{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/22102"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/22102","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"Deterministic and stochastic braking distance prediction of disc-braked rail vehicles","abstract":"A rapid modal shift to rail is inevitable to reduce transportation-related greenhouse gas emissions while meeting the ever increasing demand for mobility. Due to the nature of rail-bound traffic, the prediction of the braking performance plays a key role in improving the infrastructure capacity without compromising safety. Against this background, this works addresses the complexity of frictional brake forces and contributes to an improved brake performance prediction of disc-braked rail vehicles. By analyzing almost 2000 experimental brake applications conducted on a test rig, this work provides fundamental insights into the deterministic and stochastic behavior of a typical brake pad material applied in rail vehicles. Based on the available data and a thorough literature review, a new friction model is developed and identified for the investigated material. The model is capable of predicting the time-variant and non-linear behavior of the friction forces prevailing during the braking process and outperforms state-of-the-art friction models. In combination with a temperature model, additionally developed and identified in this work, the braking distance resulting from a single brake unit is predicted with an accuracy of 2%. The models are validated using data from more than 80 vehicle brake applications conducted with a multiple unit. The comparison of these measurements with the simulations reveals a very good agreement of instantaneous deceleration, friction forces and thermal loads occurring in the brake discs. For braking scenarios with an initial velocity of 120km/h , the braking distance of the train is predicted with an accuracy of 5%. No significant deviations are observed for other load cases. Moreover, this work presents a novel probabilistic approach that allows to consider the stochastic nature of frictional brake forces when predicting the brake performance. Based on this approach, it is found that the friction-related scatter prevailing in the brake units of rail vehicles depends on the initial velocity and is a superposition of global and individual phenomena. These are fundamental findings with respect to the meaningfulness of probabilistic analysis of brake applications, whose results are closely related to the capacity utilization during an operation with the European Train Control System (ETCS). In fact, an exemplary probabilistic analysis conducted in this work reveals that an improved consideration of the friction characteristics offers the potential to reduce the safety margins for ETCS braking curves by up to 14% without compromising safety.","abstract_html":"A rapid modal shift to rail is inevitable to reduce transportation-related greenhouse gas emissions while meeting the ever increasing demand for mobility. Due to the nature of rail-bound traffic, the prediction of the braking performance plays a key role in improving the infrastructure capacity without compromising safety. Against this background, this works addresses the complexity of frictional brake forces and contributes to an improved brake performance prediction of disc-braked rail vehicles. By analyzing almost 2000 experimental brake applications conducted on a test rig, this work provides fundamental insights into the deterministic and stochastic behavior of a typical brake pad material applied in rail vehicles. Based on the available data and a thorough literature review, a new friction model is developed and identified for the investigated material. The model is capable of predicting the time-variant and non-linear behavior of the friction forces prevailing during the braking process and outperforms state-of-the-art friction models. In combination with a temperature model, additionally developed and identified in this work, the braking distance resulting from a single brake unit is predicted with an accuracy of 2%. The models are validated using data from more than 80 vehicle brake applications conducted with a multiple unit. The comparison of these measurements with the simulations reveals a very good agreement of instantaneous deceleration, friction forces and thermal loads occurring in the brake discs. For braking scenarios with an initial velocity of 120km/h , the braking distance of the train is predicted with an accuracy of 5%. No significant deviations are observed for other load cases. Moreover, this work presents a novel probabilistic approach that allows to consider the stochastic nature of frictional brake forces when predicting the brake performance. Based on this approach, it is found that the friction-related scatter prevailing in the brake units of rail vehicles depends on the initial velocity and is a superposition of global and individual phenomena. These are fundamental findings with respect to the meaningfulness of probabilistic analysis of brake applications, whose results are closely related to the capacity utilization during an operation with the European Train Control System (ETCS). In fact, an exemplary probabilistic analysis conducted in this work reveals that an improved consideration of the friction characteristics offers the potential to reduce the safety margins for ETCS braking curves by up to 14% without compromising