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Markus Eider, M.Sc.

Forschungsfokus

  • Prescriptive Maintenance
  • Elektromobilität

Lehre

  • Grundlagen der Informatik (Bachelor Angewandte Informatik / Interaktive Systeme)
  • Einführung in die Programmierung (Bachelor Angewandte Informatik / Interaktive Systeme)

Zentrum für angewandte Forschung

Institut für angewandte Informatik - Technologie Campus Freyung

Wissenschaftlicher Mitarbeiter

Regionales Zukunftszentrum Süd

DEGG's 2.28

0991/3615-633

08551/91764-69

Sprechzeiten

Sprechstunden nach Vereinbarung per Email

Sortierung:
Beitrag in Sammelwerk/Tagungsband

  • Nicki Bodenschatz
  • Markus Eider
  • Daniel Kratschmer
  • Andreas Berl
  • A. Zimmermann

Battery-friendly charging process scheduling of electric vehicle fleets at company sites

  • (2023)
  • Angewandte Informatik
  • MOBIL
Zeitschriftenartikel

  • Markus Eider
  • B. Sick
  • Andreas Berl

Context-aware recommendations for extended electric vehicle battery lifetime

In: Sustainable Computing: Informatics and Systems vol. 37

  • (2023)

DOI: 10.1016/j.suscom.2022.100845

Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for battery-friendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice.
  • Angewandte Informatik
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • Nicki Bodenschatz
  • Markus Eider
  • Andreas Berl

Challenges and requirements of electric vehicle fleet charging at company parking sites

pg. 623-628.

  • (2021)

DOI: 10.1109/ACIT52158.2021.9548563

The uprising number of electric vehicles on company parking sites creates a variety of challenges, in terms of scheduling charging processes. In uncoordinated charging, peaks loads can occur that harm the low voltage grid and make the company pay a higher electricity bill. Further, some vehicles may not be charged enough to be used for service appointments at customer homes. In a first step, this paper highlights the individual problems in detail that can appear in a company parking scenario. Next, it is shown that charging scheduling in a company case is not trivial. Finally, requirements to a software-based solution of the challenges are derived from the individual problems.
  • Angewandte Informatik
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Nicki Bodenschatz
  • Andreas Berl

Evaluation of machine learning algorithms for the prediction of simulated company parking space occupancy

pg. 739-743.

  • (2021)

DOI: 10.1109/ACIT52158.2021.9548487

Parking lots of cities and companies are congested when employees get to work in the morning hours. Some people might need to switch to public parking spaces if there is no spot left at their workplace. This is a serious challenge, as nowadays and especially in the future, the ratio of electric vehicles in transportation is growing. Therefore, electric vehicle users require guaranteed charging spots for the continuation of their journey. To enable reservations of parking and charging spots ahead of time, companies want to gain knowledge on upcoming vehicle numbers. So, there is a need for prediction models. However, there is little research done on prediction technologies for company parking spaces. In this paper, four different models from machine learning and statistics are compared in predicting the presence of vehicles from the user groups employees, fleet and visitors. They are trained on simulated occupancy data throughout one year. An evaluation shows that decision trees and artificial neural networks perform well for the case of company parking.
  • Angewandte Informatik
  • MOBIL
Vortrag

  • Nicki Bodenschatz
  • Markus Eider
  • Andreas Berl

Mixed-Integer-Linear-Programming model for the charging scheduling of electric vehicle fleets

In: 2020 10th International Conference on Advanced Computer Information Technologies (ACIT)

Deggendorf

  • 16.-18.09.2020 (2020)
  • Angewandte Informatik
  • TC Plattling MoMo
  • NACHHALTIG
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Andreas Berl

Requirements for prescriptive recommender systems extending the lifetime of EV batteries

pg. 412-417.

