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Article

Quantitative Assessment of EV Energy Consumption: Applying Coast Down Testing to WLTP and EPA Protocols

by
Teeraphon Phophongviwat
1,
Piyawong Poopanya
2 and
Kanchana Sivalertporn
3,*
1
Department of Electrical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
Program of Physics, Faculty of Science, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
3
Department of Physics, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2025, 16(7), 360; https://doi.org/10.3390/wevj16070360
Submission received: 24 April 2025 / Revised: 21 June 2025 / Accepted: 26 June 2025 / Published: 27 June 2025

Abstract

This study presents a comprehensive methodology for evaluating electric vehicle (EV) energy consumption by integrating coast down testing with standardized chassis dynamometer protocols under WLTP Class 3b and EPA driving cycles. Coast down tests were conducted to determine road load coefficients—critical for replicating real-world resistance profiles on a dynamometer. Energy usage data were measured using On-Board Diagnostics II (OBD-II) and dynamometer measurements to assess power flow from the battery to the wheels. The results reveal that OBD-II consistently recorded higher cumulative energy usage, particularly under urban driving conditions, highlighting limitations in dynamometer responsiveness to transient loads and regenerative events. Notably, the WLTP low-speed cycle exhibited a significantly lower efficiency of 62.42%, with nearly half of the battery energy consumed by non-propulsion systems. In contrast, the EPA cycle demonstrated consistently higher efficiencies of 84.52% (low-speed) and 93.00% (high-speed). Interestingly, high-speed efficiencies between WLTP and EPA were nearly identical, despite differences in total energy consumption. These findings underscore the importance of aligning test protocols with actual driving conditions and demonstrate the effectiveness of combining coast down data with real-time diagnostics for robust EV performance assessments.

1. Introduction

Electric vehicles (EVs) are rising in popularity due to technological developments and decarbonization efforts, making the analysis of their energy consumption increasingly important for drivers, automakers, and policymakers [1]. Energy-saving strategies are designed for both city and highway driving, in both human-driven and autonomous systems [2,3]. Accurate energy consumption prediction is essential for alleviating range anxiety, supporting optimal battery sizing, energy-efficient route planning, and charging infrastructure operation [4,5,6]. Unlike traditional vehicles, EVs rely entirely on electrical energy stored in batteries, making energy efficiency a critical factor in their design, performance, and operational range. To evaluate and optimize this efficiency, standardized testing procedures are essential [7,8]. Several factors influence EV energy consumption, including vehicle-related factors, environment-related factors, and driver-related factors [9,10]. Vehicle parameters such as velocity, acceleration, braking energy regeneration, and auxiliary loads play a role [11]. Environmental factors like ambient temperature, wind speed, road condition, and traffic condition also have a significant impact [12,13]. Furthermore, driver-related factors, including driving patterns, charging habits, and route planning, can crucially affect energy consumption [14,15,16,17]. The discrepancies between standard testing results and real-world energy consumption highlight the need for further investigation into more representative evaluation methods. Standard energy-consumption testing is crucial for transparency in the electrified automotive industry, but inconsistencies with real-world driving hinder accurate energy and environmental assessments [9,18]. Examples of standardized test procedures, such as the New European Driving Cycle (NEDC), the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), and the China Light-Duty Vehicle Test Cycle for Passenger Cars (CLTC-P), have been adopted to evaluate the driving performance of EVs [19,20] in a way that aligns more closely with real-world usage. Additionally, joint estimations of battery conditions—such as state of charge (SOC), state of health (SOH), and state of power (SOP) [21]—are incorporated to reflect the battery’s energy management. Among these, coast down testing [22,23] has emerged as a robust method to characterize the energy consumption of EVs by quantifying the resistive forces acting on the vehicle, which directly influence its power requirements. Coast down testing involves measuring the time it takes for a vehicle to decelerate naturally from a predetermined speed to a standstill, providing valuable data on aerodynamic drag, rolling resistance, and mechanical losses. These data are then used to derive the road load coefficients—typically denoted as A, B, and C—which form the basis of the target road load equation [24]. This equation is pivotal in simulating driving conditions on a chassis dynamometer, a controlled testbed that replicates real-world scenarios [25]. The testing procedure begins with collecting coast down time data, followed by driving the EV onto the dynamometer. Once positioned, the vehicle is subjected to standardized drive cycles, such as those defined by the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) [26] and the Environmental Protection Agency (EPA) [27]. These cycles represent diverse driving patterns, ranging from urban low-speed conditions to high-speed highway scenarios, enabling a comprehensive analysis of energy consumption across operational ranges. This study aims to investigate the energy consumption of an electric vehicle (EV) by integrating coast down testing with chassis dynamometer-based drive cycle analysis under EPA and WLTP standards. The primary objectives are to determine road load coefficients, evaluate energy efficiency across different driving conditions, and compare the performance of standardized testing protocols. In this study, OBD-II data were utilized alongside chassis dynamometer measurements to capture real-time electrical energy consumption from the battery. While certification procedures rely on high-precision instrumentation for measuring mechanical output at the wheels, OBD-II offers additional insight by recording total electrical energy use, including contributions from auxiliary loads and regenerative braking events. This enables a more comprehensive assessment of system efficiency by comparing electrical input and mechanical output under standardized driving conditions. By combining empirical coast down testing, dynamometer simulations, and high-resolution OBD-II data, this methodology allows for a detailed characterization of EV energy consumption, effectively bridging the gap between laboratory-based testing and real-world performance. The Hybrid Pulse Power Characterization (HPPC) test was not included, as this study focused on vehicle-level energy consumption assessment using a chassis dynamometer rather than battery-level characterization.
The remainder of this paper is organized as follows. Section 2 provides an overview of the research background and highlights the current challenges in electric vehicle (EV) charging management. Section 3 presents the methodology, including the development of the scheduling model and the key assumptions used. In Section 4, the results of the proposed approach are discussed, focusing on performance evaluation under various scenarios. Finally, Section 5 concludes the paper by summarizing the key contributions and outlining directions for future research.