safety.","abstract_has_math":false,"creators":["Ehret, Marc"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Hecht, Markus"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-27T21:28:37Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-20903"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-20903","href":"https://doi.org/10.14279/depositonce-20903","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/22102","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Hecht, Markus"]},{"key":"dc:creator","label":"Author","values":["Ehret, Marc"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-08-12T08:02:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-08-12T08:02:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/22102","https://doi.org/10.14279/depositonce-20903"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A rapid modal shift to rail is inevitable to reduce transportation-related greenhouse gas emissions while meeting the ever increasing demand for mobility. Due to the nature of rail-bound traffic, the prediction of the braking performance plays a key role in improving the infrastructure capacity without compromising safety. Against this background, this works addresses the complexity of frictional brake forces and contributes to an improved brake performance prediction of disc-braked rail vehicles. By analyzing almost 2000 experimental brake applications conducted on a test rig, this work provides fundamental insights into the deterministic and stochastic behavior of a typical brake pad material applied in rail vehicles. Based on the available data and a thorough literature review, a new friction model is developed and identified for the investigated material. The model is capable of predicting the time-variant and non-linear behavior of the friction forces prevailing during the braking process and outperforms state-of-the-art friction models. In combination with a temperature model, additionally developed and identified in this work, the braking distance resulting from a single brake unit is predicted with an accuracy of 2%. The models are validated using data from more than 80 vehicle brake applications conducted with a multiple unit. The comparison of these measurements with the simulations reveals a very good agreement of instantaneous deceleration, friction forces and thermal loads occurring in the brake discs. For braking scenarios with an initial velocity of 120km/h , the braking distance of the train is predicted with an accuracy of 5%. No significant deviations are observed for other load cases. Moreover, this work presents a novel probabilistic approach that allows to consider the stochastic nature of frictional brake forces when predicting the brake performance. Based on this approach, it is found that the friction-related scatter prevailing in the brake units of rail vehicles depends on the initial velocity and is a superposition of global and individual phenomena. These are fundamental findings with respect to the meaningfulness of probabilistic analysis of brake applications, whose results are closely related to the capacity utilization during an operation with the European Train Control System (ETCS). In fact, an exemplary probabilistic analysis conducted in this work reveals that an improved consideration of the friction characteristics offers the potential to reduce the safety margins for ETCS braking curves by up to 14% without compromising safety.","Die Reduzierung der Treibhausgasemissionen bei gleichzeitig steigendem Mobilitätsbedarf erfordert eine rasche Verlagerung des Verkehrs auf die Schiene. Aufgrund des schienengebundenen Verkehrs spielt die Vorhersage der Bremsleistung eine Schlüsselrolle, um die Kapazität der Infrastruktur ohne Beeinträchtigung der Sicherheit zu erhöhen. Vor diesem Hintergrund widmet sich diese Arbeit der Komplexität von Reibungsbremskräften und leistet einen Beitrag zur Verbesserung der Vorhersage der Bremsleistung von scheibengebremsten Schienenfahrzeugen. Durch die Analyse von nahezu 2000 auf einem Prüfstand durchgeführten Bremsvorgängen liefert diese Arbeit grundlegende Erkenntnisse über das deterministische und stochastische Verhalten eines Schienenfahrzeugbremsbelages. Auf Basis der Daten und einer umfangreichen Literaturstudie wird ein neues Reibmodell entwickelt und für den untersuchten Belag identifiziert. Dieses Modell ermöglicht die Vorhersage des zeitvarianten und nichtlinearen Verhaltens der Reibungskräfte während des Bremsvorgangs und übertrifft die in der Literatur vorherrschenden Reibmodelle. In Verbindung mit einem in dieser Arbeit entwickelten Temperaturmodell wird der Bremsweg, der aus einem Bremsvorgang mit nur einer einzigen Bremsscheibe resultiert, mit einer Genauigkeit von 2% vorhergesagt. Die Modelle werden anhand von Daten aus über 80 Zugbremsversuchen validiert. Der Vergleich dieser Messungen mit den Simulationen zeigt eine sehr gute Übereinstimmung der momentanen Verzögerung, der Reibungskräfte und der in den Bremsscheiben auftretenden thermischen Belastungen. Für Bremsszenarien mit einer Ausgangsgeschwindigkeit von 120km/h wird der Bremsweg des Zuges mit einer Genauigkeit von 5% vorhergesagt. Für andere Lastfälle werden keine signifikanten Abweichungen beobachtet. Darüber hinaus wird ein neuer probabilistischer Ansatz vorgestellt, der es ermöglicht, die stochastische Natur der Reibungskräfte bei der Prognose zu berücksichtigen. Auf der Grundlage dieses Ansatzes zeigt sich, dass die in Schienenfahrzeugen vorherrschende reibungsbedingte Streuung von der Ausgangsgeschwindigkeit abhängt und eine Überlagerung von globalen und individuellen Effekten darstellt. Dies sind grundlegende Erkenntnisse im Hinblick auf die Aussagekraft probabilistischer Analysen von Bremsvorgängen, deren Ergebnisse eng mit der Streckenauslastung während des Betriebs mit dem European Train Control System (ETCS) verknüpft sind. In der Tat offenbart eine in dieser Arbeit durchgeführte probabilistische Analyse, dass eine verbesserte Berücksichtigung der Reibungscharakteristik das Potenzial bietet, die Sicherheitsmargen für ETCS Bremskurven um bis zu 14% zu reduzieren ohne die Sicherheit zu beeinträchtigen."]},{"key":"dc:title","label":"Title","values":["Deterministic and stochastic braking distance prediction of disc-braked rail vehicles"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hecht, Markus"],"dc:creator":["Ehret, Marc"],"dc:date.accessioned":["2024-08-12T08:02:50Z"],"dc:date.available":["2024-08-12T08:02:50Z"],"dc:date.issued":["2024"],"dc:description.abstract":["A rapid modal shift to rail is inevitable to reduce transportation-related greenhouse gas emissions while meeting the ever increasing demand for mobility. Due to the nature of rail-bound traffic, the prediction of the braking performance plays a key role in improving the infrastructure capacity without compromising safety. Against this background, this works addresses the complexity of frictional brake forces and contributes to an improved brake performance prediction of disc-braked rail vehicles. By analyzing almost 2000 experimental brake applications conducted on a test rig, this work provides fundamental insights into the deterministic and stochastic behavior of a typical brake pad material applied in rail vehicles. Based on the available data and a thorough literature review, a new friction model is developed and identified for the investigated material. The model is capable of predicting the time-variant and non-linear behavior of the friction forces prevailing during the braking process and outperforms state-of-the-art friction models. In combination with a temperature model, additionally developed and identified in this work, the braking distance resulting from a single brake unit is predicted with an accuracy of 2%. The models are validated using data from more than 80 vehicle brake applications conducted with a multiple unit. The comparison of these measurements with the simulations reveals a very good agreement of instantaneous deceleration, friction forces and thermal loads occurring in the brake discs. For braking scenarios with an initial velocity of 120km/h , the braking distance of the train is predicted with an accuracy of 5%. No significant deviations are observed for other load cases. Moreover, this work presents a novel probabilistic approach that allows to consider the stochastic nature of frictional brake forces when predicting the brake performance. Based on this approach, it is found that the friction-related scatter prevailing in the brake units of rail vehicles depends on the initial velocity and is a superposition of global and individual phenomena. These are fundamental findings with respect to the meaningfulness of probabilistic analysis of brake applications, whose results are closely related to the capacity utilization during an operation with the European Train Control System (ETCS). In fact, an exemplary probabilistic analysis conducted in this work reveals that an improved consideration of the friction characteristics offers the potential to reduce the safety margins for ETCS braking curves by up to 14% without compromising safety.","Die Reduzierung der Treibhausgasemissionen bei gleichzeitig steigendem Mobilitätsbedarf erfordert eine rasche Verlagerung des Verkehrs auf die Schiene. Aufgrund des schienengebundenen Verkehrs spielt die Vorhersage der Bremsleistung eine Schlüsselrolle, um die Kapazität der Infrastruktur ohne Beeinträchtigung der Sicherheit zu erhöhen. Vor diesem Hintergrund widmet sich diese Arbeit der Komplexität von Reibungsbremskräften und leistet einen Beitrag zur Verbesserung der Vorhersage der Bremsleistung von scheibengebremsten Schienenfahrzeugen. Durch die Analyse von nahezu 2000 auf einem Prüfstand durchgeführten Bremsvorgängen liefert diese Arbeit grundlegende Erkenntnisse über das deterministische und stochastische Verhalten eines Schienenfahrzeugbremsbelages. Auf Basis der Daten und einer umfangreichen Literaturstudie wird ein neues Reibmodell entwickelt und für den untersuchten Belag identifiziert. Dieses Modell ermöglicht die Vorhersage des zeitvarianten und nichtlinearen Verhaltens der Reibungskräfte während des Bremsvorgangs und übertrifft die in der Literatur vorherrschenden Reibmodelle. In Verbindung mit einem in dieser Arbeit entwickelten Temperaturmodell wird der Bremsweg, der aus einem Bremsvorgang mit nur einer einzigen Bremsscheibe resultiert, mit einer Genauigkeit von 2% vorhergesagt. Die Modelle werden anhand von Daten aus über 80 Zugbremsversuchen validiert. Der Vergleich dieser Messungen mit den Simulationen zeigt eine sehr gute Übereinstimmung der momentanen Verzögerung, der Reibungskräfte und der in den Bremsscheiben auftretenden thermischen Belastungen. Für Bremsszenarien mit einer Ausgangsgeschwindigkeit von 120km/h wird der Bremsweg des Zuges mit einer Genauigkeit von 5% vorhergesagt. Für andere Lastfälle werden keine signifikanten Abweichungen beobachtet. Darüber hinaus wird ein neuer probabilistischer Ansatz vorgestellt, der es ermöglicht, die stochastische Natur der Reibungskräfte bei der Prognose zu berücksichtigen. Auf der Grundlage dieses Ansatzes zeigt sich, dass die in Schienenfahrzeugen vorherrschende reibungsbedingte Streuung von der Ausgangsgeschwindigkeit abhängt und eine Überlagerung von globalen und individuellen Effekten darstellt. Dies sind grundlegende Erkenntnisse im Hinblick auf die Aussagekraft probabilistischer Analysen von Bremsvorgängen, deren Ergebnisse eng mit der Streckenauslastung während des Betriebs mit dem European Train Control System (ETCS) verknüpft sind. In der Tat offenbart eine in dieser Arbeit durchgeführte probabilistische Analyse, dass eine verbesserte Berücksichtigung der Reibungscharakteristik das Potenzial bietet, die Sicherheitsmargen für ETCS Bremskurven um bis zu 14% zu reduzieren ohne die Sicherheit zu beeinträchtigen."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/22102","https://doi.org/10.14279/depositonce-20903"],"dc:language.iso":["en"],"dc:rights.uri":["https://creativecommons.org/licenses/by/4.0/"],"dc:title":["Deterministic and stochastic braking distance prediction of disc-braked rail vehicles"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:37Z"}