Research on TVD Control of Cornering Energy Consumption for Distributed Drive Electric Vehicles Based on PMP

Sun, Wen; Chen, Yang; Wang, Junnian; Wang, Xiangyu; Liu, Huan

doi:10.3390/en15072641

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Assuming that the vehicle is turning and driving without wheel slip, the longitudinal velocity is constant, the lateral acceleration is small, and the tire cornering behaviors are in the linear range. The simplified 2-degrees-of-freedom motion model of the vehicle is shown in Figure 20 after neglecting the lateral tilt, pitch, and longitudinal and vertical motions [44].…”

Section: Vehicle Reference Modelmentioning

confidence: 99%

Decoupling Control of Yaw Stability of Distributed Drive Electric Vehicles

Wang,

Liu,

Yang

et al. 2024

WEVJ

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Most of the research on driving stability control of distributed drive electric vehicles is based on a yaw motion design controller. The designed controller can improve the lateral stability of the vehicle well but rarely mentions its changes to the roll and pitch motion of the body, and the uneven distribution of the driving force will also cause instability in the vehicle speed, resulting in wheel transition slip, wheel sideslip, and vehicle stability loss. In order to improve the spatial stability of distributed-driven electric vehicles and resolve the control instability caused by their motion coupling, a decoupled control strategy of yaw, roll, and pitch motion based on multi-objective constraints was proposed. The strategy adopts hierarchical control logic. At the upper level, a yaw motion controller based on robust model predictive control, a roll motion controller, and a pitch motion controller based on feedback optimal control are designed. In the lower level, through the motion coupling analysis of the vehicle yaw control process, based on the coupling analysis, the vehicle yaw, roll, and pitch decoupling controller based on multi-objective constraints is designed. Finally, the effectiveness of the decoupling controller is verified.

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Section: Vehicle Reference Modelmentioning

confidence: 99%

Decoupling Control of Yaw Stability of Distributed Drive Electric Vehicles

Wang,

Liu,

Yang

et al. 2024

WEVJ

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“…If wind power is economical and photovoltaic is not economical. In this paper, wind power annual output curve and target year power load curve EL are fitted [21].…”

Section: Fitting Wind and Light Output Curvementioning

confidence: 99%

BAS-PSO Algorithm for Integrated Energy System Optimization of Multiple Energy Supply Facilities

Xiong¹,

Chen²,

Liu³

et al. 2023

SPEE

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Regional low carbon will play an important role in the path of achieving the “double carbon” goal, and there are still many key technologies to be broken in its planning research. In this paper, an optimal scheduling model of integrated energy system with multiple energy supply devices is established by using Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) algorithm. First, in the scheduling model, a carbon trading mechanism is introduced and a stepped carbon trading cost model is constructed to constrain the carbon emissions of the plant. Then, using drosophila algorithm, the premise of whether wind power generation and photovoltaic power generation need to be built is determined by judging the economics of wind power generation and photovoltaic power generation in the construction area, and then the target power consumption curve and new energy power supply output curve are fitted. Then use the life cycle analysis method to analyze the carbon emissions generated by electric energy storage equipment, consider the carbon trading mechanism in the system economic operation model, and solve the model by BAS-PSO algorithm to overcome the problems of local optimum and slow convergence speed. Finally, a typical park integrated energy system is simulated to analyze the economic operation conditions and energy efficiency level of the system before and after participation in demand response. The innovation of this paper lies in considering the carbon trading mechanism and solving with the optimized BAS-PSO algorithm. Considering carbon trading can effectively improve the system wind power consumption capacity, and the optimized BAS-PSO algorithm improves the defects of traditional PSO. The simulation analysis results show that the established optimal scheduling model with multiple energy supply devices can realize the optimal operation of the integrated energy system of the park in the demand response environment; the improved BAS-PSO finds the lowest energy consumption within 100 iterations in 21 out of 50 iterations; the improved BAS-PSO algorithm reduces the energy consumption by 27.1 kW on average.

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“…Nevertheless, this method is computationally demanding and struggles with real-time adaptability in dynamic driving environments. The PMP-based approach has been utilized in various investigations [17,18], where it converts the optimal control problem into a constrained optimization task. Significantly, an alternative algorithm for managing kinetic energy and fuel conversion [19], influenced by the equivalent consumption minimization strategy, has been put forth.…”

Section: Introductionmentioning

confidence: 99%

Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

Xu,

Zhang,

et al. 2023

Sustainability

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Eco-driving is a driving vehicle strategy aimed at minimizing energy consumption; that is, it is a method to improve vehicle efficiency by optimizing driving behavior without making any hardware changes, especially for autonomous vehicles. To enhance energy efficiency across various driving scenarios, including road slopes, car following scenarios, and traffic signal interactions, this research introduces an energy-conserving speed planning approach for self-driving electric vehicles employing reinforcement learning. This strategy leverages vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to acquire real-time data regarding traffic signal timing, leading vehicle speeds, and other pertinent driving conditions. In the framework of rolling horizon reinforcement learning (RHRL), predictions are made in each window using a rolling time domain approach. In the evaluation stage, Q-learning is used to obtain the optimal evaluation value, so that the vehicle can reach a reasonable speed. In conclusion, the algorithm’s efficacy is confirmed through vehicle simulation, with the results demonstrating that reinforcement learning adeptly modulates vehicle speed to minimize energy consumption, all while taking into account factors like road grade and maintaining a secure following distance from the preceding vehicle. Compared with the results of traditional adaptive cruise control (ACC), the algorithm can save 11.66% and 30.67% of energy under two working conditions.

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Research on TVD Control of Cornering Energy Consumption for Distributed Drive Electric Vehicles Based on PMP

Cited by 4 publications

References 23 publications

Decoupling Control of Yaw Stability of Distributed Drive Electric Vehicles

Decoupling Control of Yaw Stability of Distributed Drive Electric Vehicles

BAS-PSO Algorithm for Integrated Energy System Optimization of Multiple Energy Supply Facilities

Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

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