# Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods

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## Abstract

**:**

## 1. Introduction

## 2. Defining Eco-Driving

## 3. Observing Eco-Driving

## 4. Linking Eco Driving with Fuel Consumption

#### 4.1. Basic Fuel Consumption Estimation Models

#### 4.1.1. Physics-Based vs. Data-Driven Models

_{tot}is equal to the sum of the work of the opposing forces plus the energy required to accelerate the vehicle [14]. More specifically:

_{air}is the work of the aerodynamic resistance, E

_{roll}the work of rolling resistance (friction), E

_{g}the work of the vehicle’s weight (positive if the vehicle is moving uphill and negative if it is moving downhill), E

_{acc}the energy required for accelerating the vehicle and E

_{id}the energy required to keep the engine running, even when the vehicle is at stop.

_{aero}is the aerodynamic resistance and F

_{RR}is the rolling resistance (Figure 2).

_{i,j}and M

_{i,j}are the emerging model regression coefficients for the MOE at a speed power “i” and an acceleration power “j” for positive and negative accelerations respectively, s (km/h) is the instantaneous speed and a (km/h/s) is the instantaneous acceleration.

#### 4.1.2. Modeling Scale

#### 4.1.3. Model Transparency

- Engine-based models, in which input variables are metrics related to the engine, such as rotational speed (rounds per minute), engine torque, and power.
- Vehicle-based models with variables such as instantaneous or average speed and acceleration.
- Models based on operating modes, with input variables such as acceleration, constant speed, deceleration and idling.

#### 4.2. Behavioral Fuel Consumption Models

^{2}of 0.58. A similar model was developed by Chen et al. (2017), although the dataset used was synthetic. Except from driving behavior, road geometry was also found as a very important factor. The R

^{2}of this model was about 0.85 and the Mean Absolute Percentage Error (MAPE) 9.3%. Walnum and Simonsen (2015) developed a linear model exploiting detailed driving behavior variables (speed, constant speed duration, gear, braking, idling and number of stops), which were also found as the most significant. Wallin (2016), on the other hand, proposed an approach that utilizes only road inclination, weather and vehicle’s weight, which is simpler yet less accurate and detailed.

^{2}of 0.70. The data exploited were speed, gear, braking, distance travelled and other. Similarly, Gilman et al. (2015) compared the performance of linear model, Neural Networks and Decision Trees. The dataset exploited included the speed and acceleration of the vehicle, road geometry and weather conditions. The models achieved Root Mean Squared Error (RMSE) of 0.012, 0.006 and 0.010 respectively.

#### 4.3. Eco Driving Effects on Fuel Consumption

## 5. The Promises and Some Caveats

#### 5.1. Data Collection

#### 5.2. Modeling Efficiency

#### 5.3. Modeling Completeness vs. Usefulness

#### 5.4. Managing Driving Behavior and Eco-Routing

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Authors and Year | Ref. | Modeling Methodologies | Naturalistic Driving Data | Synthetic Data-Simulation | Speed | Acceleration | Constant Speed Duration | Gear | Braking | Engine Operations | Air-Condition | Road Inclination | Road Geometry (Other) | Traffic Conditions | Weather Conditions | Distance travelled | Weight | Idling | Number of Stops | Routing | Vehicle Mechanical Characteristics | Monitoring of Driving Behavior |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Huang et al. (2018) | [2] | N/A | √ | √ | √ | |||||||||||||||||

Z. Xu et al. (2018) | [3] | Neural Networks, Linear Regression | √ | √ | √ | √ | √ | √ | √ | |||||||||||||

Zeng et al. (2016) | [6] | N/A | √ | |||||||||||||||||||

Zhou et al. (2016) | [8] | N/A | √ | √ | √ | √ | √ | √ | √ | √ | ||||||||||||

Meseguer et al. (2017) | [16] | N/A | √ | |||||||||||||||||||

Gilman et al. (2015) | [22] | Linear Regression, Neural Networks, Decision Trees | √ | √ | √ | √ | √ | √ | √ | |||||||||||||

Chen et al. (2017) | [33] | Linear Regression | √ | √ | √ | √ | √ | |||||||||||||||

Walnum & Simonsen (2015) | [39] | Linear Regression | √ | √ | √ | √ | √ | √ | √ | √ | √ | |||||||||||

Yao, Zhao, Zhang, et al. (2020) | [40] | Linear Regression | √ | √ | √ | √ | √ | √ | √ | |||||||||||||

Yao, Zhao, Liu, et al. (2020) | [41] | Neural Networks, Random Forests, Support Vector Regression | √ | √ | √ | √ | √ | |||||||||||||||

Wickramanayake & Bandara (2016) | [42] | Neural Networks, Random Forests, Gradient Boosting | √ | √ | √ | √ | √ | |||||||||||||||

Ping et al. (2019) | [43] | LSTM, K-means clustering | √ | √ | √ | √ | ||||||||||||||||

Wallin (2016) | [44] | Linear Regression | √ | √ | √ | √ | ||||||||||||||||

Reddy (2019) | [45] | N/A | √ | √ | √ | |||||||||||||||||

Hiraoka et al. (2009) | [46] | N/A | √ | √ | √ | |||||||||||||||||

Zhao et al. (2015) | [47] | N/A | √ | √ | √ | |||||||||||||||||

Sanguinetti et al. (2017) | [48] | N/A | √ | √ | √ | √ | √ |

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**MDPI and ACS Style**

Fafoutellis, P.; Mantouka, E.G.; Vlahogianni, E.I.
Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods. *Sustainability* **2021**, *13*, 226.
https://doi.org/10.3390/su13010226

**AMA Style**

Fafoutellis P, Mantouka EG, Vlahogianni EI.
Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods. *Sustainability*. 2021; 13(1):226.
https://doi.org/10.3390/su13010226

**Chicago/Turabian Style**

Fafoutellis, Panagiotis, Eleni G. Mantouka, and Eleni I. Vlahogianni.
2021. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods" *Sustainability* 13, no. 1: 226.
https://doi.org/10.3390/su13010226