Zoltan Pusztai

57342934600

Publications - 12

Control-Oriented Model for Energy-Efficient Electric Vehicle

Tamás Luspay Zoltan Pusztai Ferenc Friedler

Publication Name: Proceedings of the International Symposium on Applied Machine Intelligence and Informatics Sami

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 299-304

Description:

In this paper, a control-oriented Linear Parameter Varying (LPV) model of an energy efficient electric vehicle is proposed, designed for model-based control to minimize energy consumption. The control inputs of the model include the torque reference and the actual cornering radius. The LPV model assesses the impact of cornering on driving resistances and, consequently, on energy consumption, which represents a novel approach. Due to the driving characteristics and the model nonlinear dynamics of the vehicle, a velocity-linearization based method was applied to obtain the parameter-dependent form. The obtained LPV model was then validated by using logged driving data, showing a root mean square error (RMSE) of 0.4682 m/s compared to the measured speed profile, thereby confirming the model's accuracy. The proposed LPV model can be utilized to develop energy-efficient driving strategies, making it highly relevant for the design and operation of energy-efficient vehicles.

Open Access: Yes

DOI: 10.1109/SAMI63904.2025.10883184

Examination of the Resistance Components of an Energy-Efficient Electric Vehicle

Peter Koros Zoltan Pusztai Ferenc Szauter

Publication Name: Journal of Physics Conference Series

Publication Date: 2024-01-01

Volume: 2848

Issue: 1

Page Range: Unknown

Description:

The paper presents a comprehensive examination of measurement-based modelling regarding resistance forces. This work offers a detailed explanation of the experimental techniques employed to measure the resistance forces experienced by a lightweight vehicle. The modelling approach is particularly beneficial for characterizing vehicle with low resistance values. Our investigation encompasses key vehicle motion states, including cornering and straight-line motion, making it greatly useful for optimization purposes. The measurements were conducted in a proving ground and laboratory environment. The road load coefficients can be breakdown into components from total resistance force measurement. Based on breakdown, future vehicle development goals can be addressed with a focus on reducing resistance forces.

Open Access: Yes

DOI: 10.1088/1742-6596/2848/1/012011

Comparative Analysis of Driver Interface Systems in Ultra-Efficient Lightweight Electric Vehicles: a Study on Energy Efficiency and Driver Focus

Zoltan Pusztai Viktor Nagy István Kecskeméti

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 997-1002

Description:

This paper presents a comprehensive examination of two driver interface systems within the context of Ultra-Efficient Lightweight Electric Vehicles (ULEV) aimed at enhancing energy efficiency and optimizing driver focus. The vehicle employs two interface systems: a 10.1-inch touchscreen tablet with a custom Graphical User interface (GUI) that offers comprehensive data management, diagnostics, and control functionalities and a 5.5-inch wide, passive OLED display designed for ultra-low energy consumption. The tablet's advanced features come with the potential for driver distraction. In contrast, the OLED display takes a minimalist approach by presenting only critical information. This enhances driving focus and efficiency. This research utilizes a wearable eye-tracking device to measure drivers' focus and distraction levels while also logging driving performance and energy consumption data. The aim is to determine the most effective interface for promoting efficient driving practices. The study achieved significant insights into the balancing of information accessibility and cognitive load in driving while also optimizing energy efficiency. The results demonstrate the advantages of assistant systems, which reduce energy consumption by 11-15%, provide concentrated information projection, and minimize driver distraction.

Open Access: Yes

DOI: 10.3303/CET24114167

Driving Strategy Optimization in Experimental Electric Vehicles: A Study on Optimization Algorithms †

Zoltan Pusztai Ferenc Szauter Ferenc Friedler

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

In this paper, the driving strategy simulations for a single-seat, lightweight, energy-efficient experimental electric vehicle are introduced. The vehicle’s operation is simulated using a developed measurement-based vehicle model in the simulation environment. The optimization was performed for the UniTrack platform at the ZalaZone proving ground using the algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), with different optimization settings corresponding to varying iterations and initial population/swarm sizes. A 2.95% difference was observed between the least effective and the best PSO results, where both the number of iterations and swarm size were doubled. This demonstrates the effectiveness of PSO in solving the presented driving strategy problem, even when using fewer iterations and a smaller swarm size.