2. Background

The total energy consumed by an EV over a specific distance or duration can be quantified as the work needed to overcome resistive forces, accounting for system efficiencies [28]. The primary forces opposing vehicle movement are: Aerodynamic Drag, Rolling Resistance, Gravitational Force, and Acceleration Force, each of which is defined in the following Equations (1)–(4) (see Appendix A for symbol definitions). Aerodynamic Drag ( F d ), resulting from air resistance, increases proportionally to the square of the vehicle’s speed and is calculated as follows:
F d = 1 2 ρ C d A f v 2
where ρ represents air density, C d is the drag coefficient, A f denotes frontal area, and v is the velocity of the vehicle. Rolling resistance ( F r ), originating from tire deformation and frictional interactions with the road surface, can be approximated as:
F r = C r m g
In this equation, C r is the rolling resistance coefficient, m is the mass of the vehicle, and g represents gravitational acceleration (9.81 m/s2). Gravitational force ( F g ), which becomes significant when driving on inclines, is expressed as:
F g = m g s i n θ
where θ is the road slope angle. Acceleration force ( F a ), required to increase or decrease the vehicle’s velocity, is given by:
F a = m a
with a denoting acceleration. The total tractive force ( F t o t a l ) necessary for vehicle propulsion is the cumulative sum of these components:
F t o t a l = F d + F r + F g + F a
Power delivered at the wheels ( P w h e e l s ) results from multiplying this total force by the vehicle velocity:
P w h e e l s = F t o t a l v
However, battery-level energy consumption must incorporate drivetrain inefficiencies, such as those in motors, inverters, and transmissions, along with regenerative braking which recaptures kinetic energy. Thus, the power drawn from the battery ( P b a t t ) is
P b a t t = P w h e e l s η d r i v e r t r i a n P r e g e n
where η d r i v e r t r i a n represents drivetrain efficiency, and P r e g e n is the regenerative braking power, varying with driving conditions and system design [29].
Energy consumption ( E ) over a distance is calculated by integrating battery power over time, or equivalently, multiplying power by travel duration ( t = d / v in segments with constant velocity). For driving cycles like WLTP or EPA, involving variable speeds, energy consumption is calculated as
E = 0 t P b a t t t d t
Substituting the expression for P b a t t :
E = 0 t F d + F r + F g + F a v η d r i v e r t r i a n P r e g e n d t
In practical applications, this integral is discretized according to the velocity profile of test cycles (e.g., WLTP’s 1800-s cycle or EPA’s combined city/highway cycles).

