---
res:
  bibo_abstract:
  - <jats:p>In order to make rail transportation more attractive compared to motorized
    private transport, intelligent solutions are required across the entire mobility
    chain. Train stations, as places of connection and transfer, offer the greatest
    potential in this context. Thus, the deployment of autonomous service robots at
    train stations offers a wide range of possibilities for supporting passengers
    on their journeys by rail, for example, by providing information, accompanying
    them to the next link in the mobility chain, or transporting their luggage. Since
    such robots are battery-powered, one challenge is to carefully plan activities
    based on the remaining battery capacity. Therefore, the aim of this work is to
    predict battery usage and, in turn, battery state, in order to inform passengers,
    and to enable intelligent planning of its usage.In this work, realistic operational
    conditions of a service robot are systematically assessed through measurements
    on the service robot itself and through passenger surveys at a train station.
    The estimated operational conditions are experimentally replicated to simulate
    the heterogeneous use of the battery pack, while acquiring condition monitoring
    data throughout its use. The battery pack is modeled based on the single particle
    model, which can robustly predict the battery state by simulating its usage considering
    past operational conditions. The approach is validated using experimental data
    obtained from simulating realistic usage. On the one hand, additional battery
    packs are employed, and on the other hand, the load is varied. The advantage of
    this approach lies in the ability to account for future changes, such as higher
    loads than anticipated, to provide robust predictions.This enables the intelligent
    use of service robots, offering passengers an improved service experience at train
    stations.</jats:p>@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Löwen, Alexander
      foaf_surname: Löwen
      foaf_workInfoHomepage: http://www.librecat.org/personId=47233
  - foaf_Person:
      foaf_givenName: Enrique
      foaf_name: Aleman-Gallegos, Enrique
      foaf_surname: Aleman-Gallegos
  - foaf_Person:
      foaf_givenName: Osarenren
      foaf_name: Aimiyekagbon, Osarenren
      foaf_surname: Aimiyekagbon
      foaf_workInfoHomepage: http://www.librecat.org/personId=9557
  - foaf_Person:
      foaf_givenName: Sven
      foaf_name: Wachsmuth, Sven
      foaf_surname: Wachsmuth
  - foaf_Person:
      foaf_givenName: Walter
      foaf_name: Sextro, Walter
      foaf_surname: Sextro
      foaf_workInfoHomepage: http://www.librecat.org/personId=21220
  bibo_doi: 10.36001/phme.2026.v9i1.4952
  bibo_issue: '1'
  bibo_volume: 9
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2325-016X
  - http://id.crossref.org/issn/2325-016X
  dct_language: eng
  dct_publisher: PHM Society@
  dct_title: Prediction and simulation of battery pack usage for intelligent service
    robot deployment at a train station@
...