**1. Introduction**

The objective of this study is to quantify battery electric vehicles' (BEVs') energy consumption for different intersection control types, including a roundabout, a traffic signal, and a two-way stop sign using a microscopic traffic simulation model. The findings of this study can be used in developing vehicle-specific routing strategies that favor or avoid specific types of intersections depending on the vehicle technology. The market penetration rate of BEVs has significantly increased in recent years. The International Energy Agency reported that in 2018, 1.98 million BEVs were sold worldwide [1]. During the first 6 months of 2019, global sales of BEVs increased by 92% [2].

Compared to ICEVs, BEVs have significant benefits, including low maintenance costs, zero emissions, energy savings, and the ability to use electricity generated from renewable resources. Furthermore, BEVs can produce regenerative braking energy to improve energy efficiency. Specifically, in a BEV's regenerative braking system, the electric motor works as a generator by sending energy from the vehicle's wheels to the electric motor and storing it in the battery system.

Previous studies demonstrated that BEVs have different energy consumption patterns than ICEVs. An ICEVs' fuel efficiency is typically maximized under constant speed highway driving conditions, whereas BEVs are more energy efficient in intermittent driving conditions, as these conditions allow the regenerative braking system to recover more energy [3]. A previous study also investigated the fuel and energy consumption associated with various driving cycles for both BEVs and ICEVs [4]. For a test ICEV (a Nissan Versa), the "LA92" and "New York" cycles were the worst fuel economy driving cycles, whereas the "Freeway Level of Service A-C" and "Freeway Level of Service D" cycles were the best fuel economy cycles. In contrast, for the test BEV (a Nissan Leaf), the "Area" and "Ramp" cycles utilized more electricity than the other driving cycles, and the "Freeway Level of Service E" and "Freeway Level of Service F" cycles were the most energy efficient. These results suggest that BEVs' energy consumption patterns should be systematically compared to ICEVs' patterns.

A number of studies have investigated the effects of traffic controls on ICEVs and developed optimum traffic signal controls to reduce fuel consumption and emissions. Varhelyi investigated the effects of small roundabouts on emissions and fuel consumption [5]. Coelho et al. [6] utilized a vehicle-specific power method to evaluate fuel consumption and emissions impacts on roundabout intersections in Raleigh, North Carolina and in Lisbon, Portugal. Researchers from Kansas State University utilized video-recorded traffic flow through the roundabout to extract speed, acceleration, deceleration, and other characteristics, and evaluated the emission impacts of roundabouts using SIDRA software (SIDRA SOLUTIONS, Balwyn, Australia) [7]. Ahn et al. [8] investigated the impacts of fuel consumption and emissions on a high-speed roundabout using a traffic simulation model. Kwak et al. [9] investigated the effects of traffic signal optimization on fuel and greenhouse gas emissions in an urban corridor and optimized traffic signal timing plans based on vehicle fuel consumption using a genetic algorithm. Park et al. [10] introduced a sustainable traffic signal control system and demonstrated that the system could reduce fuel consumption and pollutants with moderate increases in vehicle delays and number of stops. Stevanovic et al. [11] developed a fuel optimum traffic control system by integrating the Comprehensive Modal Emission Model and Vissim-based genetic algorithm optimization of signal timings with the Vissim traffic simulation model. Michel et al. [12] investigated the potential impacts of connected and automated vehicles on fuel consumption and found that the connectivity removed some stops and strong accelerations and increased overall vehicle speed, concluding that the best fuel consumption levels for hybrid electric vehicles and BEVs are achieved with connectivity.

While the above studies investigated the effects of traffic control strategies on ICEV fuel consumption and emissions, most did not consider BEVs. Thus, a systematic analysis of the effects of various intersection controls and signal plans on BEVs' energy consumption is needed. To address this gap in knowledge, this study aimed to quantify BEVs' energy consumption for different intersection controls—a roundabout, traffic signal, and two-way stop sign—and compare BEV's energy and ICEV's fuel consumption patterns. The study also investigated the effects of various traffic signal coordination planned BEVs' energy consumption. In attempt to reduce greenhouse gas emissions, more BEVs will be utilized in the near future and some cities may adopt zero emission zones where only BEVs may be operated. The results of this study can be utilized by researchers, transportation practitioners, and politicians to make decisions related to alternative intersection control strategies for BEVs in the near future.

