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A reduced combustion mechanism of ammonia/diesel optimized with multi-objective genetic algorithm

Defence Technology（防务技术） 2024年第4期34卷 187-200页

作者： Wanchen Sun Shaodian Lin Hao Zhang Liang Guo Wenpeng Zeng Genan Zhu Mengqi Jiang State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130025China

For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 *** sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 *** optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel *** ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of *** for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by *** particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel *** kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.

关键词： Ammonia Diesel Combustion Kinetic mechanism Multi-objective optimization

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A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering Interaction Information

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automotive Innovation 2024年第1期7卷 71-81页

作者： Haitao Min Xiaoyong Xiong Pengyu Wang Zhaopu Zhang State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130012China

Trajectory prediction is an essential component in autonomous driving systems,as it can forecast the future movements of surrounding vehicles,thereby enhancing the decision-making and planning capabilities of autonomous driving *** models relying on constant acceleration and constant velocity often experience a reduction in prediction accu-racy as the forecasted timeframe *** limitation makes it challenging to meet the demands for medium to long-term trajectory ***,data-driven models,particularly those based on Long Short-Term Memory(LSTM)neural networks,have demonstrated superior performance in medium to long-term trajectory ***,this study introduces a hierarchical LSTM-based method for vehicle trajectory *** the difficulty of using a single LSTM model to predict trajectories for all driving intentions,the trajectory prediction task is decomposed into three sequential steps:driving intention prediction,lane change time prediction,and trajectory ***,given that the driving intent and trajectory of a vehicle are always subject to the influence of the surrounding traffic flow,the predictive model proposed in this paper incorporates the interactional information of neighboring vehicle movements into the model *** proposed method is trained and validated on the real vehicle trajectory dataset Next Generation *** results show that the proposed hierarchical LSTM method has a lower prediction error compared to the integral LSTM model.

关键词： Autonomous vehicles Trajectory prediction Long Short-Term Memory Driving intention prediction

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Human-Machine Shared Lateral control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

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automotive Innovation 2024年第1期7卷 102-120页

作者： Dongjian Song Bing Zhu Jian Zhao Jiayi Han State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China

Intelligent vehicle(Ⅳ)technology has developed rapidly in recent ***,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable ***-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of *** onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk *** HSLCS starts by identifying the effective areas of driver risk perception based on eye *** establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk *** upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's ***,it quantifies the reliability of the driver's risk perception by using area-matching ***,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive *** HSLCS was tested across various scenarios on a driver-in-the-loop test *** results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.

关键词： Intelligent vehicle Human-machine shared driving Risk perception Driving authority distribution

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Vehicle state and Bias Estimation Based on Unscented Kalman Filter with Vehicle Hybrid Kinematics and Dynamics Models

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automotive Innovation 2023年第4期6卷 571-585页

作者： Shouren Zhong Yang Zhao Linhe Ge Zitong Shan Fangwu Ma State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China

In recent years,vehicle state estimation methods incorporating different vehicle models have received extensive *** the vehicle is disturbed by external forces not considered in traditional vehicle models(for example,a certain slope,or wind resistance different from theoretical calculation),the problem of model mismatch will occur,which leads to the inaccurate estimation of the vehicle *** solve this problem,an Unscented Kalman Filter(UKF)algorithm is used to fuse inertial navigation data with the vehicle hybrid model in this *** hybrid model introduces a switching strategy that fuses the vehicle kinematics and the dynamics models while augmenting biases that need to be estimated in the vehicle *** switching strategy resolves the integration divergence problem of vehicle dynamics models at low speeds and the inaccurate estimation of vehicle kinematics models at high *** experiments demonstrate that the proposed method can accurately estimate biases induced by external forces,enhancing the accuracy and confidence of states by eliminating errors caused by these *** robustness of the method is validated in vehicle verification platform experiments,where errors in vehicle lateral speed and yaw rate are reduced by 9.7 cm/s and 0.012°/s,respectively,under large curvature maneuvers,and 9.6 cm/s and 0.004°/s under quarter-turn *** proposed method significantly improves lateral speed and vehicle position accuracies.

关键词： Vehicle state estimation Vehicle hybrid models Unscented Kalman filter Integration stability

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Hierarchical Parking Path Planning Based on Optimal Parking Positions

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automotive Innovation 2023年第2期6卷 220-230页

作者： Yaogang Zhang Guoying Chen Hongyu Hu Zhenhai Gao State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchunJilinChina

Automated valet parking(AVP)has attracted the attention of industry and academia in recent ***,there are still many challenges to be solved,including shortest path search,optimal time efficiency,and applicability of algorithm in complex *** this paper,a hierarchical AVP path planner is proposed,which divides a complete AVP path planning into the guided layer and the planning layer from the perspective of global *** guided layer is mainly used to divide a complex AVP path planning into several simple path plannings,which makes the hybrid A*algorithm more applicable in a complex parking *** planning layer mainly adopts different optimization methods for driving and parking path *** proposed method is verified by a large number of simulations which include the verification of the optimal parking position,the performance of the planner for perpendicular parking,and the scalability of the planner for parallel parking and inclined *** simulation results reveal that the efficiency of the algorithm is increased by more than 20 times,and the average path length is also shortened by more than 20%.Furthermore,the planner overcomes the problem that the hybrid A*algorithm is not applicable in complex parking scenarios.

