NREL/CP-5400-53864.Posted with permission. 

Presented at the 2012 SAE World Congress, 

24-26 April 2012, Detroit, Michigan 

Analyzing Vehicle Fuel Saving Opportunities 2012-01-0494

Published through Intelligent Driver Feedback 04/16/2012 

Jeffrey Gonder, Matthew Earleywine and Witt Sparks 

National Renewable Energy Laboratory 

doi:10.4271/2012-01-0494 

ABSTRACT 

While it is well known that “MPG will vary” based on how 

one drives, little independent research exists on the aggregate 

fuel savings potential of improving driver efficiency and on 

the best ways to motivate driver behavior changes. This paper 

finds that reasonable driving style changes could deliver 

significant national petroleum savings, but that current 

feedback approaches may be insufficient to convince many 

people to adopt efficient driving habits. To quantify the outer 

bound fuel savings for drive cycle modification, the project 

examines completely eliminating stop-and-go driving plus 

unnecessary idling, and adjusting acceleration rates and 

cruising speeds to ideal levels. Even without changing the 

vehicle powertrain, such extreme adjustments result in 

dramatic fuel savings of over 30%, but would in reality only 

be achievable through automated control of vehicles and 

traffic flow. Considering the effects of real-world driving 

conditions, efficient driving behaviors could reduce fuel use 

by 20% on aggressively driven cycles and by 5-10% on more 

moderately driven trips. 

To evaluate potential receptiveness to changing driving 

habits, the project team conducted a literature survey of 

driver behavior influences and observed pertinent factors 

from on-road experiments with different driving styles. This 

effort highlighted important driver influences such as 

surrounding vehicle behavior, anxiety over trying to get 

somewhere quickly, and the power/torque available from the 

vehicle. Existing feedback approaches often effectively 

deliver efficiency information and instruction, but do not 

always do so in an easy way that avoids unintended 

consequences and helps trump other driving behavior 

influences. Based on these findings the report details three 

recommendations for maximizing fuel savings from potential 

drive cycle improvement: (1) Leverage applications with 

enhanced incentives, (2) Use an approach that makes it easy 

and is widely-deployable to motivated drivers, and (3) Utilize 

connected vehicle and automation technologies to achieve 

large and widespread efficiency improvements. 

INTRODUCTION 

Data show that the reported fuel economy for a given vehicle 

can vary by plus or minus 20% across a large population of 

drivers [1]. Some of this variation results from differences in 

temperatures and road conditions encountered, but differing 

driver behavior also accounts for some of this spread. It 

stands to reason that if drivers universally adopted more 

efficient driving habits, then the spread in fuel economy 

results for any given vehicle would shift to higher values. The 

U.S. Department of Energy (DOE) asked the National 

Renewable Energy Laboratory (NREL) to evaluate the 

potential fuel saving opportunity from implementing driver 

feedback techniques and to begin evaluating which 

techniques may deliver the greatest aggregate fuel savings. 

This paper summarizes NREL's findings. 

APPROACH 

NREL divided the driver feedback investigation into three 

separate tasks: (1) quantifying the fuel saving opportunities 

from drive cycle modification; (2) identifying factors that 

influence drivers' receptiveness to adopt efficient behaviors; 

and (3) assessing various driver feedback approaches. NREL 

performed both simulations and experiments for the first task, 

and leveraged a conventional vehicle model from a previous 

study for the simulation effort [2]. The model possessed 

similar design and fuel economy characteristics as a midsize 

Chevrolet Malibu [1]. Note that this study primarily focused 

on conventional (rather than hybrid) vehicle performance 

because of DOE's interest in what driver feedback technology 

could do for the existing U.S. vehicle fleet, which primarily 

consists of conventional powertrain vehicles. 
Table 1. Simulated conventional vehicle fuel savings for ideal vs. real-world cycles 

The vehicle model was simulated over a variety of drive 

cycle conditions to observe the fuel efficiency impact. The 

analyzed cycles included those from a large set of real-world 

travel data collected in 2006 with global positioning system 

(GPS) devices as part of a study by the Texas Transportation 

Institute and the Texas Department of Transportation [3]. 

NREL examined the efficiency benefit from applying 

hypothetical modifications to the GPS drive cycles, and also 

observed the relative prevalence of inefficient vs. already 

efficient driving characteristics within the data set. 

To confirm what reasonable degree of drive cycle 

modifications could be implemented on-road, NREL 

researchers variously drove an instrumented vehicle over 

repetitions of a “city” route with a large number of stoplights 

and a “highway” route with primarily interstate driving. The 

routes were repeated by two drivers using three different 

driving styles: “normal” (as the driver would normally drive); 

“energy conscious” (making a best effort to implement 

efficiency techniques while still driving safely); and 

“aggressive” (defined as how the driver might behave if 

running late to get somewhere while still doing so safely). 

To evaluate driver receptiveness to feedback, NREL 

examined why drivers chose particular behaviors and what 

factors may serve as barriers to behavior change. These 

efforts included: (1) a literature search of driving behavior 

influences and issues, and (2) documenting the motivations 

behind driving decisions from the on-road driving 

experiments. 

