a sensitivity analysis is performed to demonstrate the robustness of the proposed method to broad changes in the system parameters and size and/or location of step load perturbation. 相似文献
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Biao Ma Yulong Liu Xiaoxiang Na Yahui Liu Yiyong Yang 《Journal of The Franklin Institute》2019,356(8):4397-4419
Shared control structure is beneficial to steering controller design of intelligence vehicles, and human-machine goal consistency is a key prerequisite for shared control. However, the goal consistency is usually given and cannot be changed, and the steering controller in low goal consistency, which directly affect the vehicle performance in case of emergency, has not been sufficiently investigated. This paper proposes a shared steering controller for path-following task based on Nash game strategy and steer-by-wire system considering different human-machine goal consistency. The driver-automation interactive path-following task is modeled by non-cooperative MPC, and authority weight of lateral displacement is used to balance the control objectives of the driver and automation. Human-machine goal consistency is determined by the driver and the automation controller steering angle. Aimed at different goal consistencies, a continuous authority weight adjustment algorithm is designed to ensure correct path following. This is especially true in low consistency in this study, when four driving modes are given to meet the different demand for control power. Simulations and hard-in-loop tests are conducted to verify the proposed control algorithm and the results show that it can perform the path-following task irrespective of human-machine goal consistency. 相似文献
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《Journal of The Franklin Institute》2022,359(2):786-815
This paper addresses the design of a sampled-data model predictive control (MPC) strategy for linear parameter-varying (LPV) systems. A continuous-time prediction model, which takes into account that the samples are not necessarily periodic and that plant parameters vary continuously with time, is considered. Moreover, it is explicitly assumed that the value of the parameters used to compute the optimal control sequence is measured only at the sampling instants. The MPC approach proposed by Kothare et al. [1], where the basic idea consists in solving an infinite horizon guaranteed cost control problem at each sampling time using linear matrix inequalities (LMI) based formulations, is adopted. In this context, conditions for computing a sampled-data stabilizing LPV control law that provides a guaranteed cost for a quadratic performance criterion under input saturation are derived. These conditions are obtained from a parameter-dependent looped-functional and a parameter-dependent generalized sector condition. A strategy that consists in solving convex optimization problems in a receding horizon policy is therefore proposed. It is shown that the proposed strategy guarantees the feasibility of the optimization problem at each step and leads to the asymptotic stability of the origin. The conservatism reduction provided by the proposed results, with respect to similar ones in the literature, is illustrated through numerical examples. 相似文献
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Weiwei Yang Jiejunyi Liang Jue Yang Paul D. Walker Nong Zhang 《Journal of The Franklin Institute》2021,358(2):1214-1239
An hybrid uninterrupted multi-speed transmission (HUMST), based on the integration of a planetary gear set and a 3-speed automatic manual transmission (3-AMT), is developed to satisfy the specific performance indexes of mining trucks. The power-split device can alleviate and eliminate the inherent torque interruption of the 3-AMT during gear shift by implementing the designed cooperative shift control strategy which is optimized by quadratic performance index. In order to achieve fast torque coordination while guaranteeing the driving comfort performance, the torque profiles of the power split device and the traction motor are optimized by Linear-quadratic regulator (LQR) algorithm. Dynamic programming (DP) is implemented as a benchmark to demonstrate the maximum fuel efficiency of the proposed HUMST. Because of the high computational cost of optimal control strategies such as DP, an improved real-time control strategy (IRTCS) using modified Gaussian distribution function is proposed to significantly reduce the computing load. As efficiency-oriented energy control strategy would result in frequent gear shifts, to achieve a desirable tradeoff between the overall efficiency and the shift stability, multi-objective genetic algorithm (MGA) is integrated to optimize the overall performance. The detail mathematical and dynamic model shows that the proposed shifting strategy with LQR can effectively suppress shift jerk, and the proposed IRTCS with MGA can reduce shift frequency by 70.78% to improve the drivability, only sacrificing 4.86% of overall efficiency compared to that of DP. 相似文献
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气候变暖对日本的冲击非常大,因此日本政府一直非常重视碳减排问题。从20世纪70年代开始,日本政府出台了很多政策和法规,如环保积分制度、阳光计划等,取得了很好的效果,极大地推动了该国的低碳经济进程,这非常值得世界各国借鉴。本文基于日本的1966-2011年时间序列数据,利用状态空间模型,对日本碳排放的影响因素进行实证分析。结果表明,日本碳排放总量呈现波动式上升的趋势,人口规模、经济增长、技术进步与碳排放之间存在长期协整关系,是日本碳排放的主要影响因素。其中,人口规模对碳排放的影响弹性为负值,这主要归功于人均生活耗能的下降,从而抑制了碳排放,并且随着时间推移,抑制效应越来越明显;经济增长是日本碳排放的重要驱动因素,但随着时间推移,驱动效应逐渐减弱;技术进步能极大地缓解日本碳排放,并且随着时间推移,减排效应越来越显著。基于上述实证结论,在对日本经验做法进行总结的基础上,针对我国实际情况,提出简要政策建议,包括低碳技术开发和可再生能源利用补贴等,以更好地推动我国的碳减排。 相似文献
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In this paper, a self-triggered model predictive controller (MPC) strategy for nonholonomic vehicle with coupled input constraint and bounded disturbances is presented. First, a self-triggered mechanism is designed to reduce the computation load of MPC based on a Lyapunov function. Second, by designing a robust terminal region and proper parameters, recursive feasibility of the optimization problem is guaranteed and stability of the the closed-loop system is ensured. Simulation results show the effectiveness of proposed algorithm. 相似文献
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《Journal of The Franklin Institute》2019,356(12):6352-6372
In precision motion systems, well-designed feedforward control can effectively compensate for the reference-induced error. This paper aims to develop a novel data-driven iterative feedforward control approach for precision motion systems that execute varying reference tasks. The feedforward controller is parameterized with the rational basis functions, and the optimal parameters are sought to be solved through minimizing the tracking error. The key difficulty associated with the rational parametrization lies in the non-convexity of the parameter optimization problem. Hence, a new iterative parameter optimization algorithm is proposed such that the controller parameters can be optimally solved based on measured data only in each task irrespective of reference variations. Two simulation cases are presented to illustrate the enhanced performance of the proposed approach for varying tasks compared to pre-existing results. 相似文献