Application to WLTP and EPA Protocols

In this study, both the WLTP and EPA standards were selected to facilitate a comparative evaluation of electric vehicle (EV) energy efficiency across diverse driving scenarios and to ensure the relevance of the results within both international and U.S. regulatory frameworks. This dual-protocol approach enhances the generalizability of the findings by enabling a comprehensive assessment of EV performance under globally recognized test conditions. The Worldwide Harmonized Light Vehicles Test Procedure (WLTP), developed by the United Nations Economic Commission for Europe (UNECE), is a globally adopted testing standard designed to better represent real-world driving behavior. It incorporates a wider range of driving parameters, including varied speeds, acceleration profiles, and idling phases, and provides a standardized methodology for assessing vehicle energy consumption and emissions in both urban and suburban contexts (Figure 1a). In contrast, the Environmental Protection Agency (EPA) testing protocols —specifically the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET)—are extensively utilized in the United States and are tailored to reflect typical American driving patterns. These procedures serve as the basis for regulatory certification of electric vehicles in the North American market. The driving tests were divided into two phases: low-speed (Figure 1b) and high-speed (Figure 1c) driving ranges.
The WLTP and EPA test protocols utilize dynamometer simulations based on coast down-derived road load coefficients, typically represented as a polynomial:
F = A + B v + C v 2
where F is the total road load force and v is the vehicle speed. The coefficients A , B , and C correspond to distinct components of resistive force: A accounts for the constant rolling resistance, primarily resulting from tire deformation and independent of speed; B represents the speed-proportional resistance, commonly attributed to bearing friction and other mechanical losses that vary linearly with velocity; and C captures the aerodynamic drag, which increases with the square of speed and becomes dominant at higher velocities. These coefficients serve as critical parameters in testing protocols to ensure a realistic representation of vehicle resistance. Accurate determination of the A , B , and C coefficients is essential for reliable energy consumption modeling and chassis dynamometer testing. Theoretical energy consumption values, calculated based on these parameters, are validated through comparison with energy consumption measured at the battery level.

3. Experiment

The physical specifications and battery details of the EV used in this research are presented in Table 1 and Table 2, respectively. The testing procedure to determine the energy consumption of an EV using a chassis dynamometer has three main steps as follows:
Step 1: Collection of Coast-Down Data for Target Road Load Parameters
In this step, a flat, straight road free from inclines and obstructions was selected for the coast down test. Suvarnabhumi 3 Road, located at Suvarnabhumi Airport, was chosen for its suitable conditions, with a total length of 5.3 km, as illustrated in Figure 2a. The electric vehicle (EV) was then accelerated to a speed of 130 km/h before shifting to neutral gear (N), allowing it to coast naturally to a speed of 15 km/h. During this process, vehicle speed and coast down time were recorded. The procedure was repeated for five trials to ensure consistency, as shown in Figure 2b.
Step 2: Determination of A , B , and C Coefficients
Using the MEIDACS-DY software (version 4.0), separate calculations were performed to determine the road load coefficients for both WLTP and EPA driving standards. These coefficients— A , B , and C —represent the rolling resistance, speed-proportional resistance, and aerodynamic drag, respectively. The differences in the A , B , and C road load coefficients between the WLTP (Worldwide Harmonized Light Vehicles Test Procedure) and EPA (Environmental Protection Agency) coast down tests arise from variations in their respective test procedures and coefficient derivation methodologies. The WLTP is based on a more dynamic and representative driving cycle that incorporates a wider range of speeds, accelerations, and decelerations to reflect real-world driving conditions. In contrast, the EPA coast down procedure adopts a more simplified approach, resulting in differing representations of rolling resistance, speed-proportional resistance, and aerodynamic drag. The coast down time data were analyzed according to the EPA and WLTP standards, as shown in Figure 2c and 2d, respectively, to determine the coefficients A , B , and C of the target road load equation. Five coast down test datasets were used, yielding five distinct sets of A , B , and C values. The coefficients were selected such that the fitting error did not exceed 10 N, and the C coefficient—representing aerodynamic drag—was minimized. The resulting road load equations derived through fitting techniques specific to each standard are F = 117.53 + 0.4056 v + 0.0265 v 2 and F = 124.68 + 0.2578 v + 0.0316 v 2 , respectively. These coefficients were subsequently employed to simulate real-world driving conditions on a chassis dynamometer in the next step.
Step 3: Chassis Dynamometer Test Configuration for Electric Vehicles
The road load coefficients obtained in Step 2 were used to simulate real-world driving conditions on a chassis dynamometer (Figure 3). The chassis dynamometer specifications are shown in Table 3. The dynamometer system was configured to replicate both WLTP and EPA driving cycles by inputting the relevant vehicle and driving parameters into the MEIDACS-DY software. Mechanical losses within the chassis dynamometer (CHDY) system—referred to as CHDY mechanical losses—were also considered. These losses arise from mechanical and electrical losses within the dynamometer system and can significantly influence the accuracy of vehicle force and power measurements. To ensure the accuracy of performance evaluations, these losses were quantified during the dynamometer calibration procedure and subtracted from the measured total forces to isolate the true road load acting on the vehicle. The EV driving tests were divided into two phases: low-speed and high-speed driving ranges. Data on total power, speed, and environmental conditions were recorded every 0.1 s and averaged over 1 s intervals. The testing room temperature was maintained at 25 °C, and the battery temperature was monitored using the battery management system (BMS) provided by the manufacturer. All measurements in this study were obtained using a calibrated chassis dynamometer, which provides high accuracy with a typical measurement uncertainty of ±0.1% of full scale (FS) for torque and ±0.02% for average speed, as specified by the manufacturer.

