Week 1 Challenge

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.