Analysis of Energy Requirements for Massive Integration of Electric Buses in Ambato City, Ecuador ^†

Abstract

Ecuador’s Energy Efficiency Law mandates that “as of 2030, all vehicles incorporated into urban public transport services must be electric”. This legal framework sets the stage for the country’s transition to electric mobility. This research examines the energy requirements for transitioning Ambato’s public bus fleet to electric vehicles, considering various technical and operational factors. The analysis evaluates the current fleet size, the expected lifespan of buses, daily operational hours, average speed, and the specific characteristics of the city’s public transport routes. Furthermore, this study delves into the technical aspects of energy consumption in electric public transport by calculating the driving force necessary to operate buses across different terrains and routes. Factors such as bus weight, passenger load, road gradient, and acceleration patterns are analyzed to assess their impact on energy consumption and vehicle range. Additionally, this study investigates the potential for regenerative braking and the necessary charging infrastructure, offering a comprehensive assessment of how these systems would function within Ambato. By forecasting future vehicle requirements and projecting growth in urban mobility, this study estimates the total energy demand for a fully electric public bus fleet. The potential for integrating renewable energy sources into the city’s grid is also explored, ensuring that the transition to electric mobility not only decreases reliance on fossil fuels but also supports cleaner energy sources. This research serves as a crucial step in understanding the infrastructure and policy changes required for the successful implementation of electric public transport in Ambato and similar Ecuadorian cities.

1. Introduction

Ecuador has emerged as a leading nation in Latin America in promoting sustainable transport, proactively advancing policies to accelerate the adoption of electric vehicles to support the decarbonization of its economy. A cornerstone of these efforts is the enactment of the Energy Competitiveness Law, which mandates that by 2030, all newly integrated vehicles in the urban public transportation system must be electric [1]. The National Transit Agency, in collaboration with transportation associations, has established a service lifespan of 22 years for public buses [2]. While the shift to electric vehicles presents substantial challenges, particularly in terms of investment and technological innovation, it also generates increased electricity demand for the transitioning fleet, as the Energy Efficiency Law will be fully applicable to public transport by 2030.

Ecuador generates approximately 36,683 GWh of electricity annually, with 74.4% sourced from renewable energy, predominantly hydropower [3]. This study offers a focused analysis of the energy requirements and infrastructure necessary to transition public transport to electric power in Ambato, a mid-sized city characterized by significant topographical and mobility challenges.

Existing research on electric vehicle integration has largely concentrated on larger metropolitan areas or regions with relatively flat terrain, leaving a gap in understanding the specific challenges faced by mid-sized cities such as Ambato. These cities, which are often characterized by more complex topographies and diverse energy demands, require specialized research [4]. Ambato, a key industrial hub in the Ecuadorian highlands, serves as a focal point for this analysis, offering insights into the complexities of electric public transport implementation in regions with varied terrain [5]. This study also seeks to address the lack of detailed projections regarding the impact of vehicle fleet transitions on local energy systems and infrastructure, particularly in Ecuador’s Highlands [6].

Mobility in the city of Ambato presents high travel times due to the continuous vehicular congestion generated on the streets and avenues, which brings with it high rates of vehicular congestion, a particular that increases year after year. According to the Sustainable Urban Mobility Plan of the city of Ambato, trips based on home to work represent 48%, followed by home-shopping trips at 17% and recreation at 12%. These values do not include trips to external areas, that is, to rural areas and neighboring cantons. For this reason, special measures need to be defined to improve mobility for the citizens [7].

Successfully transitioning Ambato’s public transportation system to electric vehicles will require careful planning and significant investment in energy infrastructure. Despite the topographical challenges, such a transition promises for both environmental and economic benefits. This research incorporates detailed projections of energy demand based on factors such as vehicle lifespan, charging infrastructure requirements, and the integration of renewable energy, thereby providing a comprehensive framework for electric vehicle deployment in Ecuador’s highlands.

2. Materials and Methods

The methodological framework begins with the collection of data, considering initial parameters and collection methods, followed by a detailed description of the study scenario [8]. Mathematical calculations were employed to determine the force required to move the bus fleet, ultimately yielding estimates of the energy necessary for large-scale integration of electric buses in Ambato, as illustrated in Figure 1.

2.1. Initial Considerations

This study evaluated the energy demand associated with the current conditions of Ambato’s urban transportation system, projecting future energy requirements as buses near the end of their useful lives. A thorough understanding of vehicle dynamics is crucial for analyzing energy demand, as factors such as vehicle mass, route characteristics, and terrain must be considered. The technical specifications of a standard bus model, widely used in public transport, served as a baseline for these calculations.

The initial considerations, based on the requirements of vehicle dynamics equations, involved key point coefficients and ideal parameters used in the calculations [9]. These also incorporated the technical specifications of a standard bus model commonly used by public transportation companies. According to data provided by the Municipal Government of Ambato, the HINO model AK chassis is the most widely used unit in the public passenger transportation sector.

In order to establish a mathematical model for energy demand analysis, the following variables were considered: slope data, number of vehicles, urban transportation route analysis, and the useful life of the vehicle fleet. In addition, the variability of speed and slope along the route, influenced by elapsed time, was incorporated in conjunction with the initial parameters outlined in

Table 1. Units for Magnetic Properties.

Characteristic	Symbol	Value	Unit
Mass bus bodywork	M	14,200	[kg]
Drag coefficient	Cd	0.73	[-]
Gravity	g	9.81	[m/s2]
Front area	A	7.42	[m2]
Air density (calculated)	pa	0.85898	[kg/m3]
Dynamic radius	Rd	0.54	[rad]
Pressure	p	71	[Pa]
Temperature	T	15	[°C]
Constant air	Ra	0.287	[kl/kg·K]
Slope	Ɵ	Variable	[rad]

below.

2.2. Data Collection

Data were collected using GPS (GPS Garmin Montana 65) devices to track route performance at one-second intervals. This information was processed using georeferenced software to determine critical variables such as speed, altitude, and route profile. The study scenario is further described in terms of Ambato’s topography, road infrastructure, and spatial dynamics, which play a fundamental role in shaping energy demand. The data were downloaded with DNR GPS software version 7.6 and processed with the following georeferenced information shown in

Table 2. Parameter obtained from Garmin GPS Montana 650.

Parameter	Unit
Speed	m/s
Altitude	[m.a.s.l.]
Weather	[s]
Latitude	°
Length	m
Data frequency	f = 1 [s]

.

2.3. Description of the Scenario Under Study

Owing to its strategic location, the city of Ambato possesses distinctive characteristics that set it apart from other cities in Ecuador. