检索结果-南通市图书馆

GOPS:A general optimal control problem solver for autonomous driving and industrial control applications

在线全文

学校读者我要写书评

暂无评论

Communications in Transportation Research

Communications in Transportation Research 2023年第1期3卷 92-106页

作者： Wenxuan Wang Yuhang Zhang Jiaxin Gao Yuxuan Jiang Yujie Yang Zhilong Zheng Wenjun Zou Jie Li Congsheng Zhang Wenhan Cao Genjin Xie Jingliang Duan Shengbo Eben Li school of vehicle and mobility Tsinghua UniversityBeijing100084China

Solving optimal control problems serves as the basic demand of industrial control tasks.Existing methods like model predictive control often suffer from heavy online computational burdens.Reinforcement learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible,high-accuracy solvers.Current Reinforcement learning(RL)solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming skills.Besides,many of them only support Python-based environments and limit to model-free algorithms.To address this gap,this paper develops General Optimal control Problems Solver(GOPS),an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial fields.GOPS is built with a highly modular structure that retains a flexible framework for secondary development.Considering the diversity of industrial control tasks,GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction,controller design,and performance validation.To handle large-scale problems,GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and samplers.It offers a variety of common approximate functions for policy and value functions,including polynomial,multilayer perceptron,convolutional neural network,etc.Additionally,constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into GOPS.Several examples,including linear quadratic control,inverted double pendulum,vehicle tracking,humanoid robot,obstacle avoidance,and active suspension control,are tested to verify the performances of GOPS.

关键词： Industrial control Reinforcement learning Approximate dynamic programming Optimal control Neural network Benchmark

智能网联汽车预期功能安全关键挑战与中国解决方案

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Engineering 2023年第12期31卷 27-30页

作者： Jun Li Wenbo Shao Hong Wang Tsinghua Intelligent vehicle Design and Safety Research Institute School of Vehicle and MobilityTsinghua UniversityBeijing 100084China

Intelligent connected vehicles(ICVs)[1]represent a crucial strategic focus for global automobile industrial transformation and advancement.ICVs also play a significant role in enhancing driving safety,improving traffic efficiency,and enabling lowcarbon transportation.Consequently,numerous countries worldwide are expediting their efforts to establish a strong foundation for ICVs,progressing from technology research,development,and testing to widespread application and commercialization.This global momentum has resulted in a thriving ICV industry,demonstrating substantial growth and encouraging prospects.

关键词：智能网联汽车 driving advancement

Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning

在线全文

学校读者我要写书评

暂无评论

Frontiers of Information Technology & Electronic Engineering 2023年第1期24卷 131-140页

作者： Huiqian LI Jin HUANG Zhong CAO Diange YANG Zhihua ZHONG school of vehicle and mobility Tsinghua UniversityBeijing 100084China Chinese Academy of Engineering Beijing 100088China

Ensuring the safety of pedestrians is essential and challenging when autonomous vehicles are involved.Classical pedestrian avoidance strategies cannot handle uncertainty,and learning-based methods lack performance guarantees.In this paper we propose a hybrid reinforcement learning(HRL)approach for autonomous vehicles to safely interact with pedestrians behaving uncertainly.The method integrates the rule-based strategy and reinforcement learning strategy.The confidence of both strategies is evaluated using the data recorded in the training process.Then we design an activation function to select the final policy with higher confidence.In this way,we can guarantee that the final policy performance is not worse than that of the rule-based policy.To demonstrate the effectiveness of the proposed method,we validate it in simulation using an accelerated testing technique to generate stochastic pedestrians.The results indicate that it increases the success rate for pedestrian avoidance to 98.8%,compared with 94.4%of the baseline method.

关键词： Pedestrian Hybrid reinforcement learning Autonomous vehicles Decision-making

Mechanism of internal thermal runaway propagation in blade batteries

在线全文

学校读者我要写书评

暂无评论

Journal of Energy Chemistry 2024年第2期89卷 184-194,I0005页

作者： Xuning Feng Fangshu Zhang Wensheng Huang Yong Peng Chengshan Xu Minggao Ouyang school of vehicle and mobility Tsinghua UniversityBeijing 100084China

Blade batteries are extensively used in electric vehicles,but unavoidable thermal runaway is an inherent threat to their safe use.This study experimentally investigated the mechanism underlying thermal runaway propagation within a blade battery by using a nail to trigger thermal runaway and thermocouples to track its propagation inside a cell.The results showed that the internal thermal runaway could propagate for up to 272 s,which is comparable to that of a traditional battery module.The velocity of the thermal runaway propagation fluctuated between 1 and 8 mm s^(-1),depending on both the electrolyte content and high-temperature gas diffusion.In the early stages of thermal runaway,the electrolyte participated in the reaction,which intensified the thermal runaway and accelerated its propagation.As the battery temperature increased,the electrolyte evaporated,which attenuated the acceleration effect.Gas diffusion affected thermal runaway propagation through both heat transfer and mass transfer.The experimental results indicated that gas diffusion accelerated the velocity of thermal runaway propagation by 36.84%.We used a 1D mathematical model and confirmed that convective heat transfer induced by gas diffusion increased the velocity of thermal runaway propagation by 5.46%-17.06%.Finally,the temperature rate curve was analyzed,and a three-stage mechanism for internal thermal runaway propagation was proposed.In Stage I,convective heat transfer from electrolyte evaporation locally increased the temperature to 100℃.In Stage II,solid heat transfer locally increases the temperature to trigger thermal runaway.In StageⅢ,thermal runaway sharply increases the local temperature.The proposed mechanism sheds light on the internal thermal runaway propagation of blade batteries and offers valuable insights into safety considerations for future design.

