Electric Vehicles’ Energy Consumption Measurement and Estimation

Use of electric vehicles (EVs) has been viewed by many as a way to significantly reduce US oil dependence, operate vehicles more efficiently, and reduce carbon emissions. Due to the potential benefits of EVs, the federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future EVs will gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: How to improve efficiency for the entire EV system? This research takes the first step in tackling this challenge by addressing a fundamental issue, how to measure and estimate EVs' energy consumption. In detail, this paper first presents a system which can collect in-use EV data from both the battery system, including battery state of charge, pack current, pack voltage, pack power and vehicle driving data including velocity, acceleration, and vehicle position (latitude, longitude, and elevation). Then this system has been installed in an EV conversion vehicle built for this research. Approximately five months of EV data have been collected and these data have been used to analyze both EV performance and driver behaviors. The analysis shows that the EV is more efficient when driving on in-city routes than driving on freeway routes. This has become one of the major reasons that EV driver prefers in-city routes over freeway routes. This research further analyzes the relationships among the EV's power use, the vehicle's velocity, acceleration, and the roadway grade. This is done by categorizing data according to the specific ranges of velocity, acceleration, and roadway grade. In addition, through statistical analysis, we determined that the EV's power within a specific range of velocity, acceleration, and grade could be described as a normal distribution. Based on the analysis presented, this paper further proposes an analytical EV power estimation model using the fundamental theory of vehicle dynamics and basic relationships among power, force, torque, voltage, and current. This model has been evaluated using the test vehicle. The results show that this model can successfully estimate EV's instantaneous power and trip energy consumption. Future research will focus on applying the proposed EV power estimation model to improve EVs' energy efficiency.