Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338645
Feihua Huang, Yan Gao, Chunyun Fu, A. Gostar, R. Hoseinnezhad, Minghui Hu
The performance of vehicle chassis control systems relies on the accuracy of input information to the control systems. Some important vehicle states which are necessary for chassis control cannot be directly measured at low cost, such as the vehicle longitudinal and lateral velocities. In the existing literature, many vehicle state estimation solutions are designed based on vehicle dynamic models. These models inevitably involve the acquisition of tire forces which cannot be easily measured or estimated. In this paper, a vehicle state estimator is proposed based on a straightforward vehicle kinematic model, which does not rely on any tire force information. The complexity and computation load of the proposed state estimator is low. Besides, to ensure competitive estimation performance, the state transition model used in this estimator is designed to be adaptive to the on-board sensor measurements. In the simulation studies, the proposed estimator is able to provide accurate estimation results under different simulation conditions, which verifies the effectiveness of the proposed vehicle state estimator.
{"title":"Vehicle State Estimation Based on Adaptive State Transition Model","authors":"Feihua Huang, Yan Gao, Chunyun Fu, A. Gostar, R. Hoseinnezhad, Minghui Hu","doi":"10.1109/CVCI51460.2020.9338645","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338645","url":null,"abstract":"The performance of vehicle chassis control systems relies on the accuracy of input information to the control systems. Some important vehicle states which are necessary for chassis control cannot be directly measured at low cost, such as the vehicle longitudinal and lateral velocities. In the existing literature, many vehicle state estimation solutions are designed based on vehicle dynamic models. These models inevitably involve the acquisition of tire forces which cannot be easily measured or estimated. In this paper, a vehicle state estimator is proposed based on a straightforward vehicle kinematic model, which does not rely on any tire force information. The complexity and computation load of the proposed state estimator is low. Besides, to ensure competitive estimation performance, the state transition model used in this estimator is designed to be adaptive to the on-board sensor measurements. In the simulation studies, the proposed estimator is able to provide accurate estimation results under different simulation conditions, which verifies the effectiveness of the proposed vehicle state estimator.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126132965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338647
Qiang Ai, Hongqian Wei, Youtong Zhang
The optimization of flux-intensifying interior permanent magnet motor with the reverse salient rotor for electric vehicles is considered and explained. Firstly, the size parameters of an initial motor are selected and then the finite element model is established based on parametric variables. Secondly, to avoid the frequent usage of finite element analysis, a well-trained back propagation neural network model is used to replace the finite element model. Thirdly, the sequential unconstrained minimization technique and non-dominated sorting genetic algorithm-II algorithm are combined together to solve the multi-objective optimization solution with inequality constraints. Finally, the electric machine is reconstructed based on the optimal parameters extracted from Pareto front. The effectiveness of proposed approach is verified by the simulation results.
{"title":"Optimal design for Flux-intensifying Permanent Magnet Machine Based on Neural Network and Multi-objective optimization","authors":"Qiang Ai, Hongqian Wei, Youtong Zhang","doi":"10.1109/CVCI51460.2020.9338647","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338647","url":null,"abstract":"The optimization of flux-intensifying interior permanent magnet motor with the reverse salient rotor for electric vehicles is considered and explained. Firstly, the size parameters of an initial motor are selected and then the finite element model is established based on parametric variables. Secondly, to avoid the frequent usage of finite element analysis, a well-trained back propagation neural network model is used to replace the finite element model. Thirdly, the sequential unconstrained minimization technique and non-dominated sorting genetic algorithm-II algorithm are combined together to solve the multi-objective optimization solution with inequality constraints. Finally, the electric machine is reconstructed based on the optimal parameters extracted from Pareto front. The effectiveness of proposed approach is verified by the simulation results.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125875218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338661
Shuai Zhang, Guidong Yang, Yafei Wang, Qinghui Ji, Huimin Zhang
Driving comfort, which is mainly influenced by vibration and shock, is an essential factor to evaluate the performance of intelligent vehicles. The evaluation methods of driving comfort mainly contain subjective and objective evaluation. Subjective evaluation is time-consuming, expensive and sensitive to personal feelings. And objective evaluation is difficult to exactly define the relationship between objective parameters and driving comfort. In order to combine the advantages of subjective and objective evaluation, a neural network that adopt objective indicators as input and subjective ratings as output was established for evaluating driving comfort. First, a road test with about 9000 km was conducted and key parameters of vehicle status were recorded, as well as subjective ratings. Secondly, 25,165 segments were extracted from the naturalistic driving data. Then, total weighted root-mean-square accelerations of all segments were computed according to ISO 2631–1997 Standard. And the result shows that the comfort levels calculated by weighted root-mean-square accelerations cannot match the subjective ratings given by professional evaluators very well. Finally, a 20-128-256-256-128-6 BP neural network was established and trained. And the accuracy of evaluation based on neural network is better than evaluation based on weighted root-mean-square value. The result reveals that it is feasible to establish a neural network model based on collected naturalistic driving data to evaluate the driving comfort of vehicles.