To complete the final assessment task, NREL tested several 

devices and evaluated various feedback approaches in the 

context of the first two tasks' findings-i.e., could the approach 

result in quantitative fuel savings, and are people likely to 

start driving more efficiently in response to it? 

QUANTIFYING FUEL-SAVING 

OPPORTUNITIES 

Hypothetical Drive Profile Adjustments 

To quantify the outer boundary fuel savings that could be 

achieved from drive cycle modification, the authors examined 

the idealized results of fully optimizing several cycles from 

the real-world data set mentioned above [3]. The cycles were 

selected to reflect a range of kinetic intensity (KI) values. KI 

represents a ratio of characteristic acceleration to 

aerodynamic speed and has been shown to be a useful drive 

cycle classification parameter [4]. The real-world cycles were 

converted into equivalent “ideal” cycles using the following 

steps: 

1. Calculate the trip distance of each sample trip. 

2. Eliminate stop-and-go and idling within each trip. 

3. Set the acceleration rate to 3 mph/s. 

4. Set the cruising speed to 40 mph. 

5. Continue cruising at 40 mph until the trip distance is 

reached. 

As summarized in Table 1, the results indicate a fuel savings 

potential in excess of 30%-40% from full cycle optimization. 

While these results would not be achievable in reality without 

full automation of vehicles and traffic control, it is promising 

to note such significant fuel savings from cycle modifications 

alone with no changes to the vehicle or powertrain. 

Figures 1 and 2 examine the impact of applying a few of the 

cycle optimization steps in isolation. For instance, the points 

along the vertical axis in Figure 1 represent the fuel savings 

benefit solely from eliminating stops in the five sample 

cycles. This elimination of stop-and-go driving yields the 

greatest benefit for the high KI cycles (for which acceleration 

power demands tend to dominate due to a large number of 

stops in the unmodified cycles). The points to the right of the 

vertical axis in Figure 1 represent the benefit of eliminating 

slow-and-go events below the indicated speed in addition to 

eliminating the stop-and-go, which results in a small 

additional savings for each cycle. Other simulations 

conducted as part of this study identified a small fuel savings 

benefit simply from reducing the rate of acceleration events, 

but the savings were not nearly as great as from avoiding 

accelerations and decelerations (accel/decel) altogether. This 

suggests that driving style improvements should focus on 

reducing the number of stops in high KI/city-type cycles, and 

not just the rate of accelerating out of a stop. 

One way for drivers to reduce stop-and slow-and-go driving 

is to select routes with fewer stops and drive at less congested 
Figure 1. Fuel savings from eliminating stop- and slow-and-go driving
	

Figure 2. Fuel savings from optimizing vehicle speed 

times of day. However, drivers can also reduce stops over the Figure 2 examines the fuel savings from incremental speed 

course of a given driving route by watching far enough ahead optimization and highlights that the largest speed 

to anticipate when maintaining speed will result in a stop at a improvement savings result from cycles with the highest 

red light or traffic bottleneck. If the driver instead eases off of driving speeds. These tend to be the cycles with the lowest KI 

the gas pedal to start gradually decelerating early, the light values (where aerodynamic road loads dominate in highwaycould change and the traffic could clear before the driver gets type driving). Since aerodynamic drag increases with the 

there. Such an approach not only decreases accel/decel rates square of vehicle velocity, even modest reductions from very 

but also (and more importantly) their frequency of high speeds result in significant fuel savings. 

occurrence. 
Figure 3. Fuel use comparison for “city” driving experiments
	

Implementing Efficiency Strategies Over Repeated 

On-Road Routes 

The analysis discussed to this point reflects theoretical 

savings from targeted cycle improvements that may be 

difficult to implement on-road. To observe the obstacles to 

efficient driving first-hand and to perform a sanity check on 

potential fuel savings over a constant driving route, the 

project team conducted a series of on-road driving 

experiments in the Denver area. 

The two selected routes (one with city-type driving and one 

with highway-type driving) were designed to have cycle 

characterization parameters that fell in the mid range of 

corresponding parameters from the Texas travel survey onroad cycles [3]. Two drivers (M. Earleywine and J. Gonder) 

took turns driving the routes. While one person drove, the 

other sat in the passenger seat to record test observations, 

including apparent influences on the driver's behavior. A 

Mercedes C100 test vehicle was used for the driving 

experiments, and second-by-second data were collected from 

the vehicle's on-board diagnostic (OBD) port. Chassis 

dynamometer testing verified the accuracy of the vehicle 

speed data collected from the OBD port, but unfortunately 

reliable fuel use data could not be collected. Instead, fuel use 

estimates were generated by simulating the collected drive 

profiles using the aforementioned conventional vehicle model 

(to be consistent with the travel survey data analysis). As 

described in the Approach section, each route was repeated 

with three different driving styles. 

