Krisztian Enisz

54395276100

Publications - 5

Localization robustness improvement for an autonomous race car using multiple extended Kalman filters

Krisztian Enisz Ernö Horváth Istvan Szalay

Publication Name: Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering

Publication Date: 2025-08-01

Volume: 239

Issue: 9

Page Range: 3771-3783

Description:

In this paper, we introduce a vehicle localization method designed for the SZEnergy race car, which competes in the Shell Eco-marathon. The proposed method comprises four different extended Kalman filter-based localization algorithms and a selection algorithm that determines the most suitable one based on vehicle speed, GNSS availability, and signal quality. The low-speed Kalman filters are based on a kinematic vehicle model while the high-speed variants are based on a dynamic vehicle model. Several measurements were performed during test maneuvers to evaluate the performance of the filters. The proposed method succesfully handles sensor miscalibration and GNSS outages.

Open Access: Yes

DOI: 10.1177/09544070241266281

Performance Analysis of Position Estimation and Correction Methods †

Krisztian Enisz Ernö Horváth Zoltan Szeli József Gábor Pázmány Norbert Marko

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

There are several global and local position estimation and refinement techniques based on the GNSS (Global Navigation Satellite System) and environmental monitoring (e.g., LIDAR, Light Detection and Ranging). These are usually based on a combination of multiple sensors using some form of sensor fusion, together with a filtering or observation technique. The behavior of these algorithms may vary depending on the applied sensor signals and on their accuracy under different environmental conditions and for different vehicle types. In the case of systems that also use GNSS signals, different procedures must also be prepared for signal dropouts and, in the worst case, drastic fluctuations in accuracy. The aim of this research is to present and compare the performance of different estimation procedures for different vehicles and environmental conditions.

Open Access: Yes

DOI: 10.3390/engproc2024079061

Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis

Krisztian Enisz Ernö Horváth Istvan Szalay Norbert Marko

Publication Name: Machines

Publication Date: 2023-12-01

Volume: 11

Issue: 12

Page Range: Unknown

Description:

In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements. The current paper proposes an AI-based long short-term memory (LSTM) localization approach and evaluates its performance against the ground truth.

Open Access: Yes

DOI: 10.3390/machines11121079

Permanent Magnet Synchronous Motor Model Extension for High-Frequency Signal Injection-Based Sensorless Magnet Polarity Detection

Krisztian Enisz Istvan Szalay Hunor Medve Denes Fodor

Publication Name: Energies

Publication Date: 2022-02-01

Volume: 15

Issue: 3

Page Range: Unknown

Description:

In this paper, a novel extended permanent magnet synchronous motor model is presented that incorporates a quadratic flux-current function to represent the polarity-dependent saliency. The proposed model enables the design of sensorless polarity detection algorithms required by the initial position detection of permanent magnet synchronous motors. The novelty of the model is that it integrates the polarity-dependent saliency into the traditional machine model and introduces a new machine parameter, the polarity-dependent saliency coefficient, to specify the Hessian matrix of the flux-current function. A measurement method is presented for determination of the elements of the Hessian and the polarity-dependent saliency coefficient. The solution of the model is given for high-frequency sinusoidal pulsating voltage injection. Experimental results show that the proposed extended model accurately predicts the amplitudes and phases of the second harmonics of the motor currents, which are the carriers of the polarity-dependent information. This information enables a current measurement-based polarity detection algorithm using the phase difference between the fundamental and second harmonic of the apparent d-axis current. Both the presented measurement data and the proposed model show that injection in the d-direction is optimal for polarity detection.

Open Access: Yes

DOI: 10.3390/en15031131

Comparison of square-wave and sinusoidal signal injection in sensorless polarity detection for PMSMs

Krisztian Enisz Istvan Szalay Denes Fodor

Publication Name: 2022 IEEE 20th International Power Electronics and Motion Control Conference Pemc 2022

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: 583-589

Description:

This paper compares the non-modulated square-wave voltage injection-based and sinusoidal voltage injection-based polarity detection methods applicable in sensorless PMSM drives. A quadratic PMSM model extension is presented that serves as a basis for the polarity detection techniques. The comparison includes the polarity-dependent current components produced by the injected signals, the design parameters related to the injected signal features and their effect on the probability of correct polarity detection. The performance differences and possible application areas are also discussed.

Open Access: Yes

DOI: 10.1109/PEMC51159.2022.9962876