关键词： Lithium-ion battery Blade battery Thermal runaway Internal thermal runaway propagation

Energy Management Optimization Based on Aging Adaptive Functional State Model of Battery for Internal Combustion Engine vehicles

在线全文

学校读者我要写书评

暂无评论

Automotive Innovation 2023年第1期6卷 62-75页

作者： Weiwei Kong Tianmao Cai Yugong Luo Xiaomin Lian Fachao Jiang College of Engineering China Agricultural UniversityBeijing 100083China school of vehicle and mobility Tsinghua UniversityBeijing 100084China

This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batteries in ICEVs are investigated.Then,an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life.A battery protection scheme is developed,including over-discharge and graded over-current protection to improve battery safety.A model-based energy management strategy is synthesized to comprehensively optimize fuel economy,battery life preservation,and vehicle performance.The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests.The results show that the proposed energy management algorithm can effectively improve fuel economy.

关键词： Internal combustion engine vehicles Aging adaptive functional state model Energy management

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Engineering 2024年第2期33卷 133-145页

作者： Jian Wu Yang Yan Yulong Liu Yahui Liu school of Mechanical and Automotive Engineering Liaocheng UniversityLiaocheng 252000China 2 school of vehicle and mobility Tsinghua UniversityBeijing 100084China

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

关键词： Obstacle avoidance trajectory planning Inverse reinforcement theory Anthropomorphic Adaptive driving scenarios

Numerical simulation method of surge experiments on gas turbine engines

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2023年第3期36卷 107-120页

作者： Xinqian ZHENG Hanxuan ZENG Baotong WANG Mengyang WEN Heli YANG Zhenzhong SUN school of vehicle and mobility Tsinghua UniversityBeijing 100084China Institute for Aero Engine Tsinghua UniversityBeijing 100084China

In order to obtain the surge margin of an aero-engine during its operation,an engine surge experiment is required.A multi-dimensional simulation method for an aero-engine is established in this paper.The simulation of a surge experiment using high-pressure air-injection is then carried out on a turbo-shaft engine to obtain the surge boundary using this method.More specifically,firstly,a body-force model is employed to calculate the compressor performance owing to its capability of capturing the main three-dimensional features of compressor surge and avoiding excessive simulation time required by the traditional fully-three-dimensional Reynolds Averaged Navier-Stokes(RANS)method.Then,a one-dimensional model combining a lumped-parameter plenum model is used for the combustor to account for the propagation of pressure waves and the heat-release process,and a zero-dimensional throttle model is used to mimic the choking effect at the turbine nozzle.Finally,the air-injection system is modeled by imposing an injection boundary condition,which can be used conveniently in changing injection parameters.Based on the established method,the influences of different test parameters,such as the air-injection location,the pressure,the orifice size,the number of injection orifices,and the injection time duration on the surge characteristics and boundary are further studied,which offer effective guidance to optimize an actual experimental design.

关键词： Air-injection Body-force model Gas turbine engine Numerical method Surge experiment

Research on Optimal Shift Schedule for Multi-Speed Electric vehicle Based on Trajectory Prediction

在线全文

学校读者我要写书评

暂无评论

Research on Optimal Shift Schedule for Multi-Speed Electric ...

Predictive cruise control for heavy trucks based on slope information under cloud control system

2023中国汽车工程学会年会暨展览会

作者： Lu Zhenghong Dong Wenlong Huang Yong school of vehicle and mobility Tsinghua University

Based on dynamic programming in the frame of moving horizon control,an optimal control strategy for multi-speed electric vehicle shifting schedule considering the curvature of the road ahead is proposed.Based on the on-board visual perception capacity with map information,the obtained information of the lane line in front of the vehicle is obtained as the future trajectory.With the multipoint preview longitudinal driver model,the vehicle speed prediction is performed on multiple preview points on the future remote trajectory,acting as predicted information for following optimization.With the obtained predicted vehicle speed sequence,the dynamic programming method is used to perform optimal predictive control in the prediction domain,and the optimization objective function including the current driver’s dynamic intention,battery energy consumption,shift frequency and other factors is investigated.A simulation model is built to compare the proposed strategy with classic dual-parameter economy and dynamic shifting schedule.The results show that the optimal shift schedule strategy based on preview trajectory can save the power consumption of multi-gear electric vehicles,ensure the dynamic performance and reduce the shifting frequency.

关键词：

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2022年第4期33卷 812-826页

作者： LI Shuyan WAN Keke GAO Bolin LI Rui WANG Yue LI Keqiang College of Engineering China Agricultural UniversityBeijing 100083China school of vehicle and mobility Tsinghua UniversityBeijing 100084China

With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

关键词： predictive cruise control(PCC) cloud control system(CCS) layered architecture road point segmentation method(RPSM) economic driving