Figure 3 shows the spread in fuel consumption results for the 

city route plotted against the characteristic acceleration for 

each measured profile. The different colors represent 

different driving behaviors, and the different shapes represent 

different drivers. The legend provides an abbreviation for the 

driving type (“ec” for energy conscious, “norm” for normal, 

or “ag” for aggressive) followed by the initials of the driver 

for the indicated cycles. Figure 4 provides a similar plot for 

the highway route, where the relative fuel consumption 

results are instead plotted against the aerodynamic driving 

speed for each cycle [4]. 

Both figures show some variability in fuel consumption even 

when repeating a route with the same driving style. These 

variations likely result from differences in traffic conditions 

and stoplight timing from one repetition to the next. 

Nevertheless, fuel consumption for both routes does seem to 

trend lower for “energy conscious” as compared to “normal” 

driving and to trend higher for “aggressive” driving. 

Figure 3 shows a 30% spread between the lowest fuelconsuming energy-conscious repetition and the highest fuelconsuming aggressive repetition of the city route. The 

apparent correlation between fuel consumption differences 

and characteristic acceleration is consistent with the earlier 

finding that reducing accel/decel seems to carry the largest 

benefit in city-type driving with significant stop- or slow-andgo events. 

Figure 4 reveals an approximate 20% spread between the 

lowest fuel-consuming energy-conscious repetition and the 

highest fuel-consuming aggressive repetition of the highway 

route. The fuel consumption differences for this route seem to 

roughly correlate with aerodynamic speed, which is again 

consistent with the earlier findings that reducing high speeds 

seems to be the dominant factor for improving highway 
Figure 4. Fuel use comparison for “highway” driving experiments
	

driving efficiency. While the maximum predicted savings is 

quite a bit higher for some of the travel survey cycle 

adjustments, it should be noted that the top speeds for the 

“aggressive” NREL experiments were much lower than those 

for the fastest driving vehicles in the travel survey sample. 

Prevalence of Inefficient/Sub-optimal Driving 

While the above discussion focused on the fuel saving 

potential from specific behavior changes, this section 

considers the prevalence of inefficient behaviors in a large, 

real-world driving sample. The Texas travel survey data 

again provide a useful reference for this analysis [3]. Figure 5 

illustrates the simulated fuel consumption results (using the 

color scale on the right-hand side) for each individual trip in 

the GPS driving sample that traversed one or more miles. The 

figure represents each of these nearly 4,000 trips with a small 

circle and positions it relative to the two axes based on the 

cycle metrics (aerodynamic speed and characteristic 

acceleration) that seemed to correlate with fuel efficiency for 

the on-road driving experiments. 

Consistent with the on-road experiment observations for lowspeed city driving, higher characteristic acceleration 

corresponds with higher fuel consumption in the scatter plot 

(although as aerodynamic speed drops below 25 mph, trip 

fuel consumption increases even with low characteristic 

acceleration). For the higher-speed (highway) trips, Figure 5 

shows increasing fuel consumption as aerodynamic speed 

rises above 60 mph (again consistent with the previous 

analysis results). Note, though, that increased characteristic 

acceleration in highway trips also results in higher fuel 

consumption. 

The figure highlights that the lowest fuel consuming trips 

have aerodynamic speeds between roughly 25-60 mph and 

characteristic accelerations below about 0.3 mph/s. The large 

number of trips with slightly higher fuel consumption has 

slightly higher characteristic acceleration, and/or 

aerodynamic speeds that fall outside of the 25-60 mph range. 

The small number of very high fuel-consuming trips has 

characteristic accelerations and/or aerodynamic speeds far 

from the minimum-consumption operating conditions. 

DRIVER RECEPTIVENESS 

CONSIDERATIONS 

NREL consulted with experts in social science and human 

factors engineering for advice on what type of feedback 

drivers would be most receptive to receiving. The experts 

recommended focusing on the context of those behaviors that 

could lead to fuel savings if changed. Assessing feedback 

approaches against the specific context would be more 

fruitful than trying to identify the “best” specific stimulation 

type (light, sound, color, text, etc.). To improve contextual 

understanding of driving behavior influences, the results 

summarized here are from a literature review and from 

observations during the on-road driving experiments. 

LITERATURE REVIEW OF DRIVING 

BEHAVIOR INFLUENCES AND ISSUES 

A review of prior research found a number of studies 

involving fuel economy, driving behavior, and general human 

behavior as related to energy-saving practices. These findings 

are summarized below. 
Figure 5. Cycle metrics and simulated fuel consumption for nearly 4,000 real-world trips
	

Potential for Fuel Savings 

There is broad consensus from prior research that vehicle fuel 

savings of 10% are possible through modified driver behavior 

(a finding also supported by this study). Such savings can be 

attained through reasonably moderate behavior modification 

without resorting to extreme “hyper-miling” techniques. One 

study estimates that if a third of all U.S. drivers adopted ecodriving techniques, it would result in an annual savings of 33 

million metric tons of carbon dioxide (CO2) and a cost 

savings of $7.5-$15 billion [5]. 

