Deep Reinforcement Learning-Based Integrated Control of Hybrid Electric Vehicles Driven by Lane-Level High-Definition Map

Chen, Jiaxin; Li, Shen; Yang, Kai; Wei, Chongfeng

doi:10.1109/tte.2023.3288364

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…A double-helical planetary gear transmission (PGT) system has significant advantages, including small axial force, low noise, high load-bearing capacity, and high transmission power, which is widely used in heavy-load and high-power equipment such as marine, aerospace, aviation, and petroleum. 1–3 There is an impact load in the gear transmission process, which is due to the influence of transmission error, time-varying mesh stiffness, tooth surface friction, and tooth wear. As a result, the thickness, pressure, and temperature distribution of oil film in the lubricating contact area are also changed.…”

Section: Introductionmentioning

confidence: 99%

Research on the dynamic performance of double-helical planetary gear transmission under friction and wear

Wang,

Yang,

Tang

2024

Proceedings of the Institution of Mechanical Engineers, Part K:

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In this paper, the main research is to investigate the effects of mechanical, friction, temperature, and tooth wear on the dynamic performance of a double-helical planetary gear transmission system. To achieve this, a new lumped parameters model is presented, which combines dynamics and lubrication. The model allows for the study of excitation effects and considers various parameters such as material, lubricating oil, and working conditions. By proposing a multi-source excitation calculation method, the paper provides a novel approach that has not been reported in the existing literature. Additionally, the paper establishes a tooth wear model and a lubrication model for the double-helical gear pair. It shows that under constant torque, the displacement and acceleration amplitudes of the system initially decrease and then increase with increasing input speed. Under constant speed, the displacement and acceleration amplitudes increase with increasing torque. Amplitudes decrease during running-in wear, increase in stable wear, and rapidly increase in severe wear. Boundary lubrication minimizes vibration, while elastohydrodynamic lubrication maximizes it.

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Section: Introductionmentioning

confidence: 99%

Research on the dynamic performance of double-helical planetary gear transmission under friction and wear

Wang,

Yang,

Tang

2024

Proceedings of the Institution of Mechanical Engineers, Part K:

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“…Addressing complex dynamics, Peng et al [17] introduced a model-based deep reinforcement learning approach to motion control, leveraging deep reinforcement learning techniques to enhance control performance. Lastly, Chen et al [18] employed two deep reinforcement learning algorithms for controlling the speed, steering angle, and power distribution of the powertrain. This approach achieved stable and effective control outcomes, particularly in complex control scenarios.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Autonomous Vehicle Lateral Control: A Linear Complementarity Model-Predictive Control Approach

Ye,

Wang,

Dai

2023

Applied Sciences

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Model-predictive control (MPC) offers significant advantages in addressing constraint-related challenges and plays a pivotal role in self-driving car technology. Its primary goal is to achieve precise trajectory tracking while prioritizing vehicle stability and safety. However, real-time operations often face challenges related to computational demands and low computational efficiency. To address these challenges, this paper introduces a novel lateral control algorithm for self-driving vehicles, which utilizes the linear complementarity problem (LCP) instead of the conventional quadratic programming (QP) method as the MPC optimization solution. This innovative approach incorporates the electric steering system into the vehicle dynamics model, allowing for precise torque regulation of the steering motor and enhancing control accuracy. The MPC algorithm adopts the LCP solution method to calculate control signals based on the vehicle’s state, ensuring both rapid and stable vehicle control. Simulation results demonstrate that the proposed MPC algorithm, utilizing the LCP solution method, effectively addresses efficiency issues in the lateral motion of self-driving cars. This leads to improvements in both driving stability and real-time performance. Overall, this innovative approach lays a solid foundation for the practical implementation of self-driving cars.

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A unified benchmark for deep reinforcement learning-based energy management: Novel training ideas with the unweighted reward

Chen,

Tang,

Yang

2024

Energy

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Deep Reinforcement Learning-Based Integrated Control of Hybrid Electric Vehicles Driven by Lane-Level High-Definition Map

Cited by 4 publications

References 40 publications

Research on the dynamic performance of double-helical planetary gear transmission under friction and wear

Research on the dynamic performance of double-helical planetary gear transmission under friction and wear

Enhancing Autonomous Vehicle Lateral Control: A Linear Complementarity Model-Predictive Control Approach

A unified benchmark for deep reinforcement learning-based energy management: Novel training ideas with the unweighted reward

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