{"title":"Objective Evaluation for the Driving Comfort of Vehicles Based on BP Neural Network","authors":"Shuai Zhang, Guidong Yang, Yafei Wang, Qinghui Ji, Huimin Zhang","doi":"10.1109/CVCI51460.2020.9338661","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338661","url":null,"abstract":"Driving comfort, which is mainly influenced by vibration and shock, is an essential factor to evaluate the performance of intelligent vehicles. The evaluation methods of driving comfort mainly contain subjective and objective evaluation. Subjective evaluation is time-consuming, expensive and sensitive to personal feelings. And objective evaluation is difficult to exactly define the relationship between objective parameters and driving comfort. In order to combine the advantages of subjective and objective evaluation, a neural network that adopt objective indicators as input and subjective ratings as output was established for evaluating driving comfort. First, a road test with about 9000 km was conducted and key parameters of vehicle status were recorded, as well as subjective ratings. Secondly, 25,165 segments were extracted from the naturalistic driving data. Then, total weighted root-mean-square accelerations of all segments were computed according to ISO 2631–1997 Standard. And the result shows that the comfort levels calculated by weighted root-mean-square accelerations cannot match the subjective ratings given by professional evaluators very well. Finally, a 20-128-256-256-128-6 BP neural network was established and trained. And the accuracy of evaluation based on neural network is better than evaluation based on weighted root-mean-square value. The result reveals that it is feasible to establish a neural network model based on collected naturalistic driving data to evaluate the driving comfort of vehicles.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124087097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338561
Zhe Sun, Shujie Hu, Nengzhuo Li, Defeng He
In this paper, a fuzzy nonsingular terminal sliding mode (FNTSM) control strategy is proposed for the trajectory-following control problem of a Mecanum-wheeled automated guided vehicle (MWAGV). Initially, a plant model with 4 inputs and 3 outputs is identified to describe the kinematics and dynamics of the MWAGV's trajectory-tracking behavior. Then, an FNTSM controller is designed for the MWAGV, and the control system's stability is verified via Lyapunov. Lastly, simulations are executed to test the control performance in the cases of lateral motion and circular motion with an initial offset. The simulation results indicate that compared with conventional sliding mode (CSM) control, the developed FNTSM control algorithm owns remarkable superiority reflected in higher tracking accuracy, stronger robustness and a better balance between the tracking precision and control smoothness.
