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

Paper #:
  • 2016-01-1238

  • 2016-04-05
Karoshi, P., Tieber, K., Kneissl, C., Peneder, G. et al., "A History-Based Load Requirement Prediction Algorithm, for Predictive Hybrid- and Thermal Operation Strategies," SAE Technical Paper 2016-01-1238, 2016, https://doi.org/10.4271/2016-01-1238.
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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