  • (2020)

DOI: 10.1109/ACIT49673.2020.9209011

Lithium-ion batteries in electric vehicles are subject to degradation, which is strongly influenced by the actions of vehicle users. Hereby, inexperienced users can cause the battery to reach its end of life state earlier than average. For this reason, this paper proposes the concept of a prescriptive recommender system that supports users in planning their utilization actions. The paper identifies functionalities of decision support systems relevant to extend the lifetime of electric and electronic systems. This allows to determe generic functional and non-functional requirements for prescriptive recommender systems. Further, the derived requirements are discussed in connection to the practicability of a prescriptive recommender system.
  • Angewandte Informatik
  • TC Plattling MoMo
  • MOBIL
  • NACHHALTIG
Vortrag

  • Markus Eider

Projekt CITRAM - Citizen Science for Traffic Management

In: 2. TRIOKON 2020 – Die ostbayerische Transferkonferenz für Wirtschaft, Wissenschaft und Gesellschaft

Hochschule für angewandte Wissenschaften Landshut Landshut

  • 29.09.2020 (2020)
  • Angewandte Informatik
  • TC Plattling MoMo
  • NACHHALTIG
  • DIGITAL
Vortrag

  • Markus Eider
  • Andreas Berl

Requirements for prescriptive recommender systems extending the lifetime of EV batteries

In: 2020 10th International Conference on Advanced Computer Information Technologies (ACIT)

Deggendorf

  • 16.-18.09.2020 (2020)
  • TC Plattling MoMo
  • Angewandte Informatik
  • NACHHALTIG
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Nicki Bodenschatz
  • Markus Eider
  • Andreas Berl

Mixed-Integer-Linear-Programming model for the charging scheduling of electric vehicle fleets

pg. 741-746.

  • (2020)

DOI: 10.1109/ACIT49673.2020.9208875

The number of electric vehicles is steadily increasing of the past few years. This transition to electric vehicles bears the challenge, to integrate the charging processes into the grid without overstressing it. To prevent this, research has tackled lately the scheduling of electric vehicle charging. Especially the charging of electric vehicle fleets is in the focus of research. There are already different solution approaches to increase the grid stability, to increase the intake of locally produced renewable energy or simply to reduce the cost. However, all these solution approaches use different mathematical models with different parameters to represent the charging scheduling problem. This results in the problem that each model is applicable for a special use case only, other use cases might need other parameters for the scheduling of the electric vehicle fleet. To ease this problem, this paper provides a detailed mathematical model for the cost minimization of a general electric fleet in the form of a mixed-integer-linearprogram. In order to do this, the paper shows that different research approaches use different parameters in their solutions. Afterwards, the paper presents a general overview of technical limitations for the electric fleets. On foundation of these limitations a mixed-integer-linear-program model for a wide range of electric fleets is established. Also, the paper provides options to extend the model in order to improve the result of an optimal schedule.
  • Angewandte Informatik
  • TC Plattling MoMo
  • DIGITAL
  • MOBIL
Vortrag

  • Markus Eider

Nutzerorientierte Empfehlungen für Lithium-Ionen-Batterien in Elektrofahrzeugen . Posterpräsentation

In: 6. Tag der Forschung

Technische Hochschule Deggendorf Deggendorf

  • 10.04.2019 (2019)
  • Elektrotechnik und Medientechnik
  • TC Freyung
  • MOBIL
  • NACHHALTIG
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Andreas Berl

Dynamic Generation of Recommendations for EV Battery Health

  • (2018)

DOI: 10.23919/EETA.2018.8493182

Electric vehicles equipped with Lithium-ion batteries face performance loss due to battery ageing. This effect can be actively influenced through behaviour introduced by vehicle users. Therefore, this paper proposes a dynamic recommendation architecture to automatically generate dynamic recommendations in order to prolong battery lifetime. We propose dynamic recommendations as well as requirements for them. The recommendations suggest a certain user behaviour for a specific chronological scope in the future as well as a weight based on their impact on maintaining battery health. Furthermore, we present an exemplary architecture, based on the requirements. Using historical electric vehicle driving data, it can automatically derive dynamic recommendations.
  • TC Freyung
  • NACHHALTIG
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Diana Schramm
  • Nicki Bodenschatz
  • Andreas Berl
  • P. Danner
  • H. Meer

A Novel Approach on Battery Health Monitoring

  • (2018)

  • TC Freyung
  • DIGITAL
Vortrag

  • Nicki Bodenschatz
  • Markus Eider
  • Diana Schramm
  • Andreas Berl

Optimierte Ladeplanung von Elektrofahrzeugflotten . Posterpräsentation

In: 5. Tag der Forschung

Technische Hochschule Deggendorf Deggendorf

  • 08.03.2018 (2018)
  • TC Freyung
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Andreas Berl

Dynamic EV Battery Health Recommendations

pg. 586-592.