{"title":"Trajectory-Following Control of Mecanum-Wheeled AGV Using Fuzzy Nonsingular Terminal Sliding Mode","authors":"Zhe Sun, Shujie Hu, Nengzhuo Li, Defeng He","doi":"10.1109/CVCI51460.2020.9338561","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338561","url":null,"abstract":"In this paper, a fuzzy nonsingular terminal sliding mode (FNTSM) control strategy is proposed for the trajectory-following control problem of a Mecanum-wheeled automated guided vehicle (MWAGV). Initially, a plant model with 4 inputs and 3 outputs is identified to describe the kinematics and dynamics of the MWAGV's trajectory-tracking behavior. Then, an FNTSM controller is designed for the MWAGV, and the control system's stability is verified via Lyapunov. Lastly, simulations are executed to test the control performance in the cases of lateral motion and circular motion with an initial offset. The simulation results indicate that compared with conventional sliding mode (CSM) control, the developed FNTSM control algorithm owns remarkable superiority reflected in higher tracking accuracy, stronger robustness and a better balance between the tracking precision and control smoothness.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129055079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338629
Zhishuai Yin, Yuwei Pan
Driver-automation co-piloting, a driving mode under which autonomous driving systems and human drivers accomplish driving tasks cooperatively is expected to be widely used to reduce driver workload in future driving. The work presented in this paper focuses on safety evaluation of the transition mechanism between autonomous system and human drivers. A group of two-factor experiments, in which two factors are: (1) advance responding time for drivers: 15s,45s, (2) notification modes to drivers: audio, visual, audio/visual, were performed to quantitatively measure driver workload by using eye tracking data, which is highly relevant to driving safety. The results of these experiments indicate that drivers' workloads increased more smoothly when given audio notification and more responding time during transitions. The research has brought about a solution to ensure a good level of driving safety in co-piloting.
{"title":"Evaluating Safety of Mechanisms that Transit Control from Autonomous Systems to Human Drivers","authors":"Zhishuai Yin, Yuwei Pan","doi":"10.1109/CVCI51460.2020.9338629","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338629","url":null,"abstract":"Driver-automation co-piloting, a driving mode under which autonomous driving systems and human drivers accomplish driving tasks cooperatively is expected to be widely used to reduce driver workload in future driving. The work presented in this paper focuses on safety evaluation of the transition mechanism between autonomous system and human drivers. A group of two-factor experiments, in which two factors are: (1) advance responding time for drivers: 15s,45s, (2) notification modes to drivers: audio, visual, audio/visual, were performed to quantitatively measure driver workload by using eye tracking data, which is highly relevant to driving safety. The results of these experiments indicate that drivers' workloads increased more smoothly when given audio notification and more responding time during transitions. The research has brought about a solution to ensure a good level of driving safety in co-piloting.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125734722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338655
Weining Ren, Kun Jiang, Xinxin Chen, Tuopu Wen, Diange Yang
The Visual-Inertial navigation system(VINS) has become a popular navigation approach in the field of unmanned aerial vehicles(UAV) or robotics. While its performance under autonomous driving scenario is not satisfactory due to the fact that autonomous driving scenario is more challenging and dynamic than the UAV scenario. Thus, the Visual-Inertial navigation system will collapse occasionally and thus undermine the navigation result. In this work, we propose a adaptive mechanism that could switch between three modes, only VINs, only GNSS and VINS&GNSS fusion. When Visual-Inertial component breaks down, our algorithm could only rely on the GNSS signal until VINS recovers. Similarly, when GNSS signal is not very accurate, our system could only rely on the VINS-Mono. We demonstrate our algorithm under challenging scenarios such as night sight and high speed road and do both qualitative analysis and quantitative analysis.
