HIL Simulation of a Tram Regenerative Braking System

Pavlović, Tomislav; Župan, Ivan; Šunde, Viktor; Ban, Ž.

doi:10.3390/electronics10121379

Cited by 7 publications

(11 citation statements)

References 17 publications

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“…In addition, the motor drive should have a highfrequency decoupling capacitor to absorb the rapid transients of the reverse current. Figure 10 shows the Simulation Model of the Regenerative Braking System an autonomous EV in MATLAB/Simulink schematic [16].…”

Section: F Anti-lock System and Power Regenerationmentioning

confidence: 99%

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Overview Analysis of Recent Developments on Self-Driving Electric Vehicles

Ajao¹

2023

Preprint

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This paper provides a comprehensive overview of recent advancements in autonomous electric vehicle (AEV) within the specified region. It elaborates on the progress and comparative analysis of diverse subsystems, including energy storage, cell balancing for battery systems, vehicle charger layouts, electric vehicle motor mechanisms, and braking systems. Furthermore, this paper showcases several prototype autonomous electric vehicles as conclusive study findings.

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Section: F Anti-lock System and Power Regenerationmentioning

confidence: 99%

“…In levels 1 and 2, the autonomous driving systems can assist the driver, but the driver cannot divert their attention from the road. Level 1 provides assistance either for braking or steering, while level 2 offers a combination of both, equipping the car with lane centering and adaptive cruise control capabilities [17,18].…”

Section: Future Developmentmentioning

confidence: 99%

Overview Analysis of Recent Developments on Self-Driving Electric Vehicles

Ajao¹

2023

Preprint

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“…With the support of these multiple parameters, different systems in the vehicle will execute the necessary control operations. 187 Then, during the second phase, the vehicle influential input characteristics are used to build an efficient RBS model. In model-inloop (MIL) simulation, the base RBS model is used to improve the accuracy of the original model and to predict the performance of the updated model.…”

Section: Development Process Of Regenerative Braking Control Systemmentioning

confidence: 99%

“…The procedure consists of four phases; the first phase is to calculate the numerous parameters of the vehicle, as well as other critical factors such as the motor, battery, brake, etc. With the support of these multiple parameters, different systems in the vehicle will execute the necessary control operations 187 . Then, during the second phase, the vehicle influential input characteristics are used to build an efficient RBS model.…”

Section: Development Process Of Regenerative Braking Control Systemmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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“…They do require a lot of time and professional expertise, but some approaches have also appeared to shorten and simplify the process, as proposed in [45]. The hardware-in-theloop (HiL) method has been applied in many recent projects, e.g., in a tram regenerative braking system [46], for evaluation of the active safety of vehicles equipped with electronic stability control systems [47], or for simulation of a photovoltaic system in micro-grids [48].…”

Section: Introductionmentioning

confidence: 99%

Overview of Control Algorithm Verification Methods in Power Electronics Systems

et al. 2021

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The paper presents the existing verification methods for control algorithms in power electronics systems, including the application of model checking techniques. In the industry, the most frequently used verification methods are simulations and experiments; however, they have to be performed manually and do not give a 100% confidence that the system will operate correctly in all situations. Here we show the recent advancements in verification and performance assessment of power electronics systems with the usage of formal methods. Symbolic model checking can be used to achieve a guarantee that the system satisfies user-defined requirements, while statistical model checking combines simulation and statistical methods to gain statistically valid results that predict the behavior with high confidence. Both methods can be applied automatically before physical realization of the power electronics systems, so that any errors, incorrect assumptions or unforeseen situations are detected as early as possible. An additional functionality of verification with the use of formal methods is to check the converter operation in terms of reliability in various system operating conditions. It is possible to verify the distribution and uniformity of occurrence in time of the number of transistor switching, transistor conduction times for various current levels, etc. The information obtained in this way can be used to optimize control algorithms in terms of reliability in power electronics. The article provides an overview of various verification methods with an emphasis on statistical model checking. The basic functionalities of the methods, their construction, and their properties are indicated.

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HIL Simulation of a Tram Regenerative Braking System

Cited by 7 publications

References 17 publications

Overview Analysis of Recent Developments on Self-Driving Electric Vehicles

Overview Analysis of Recent Developments on Self-Driving Electric Vehicles

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Overview of Control Algorithm Verification Methods in Power Electronics Systems

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