New York, NY

  • (2018)
  • TC Freyung
  • MOBIL
  • NACHHALTIG
Beitrag in Sammelwerk/Tagungsband

  • Nicki Bodenschatz
  • Diana Schramm
  • Markus Eider
  • Andreas Berl

Classification of Electric Vehicle Fleets Considering the Complexity of Fleet Charging Schedules . [Status: Presented]

New York, NY

  • (2018)
  • TC Freyung
  • MOBIL
Vortrag

  • Markus Eider

A Novel Approach on Battery Health Monitoring

In: 7th Conference on Future Automotive Technology (CoFAT)

Fürstenfeldbruck

  • 09.05.2018 (2018)
  • TC Freyung
  • MOBIL
Vortrag

  • Markus Eider
  • Andreas Berl

Verlängerte Batterielebensdauer von Elektrofahrzeugen durch dynamische Nutzungsempfehlungen

In: 4. Jahreskonferenz des Netzwerks INDIGO (Internet und Digitalisierung Ostbayern)

Technische Hochschule Deggendorf Deggendorf

  • 23.11.2018 (2018)
  • TC Freyung
  • Elektrotechnik und Medientechnik
  • MOBIL
Vortrag

  • Markus Eider

Dynamic Generation of Recommendations for EV Battery Health

In: International Conference of Electrical and Electronic Technologies for Automotive (AUTOMOTIVE)

Mailand, Italien

  • 09.07.2018 (2018)
  • Elektrotechnik und Medientechnik
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • B. Kirpes
  • S. Klingert
  • R. Basmadjian
  • H. Meer
  • Markus Eider
  • M. Ortega

EV Charging Coordination to Secure Power Grid Stability

  • (2017)
  • Elektrotechnik und Medientechnik
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Diana Schramm
  • Andreas Berl
  • R. Basmadjian
  • H. Meer
  • S. Klingert
  • T. Schulze
  • F. Kutzner
  • C. Kacperski
  • M. Štolba

Seamless Electromobility

pg. 316-321.

New York NY

  • (2017)

DOI: 10.1145/3077839.3078461

The existing electromobility (EM) is still in its fledgling stage and multiple challenges have to be overcome to make Electric Vehicles (EVs) as convenient as combustion engine vehicles. Users and Electric Vehicle Fleet Operators (EFOs) want their EVs to be charged and ready for use at all times. This straightforward goal, however, is counteracted from various sides: The range of the EV depends on the status and depletion of the EV battery which is influenced by EV use and charging characteristics. Also, most convenient charging from the user's point of view, might unfortunately lead to problems in the power grid. As in the case of a power peak in the evening when EV users return from work and simultaneously plug in their EVs for charging. Last but not least, the mass of EV batteries are an untapped potential to store electricity from intermittent renewable energy sources. In this paper, we propose a novel approach to tackle this multi-layered problem from different perspectives. Using on-board EV data and grid prediction models, we build up an information model as a foundation for a back end service containing EFO and Charging Station Provider (CSP) logic as well as a central Advanced Drivers Assistant System (ADAS). These components connect to both battery management and user interfaces suggesting various routing and driving behaviour alternatives customized and incentivized for the current user profile optimizing above mentioned goals.
  • TC Freyung
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • Stefan Kunze
  • Rainer Pöschl
  • Alexander Faschingbauer
  • Markus Eider

Artificial Neural Networks based Age Estimation of Electronic Devices

pg. 827-832.

  • (2017)
  • TC Freyung
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Stefan Kunze
  • Rainer Pöschl

FPGA Based Emulation of Multiple 1-Wire Sensors for Hardware in the Loop Tests

pg. 279-284.

  • (2016)
  • TC Freyung
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Markus Eider
  • Stefan Kunze
  • Wolfgang Dorner

A Customizable Software Tool for Hardware in the Loop Tests

pg. 69-74.

  • (2016)
  • TC Freyung
  • DIGITAL
Vortrag

  • Markus Eider
  • Peter Faber
  • F. Haselbeck
  • Cordula Krinner

Travel Mate Matching – Strengthening Shared Mobility through the Formation of Interest Groups

In: Abschlusskonferenz des AI-Clash „Clashing Approaches to Artificial Intelligence“

Technische Hochschule Deggendorf Deggendorf; Online

  • 13.05.2022
  • Angewandte Wirtschaftswissenschaften