Multi-Objective Optimal Gearshift Control for Multispeed Transmission Electric Vehicles

Liu, Yanwei; Chen, Yuzhong; Li, Zhenye; Zhao, Kegang; Lin, Zhongqin

doi:10.1109/access.2020.3009481

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For four different positions of the piezoelectric actuator, the PID control and FxLMS control algorithm can excellently reduce the single frequency vibration of the system at 4 times the engagement base frequency [20]. For position 1, at the target frequency, the vibration can decrease by about 3 dB by the PID control.…”

Section: Optimization Of Piezoelectric Actuator Operationmentioning

confidence: 95%

Active Vibration Control of Robot Gear System Based on Adaptive Control Algorithm

Zhang

Guan

2022

Journal of Sensors

View full text Add to dashboard Cite

With the aim of solving the errors of gear transmission system in the actual manufacturing process, processing and installation, and solving the the vibration and noise of the gear system caused by the deformation brought about by external excitation such as motor load and actuator, which seriously threaten the safety and stability of unit equipment, a novel active vibration suppression structure of multistage gear system with built-in piezoelectric actuator is designed to generate active control force, and it can be used on the shaft. An active controller is designed and established using FxLMS adaptive algorithm. The results of this method show that by measuring the vibration signal system, the base frequency of the high-speed gear pair is 310 Hz, and the basic frequency of the low-speed gear pair is 192 Hz. I had the adaptation snare go for almost 0.5 seconds, with a difference of 0.52%. Adaptive Trap II reached in 1 second, with a difference of 0.96%. In the active vibration suppression test, the basic frequency of the high-speed gear pair is 804 Hz, and the basic frequency of the low-speed gear connector is 500 Hz. Using the FxLMS adaptive algorithm, it is able to effectively suppress the frequency vibrations of the high-speed dual-gear and low-speed dual-gear coupling systems of multispeed gears. After being controlled by FxLMS algorithm at the second frequency of the high-speed gear, the vibration reduction is about 10 dB at the third frequency of the low-speed gear. The vibration reduction is also approximately 7 dB. This has proved that a new experiment of industrial safety can be used to accelerate the movement of gear vibrations using the FxLMS adaptation algorithm.

show abstract

Section: Optimization Of Piezoelectric Actuator Operationmentioning

confidence: 95%

Active Vibration Control of Robot Gear System Based on Adaptive Control Algorithm

Zhang

Guan

2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…This approach enables precise and efficient control optimization with only a few experiments [22], [23]. Previous studies have applied PILCO to different engineering disciplines, including an autopilot underwater vehicle control problem [24], [25], constant force control of a robot surface [26], autonomous optimization of PID parameters in the control of a flight attitude simulator [27], and experiments on a planetary gear transmission shift console frame [28]. These studies have shown that PILCO can obtain the ideal controller strategy for the gearbox with a relatively small number of experiments.…”

Section: Introductionmentioning

confidence: 99%

The Optimization of RBFNN Gearshift Controller Parameters for Electric Vehicles Using PILCO Reinforcement Learning

Liu,

Zhang,

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

In order to improve the efficiency of gearshift controller parameter optimization and obtain a good gearshift controller control effect, this article proposes an optimization method for electric vehicle transmission gearshift controller, and selects dual clutch transmission as the research object that establishes an 11-degree-of-freedom gearshift dynamics model and a feedforward-feedback gearshift control model. The feedforward control is chosen from the target trajectory given by the Legendre pseudo-spectral approach, and the feedback controller is a Gaussian kernel radial basis function neural network controller. The feedback controller performs parameter optimization by the Probabilistic Inference for Learning Control (PILCO) reinforcement learning algorithm to obtain a control strategy that matches the actual gearshift conditions. By comparing how well the master-slave moving disk can follow the target trajectory during various optimization iterations, it is verified that the algorithm requires only a few experiments to complete the optimization, and the optimized Radial Basis Function Neural Network (RBFNN) control has a better control effect by comparing the results of different iterations. Applying the learned controller to various slope and load circumstances yields data that demonstrate that all can have an obvious optimization effect with good robustness. Additionally, the reinforcement learning technique suggested in this research can be used for various gearshift controller parameter optimization to assist engineers and technicians in increasing the effectiveness of their Research and Development.INDEX TERMS Electric vehicle, dual clutch transmission, gearshift control, reinforcement learning.

show abstract

“…As a typical algorithm of direct method, the pseudospectral method mainly includes three types: the Gauss pseudospectral method, the Legendre pseudospectral method, and the Radau pseudospectral method. Recently, the pseudospectral method has attracted increasing interest due to fast convergence and high precision, and it is widely used in many fields: shifting process control for transmission, 17,18 vehicle economical driving, 19,20 and spacecraft trajectory optimization. 21 The pseudospectral method discretizes the state and control variables and approximates them through the Lagrange interpolation base function.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

Liu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

show abstract

Multi-Objective Optimal Gearshift Control for Multispeed Transmission Electric Vehicles

Cited by 5 publications

References 26 publications

Active Vibration Control of Robot Gear System Based on Adaptive Control Algorithm

Active Vibration Control of Robot Gear System Based on Adaptive Control Algorithm

The Optimization of RBFNN Gearshift Controller Parameters for Electric Vehicles Using PILCO Reinforcement Learning

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

Contact Info

Product

Resources

About