Browsing by Author "Ewin, Nathan"

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    Electric vehicle energy consumption modelling and estimation—A case study
    (Wiley, 2020-07-12) Miri, Ilyès; Fotouhi, Abbas; Ewin, Nathan
    Electric vehicles (EVs) have a limited driving range compared to conventional vehicles. Accurate estimation of EV's range is therefore a significant need to eliminate “range anxiety” that refers to drivers' fear of running out of energy while driving. However, the range estimators used in the currently available EVs are not sufficiently accurate. To overcome this issue, more accurate range estimation techniques are investigated. Nonetheless, an accurate power‐based EV energy consumption model is crucial to obtain a precise range estimation. This paper describes a study on EV energy consumption modelling. For this purpose, EV modelling is carried out using MATLAB/Simulink software based on a real EV in the market, the BMW i3. The EV model includes vehicle powertrain system and longitudinal vehicle dynamics. The powertrain is modelled using efficiency maps of the electric motor and the power electronics' data available for BMW i3. It also includes a transmission and a battery model (ie, Thevenin equivalent circuit model). A driver model is developed as well to control the vehicle's speed and to represent human driver's behaviour. In addition, a regenerative braking strategy, based on a series brake system, is developed to model the behaviour of a real braking controller. Auxiliary devices are also included in the EV model to improve energy consumption estimation accuracy as they can have a significant impact on that. The vehicle model is validated against published energy consumption values that demonstrates a satisfactory level of accuracy with 2% to 6% error between simulation and experimental results for Environmental Protection Agency and NEDC tests.
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    A multi-mode electric vehicle range estimator based on driving pattern recognition
    (Sage, 2021-07-20) Mao, Lang; Fotouhi, Abbas; Shateri, Neda; Ewin, Nathan
    Limited driving range and availability of charging infrastructures are still among the main barriers of adoption of electric vehicles (EVs) in the market. Combination of those limiting factors causes ‘range anxiety’ in EV users. While different EV battery technologies and charging infrastructures are under development, one short-term solution to reduce EV users’ range anxiety is to provide the EV user with an accurate range estimation. In this study, an EV range estimation technique is proposed that recognises the current driving pattern and then classifies it into one of the predefined clusters (driving modes). The future energy consumption per kilometre is then tuned according to the average energy consumption of each cluster. Having an updated energy consumption rate, the EV range is calculated based on the battery state-of-charge. Different features are considered for driving pattern clustering where ‘average speed’ and ‘average power’ were identified as the best choices for this application. The effectiveness of the proposed EV range estimator is validated using real driving data that gives an average error of 9% in EV energy consumption estimation ahead