Energy and Time Optimal Autopilot for Electric Vehicles Performing Ackerman Cornering

This paper studies energy and time optimality of electric vehicles during constant Ackerman steering along a quad-circle, and proposes an autopilot assimilating the optimal results. The energy and time optimal solutions satisfying the steering and battery limitations are generated and a Pareto-front analysis is carried out with multi-objective optimization using NSGA-II algorithm. In the autopilot design, the indicators for the energy and time optimality performances are merged in a vehicle status vector. At each control cycle of the optimal drives, the torque commands and the vehicle status vectors are stored and later clustered using the k-means algorithm. At each cluster centre, a pair of the vehicle status vector and the control command vector is acquired and these pairs designate the rules that generate the unique optimal control command associated with a particular vehicle status. A convex combination of these rules constitutes the autopilot design. The proposed autopilot is tested against the energy-time Pareto-front extracted, and it is observed that in terms of the performance measures, the optimality has been preserved. Additionally, for various weightings of time and energy optimality objectives, the performance of the autopilot is compared with that of optimal controllers with weighted objectives. The results verify the quality of the autopilot design using a decision-making process over a rule set inferred from the solutions acquired with optimal solutions.


Energy Optimal Controller for Electric Vehicles on Partially Icy Roads with Heuristic Skidding Compensation
Ahiska, Kenan; Özgören, Mustafa Kemal; Leblebicioğlu, Mehmet Kemal (2015-10-16)
In this study, a mathematical model is constructed for an electric vehicle. An energy optimal controller is designed for the gross motion model of the vehicle moving on a positive constant slope road with some icy parts. The energy optimal controller takes torque, speed and battery constraints into account. The loss of control during skidding period is compensated with an additional control command based on heuristics. The compensation is based on an enrichment of applied control input throughout the sectio...
Efficiency Optimization of a Direct Torque Controlled Induction Motor used in Hybrid Electric Vehicles
Sergaki, Eleftheria S.; Moustaizis, Stavros D. (2011-09-10)
The main contribution of this paper is the application of Loss Minimization control algorithm of a three-phase squirrel-cage induction motor which is used in parallel with an internal combustion engine (ICE), in hybrid electric vehicles (HEY). During steady state operation of the electric motor, the electric motor's optimal motor flux profile minimizes the electric motor losses and maximizes the overall HEN efficiency, hybridization factor (HF). During steady state operation of the direct torque controlled ...
Multi-objective charging scheduling utilizing electric vehicle load models
Güzel, İven; Göl, Murat; Department of Electrical and Electronics Engineering (2022-1)
Utilization of electric vehicle (EV) load models can improve the performance of smart charging strategies, which increase the reliability of the grid by harnessing the flexibility of EV loads. This thesis presents methods for utilizing EV load models in real time stochastic charging control with single and finite system-time horizons. First, the drivers’ load models are found with kernel density estimation. A single system time horizon coordinated charging control algorithm is devised to ensure each EV is c...
Parameter Estimation of Electric Vehicles for Improved Range Prediction
Saglam, Berkay; Bostancı, Emine; Göl, Murat (2021-01-01)
© 2021 IEEE.In order to improve performance of range estimation of electric vehicles, parameters that affect energy consumption should be determined accurately. This paper presents a parameter estimation methodology for electric vehicles based on least squares method. In this study, the power and angular velocity of wheels are measured from the vehicle directly. In addition to those, the directional velocity data is extracted from the GPS signal, in order to avoid the parameter dependency between the angula...
Assessment of Impacts of Electric Vehicles on LV Distribution Networks in Turkey
TEMIZ, Armagan; Güven, Ali Nezih (2016-04-08)
This study proposes a methodology to analyze the impacts of Electric Vehicles (EVs) on Low Voltage (LV) distribution networks based on probabilistic models developed for the charging process of EVs. In addition to the battery charging characteristics, Gaussian distribution function for EV plug-in times and Weibull distribution function for daily travel times are utilized in simulations. Monte Carlo based load flow simulations are performed in order to evaluate the response of the LV networks to various EV a...
Citation Formats
K. Ahiska, M. K. Özgören, and M. K. Leblebicioğlu, “Energy and Time Optimal Autopilot for Electric Vehicles Performing Ackerman Cornering,” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: