A History-Based Load Requirement Prediction Algorithm, for Predictive Hybrid- and Thermal Operation Strategies

Paul Karoshi, Karin Tieber, Christopher Kneissl, Georg Peneder, Harald Kraus, Martin Hofstetter, Jürgen Fabian, Martin Ackerl

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

In hybrid electric vehicles (HEV), the operation strategy strongly influences the available system power, as well as local exhaust emissions. Predictive operation strategies rely on knowledge of future traction-force demands. This predicted information can be used to balance the battery’s state of charge or the engine’s thermal system in their legal operation limits and can reduce peak loads. Assuming the air and rolling drag-coefficient to be constant, the desired vehicle velocity, vehicle-mass and longitudinal driving resistances determine the vehicle’s traction-force demand. In this paper, a novel methodology, combining a history-based prediction algorithm for estimating future traction-force demands with the parameter identification of road grade angle and vehicle mass, is proposed. It is solely based on a route-history database and internal vehicle data, available on its on-board communication and measuring systems. It complements state-of-the-art navigation software, as these systems usually are not activated on frequently driven routes. In a first step, a Kalman filter estimates the vehicle mass and the current grade angle of the road online, using the vehicle’s longitudinal equation of motion. In a second step, velocity and road gradient are predicted. This is done by comparing online vehicle data with data stored in a route history. As the steering-wheel angle correlates well with the position on a given route, it is chosen as distinctive parameter for route identification. A longitudinal vehicle model calculates the approximated future traction-force demand from the predicted velocity and road gradient trajectory, considering the online estimated vehicle mass. Then the operation strategy can determine control variables, such as the upcoming loads to the propulsion units, for a certain prediction horizon ahead of the vehicle. Validation results of the prediction system are presented for an all-electric passenger car. However, computing and memory requirements for a real-time capable hardware are not considered.
Original languageEnglish
Title of host publicationSAE - World Congress 2016
PublisherSAE International
Publication statusPublished - 14 Apr 2016
EventSAE World Congress 2016 - Detroit, United States
Duration: 12 Apr 201614 Apr 2016

Conference

ConferenceSAE World Congress 2016
CountryUnited States
CityDetroit
Period12/04/1614/04/16

Keywords

  • Predictive operation strategy
  • mass estimation
  • road grade estimation
  • real time capability
  • commercial vehicle

Cite this

Karoshi, P., Tieber, K., Kneissl, C., Peneder, G., Kraus, H., Hofstetter, M., ... Ackerl, M. (2016). A History-Based Load Requirement Prediction Algorithm, for Predictive Hybrid- and Thermal Operation Strategies. In SAE - World Congress 2016 [2016-01-1238] SAE International.

A History-Based Load Requirement Prediction Algorithm, for Predictive Hybrid- and Thermal Operation Strategies. / Karoshi, Paul; Tieber, Karin; Kneissl, Christopher ; Peneder, Georg; Kraus, Harald; Hofstetter, Martin; Fabian, Jürgen; Ackerl, Martin.

SAE - World Congress 2016. SAE International, 2016. 2016-01-1238.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Karoshi, P, Tieber, K, Kneissl, C, Peneder, G, Kraus, H, Hofstetter, M, Fabian, J & Ackerl, M 2016, A History-Based Load Requirement Prediction Algorithm, for Predictive Hybrid- and Thermal Operation Strategies. in SAE - World Congress 2016., 2016-01-1238, SAE International, SAE World Congress 2016, Detroit, United States, 12/04/16.
Karoshi P, Tieber K, Kneissl C, Peneder G, Kraus H, Hofstetter M et al. A History-Based Load Requirement Prediction Algorithm, for Predictive Hybrid- and Thermal Operation Strategies. In SAE - World Congress 2016. SAE International. 2016. 2016-01-1238
Karoshi, Paul ; Tieber, Karin ; Kneissl, Christopher ; Peneder, Georg ; Kraus, Harald ; Hofstetter, Martin ; Fabian, Jürgen ; Ackerl, Martin. / A History-Based Load Requirement Prediction Algorithm, for Predictive Hybrid- and Thermal Operation Strategies. SAE - World Congress 2016. SAE International, 2016.
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AB - In hybrid electric vehicles (HEV), the operation strategy strongly influences the available system power, as well as local exhaust emissions. Predictive operation strategies rely on knowledge of future traction-force demands. This predicted information can be used to balance the battery’s state of charge or the engine’s thermal system in their legal operation limits and can reduce peak loads. Assuming the air and rolling drag-coefficient to be constant, the desired vehicle velocity, vehicle-mass and longitudinal driving resistances determine the vehicle’s traction-force demand. In this paper, a novel methodology, combining a history-based prediction algorithm for estimating future traction-force demands with the parameter identification of road grade angle and vehicle mass, is proposed. It is solely based on a route-history database and internal vehicle data, available on its on-board communication and measuring systems. It complements state-of-the-art navigation software, as these systems usually are not activated on frequently driven routes. In a first step, a Kalman filter estimates the vehicle mass and the current grade angle of the road online, using the vehicle’s longitudinal equation of motion. In a second step, velocity and road gradient are predicted. This is done by comparing online vehicle data with data stored in a route history. As the steering-wheel angle correlates well with the position on a given route, it is chosen as distinctive parameter for route identification. A longitudinal vehicle model calculates the approximated future traction-force demand from the predicted velocity and road gradient trajectory, considering the online estimated vehicle mass. Then the operation strategy can determine control variables, such as the upcoming loads to the propulsion units, for a certain prediction horizon ahead of the vehicle. Validation results of the prediction system are presented for an all-electric passenger car. However, computing and memory requirements for a real-time capable hardware are not considered.

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