Vehicle Independent Road Segment Resistance Estimation
Paper i proceeding, 2017

Road resistance is commonly divided into three different components; rolling resistance, wind resistance and resistance from road gradient (hills). The total sum of road resistance is the force that must be delivered by the powertrain to the wheels of the vehicle in order to maintain speed. The idea pursued in this paper is that it is possible to find models for each of the different components of the road resistance where the input parameters used are separated into purely vehicle dependent and purely vehicle independent parameters and that it is possible to estimate vehicle independent parameters from log data from a large population of vehicles (big data). The advantages with this approach is that data from any vehicle can be used to improve the estimation and that all vehicles can benefit from the estimated data. In the long run, this can lead to a system that dynamically calculates the surrounding parameters of the road resistances and that adapts rapidly to changing conditions such as wind and wet road surface. The main benefit from using the results is improved range estimation of battery electric vehicles but it can also be used for less computational route planning and improved vehicle energy management.

Författare

Mikael Askerdal

Swedish Electromobility Centre

Chalmers, Signaler och system, System- och reglerteknik, Mekatronik

Jonas Fredriksson

Chalmers, Signaler och system, System- och reglerteknik, Mekatronik

Proceedings of 30th Electric Vehicle Symposium

Road resistance is commonly divided into three different components; rolling resistance, wind resistance and resistance from road gradient (hills). The total sum of road resistance is the force that must be delivered by the powertrain to the wheels of the vehicle in order to maintain speed. The idea pursued in this paper is that it is possible to find models for each of the different components of the road resistance where the input parameters used are separated into purely vehicle dependent and purely vehicle independent parameters and that it is possible to estimate vehicle independent parameters from log data from a large population of vehicles (big data). The advantages with this approach is that data from any vehicle can be used to improve the estimation and that all vehicles can benefit from the estimated data. In the long run, this can lead to a system that dynamically calculates the surrounding parameters of the road resistances and that adapts rapidly to changing conditions such as wind and wet road surface. The main benefit from using the results is improved range estimation of battery electric vehicles but it can also be used for less computational route planning and improved vehicle energy management
Stuttgart, Germany,

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Senast uppdaterat

2018-12-05