Reference [5] proposes that achieving significant fuel savings 

through driver behavior changes requires substantial 

investment in a multi-faceted approach involving: 

1. Public education 

2. Driver feedback 

3. Regulatory actions 

4. Economic and policy incentives 

5. Social marketing. 

Driver Behaviors 

The driving behaviors that affect fuel economy are well 

understood through existing research and are further 

supported by this study's findings. Those behaviors include: 

1. Speed during highway driving. 

2. KI or frequency and intensity of braking and acceleration 

[6]. 

3. Frequency of stops - faster acceleration is acceptable if it 

means that a stop is avoided [7]. 

4. Timing of gear changes [6]-not a focus in the present 

study due to the current market dominance of automatic 

transmission vehicles. 

5. Time to Collision (TTC) values [6]-metric defined as the 

following distance from one car to another divided by the 

speed difference (when the second car is overtaking the first). 

The study observed that drivers following an efficiency 

regimen tended to have fewer instances of small TTC values. 

This metric directly relates to KI: if the driver is trying to 

reduce KI, he or she will avoid rapidly approaching another 

vehicle and then braking to avoid collision. 

Effect of Social Norms on Driver Behavior 

Reference [8] identified two “axes” of norms that influence 

driver behavior: Legal or regulatory norms, and informal or 

social norms. The study posits that drivers who violate either 

or both of those norms are more likely to be involved in 

accidents because other drivers are unable to predict their 

actions. This conclusion is supported by evidence that drivers 

who travel at close to the median speed of traffic on a 

highway are less likely to be involved in accidents than 

drivers who tend to drive either faster or slower than that 

speed. This finding suggests that: 

1. Harnessing the natural pressure to conform to surrounding 

traffic behavior may help a driver feedback approach succeed 

at getting drivers with very high speeds and accelerations to 

slow down. 

2. A feedback device that encourages behavior outside the 

social norm for a given situation may encounter driver 

resistance to deviate from the informal norm. 

3. Drivers who do follow feedback advising behavior outside 

of the norm (for example, driving at a more efficient 55 mph 
on a roadway where the median speed is 70 mph) may 

increase their risk of being in an accident. 

Other research into consumer behavior related to home 

energy efficiency indicates that significant and lasting 

behavior change is possible under the right circumstances. 

Following a natural disaster that interrupted electricity 

distribution, the city of Juneau, Alaska, cut its electricity 

consumption by more than 30 percent. After repairs were 

completed, energy consumption remained 10% lower than 

pre-event levels [9]. Another study reports that providing 

social comparisons can motivate households to reduce energy 

consumption [10]. The residential energy consumption data 

required to make such comparisons are already collected by 

the local utility. However, since an analogous real-time 

dataset does not exist for transportation fuel use, it would be 

more difficult to apply such social comparisons to this 

domain. 

The iPhone driver feedback application Bliss Trek (no longer 

available) attempted to do this by linking to a user's Twitter 

account and allowing the user to broadcast driving efficiency 

scores-in effect creating a competition among users. While 

this feature was broadcast-only, one could envision that 

adding the current high score(s) to the application's real-time 

display would enhance the immediacy of the competition. 

Potential for Adoption and Use of Feedback 

Systems 

To assess the impact fuel economy has on vehicle purchase 

decisions, a research study interviewed 57 households prior 

to 2007. The study found that most households do not have a 

realistic grasp of how much money they spend in a week, 

month or year on fuel. This result indicates that the modest 

fuel savings associated with driver behavior modifications 

may be insufficient to incentivize their adoption. The study 

also found that fuel economy was not named as a concern 

when purchasing a vehicle among most of the middle-to 

high-income participants [11]. 

In a more recent survey [12], Consumer Reports found that 

prospective vehicle buyers listed fuel economy as a top factor 

of interest. This survey was conducted in the wake of the 

2008 fuel price spike, suggesting a possible temporary 

increase in concern for fuel economy. In a recent survey in 

Japan [13], J.D. Power Asia Pacific found improved customer 

satisfaction with auto dealers who provided advice for 

maximizing fuel economy when driving. This further 

supports the thesis that consumers do have some interest in 

fuel economy with the right encouragement. 

Taken together, these studies suggest that although 

consumers do have an interest in improving fuel economy, 

that interest is closely tied with fuel prices and perhaps also 

an emotional response to rapid increases in fuel price. Absent 

such high or increasing price conditions, potential fuel cost 

savings alone may be insufficient to motivate behavior 

change. 

A recent California study of the effect of driver feedback on 

fuel economy included a user survey of background, 

likelihood, and attitude toward eco-driving. The 20 

participants in the survey were concerned about climate 

change (7.3 on a scale of 10) and likely to adopt eco-driving 

techniques in the near future (7.4). They were unlikely (3.9) 

to purchase a driver feedback device, but likely to use one 

(8.0) if it comes standard with their next vehicle [14]. 

This research indicates that simply building a driver feedback 

device and hoping that consumers will purchase and use it 

will not lead to significant fuel savings overall. There must be 

some additional incentive or partnership that either offsets the 

cost of the device or provides some other benefit to 

incentivize its use. 