According to the definition given by the US Department of Energy, plug-in electric vehicles include BEVs (e.g., Nissan Leaf) and plug-in hybrid electric vehicles (PHEVs). The latter include blended/parallel vehicles (e.g., Toyota Prius Plug-In) along with extended-range electric vehicles (EVs) (e.g., Chevy Volt) which have a variety of configurations. In this study, EVs refer to battery-only EVs (i.e., BEVs), since a Nissan Leaf was utilized as a test vehicle.

The contributions of the study include the following: (a) we attempted to identify the impacts of various traffic controls on ICEV and BEV fuel/energy consumption; (b) we used a microscopic analysis tool to evaluate instantaneous ICEV and BEV fuel/energy consumption; and (c) while most other studies have utilized simplified BEV energy models which consider an average regenerative braking energy efficiency or a regenerative braking factor that depends on vehicle speed, this study utilized a BEV energy model that can estimate the instantaneous energy consumed (kWh) and the instantaneous energy regenerated (kWh) required to accurately capture the regenerative braking energy.

The remainder of this paper is organized as follows. The next sections present the methodology utilized in the study; the energy model for BEVs and the fuel consumption model for ICEVs; and the simulation model and results. The discussion and future works are summarized in the final section.

### **2. Methodology**

This study evaluated the effects on energy/fuel efficiency of two factors. First, the effects of different intersection controls (i.e., a roundabout, a traffic signal, and a stop sign) were investigated for both BEV and ICEV energy/fuel consumption. Second, the effects of fixed-time traffic signal control strategies with various signal coordination plans were assessed by evaluating BEV and ICEV energy/fuel consumption levels in a corridor with three intersections using three signal coordination plans: a well-coordinated plan, a partially coordinated plan, and a poorly coordinated plan.

The study required two main tasks: (1) the simulation or measurement of vehicle driving patterns for different intersection control types; and (2) measurements and/or estimates both BEV and ICEV energy/fuel consumption. Typically, field measurements and simulation are used to identify vehicle driving patterns. The use of a probe vehicle is a popular method to record vehicle driving patterns; however, it is difficult to collect the speed profiles of all approaching vehicles for different intersection control strategies. Thus, this study used the INTEGRATION software (Hesham Rakha, Blacksburg, VA, USA), a microscopic traffic simulation software, to obtain realistic representations of individual vehicles for different traffic control types. INTEGRATION software was validated against standard traffic flow theory and has also been used to evaluate various intelligent transportation system applications [13].

The energy consumption of BEV and the fuel consumption of ICEV can be measured with on-board measurement devices or dynamometer testing. While field measurements are relatively accurate in measuring the BEV's battery state-of-charge (SOC) and ICEV fuel consumption, data collection is limited to test vehicles equipped with measurement devices. This limits the usefulness of this method, because it is difficult to recruit test vehicles, install the data collection equipment, and collect sufficient field data. Thus, this study employed a microscopic BEV energy model to estimate BEV energy consumption and a microscopic fuel consumption model to estimate ICEV fuel consumption.

A number of BEV energy models and ICEV fuel consumption models use an average speed as an input variable. However, this approach is not suitable for the purposes of this study. In particular, the average speed-based models cannot identify the effects of transient changes in a vehicle's speed and acceleration which have significant effects on energy/fuel consumption as demonstrated in earlier studies [14]. Significant and frequent speed changes are observed while approaching and traversing intersection controlled approaches including roundabouts, traffic signals, and stop signs [8]. Thus, this study utilized microscopic energy/fuel consumption models to estimate the vehicle energy/fuel consumption for various scenarios. These models are described in more detail below.