Open Access: Yes

DOI: 10.3390/engproc2024079042

Optimized Eco-Driving with Real-Time Telemetry in a Lightweight Electric Vehicle

Zoltan Pusztai István Kecskeméti Ferenc Friedler

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 877-882

Description:

In this paper, the application of an advanced telemetry system is introduced, which is used to monitor an electric, energy-efficient experimental urban vehicle. The system enables real-time observation of both the pilot's actions and vehicle parameters. The vehicle's pilot drives according to a predetermined driving strategy, optimized for minimizing energy consumption during vehicle operation. The telemetry system aims to provide real-time information about the pilot's driving and deviations from the predetermined strategy, offering additional opportunities for correction during operation. Additionally, it facilitates real-time observation of all vehicle and sensor data on the vehicle's CAN network. The paper discusses the determination of the driving strategy and presents its graphical representation for the pilot. A detailed description of the telemetry system's operation through wireless connection is provided in the paper. In terms of implementation, the driving strategy was formulated using MATLAB through optimization, while graphical display, data collection, and telemetry system development were implemented in the LabVIEW environment. The functionality of the created energy-efficient driving support framework was examined under real driving conditions. The application of the telemetry system and proposed hybrid optimization approach helped to further reduce the energy consumption by 8.54%.

Open Access: Yes

DOI: 10.3303/CET24114147

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

Peter Koros György Istenes Zoltan Pusztai Ferenc Friedler Zoltán Horváth

Publication Name: Energies

Publication Date: 2023-06-01

Volume: 16

Issue: 12

Page Range: Unknown

Description:

In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage.

Open Access: Yes

DOI: 10.3390/en16124713

Implementation of Optimized Regenerative Braking in Energy Efficient Driving Strategies

Peter Koros Zoltan Pusztai Ferenc Szauter Ferenc Friedler

Publication Name: Energies

Publication Date: 2023-03-01

Volume: 16

Issue: 6

Page Range: Unknown

Description:

In this paper, determination of optimized regenerative braking-torque function and application in energy efficient driving strategies is presented. The study investigates a lightweight electric vehicle developed for the Shell Eco-Marathon. The measurement-based simulation model was implemented in the MATLAB/Simulink environment and used to establish the optimization. The optimization of braking-torque function was performed to maximize the recuperated energy. The determined braking-torque function was applied in a driving strategy optimization framework. The extended driving strategy optimization model is suitable for energy consumption minimization in a designated track. The driving strategy optimization was created for the TT Circuit Assen, where the 2022 Shell Eco-Marathon competition was hosted. The extended optimization resulted in a 2.97% improvement in energy consumption when compared to the result previously achieved, which shows the feasibility of the proposed methodology and optimization model.

Open Access: Yes

DOI: 10.3390/en16062682

Energy Efficient Drive Management of Lightweight Urban Vehicle

Zoltan Pusztai Ferenc Szauter Ferenc Friedler

Publication Name: Chemical Engineering Transactions

Publication Date: 2023-01-01

Volume: 103

Issue: Unknown

Page Range: 253-258

Description:

In this paper, the energy saving effect of optimized driving strategy is presented and compared to human driving strategy. The driving strategy of a one-seated experimental electric vehicle is investigated and optimized in this study, where the objective function of optimization is the minimization of the consumed energy. Measurement-based vehicle model is used during the optimization process. The initialization and constraints of optimization are set up by analyzing the acquired vehicle data of the driver. The analyzation is done using a transform algorithm, making the initialization of optimization automated. Genetic algorithm is used with mixed initial population acquired from measured driving data and from creation function. Using this hybrid initial population helped to decrease the time of optimization. The resulted velocity profile of the optimized driving strategy was used in field test measurements, where 4.28% energy savings was achieved compared to the results prior to optimization.