4. Results and Discussion

This study presents a comparative analysis of the energy consumption of a battery electric vehicle (BEV) under standardized WLTP Class 3b and EPA test procedures. Data were acquired using the vehicle’s On-Board Diagnostics II (OBD-II) system and a chassis dynamometer (DYNO), representing electrical input power and mechanical output power at the wheels, respectively. The OBD-II system captures high-resolution electrical parameters directly from the vehicle’s control unit, including battery voltage, current, and total power consumption. It accounts for the overall battery energy usage, encompassing both traction-related energy and consumption by auxiliary systems such as air conditioning, lighting, and control electronics. The data were recorded at a sampling rate of 10 Hz and subsequently averaged to 1 Hz to facilitate direct comparison with dynamometer outputs. In contrast, the DYNO measures the mechanical power delivered to the wheels based on road load simulation parameters derived from coast down testing. It reflects only the energy transferred to vehicle propulsion, thereby excluding auxiliary loads.
In both WLTP and EPA test cycles, the energy consumption analysis is conducted separately for two scenarios: low-speed (city) driving and high-speed driving. It was consistently observed that the electrical energy drawn from the battery exceeded the mechanical energy measured at the wheels, which is expected due to drivetrain and system losses. Figure 4 presents the energy consumption data measured from both OBD-II and DYNO under low-speed (city) and high-speed driving conditions, across the two testing protocols. The corresponding standard driving profiles are also illustrated for reference.
Under the WLTP low-speed cycle (Figure 4a) the OBD-II data exhibited an earlier and more continuous accumulation of energy compared to the DYNO output. This discrepancy suggests that the OBD-II system is more responsive in capturing transient power draw, particularly during the initial acceleration phases. In contrast, the DYNO values remained at zero over a considerable initial distance, likely due to threshold limitations or delays in mechanical resistance modeling. An interesting observation is that when the vehicle velocity is zero—specifically during the time intervals of 100–135 s and 450–500 s—there is an increase in energy measured by the OBD-II system, while the energy recorded by the DYNO remains constant. This indicates that energy drawn from the battery during these stationary periods is used to power auxiliary systems such as HVAC, lighting, and infotainment. To further investigate this behavior, the energy usage per time step (per second) was analyzed, as illustrated in Figure 5 (green dots). It was found that during periods of constant vehicle state, the average energy usage is approximately 0.00105 kWh/s. By subtracting this auxiliary load from the cumulative energy profile, the net energy associated solely with vehicle mechanical dynamics can be isolated. The resulting corrected energy profile exhibits flat regions during vehicle idling, as shown in Figure 5 (an orange line).
In the WLTP high-speed cycle (Figure 4b), DYNO values begin to accumulate more significantly during the mid-to-late segments of the drive cycle. However, the OBD-II readings consistently report higher cumulative energy consumption than DYNO, as OBD-II captures the total electrical energy drawn from the battery, including not only the energy used for propulsion but also that consumed by auxiliary systems such as HVAC, lighting, and infotainment. The abrupt drop in energy consumption observed between 100 and 130 s corresponds to regenerative braking—from approximately 70 km/h to 10 km/h—during which kinetic energy is partially recovered. A similar fluctuation is also observed later in the cycle during deceleration phases. Notably, after approximately 250 s, even though the vehicle decelerates from its peak speed, the cumulative energy recorded by OBD-II continues to increase gradually. This is attributed to the heat generated during high-speed operation, which activates the vehicle’s cooling systems and contributes to additional energy consumption.
In the EPA cycle (Figure 4c,d), both OBD-II and DYNO exhibited similar trends in energy consumption throughout the drive cycle. However, the OBD-II system consistently reported higher energy consumption due to additional losses, as previously discussed, leading to a more pronounced divergence between the two measurement methods. A comparison of the WLTP and EPA standards is presented in Figure 6a,b, illustrating energy consumption as a function of distance for low- and high-speed driving profiles, respectively. Despite covering a shorter distance, the WLTP cycle demonstrates higher energy consumption in both profiles. This is attributed to its more complex driving pattern, which includes frequent variations in speed, acceleration, and deceleration—features that more accurately reflect real-world driving conditions compared to the EPA cycle.
As summarized in Table 4 and Table 5, the measured energy consumption and efficiency values are consistent with those reported in previous studies [11,20], further validating the methodology and the application of WLTP and EPA protocols in assessing EV performance. Under the EPA cycle, energy consumption at both the battery and wheel levels are higher in the high-speed profile compared to the low-speed profile. In contrast, the WLTP cycle reveals the opposite trend. Notably, in the WLTP low-speed cycle, battery-level energy consumption is considerably higher than at the wheel level, resulting in a relatively low efficiency of 62.42%. This suggests that nearly half of the energy drawn from the battery is consumed by internal systems unrelated to mechanical propulsion. As evidenced in Figure 5, which illustrates the energy consumption attributable solely to the mechanical components, the total energy at the end of the WLTP low-speed cycle decreased from 0.692 kWh (Figure 4a) to 0.462 kWh (Figure 5). In contrast to WLTP, the EPA cycle yields consistently higher efficiencies in both speed profiles, with values of 84.52% (low-speed) and 93.00% (high-speed). Interestingly, the high-speed efficiencies of both standards are nearly identical—92.98% for WLTP and 93.00% for EPA—despite the EPA showing higher total energy consumption, which can be attributed to its longer distance and higher average speed.