关键词： Automated valet parking Path planning Hybrid A Visibility graph Shortest path

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Clutch Torque Optimization for Heavy-Duty Vehicles Starting Based on Intelligent Identification of Driver's Starting Intention, Vehicle Mass, and Road Grade

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SAE International Journal of Commercial Vehicles 2024年第3期17卷 303-331页

作者： Geng, Xiaohu Liu, Weidong Lei, Yulong Fu, Yao Xue, Maohan Jilin University State Key Laboratory Automotive Simulation & Control China

For heavy-duty vehicles equipped with automated mechanical transmission (AMT), the control of automatic clutch torque is crucial during the start-up process. However, the difficulty of controlling clutch torque is exacerbated by differences in driver's starting intentions, changes in vehicle mass, and road gradient. Therefore, this article proposes the clutch starting torque optimization strategy based on intelligent recognition of driver's starting intention, vehicle mass, and road gradient. First, an intelligent recognition strategy is proposed based on the combination of data-driven and onboard transmission control unit (TCU) algorithms, which improves the accuracy of recognizing the driver's intention to start as well as the vehicle mass and road gradient. Based on the vehicle's historical state data information, the predictive model is trained offline using a long-short-term memory (LSTM) network to obtain predicted parameter identification results, which are then used to calibrate the computed values of the onboard TCU algorithm. Second, the clutch torque optimization strategy is designed based on the driver's starting intention, while considering the effects of road gradient and vehicle mass on the clutch starting resistance torque. The weight coefficients of the objective performance function are adjusted according to the driver's starting intention, and the Pontryagin's minimum principle (PMP) is used to solve the clutch target torque. Finally, offline data training and real-vehicle testing are performed. The results show that the optimization strategy can effectively reduce the friction work and the degree of impact during the starting process, minimize the clutch slipping time, and improve the smoothness of vehicle starting and driving comfort. © 2024 SAE International.

关键词： Clutch torque optimization Driver's starting intention recognition Heavy-duty vehicle Road grade estimation Vehicle mass estimation

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Open-source dataset for traffic light and countdown display detection in urban environment

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Science China(Information Sciences) 2025年第5期68卷 407-408页

作者： Shuchun WAN Haitao DING Jianwei ZHANG Yunfeng HU Hong CHEN State Key Laboratory of Automotive Simulation and Control Jilin University Department of Control Science and Engineering Jilin University College of Electronic and Information Engineering Tongji University

Here we introduce an open-source dataset for traffic light and countdown display detection,which includes three subsets:a subset of traffic light data,a subset of traffic light and countdown display data,and a subset ...

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state Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 99-113页

作者： Zixu Wang Chaoning Chen Quan Jiang Hongyu Zheng Chuyo Kaku State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China Jiangsu Chaoli Electric Co.Ltd. Zhenjiang 212321China

Accurate vehicle dynamic information plays an important role in vehicle driving ***,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state *** paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis *** the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during *** performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter *** effectiveness of the algorithm is further verified by driving simulator *** this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

关键词： Drive-by-wire chassis vehicle Vehicle state estimation Dual unscented particle filter Tire force estimation Unscented particle filter

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Adaptative Pressure Estimation and control Architecture for Integrated Electro-Hydraulic Brake System

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Chinese Journal of Mechanical Engineering 2025年第1期38卷 353-381页

作者： Zhenhai Gao Yi Yang Guoying Chen Liang Yuan Jianguang Zhou Jie Zhang State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China Chongqing Research institute Jilin UniversityChongqing 404100China China Automotive Innovation Corporation Nanjing 210000China

The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle *** they are not independently applicable to any environment and have their own scope of *** addition,about the master cylinder pressure control,there are few studies that can simultaneously balance pressure building accuracy,speed,and prevent pressure overshoot and *** this paper,an adaptative fusion method based on electro-hydraulic characteristic and vehicle mode is proposed to estimate the master cylinder *** fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds,pressures,and ABS *** from this,this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic *** order to simultaneously balance pressure control accuracy,response speed,and prevent overshoot and jitter,this article proposes an adaptative LQR controller for MC pressure control which uses fuzzy-logic controller to adjust the weights of LQR controller based on target pressure and difference compared with actual *** mode-in-loop and hardware-in-loop tests in ramp,step and sinusoidal response,the whole estimation and control system is verified based on real hydraulic system and the performance is satisfactory under these *** research proposes an adaptative pressure estimation and control architecture for integrated electro-hydraulic brake system which could eliminate pressure sensors in typical scenarios and ensure the comprehensive performance of pressure control.

关键词： Brake-by-wire(BBW) MC pressure estimation MC pressure control Integrated electro-hydraulic brake system(IEHB) Adaptative sliding mode observer(ASMO) Adaptative LQR controller

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Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information

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control Theory and Technology 2025年第1期23卷 74-90页

作者： Qifang Liu Dazhen Sun Haowen Chen Dongzi Li Ping Wang Department of Control Science and Engineering Jilin UniversityChangchun130022JilinChina State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun130022JilinChina

Eco-driving has always been an ongoing *** urban driving conditions,traffic regulations,other vehicle behaviors,and special driving scenarios will have a major impact on the energy consumption of autonomous *** a representative algorithm of artificial intelligence,reinforcement learning has the ability to perform well under complex *** paper uses deep reinforcement learning algorithms to design the economical driving strategies of autonomous vehicles in three driving scenarios:driving at signalized intersection under free traffic flow,car-following on ramps,and driving at signalized intersection considering queue *** the above three driving scenarios,the driving strategy proposed in this paper achieves economical driving performance while satisfying the driving scenario requirements.

关键词： Eco-driving strategy Reinforcement learning Signalized intersection Car-following

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