{"title":"Adaptive Sensor Fusion of Camera, GNSS and IMU for Autonomous Driving Navigation","authors":"Weining Ren, Kun Jiang, Xinxin Chen, Tuopu Wen, Diange Yang","doi":"10.1109/CVCI51460.2020.9338655","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338655","url":null,"abstract":"The Visual-Inertial navigation system(VINS) has become a popular navigation approach in the field of unmanned aerial vehicles(UAV) or robotics. While its performance under autonomous driving scenario is not satisfactory due to the fact that autonomous driving scenario is more challenging and dynamic than the UAV scenario. Thus, the Visual-Inertial navigation system will collapse occasionally and thus undermine the navigation result. In this work, we propose a adaptive mechanism that could switch between three modes, only VINs, only GNSS and VINS&GNSS fusion. When Visual-Inertial component breaks down, our algorithm could only rely on the GNSS signal until VINS recovers. Similarly, when GNSS signal is not very accurate, our system could only rely on the VINS-Mono. We demonstrate our algorithm under challenging scenarios such as night sight and high speed road and do both qualitative analysis and quantitative analysis.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114565469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338564
Honghui Zhang, Zhiyuan Zou, Hang Su
Magnetorheological (MR) controllable damping is promising in suspension control and almost commercialized in luxuries. However, the development of MR semi-active control for vehicles is complicated because of the messed interdisciplinary process both in the suspension control and the MR damper control. In this paper, a new scheme of driving control based on BP neural network is proposed to package the MR damper as a black box implementing the strong nonlinearity mapping between the excitation current and damping force by the embedded driver. The sensor also embedded in the MR damper for integrated solution, and a mechanism for tackling the sedimentation problem of the MR damper are also pointed out.
{"title":"A New Scheme for Semi-active Suspension Control based on BP Neural Network Model of Magnetorheological Damper","authors":"Honghui Zhang, Zhiyuan Zou, Hang Su","doi":"10.1109/CVCI51460.2020.9338564","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338564","url":null,"abstract":"Magnetorheological (MR) controllable damping is promising in suspension control and almost commercialized in luxuries. However, the development of MR semi-active control for vehicles is complicated because of the messed interdisciplinary process both in the suspension control and the MR damper control. In this paper, a new scheme of driving control based on BP neural network is proposed to package the MR damper as a black box implementing the strong nonlinearity mapping between the excitation current and damping force by the embedded driver. The sensor also embedded in the MR damper for integrated solution, and a mechanism for tackling the sedimentation problem of the MR damper are also pointed out.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129215040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338560
Song Yan, Yi Zhang, Jun-li Wang, X. Pei
Most of the existing researches only consider vehicles and signals as control objects, and there are also problems of loss of space and time resources caused by unreasonable distribution of spatiotemporal-right. In this paper, an overall collaborative control model for intersections considering the distribution of spatiotemporal right, vehicle trajectory and signal timing was established. A solution algorithm for the assignment of spatiotemporal-rights based on decision tree C4.5 is proposed. A high-dimensional solution based on genetic algorithm and an enumerated low-dimensional solution for signal timing and vehicle trajectory optimization are proposed respectively. Finally, an overall control model including the phase and lane, signal timing and vehicle trajectory was established. The simulation program was developed with python3.7, and the effectiveness of algorithm proposed in this paper was verified by experiments. When flow intensity is 0.23, the algorithm has the best improvement effect, the high-dimensional and low-dimensional algorithms can reduce the delay by 57.6% and 44.8% respectively. It also verified that the algorithm has better adaptability to the change of traffic demand than the algorithm that only considers the vehicle trajectory or signal timing.
{"title":"Spatiotemporal-rights-based coordinate control of isolated intersections under i-VICS","authors":"Song Yan, Yi Zhang, Jun-li Wang, X. Pei","doi":"10.1109/CVCI51460.2020.9338560","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338560","url":null,"abstract":"Most of the existing researches only consider vehicles and signals as control objects, and there are also problems of loss of space and time resources caused by unreasonable distribution of spatiotemporal-right. In this paper, an overall collaborative control model for intersections considering the distribution of spatiotemporal right, vehicle trajectory and signal timing was established. A solution algorithm for the assignment of spatiotemporal-rights based on decision tree C4.5 is proposed. A high-dimensional solution based on genetic algorithm and an enumerated low-dimensional solution for signal timing and vehicle trajectory optimization are proposed respectively. Finally, an overall control model including the phase and lane, signal timing and vehicle trajectory was established. The simulation program was developed with python3.7, and the effectiveness of algorithm proposed in this paper was verified by experiments. When flow intensity is 0.23, the algorithm has the best improvement effect, the high-dimensional and low-dimensional algorithms can reduce the delay by 57.6% and 44.8% respectively. It also verified that the algorithm has better adaptability to the change of traffic demand than the algorithm that only considers the vehicle trajectory or signal timing.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129266229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338658
Mingxin Kang, Ran Chen, Yuzhe Li
Most vehicle active suspension control systems assume that the dynamic system model descriptions are accurate. However, there may exist modeling error and external disturbances for real world applications. While extensive research in robust model predictive control has been considered to handle such issues, the control performance may degrade due to the conservation of the prior uncertainty set. In this work, a vehicle active suspension control problem with modeling error and external disturbances is studied. We propose an adaptive tube-based model predictive controller to identify parameter uncertainty set and optimize reformulated quadratic optimization problem (QOP) for increasing control performance. The recursive feasibility and stability analysis of the proposed method is presented, and simulation results are demonstrated to indicate the effectiveness of the proposed algorithm.