5. Conclusions

This study provides a detailed analysis of electric vehicle energy consumption using a hybrid approach that combines coast down testing and chassis dynamometer-based drive cycle simulations under WLTP and EPA standards. By determining road load coefficients through empirical coast down data and applying them to standardized test cycles, we effectively replicated real-world driving resistance on the dynamometer platform. The results show consistent discrepancies between OBD-II and DYNO outputs, with OBD-II detecting higher energy consumption, especially under urban and low-speed conditions. These differences are attributed to the increased sensitivity to auxiliary loads and transient power demands. Energy efficiency varied notably across test protocols, with the EPA highway cycle delivering the highest conversion efficiency of 93.00%, and WLTP low-speed exhibiting the lowest at 62.42%. This variation highlights the importance of aligning test procedures with actual driving conditions for accurate EV performance evaluation. These findings emphasize the necessity of matching testing standards with actual vehicle usage patterns to ensure reliable evaluations. However, this study is limited to a single EV model tested under typical environmental conditions in Thailand, where moderate temperatures and humidity levels may affect energy consumption. Therefore, the results may not be directly applicable to other vehicle types or different climatic regions. Future work may expand this methodology to include diverse vehicle types, environmental conditions, and multi-modal energy recovery systems to further enhance the fidelity of laboratory-based efficiency assessments.

