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Stable trajectory planning and energyefficience control allocation of lane change maneuver for autonomous electric vehicle

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Journal of Intelligent and Connected vehicles 2018年第2期1卷 55-65页

作者： Liwei Xu Guodong Yin Guangmin Li Athar Hanif Chentong Bian School of Mechanical Engineering Southeast UniversityNanjingChina Southeast University NanjingChina Department of electrical Engineering COMSATS Institute of Information TechnologyLahorePakistan

Purpose–The purpose of this paper is to investigate problems in performing stable lane changes and toﬁnd a solution to reduce energy consumption of autonomous electric ***/methodology/approach–An optimization algorithm,model predictive control(MPC)and Karush–Kuhn–Tucker(KKT)conditions are adopted to resolve the problems of obtaining optimal lane time,tracking dynamic reference and energy-efﬁcient *** this paper,the dynamic constraints of vehicles during lane change areﬁrst established based on the longitudinal and lateral force coupling characteristics and the nominal reference ***,by optimizing the lane change time,the yaw rate and lateral acceleration that connect with the lane change time are ***,to assure the dynamic properties of autonomous vehicles,the real system inputs under the restraints are obtained by using the MPC *** on the gained inputs and the efﬁcient map of brushless direct-current in-wheel motors(BLDC IWMs),the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is ***–The effectiveness of the proposed control system is veriﬁed by numerical ***,the proposed control system can successfully achieve stable trajectory planning,which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries,which accomplishes accurate tracking control and decreases obvious energy ***/value–This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric *** from previous path planning researches in which only the geometric constraints are involved,this paper considers vehicle dynamics,and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.

关键词： autonomous electric vehicle Energy-efﬁcient control allocation Lane change maneuver Stable trajectory planning

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AI Based Traffic Flow Prediction Model for Connected and autonomous electric vehicles

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Computers, Materials & Continua 2022年第2期70卷 3333-3347页

作者： P.Thamizhazhagan M.Sujatha S.Umadevi K.Priyadarshini Velmurugan Subbiah Parvathy Irina V.Pustokhina Denis A.Pustokhin Department of electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationVijayawada522502India Centre for Nanoelectronics and VLSI Design SENSEVIT UniversityChennai CampusChennai600127India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential *** and autonomous electric vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and ***,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)*** this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in *** presented model involves two major processes namely,feature engineering and *** feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature *** addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw ***,AIPAE model is applied to determine the predictive values of traffic *** order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct *** experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

关键词： autonomous electric vehicle traffic flow predictive automation industry connected vehicles seep learning

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Dynamic Cell Modeling for Accurate SOC Estimation in autonomous electric vehicles

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Journal of Power and Energy Engineering 2023年第8期11卷 1-15页

作者： Qasim Ajao Lanre Sadeeq Department of electrical Engineering Georgia Southern University Statesboro GA USA

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.

关键词： autonomous electric vehicle Modeling Battery Model Battery Management Systems (BMS) Lithium Polymer State of Charge Kalman-Filter

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