{"title":"Adaptive Tube-based Model Predictive Control for Vehicle Active Suspension System","authors":"Mingxin Kang, Ran Chen, Yuzhe Li","doi":"10.1109/CVCI51460.2020.9338658","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338658","url":null,"abstract":"Most vehicle active suspension control systems assume that the dynamic system model descriptions are accurate. However, there may exist modeling error and external disturbances for real world applications. While extensive research in robust model predictive control has been considered to handle such issues, the control performance may degrade due to the conservation of the prior uncertainty set. In this work, a vehicle active suspension control problem with modeling error and external disturbances is studied. We propose an adaptive tube-based model predictive controller to identify parameter uncertainty set and optimize reformulated quadratic optimization problem (QOP) for increasing control performance. The recursive feasibility and stability analysis of the proposed method is presented, and simulation results are demonstrated to indicate the effectiveness of the proposed algorithm.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131724756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338472
Haoxuan Dong, Weichao Zhuang, Yan Wang, Haonan Ding, Guo-dong Yin
To improve the regeneration energy of electric vehicle, an energy-optimal braking strategy is developed. First, the vehicle braking intention is accessed by using vehicle-to-everything communication, i.e., braking distance and terminal velocity. Then, an optimal control problem with consideration of braking intention is formulated for maximizing regeneration energy. The control problem is solved by distance-based dynamic programming algorithm to plan the energy-optimal braking velocity. Finally, the effectiveness of proposed strategy is evaluated by simulation. The results show the regeneration energy efficiency of proposed strategy achieves improvement is over 10% compared with the constant speed strategy. Furtherly, the energy-optimal braking suggestions is investigated based on several traffic scenarios, i.e., a larger braking force in a high-velocity range can reduce vehicle resistance and make full use of motor generation power; the braking force was adjusted in moderated-velocity range for reducing friction braking, and a larger braking force should be used for parking quickly.
{"title":"Energy-optimal Braking Velocity Planning of Connected Electric Vehicle","authors":"Haoxuan Dong, Weichao Zhuang, Yan Wang, Haonan Ding, Guo-dong Yin","doi":"10.1109/CVCI51460.2020.9338472","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338472","url":null,"abstract":"To improve the regeneration energy of electric vehicle, an energy-optimal braking strategy is developed. First, the vehicle braking intention is accessed by using vehicle-to-everything communication, i.e., braking distance and terminal velocity. Then, an optimal control problem with consideration of braking intention is formulated for maximizing regeneration energy. The control problem is solved by distance-based dynamic programming algorithm to plan the energy-optimal braking velocity. Finally, the effectiveness of proposed strategy is evaluated by simulation. The results show the regeneration energy efficiency of proposed strategy achieves improvement is over 10% compared with the constant speed strategy. Furtherly, the energy-optimal braking suggestions is investigated based on several traffic scenarios, i.e., a larger braking force in a high-velocity range can reduce vehicle resistance and make full use of motor generation power; the braking force was adjusted in moderated-velocity range for reducing friction braking, and a larger braking force should be used for parking quickly.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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