Author Contributions

Conceptualization, P.P. and K.S.; methodology, T.P. and P.P.; validation, T.P., P.P. and K.S.; formal analysis, P.P. and K.S.; writing—original draft preparation, T.P. and P.P.; writing—review and editing, K.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Fund (Ubon Ratchathani University, Project ID: 4694796), supported by National Science Research and Innovation Fund (NSRF).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BEVBattery Electric Vehicle
BMSBattery Management System
DYNOChassis Dynamometer
EPAEnvironmental Protection Agency
EVElectric Vehicle
HPPCHybrid Pulse Power Characterization
IP67Ingress Protection Rating 67 (Dust-tight and water immersion protection)
LIBLithium Ion Battery
NEDCNew European Driving Cycle
OBD-IIOn-Board Diagnostics II
WLTPWorldwide Harmonized Light Vehicles Test Procedure

Appendix A

To support the interpretation of equations and technical discussions presented in this study, the key symbols used throughout the manuscript are summarized below, along with their definitions and corresponding units.
Table A1. Table of Symbols.
Table A1. Table of Symbols.
SymbolDescriptionUnit
ρ Air densitykg/m3
C d Drag coefficient-
A f Frontal area of the vehiclem2
v Vehicle velocitym/s
C r Rolling resistance coefficient-
m Mass of the vehiclekg
g Gravitational acceleration9.81 m/s2
θ Road slope angleradians or °
a Accelerationm/s2
F t o t a l Total tractive forceN
F d Aerodynamic drag forceN
F r Rolling resistance forceN
F g Gravitational forceN
F a Acceleration forceN
F r o a d Road load forceN
P b a t t Power drawn from the batteryW
P w h e e l s Power delivered at the wheelsW
P r e g e n Regenerative braking powerW
η d r i v e r t r i a n Drivetrain efficiency-
E Energy consumptionkWh
t Times
A ,   B ,   C Road load coefficients (polynomial terms in resistance model)N, N/(m/s), N/(m/s)2