Other considerations that can impact adoption and use of 

feedback systems include: 

1. A finite time window may exist in which to educate 

feedback device users. After a few weeks of observing what 

the device does under different conditions, a driver will likely 

start paying less and less attention to it. 

2. Feedback provided at different time scales and tied in with 

different events may be useful. For instance, in addition to 

real-time instruction while driving, cumulative feedback 

could be provided every few hundred miles when refueling 

and/or every few thousand miles when getting an oil change. 

3. Different solutions may work better for people with low 

motivation vs. those with high motivation to adopt efficient 

driving behaviors. 

4. Feedback is most effective when it highlights benefits/ 

savings rather than punishment/penalty, when it helps 

minimize any negative impacts of the behavior change, and 

when it specifically relates to a person's own experience/ 

context (e.g., provides fuel economy relative to that for other 

drivers of the same vehicle and in the same area). 

Design of Driver Feedback Systems 

The simplest driver feedback system, an instantaneous fuel 

economy gauge, was evaluated in a 1977 study involving 140 

test vehicles, half of which were equipped with an 

instantaneous fuel economy meter. Drivers were tasked with 

reducing their fuel use over a 12-week period. The study 

found that meter-equipped vehicles had on average 3% lower 

fuel consumption than non-equipped vehicles, but the author 

reported that this difference was not statistically significant 

[7]. 

Instantaneous fuel economy gauges are not especially helpful 

in urban driving because they do not address specific 
behaviors and because instantaneous fuel economy changes 

so quickly and significantly when accelerating and 

decelerating. A gauge by itself also has limited utility in the 

absence of accompanying instruction or intuitive driver 

understanding of how to improve fuel economy. 

A more recent study of driver feedback systems built into 

existing vehicles concluded that the most effective systems 

provided a binary (yes/no) indication of whether or not 

current behavior is efficient [15]. This study focused only on 

existing displays, so it provides little insight into which other 

general design elements are most effective. It also focused on 

a person's initial ability to interpret a display and did not take 

into account the display's 

became accustomed to it. 

effectiveness after the driver 

Potential for Driver Distraction 

A 2007 study examined research into driver distraction 

caused by a variety of in-car devices, including entertainment 

systems, navigation systems, and mobile phones [16]. The 

most relevant research to this study involves the level of 

driver distraction caused by navigating a vehicle with the aid 

of a navigation system. This line of research was chosen for 

comparison because driver feedback systems do not require 

the driver to provide any information to the device, only to 

receive and process information from the device. 

The review cited research that compared a head-down 

electronic map, a head-up turn-by-turn guidance display with 

head-down electronic map, voice guidance with head-down 

electronic map, and paper map. The study measured driving 

speed, workload, navigation errors, and reaction time to 

external events while interacting with the navigation system. 

The voice guidance/electronic map system resulted in better 

performance with lower workload ratings, faster mean 

speeds, and lower numbers of navigational errors. The study 

also reports that eyes-off-road times and the level of 

cognitive effort required to complete a task are indicators of 

the level of driver distraction. 

From this research, the authors conclude that: 

1. A voice or audible feedback mechanism may be preferable 

from a driver distraction point of view because it does not 

require the driver to look away from the road to take in the 

information. An audio-only approach may also be more 

convenient for the user if it eliminates the need to mount an 

aftermarket display and potentially run wires to it. 

2. The information provided should be made as simple to 

understand as possible to minimize the cognitive effort 

required to process it. 

Reference [6] suggests two approaches to providing 

feedback: tactical and strategic. Tactical is instantaneous 

feedback on acceleration, braking, and speed. This, to a large 

degree, is the approach taken by most of the devices surveyed 

as part of this study. Strategic feedback analyzes driving 

behavior over some time period and advises the driver in 

specific terms on how to improve fuel economy (for example, 

“let off the accelerator sooner when slowing”). The 

DriveGain iPhone application provides this type of advice in 

addition to a visual tactical display. 

Because of its real-time nature, it would be difficult to 

present tactical feedback audibly, but strategic feedback can 

certainly be presented this way. Reference [6] did not 

investigate the effects of these two feedback approaches 

separately. Future research may be warranted to determine 

whether a tactical, strategic, or combined approach is best [6, 

7]. 

In reviewing existing feedback devices, NREL found the 

cognitive effort required to interpret and understand the 

visual and audible feedback was not excessive. However, 

because visual systems do require taking eyes off the road, 

they should be designed and placed so that information can 

be pulled off of them with only a quick glance (less than two 

seconds) and conform to guidelines for mitigating driver 

distraction potential [17]. 

FACTORS IMPACTING EFFICIENT 

DRIVING DECISIONS 

To evaluate the effectiveness of a driver feedback 

mechanism, it is important to understand how other 

influences compare with a driver's potential desire to follow 

the mechanism's advice. To gain a better understanding of 

these issues, the vehicle passenger during the on-road driving 

experiments described in the previous section recorded 

observations about various factors that influenced the driver. 

