Characterization of urban commuter driving profiles to optimize battery size in light-duty plug-in electric vehicles

doi:10.1016/j.trd.2010.09.001

Transportation Research Part D: Transport and Environment

Volume 16, Issue 3, May 2011, Pages 218-224

https://doi.org/10.1016/j.trd.2010.09.001 Get rights and content

Abstract

Electrification of light duty vehicles using plug-in electric vehicles in conjunction with new generation of renewable energy to match the new electrical load addresses global concerns of greenhouse gas emissions and is being considered as a way to reduce environmental damage from the transportation sector. A database was gathered over a year from a fleet of 76 vehicles in Winnipeg, Canada and is used to develop a daily driving profile approximating actual driving power demand and parking times for charging these vehicles. The data are utilized to construct a commuter driving cycle based on the parameters defining functionality of a light duty vehicle. The cycle is then used in an energy-based simulation to optimize battery size for a commuter sedan car. Overnight only versus overnight and day charging scenarios are then compared. It is found that the battery storage size can be decreased by up to 40% without loss of functionality with a 2.4 h daily charge.

Research highlights

► From a database of 76 car drivers in a Canadian city were monitored over a year using, a 24 h commuter duty cycle including parking times is developed to assesses efficient battery charging cycles. ► Parking times were broken down into 4 groups: home, work, commercial and other when considering charging options ► The driving cycle including parking times is compared assuming two battery chemistries and two types of charging for various battery pack sizes.

Introduction

Battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV)¹ are seen as having potential for reducing oil dependency and greenhouse gas emissions in transportation use. Design of plug-in powertrains requires optimizing the battery storage capacity considering the possibility of recharging from the electric grid between trips. Optimal battery capacity leads to a lighter weight, lower cost and more efficient PEV but it requires information on the driving profile of an average driver and parking times of the vehicle. Duty cycles provide comprehensive histories of vehicle over a typical 24-h span, including parking times and associated idling times, while a driving cycle refers only to driving history of a vehicle.

Standard cycles such as FTPs, LA92, UDDS andUS06 are adjusted to the limitations of the lab testing equipment, but they cannot completely emulate real world driving (Lin and Niemeier, 2003). More importantly for designing of PEVs, standard cycles do not provide information on vehicle parking times nor do they include a wide array of routes driven by commuters.

Using global positioning system (GPS) based data loggers is now widely practiced to track and record driving events, determine trip purpose, traffic congestion levels and develop driving cycles (Dai et al., 2008). The steps to construct a driving cycle include data collection, segmenting data into meaningful snippets or micro-trips, constructing cycles from the snippets, and evaluating the constructed cycle. Different methods for classifying micro-trips to create the duty cycle can be used: calculations of micro-trip probability and frequency (Tong et al., 2005); classifications of urban, suburban and freeway driving based on acceleration and speed ranges (Andre et al., 1995); and quasi-random micro-trip selection methods (Lin and Niemeier, 2002).

We examine the habits characterizing a commuter’s driving pattern, create a large database of driving and parking times, and develop a duty cycle based on functionality of vehicle use. It should be possible to implement the methodologies developed for Winnipeg in Canada, a city with a population of 0.7 million, for other urban settings with appropriate data on vehicle driving patterns. The data was collected at a time when only two plug-in electric vehicles were operating in Winnipeg. Therefore GPS data was gathered using gasoline vehicles assuming that driver behavior is the same irrespective of the power source of a vehicle.

Section snippets

Data collection and analysis

The data logger used is a GPS receiver that records position, speed, date and time at one-second intervals (Persen Technologies Inc., 2010). Speed data is calculated by the logger from the position records with a minimum precision of 0.37 km/h. In addition to positional and speed data, the logger also numbers vehicle trips sequentially and records the speed limit.

Social factors that likely influence the driving patterns of the volunteers are considered as much as possible to ensure that the

Duty cycle construction methodology

Various driving characteristics such as idling, high traffic congestion, free moving, creep and high-speed roadways necessitate data reduction and clustering. The daily trips divided into micro-trips are classified according to their ranges of speed and acceleration into certain traffic groups. A random selection method is adopted to select the number of classified micro-trips required to recreate an average trip. The number of trips per cycle, the hours of day that the vehicle is being used,

Database specifications

The data was collected subject to a confidentiality agreement with the volunteer drivers.

Battery size optimization

Even though battery technology is likely to continue to improve, batteries may remain the most expensive propulsion component. The challenge is then to find the minimum battery capacity that would respond well to the demands of an urban commuter.

The battery technologies for wide adoption in PEVs are still evolving as lower capital costs are being sought. PEVs economically perform best, in terms of overall cost per kilometer, when the battery size relates more closely to the charging patterns of

Conclusion

In constructing a duty cycle using data collected in Winnipeg the emphasis has been on commuters. A cycle representing weekday commuting in the city and, including vehicle down-times suitable for charging, is developed aimed at exploring optimal PEV battery optimization and powertrain design. The results of simulations show that the Ni–MH battery technology, along with home and work charging, currently provides the lowest cost per kilometer for an urban commuter. Since commuting represents more

Acknowledgement

Data collection and analysis was supported by a grant from the AUTO21 Network of Centers of Excellence, Project number DF302-DBS. The authors would like to thank Arne Elias from the Centre for Sustainable Transportation at the University of Winnipeg and Frank Franczyc from Persen Technologies Inc. Discussions with Emerging Energy Systems at Manitoba Hydro and Tom Molinski are acknowledged.

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