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Figure 1. Standard driving test cycles: (a) WLTP Class 3b [26]; EPA test procedures for (b) low-speed and (c) high-speed conditions [27].
Figure 1. Standard driving test cycles: (a) WLTP Class 3b [26]; EPA test procedures for (b) low-speed and (c) high-speed conditions [27].
Wevj 16 00360 g001
Figure 2. Coast down test: (a) test route on Suvarnabhumi 3 Road; (b) EV deceleration; (c) EPA; and (d) WLTP standards for road load forces.
Figure 2. Coast down test: (a) test route on Suvarnabhumi 3 Road; (b) EV deceleration; (c) EPA; and (d) WLTP standards for road load forces.
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Figure 3. Schematic diagram and experimental setup of the chassis dynamometer test system, including the operation unit, AC drive, cooling fan, control panel, and actual test setup with the EV positioned on the dynamometer rolls.
Figure 3. Schematic diagram and experimental setup of the chassis dynamometer test system, including the operation unit, AC drive, cooling fan, control panel, and actual test setup with the EV positioned on the dynamometer rolls.
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Figure 4. Energy consumption of WLTP standards at (a) low- and (b) high-speed ranges and EPA standard at (c) city- and (d) high-speed ranges. The green line represents the standard driving cycle used in the test protocol.
Figure 4. Energy consumption of WLTP standards at (a) low- and (b) high-speed ranges and EPA standard at (c) city- and (d) high-speed ranges. The green line represents the standard driving cycle used in the test protocol.
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Figure 5. Energy usage per unit time during the WLTP low-speed driving test. The yellow-shaded areas indicate time intervals during which the vehicle velocity is zero.
Figure 5. Energy usage per unit time during the WLTP low-speed driving test. The yellow-shaded areas indicate time intervals during which the vehicle velocity is zero.
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Figure 6. Comparison of energy consumption between standards WLTP and EPA at (a) city- and (b) high-speed ranges.
Figure 6. Comparison of energy consumption between standards WLTP and EPA at (a) city- and (b) high-speed ranges.
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Table 1. Test electric vehicle specifications.
Table 1. Test electric vehicle specifications.
Dimensions and Weight
Length:Width:Height 4612 mm:1852 mm:1640 mm
Wheelbase (front/rear) 2765 mm
Vehicle weight 2120 kg
Wheel and tire size20-inch alloy wheels,
235/50R20 (front), 255/50R20 (rear)
Performance
Electric motor typePermanent magnet synchronous motor
Battery typeLithium-ion battery
Max power201 hp (150 kW)
Max torque310 N-m
Battery capacity83.4 kWh
Max range (NEDC mode)555 km
Driving modesSport/Normal/Eco/Comfort
Table 2. Battery specifications.
Table 2. Battery specifications.
Battery CellTypeLithium Iron Phosphate (LiFePO4)
Cathode/Anode materialsLithium Iron Phosphate/Graphite
Nominal Voltage (Volts)3.2
Nominal Capacity (Ah)131
Battery PackBattery Dimensions (mm)1853 × 1129 × 183.5
Total Voltage Range (Volts)300–438
Nominal Capacity (Ah)131
Nominal Voltage (Volts)384
Weight (kg)396
Water Protection LevelIP67 (Dust-tight and protected against immersion in water up to 1 m for 30 min)
Table 3. Chassis dynamometer specifications for electric vehicle testing.
Table 3. Chassis dynamometer specifications for electric vehicle testing.
UnitSystem ConfigurationSpecification
Dynamometer
MechanicalDyno structureFrame floated roller over—huge type, with remote torque calibration unit, 2-axle configuration (Front and Rear axle)
Roller diameter1219.2 mm (48 inch)
Maximum speed250 km/h
Absorbing (kW/Nm)2WD: 150 kW/5400 N
Motoring (kW/Nm)2WD: 110 kW/3960 N
Tire centering deviceCentering method: Air cylinder type
Lifting force: 20 kN per axle
ElectricDyno control unit
(AC drive)
VT340DY-H4700 series
Operation unitDesktop console type with touch panel
Measuring rack19-inch rack type (2 sets)
Computer systemMEIDACS-DY 6200 P
OtherVehicle cooling fanSimulates frontal airflow; wind speed (up to 140 km/h) is controlled according to the driving profile
Vehicle restrain deviceForward rush-out prevention (2 sets, Font and Rear)
Side run-out prevention (2 sets, Font and Rear)
Tire restraint deviceFix on-drive wheel of 2WD vehicle
Drive robotSeat mount type drive robot system (Accel, Clutch, Brake, Shift, Select)
Driver’s aidDriver’s monitor with stand, remote controller
Measuring tool
Power meterWT1800 (Yokogawa)
DC power supply and discharging unit (Load bank)DC power supply for electric device: PAT60-133T (×2 sets) (Kikusui)
DC load unit electric device: PLZ12005WH (Kikusui)
Battery simulatorBS3030 (MEIDENSHA), Voltage: 500 V, Current: 500 A, Power: max. 200 kW
Table 4. Energy consumption rate per kilometer.
Table 4. Energy consumption rate per kilometer.
Test StandardsSpeed RangesDistance
(km)
Average Speed
(km/h)
Energy Consumption at Battery Level
(kWh/km)
Energy Consumption at Wheel Level
(kWh/km)
WLTP Class 3blow3.0918.90.2240.140
high7.1656.70.1250.116
EPAlow7.4521.20.1680.134
high16.5177.70.2100.189
Table 5. The use of stored electrical energy in the battery and mechanical energy at the wheels to determine the efficiency of electric vehicles.
Table 5. The use of stored electrical energy in the battery and mechanical energy at the wheels to determine the efficiency of electric vehicles.
Test StandardsSpeed RangesEnergy Drawn from the Battery
(kWh)
Energy Usage Measured at the Wheels
(kWh)
Efficiency (%)
WLTPlow0.6920.43262.42
high0.8980.83592.98
EPAlow0.4210.35584.52
high1.5021.39793.00
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MDPI and ACS Style

Phophongviwat, T.; Poopanya, P.; Sivalertporn, K. Quantitative Assessment of EV Energy Consumption: Applying Coast Down Testing to WLTP and EPA Protocols. World Electr. Veh. J. 2025, 16, 360. https://doi.org/10.3390/wevj16070360

AMA Style

Phophongviwat T, Poopanya P, Sivalertporn K. Quantitative Assessment of EV Energy Consumption: Applying Coast Down Testing to WLTP and EPA Protocols. World Electric Vehicle Journal. 2025; 16(7):360. https://doi.org/10.3390/wevj16070360

Chicago/Turabian Style

Phophongviwat, Teeraphon, Piyawong Poopanya, and Kanchana Sivalertporn. 2025. "Quantitative Assessment of EV Energy Consumption: Applying Coast Down Testing to WLTP and EPA Protocols" World Electric Vehicle Journal 16, no. 7: 360. https://doi.org/10.3390/wevj16070360

APA Style

Phophongviwat, T., Poopanya, P., & Sivalertporn, K. (2025). Quantitative Assessment of EV Energy Consumption: Applying Coast Down Testing to WLTP and EPA Protocols. World Electric Vehicle Journal, 16(7), 360. https://doi.org/10.3390/wevj16070360

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