One of the main observations from the on-road driving 

experiments was that driving at slow speeds and slow 

accelerations can annoy other drivers on the road. During 

more than one of the energy-conscious test drives, another 

driver honked impatiently in response to gradual 

accelerations. Slow speeds also cause the driver to pay more 

attention to vehicles behind rather than vehicles ahead since 

the vehicles ahead were often pulling away and the vehicles 

behind were tailgating. It was also observed that free-flowing 

traffic will often drive slightly above the posted speed limit. 

Light traffic conditions can make it easier for a motivated 

driver to implement fuel-efficient techniques by alleviating 

concerns about holding up the vehicles behind. However, 

light traffic can discourage a marginally motivated driver 

from driving efficiently if the other cars on the road speed 

past on the left. Heavier traffic will increase the number of 

stops, but can also decrease the magnitude of accelerations 

and of high speeds. Therefore, heavy traffic has the potential 

to decrease the fuel consumption of an aggressive driver, 
although it will likely increase the fuel consumption of most 

vehicles if it forces additional stop-and-go driving. 

A given driver may not exhibit the same types of driving 

behavior all of the time. If a driver is pressed for time and in 

a hurry, he or she will most likely drive more aggressively. If 

someone is sightseeing and driving with no specific time 

frame or destination in mind, he or she will tend to drive and 

accelerate more slowly, making it easier to drive efficiently. 

Relative to driving aggressively, driving energy-efficiently at 

slower speeds and lower accelerations requires less attention 

from the driver. An aggressive driver constantly looks ahead 

for opportunities to change lanes and pass other vehicles, 

whereas a more fuel-efficient driver can just stay in the right 

lane and let other vehicles pass him or her. 

For powerful vehicles, it can be difficult to maintain the light 

accelerator pedal pressure required for steady speed cruising. 

In addition, because a vehicle's purchase price depends 

greatly on its engine size, owners may be reluctant not to take 

advantage of the full power capability for which they paid. 

Fuel efficiency feedback may have to compete with the 

enjoyment many people have from driving in a sporty 

manner. Note that over time the vehicle market as a whole 

has demonstrated consumer preferences for weight and 

performance rather than for less powerful and slower vehicles 

(which inherently cost less and have better fuel efficiency 

[18]). 

While considering whether a financial hardship might lead a 

person to change driving habits, it was noted that other 

alternatives may be more strongly considered, such as taking 

public transportation, walking, biking, or carpooling. A driver 

with an acute need to reduce fuel cost might expect greater 

savings from such a mode change. For instance, starting a 

carpool and splitting fuel cost could cut expenses in half, 

whereas it would be difficult to achieve 50% fuel savings just 

by driving more efficiently. 

ASSESSING VARIOUS DRIVER 

FEEDBACK APPROACHES 

The approach assessment involved applying the project 

insights described in the previous two sections to consider 

whether a given approach could work (e.g., if it effectively 

conveys appropriate information and instruction) and whether 

it would likely trigger significant behavior changes (e.g., if it 

is easy to use, avoids unintended consequences, and trumps 

other behavior influences). The observations about a number 

of different feedback approaches are summarized below. 

• General Advice Sources - readily accessible but do not 

tailor feedback based on someone's actual driving behavior. 

They have the potential for a moderate overall impact. 

• Driver Training Courses - unlikely to be attended by large 

numbers of drivers. They provide feedback on actual driving 

behavior, but because of limited participation their overall 

impact is expected to be low. 

• Conventional Dashboards - many new vehicles provide both 

instantaneous and average fuel economy readouts. These 

displays have the potential to reach large numbers of people, 

but in the absence of accompanying feedback advice (and 

given that many vehicles in the legacy fleet have no such 

display) the overall impact would be moderate at best. 

• Hybrid Vehicle Dashboards - typically have robust 

feedback displays conveniently accessible by the driver. 

Hybrid powertrains also alleviate some cycle-related 

efficiency concerns (such as through use of engine shut off 

and regenerative braking during stop-and-go driving), though 

at a cost premium. The combined technology and feedback 

impact for individual hybrid owners will be very large, but 

the aggregate impact remains limited due to relatively low 

hybrid vehicle penetration (both in new sales and in the 

existing vehicle fleet). 

• Smart Phone Applications - have robust feedback, may have 

a competitive theme and can be adapted to existing vehicles. 

However, the barriers to use are significant-requiring 

availability of a smart phone, acquisition of software, 

purchase and installation of a vehicle mounting device, and 

use of the phone for driver feedback rather than other 

functions. Impact is expected to be moderate at best due to 

limited adoption rates. 

• GPS Navigation Systems - some recent systems have driver 

feedback functionality built in. Without an accelerometer, 

though, the feedback is low-fidelity. As with smart phone 

applications, users must purchase and install the device. 

Expected impact is low. 

• Offline Feedback Systems - largely limited to fleet users 

and require professional hardware installation. They do 

provide robust feedback and have the added advantage of not 

requiring driver attention. Because fleets may have more 

influence over their drivers' behavior, the impacts for fleets 

that use this approach may be significant. 

