Font Size: 

1. Target of paper

The run out of energy during a journey is commonly designated as “range anxiety”. The paper addresses this phenomenon to which an EV driver may likely be met along long-haul journeys. By developing a power loss model of the full drivetrain of an electric vehicle, it is possible to predict its energy consumption across a usual journey composed of different manoeuvres. Since each driver has its own driving strategies, the energy consumption may vary significantly so that eco-driving tips are welcome to better manage the remaining range of the vehicle. Additionally, a smart charging strategy may avoid range anxiety situations. Identifying the adequate charging scenario is thus of general interest. Therefore, the paper aims at helping the drivers optimize the use of their vehicle by giving some advice related to driving and charging strategies.

2. Methods and Equipment used

Based on a large literature review composed of different theoretical and numerical physical models, we propose a set of original physics-based models of the full drivetrain. The main components for which power loss maps are generated are the transmission stage, the electric motor/generator, the inverter and the battery. The models are then implemented into a MATLAB code to perform the simulation of driving manoeuvres. The numerical values of the model parameters refer to an equivalent C-segment modern full electric car and are based on data measurements published in the literature.

Several case studies inspired by real life are investigated using MATLAB simulation, such as the basic acceleration, constant speed, deceleration pattern applied in urban conditions. For all case studies, some speed profiles are compared in terms of energy consumption and an adequate mean speed is fixed. This pattern is modelled as a symmetrical trapezoid speed profile inspired from the NEDC, which is also implemented to validate the model against official data. The energy consumption is computed using a backward dynamic scheme, i.e., power losses are added to the power demand necessary to follow the driving cycle.

Concerning the charging strategy, the method relies on the driver needs, the battery capacity and the charging power requirements.

3. Expected results

The numerical applications are performed with a test car having the mass and the aerodynamic properties of the Nissan Leaf. The simulation tool applied on a 500m trapezoid speed profile case study with 30kph average speed turns out that the minimum energy consumption is 42.11Wh. The other investigated speed profiles indicate up to 9% more in energy consumption. The model is thus expected to be very accurate compared to published data. The analysis of the results highlight a balance between the power losses in the battery (linked to the acceleration) and the aerodynamic drag (linked to the maximum speed). Therefore, a compromise has to be found. A similar conclusion can be drawn for the other case studies.

Moreover, the more adequate charging strategy learns that advanced charging infrastructures help to reduce range anxiety comparing to big batteries.

Electric vehicles; Eco-driving; Vehicle Simulation; Drivetrain Modelling; Charging strategy