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This paper describes extensions to the widely used TNO MF-Tyre 5.2 Magic Formula tyre model. The Magic Formula itself has been adapted to cope with large camber angles and inflation pressure changes. In addition the description of the rolling resistance has been improved. Modelling of the tyre dynamics has been changed to allow a seamless and consistent switch from simple first order relaxation behaviour to rigid ring dynamics. Finally the effect of inflation pressure on the loaded radius and the tyre enveloping properties is discussed and some results are given to illustrate the capabilities of the model.

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Drivers of battery electric vehicles (BEVs) require an accurate and reliable energy consumption prediction along a chosen route to reduce range anxiety. The energy consumption for a future trip depends on a number of factors such as driving behavior, road topography information, weather conditions and traffic situation. This paper discusses an algorithm to predict the energy consumption for a future trip considering these influencing factors. The route information is obtained from OpenStreetMap and Shuttle Radar Topography Mission. The algorithm consists of an offline algorithm and an online algorithm. The offline algorithm is designed to provide information for the driver to make future driving plans, which provides a nominal energy consumption value and an energy consumption range before a trip begins. The online algorithm is designed to adjust the energy consumption prediction result based on current driving, which includes a vehicle parameter estimation algorithm and a driving behavior correction algorithm. The energy consumption prediction algorithm is verified by 30 driving tests, including city, rural, highway and hilly driving. A comparison shows that the measured energy consumption of all trips is within the energy consumption range provided by the offline algorithm and most of the differences between the measurement and nominal prediction are smaller than 10%. The offline prediction is used as a starting point and is corrected by the online algorithm during driving. The mean absolute percentage error between the measured energy consumption value and online prediction result of all trips is within 5%.

The limited driving range is considered as a significant barrier to the spread of electric vehicles. One effective method to reduce "range anxiety" is to offer accurate information to the driver on the remaining driving range. However, the energy consumption during driving is largely determined by driving behaviour, road topography information and traffic situation, which are hard to predict. This paper will discuss an accurate electric vehicle energy consumption model validated using driving tests on different public roads, and then the model is used to predict future energy consumption based on road information. The energy consumption model includes five parts: the road load model, the powertrain loss model, the regenerative braking model, the auxiliary system model and the battery model. The parameters of these models are obtained through driving tests on public road and dynamometer tests in the TU/e Automotive Engineering Science lab. The results show that the model can calculate the energy consumption with a maximum error of 5% based on driving speed under different circumstances. To predict the future energy consumption, the road information is obtained from OpenStreetMap and Shuttle Radar Topography Mission. An offline algorithm is built to predict the energy consumption for a future trip based on the road information. The algorithm gives two energy consumption results: one is for the fastest driving speed; the other one is for the most economic driving speed. The results show that the measured energy consumption results for different types of road driving are all within the algorithm's prediction scope. Therefore, the offline algorithm can give an accurate energy consumption estimation to the driver before a trip begins.

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