• Dedicated Aftermarket Feedback Devices - These are 

generally dashboard-mounted devices with a wired or 

wireless connection to the vehicle's OBD port. The OBD 

connection provides the device with a high-quality data feed, 

including fuel flow rate, engine load, and vehicle speed. This 

allows the device to present throttle intensity as a surrogate 

for acceleration without the need for an accelerometer and the 

associated calibration requirements. However, these devices 

tend to be costly (on the order of $200) and still require 

installation and setup, which will tend to limit their overall 

penetration and impact. 

• Haptic Pedal Feedback - In this approach, driver feedback is 

provided by means of a vibrating accelerator pedal. When the 
driver accelerates at a rate greater than what the on-board 

computer deems efficient, the accelerator pedal vibrates to 

notify the driver to accelerate more gently. This approach 

would need to be built into the vehicle and would have 

similar advantages to manufacturer-integrated dashboard 

feedback. While manufacturers have researched this 

approach, it has not been incorporated into production 

vehicles, perhaps due to consumer acceptance concerns. 

Assessment Highlights 

The assessment identified seamless integration of 

comprehensive feedback into the manufacturer instrument 

cluster as one of the most promising approaches. 

Unfortunately, of vehicles on the road today, only hybrid and 

other advanced-technology vehicles seem to provide such 

comprehensive and seamlessly integrated feedback. (This 

perhaps reflects the heightened efficiency awareness of 

consumers in the hybrid vehicle market segment.) Some 

conventional vehicles do display instantaneous fuel economy, 

but few provide rigorous coaching on how to improve it. 

Many vehicles do not even provide this level of feedback, 

which obviously hinders the benefit that dashboard-integrated 

approaches can deliver to the existing vehicle fleet. 

The most promising aftermarket approaches include getting 

feedback from an application or “app” on a driver's existing 

smart phone and/or using a dedicated device that incorporates 

information from the vehicle's OBD port. Regarding the 

quality of feedback information and instruction, OBD 

connection provides a slight advantage from data inputs such 

as vehicle-reported fuel rate and idle time. However, speed/ 

acceleration data either from the OBD or from the GPS/ 

accelerometer on a non-connected smart phone can provide 

the feedback basis for the most critical fuel efficiency 

behaviors. 

With respect to adoption barriers, these two aftermarket 

approaches do not fare as well. Both require mounting a 

device in the vehicle such that the information display is 

visible but does not distract the driver. An OBD connection 

requires purchasing a dedicated device to obtain the vehicle 

data. The app approach gets around the expensive device 

purchase requirement for those who already own a smart 

phone, but requires users to firmly mount and calibrate the 

device (to get quality accelerometer data) and to give up other 

uses of the phone while driving (such as making a phone call, 

listening to music or getting directions). These barriers are 

not insurmountable, but can be significant for people who 

may not be enthusiastic about changing their driving style in 

the first place. 

CONCLUSIONS AND 

RECOMMENDATIONS 

SUMMARY FINDINGS 

This study has shown that adopting efficient driving 

behaviors can result in fuel savings on the order of 20% for 

aggressively driven trips. Even starting from more moderate 

driving styles, efficient behaviors can reduce fuel 

consumption by 5%-10%. Wide-spread penetration of such 

efficiency improvements could result in significant aggregate 

fuel savings. However, unlike efficiency technologies 

inherently integrated into a vehicle, realizing such aggregate 

fuel savings requires first motivating drivers to change how 

they drive. Important driver behavior influences include the 

actions of surrounding vehicles, the general flow of traffic, 

anxiety over trying to get somewhere quickly, and the power/ 

torque available from the vehicle. For many drivers, the 

perceived value of a fractional reduction in their fuel budget 

may be insufficient (relative to these other influences) to 

trigger them to make a concerted behavior change. 

For individuals who are willing to change how they drive, the 

feedback mechanism they turn to for guidance will need to 

give them effective instruction on how to drive more 

efficiently and provide them useful reference point 

information (such as current fuel economy, acceleration rate 

and/or vehicle speed). Other important considerations for a 

feedback approach include its ease of use and how well it 

avoids unintended consequences that could add to a driver's 

reasons not to bother with the effort. Maximizing aggregate 

fuel savings requires feedback techniques that minimize 

adoption rate limitations and that push per-vehicle fuel 

savings towards the outer boundary of what an individual can 

achieve. 

RECOMMENDATIONS TO MAXIMIZE 

FUEL SAVINGS 

Leverage Applications with Enhanced Incentives 

Commercial vehicle fleets present one application where fuel 

savings motivation can significantly outweigh the influences 

that work against behavior change in personal vehicles. 

Commercial vehicles tend to use a lot of fuel, so fleet 

managers strongly encourage their drivers to adopt efficient 

behaviors. Fleets may in many cases already implement some 

sort of driver training and incentive program. Working with 

such a motivated segment of drivers could allow larger scale 

refinement and evaluation of particular feedback techniques, 

leading to further deployment of the best fuel saving 

approach or approaches. 

Getting large numbers of people to drive their personal 

vehicles more efficiently could also be accomplished through 

increasing their incentive to do so. One way to achieve this 
Figure 6. Example general advice with simple accompanying in-vehicle reference tool
	

could be to work collaboratively with insurance companies 

that are beginning to implement usage-based insurance [19]. 