Open Access: Yes

DOI: 10.3303/CET23103043

Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle

Peter Koros Zoltan Pusztai Ferenc Szauter Ferenc Friedler

Publication Name: Energies

Publication Date: 2022-05-01

Volume: 15

Issue: 10

Page Range: Unknown

Description:

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated power-train was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory.

Open Access: Yes

DOI: 10.3390/en15103631

Regenerative Braking Optimization of Lightweight Vehicle based on Vehicle Model

Peter Koros Zoltan Pusztai Ferenc Szauter Ferenc Friedler

Publication Name: Chemical Engineering Transactions

Publication Date: 2022-01-01

Volume: 94

Issue: Unknown

Page Range: 601-606

Description:

The usage of regenerative braking highly improves the overall energy efficiency of electric vehicles. In this paper, the model-based optimization of the torque profile is determined in the regenerative braking process of a lightweight electric vehicle. For the optimization, measurement-based vehicle model was used, where the extended powertrain model was set up, including the regenerative operation. The whole model was elaborated in MATLAB Simulink environment, where genetic algorithm (GA) was applied for the optimization. The resulted optimized braking curve was applied to control the experimental vehicle and field test were made to validate the optimization results. The results of the presented work can be directly used to further improve the drive cycle efficiency of the urban electric vehicles. The application of optimized driving strategies, including regenerative braking, could contribute to further energy and pollution reduction in urban transportation.

Open Access: Yes

DOI: 10.3303/CET2294100

Vehicle Model for Driving Strategy Optimization of Energy Efficient Lightweight Vehicle

Peter Koros Zoltan Pusztai Ferenc Friedler

Publication Name: Chemical Engineering Transactions

Publication Date: 2021-01-01

Volume: 88

Issue: Unknown

Page Range: 385-390

Description:

The energy consumption and CO2 emission of urban vehicles are highly dependent on their operation. Vehicle models can be used for optimizing driving strategy for emission reduction. This paper proposes a novel vehicle model of a one-seat electric vehicle dedicated for Shell Eco-marathon (SEM), the most famous and largest race of energy efficient vehicles. The available vehicle dynamical formulas cannot be directly used to describe the characteristics of lightweight vehicles. In the current work, a novel grey-box vehicle model has been introduced, based on measurement scenarios. The whole model has been elaborated in MATLAB Simulink environment, where individual subassemblies were defined for driving resistance model, powertrain model, and the racetrack characteristics. The resistance force model manages the forces in straight line moving and also takes the effect of cornering into account. Based on test bench measurements the complete efficiency map of the drivetrain was created and implemented into the vehicle model. The presented vehicle model is suitable for driving strategy optimization. By optimizing this model, 7.1 % energy savings have been achieved compared to best human driven lap. Driving strategy optimization will be essential, especially for autonomous vehicles, expressing the importance of the presented results in the future.

Open Access: Yes

DOI: 10.3303/CET2188064

Self-Driving Vehicle Sensors from One-Seated Experimental to Road-legal Vehicle

Ernö Horváth Zoltan Szeli Peter Koros Gábor Szakállas Peter Gulyas Zoltan Pusztai

Publication Name: Ines 2020 IEEE 24th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2020-07-01

Volume: Unknown

Issue: Unknown

Page Range: 97-101

Description:

Our university is determined to research and educate self-driving and autonomous technology. These two field require different necessities and attitude. In his paper we would like to summarize the migration from a road-legal vehicle to a self-developed, one-seated vehicle. We will describe the challenges of the migration process and of course how to overcome these challenges. The current paper also proposes recommendations and use-cases regarding self-driving vehicle sensory system.

Open Access: Yes

DOI: 10.1109/INES49302.2020.9147181