Markus Eider, M.Sc.

Academic Staff

Research project “SmiLE”

DEGG’s 2.28

0991/3615-633

0991/3615-297


Beitrag (Sammelband oder Tagungsband)
  • Nicki Bodenschatz
  • Markus Eider
  • Andreas Berl
Challenges and requirements of electric vehicle fleet charging at company parking sites[Accepted for publication]
  • 2021
  • Angewandte Informatik
  • MOBIL
Beitrag (Sammelband oder Tagungsband)
  • Markus Eider
  • Nicki Bodenschatz
  • Andreas Berl
Evaluation of machine learning algorithms for the prediction of simulated company parking space occupancy[Accepted for publication]
  • 2021
  • 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)

  • 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)

  • 2020
  • Angewandte Informatik
  • TC Plattling MoMo
  • NACHHALTIG
  • DIGITAL
Beitrag (Sammelband oder 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
Beitrag (Sammelband oder 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
  • MOBIL
  • DIGITAL
Vortrag
  • Markus Eider
Projekt CITRAM - Citizen Science for Traffic Management

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

  • 2020
  • TC Plattling MoMo
  • Angewandte Informatik
  • NACHHALTIG
  • DIGITAL
Vortrag
  • Markus Eider
Nutzerorientierte Empfehlungen für Lithium-Ionen-Batterien in ElektrofahrzeugenPosterpräsentation

In: 6. Tag der Forschung

  • 2019
  • TC Freyung
  • Elektrotechnik und Medientechnik
  • MOBIL
  • NACHHALTIG
Beitrag (Sammelband oder Tagungsband)
  • Nicki Bodenschatz
  • Diana Schramm
  • Markus Eider
  • Andreas Berl
Classification of Electric Vehicle Fleets Considering the Complexity of Fleet Charging Schedules[Status: Presented]
  • 2018
  • TC Freyung
  • MOBIL
Beitrag (Sammelband oder 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 ElektrofahrzeugflottenPosterpräsentation

In: 5. Tag der Forschung

  • 2018
  • TC Freyung
  • MOBIL
Beitrag (Sammelband oder Tagungsband)
  • Markus Eider
  • Andreas Berl
Dynamic EV Battery Health Recommendations, pg. 586-592.
  • 2018
  • TC Freyung
  • MOBIL
  • NACHHALTIG
Vortrag
  • Markus Eider
  • Andreas Berl
Verlängerte Batterielebensdauer von Elektrofahrzeugen durch dynamische Nutzungsempfehlungen

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

  • 2018
  • Elektrotechnik und Medientechnik
  • TC Freyung
  • MOBIL
Vortrag
  • Markus Eider
Dynamic Generation of Recommendations for EV Battery Health

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

  • 2018
  • Elektrotechnik und Medientechnik
  • MOBIL
Vortrag
  • Markus Eider
A Novel Approach on Battery Health Monitoring

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

  • 2018
  • TC Freyung
  • MOBIL
Beitrag (Sammelband oder 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 (Sammelband oder 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 (Sammelband oder 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 (Sammelband oder 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.
  • 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 (Sammelband oder Tagungsband)
  • Markus Eider
  • Stefan Kunze
  • Wolfgang Dorner
A Customizable Software Tool for Hardware in the Loop Tests, pg. 69-74.
  • 2016
  • TC Freyung
  • DIGITAL
Beitrag (Sammelband oder 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