The principle behind usage-based insurance is that insurance 

companies can better assess risk if they have direct 

measurements of things like distance driven and frequency of 

high speeds and accelerations. The companies are willing to 

give policyholders a discount on their premiums in exchange 

for installing a device in their vehicle that can provide this 

information. Because these same factors that contribute to 

insurance risk also increase fuel use, a single device could be 

enhanced to monitor as well as give feedback on improving 

such behaviors. Drivers could then realize a double benefit 

from adjusting their driving style: reducing their expenses for 

fuel as well as for auto insurance. 

Prepare a Simple and Widely Deployable 

Approach 

Many people may remain reluctant to change driving habits 

as long as fuel prices hold steady (in order, for instance, to 

avoid potential angry honks or scowls from others for driving 

or accelerating too slowly). However, recent history 

illustrates the potential for gasoline prices to rise year after 

year by 10%-20% [20] and for this to trigger significantly 

increased public interest in saving fuel [12]. Gasoline prices 

may soon resume their upward rise, which could create a 

receptive environment for large deployment of an easy and 

straightforward driving efficiency tool. 

The authors devised the simple concept described below 

while considering how to implement the project findings and 

the benefits of various existing approaches into an 

understandable and unobtrusive efficiency guide [21]. The 

general principle behind the proposed method is that an 

interested driver can learn the basic tenets of efficient driving 

from a short set of instructions, but having real-time reference 

information would help the driver really understand how to 

implement efficiency techniques. This can be accomplished 

with aftermarket devices or manufacturer-integrated feedback 

displays, albeit with limited penetration potential as discussed 

earlier. However, all vehicles contain a basic set of status 

gauges (speedometer, tachometer, etc.) that could be used to 

provide the most important driving efficiency reference 

information. Simply adding “landmarks” to those gauges 

would provide drivers with distinct vehicle operation 

reference points to help implement efficient driving 

instructions. 

Figure 6 shows an example of how a simple set of 

instructions combined with speedometer enhancements could 

provide driving efficiency feedback. This concept could 

accomplish with inexpensive stickers on an existing analog 

gauge what would otherwise require a legacy vehicle owner 

to re-purpose a smart phone or purchase and mount another 

expensive device. Ideally, a website (also viewable by smart 

phone) could expand upon the six listed tips and encourage 

driver interaction after refueling in order to track progress. 

Note how the focus to remain between roughly 20-60 mph 

and to minimize speed fluctuation (keeping acceleration low) 

aligns with the most fuel-efficient trips plotted in Figure 5. 

Make It Increasingly Automatic 

Another way to address the challenges of driver motivation 

and to increase adoption of efficient behavior is to give the 

vehicle more responsibility. New vehicles increasingly 

include safety and convenience features such as lane keep 

assist, adaptive cruise control, and early brake application for 

imminent collision avoidance. The technologies to produce 

these features could be used to create “green driving assist” in 

which the vehicle intelligently selects optimal acceleration/ 

deceleration rates and cruising speeds. The driver's full 

attention could then remain on the road to ensure safe 

operation (rather than occasionally diverting attention to a 

feedback device). 

Over the past 25 years, research driven by highway safety, 

capacity improvement, and defense applications has 

dramatically advanced intelligent vehicle technologies toward 

even further levels of automated control. Google recently 

installed some of the developed component technologies in 

several vehicles, which have since autonomously driven 
thousands of miles on California roads [22]. An Italian 

company has similarly retrofitted vehicles and last year 

demonstrated an autonomous drive from Rome, Italy, to 

Shanghai, China [23]. 

Continued autonomous driving advancements with an 

energy-efficiency focus could enable fuel savings to approach 

those this project calculated for complete drive profile 

optimization. Those savings exceeded 30%-40% over a 

variety of drive cycles with no changes to the vehicle. 

Another benefit of automation is that rather than having to 

convince drivers to change, people may instead demand the 

technology (regardless of fuel price) for value additions such 

as increased convenience and productivity, and reduced 

accidents and time spent in congestion. The improved safety, 

traffic flow, and guidance aspects could also enable 

compounding benefits from reducing vehicle weight and 

powertrain size, or even connecting vehicles to electric power 

while driving-each of which has been shown to deliver 

dramatic additional fuel savings. 

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CONTACT INFORMATION 

Jeffrey Gonder can be reached by e-mail at 

jeff.gonder@nrel.gov. 

ACKNOWLEDGMENTS 

The authors gratefully acknowledge the programmatic 

support of the U.S. Department of Energy, Office of Energy 

Efficiency and Renewable Energy, Vehicle Technologies 

Program, and wish to particularly thank Dr. Yury Kalish for 

his guidance and support. The authors also express their 

gratitude to Mekayla Beaver of Gloworm Insights and Dr. 

Barbara Farhar from the University of Colorado Institute of 

Behavioral Science for their advice on exploring the human 

factors and social science issues regarding driver influences 

and feedback receptiveness. 

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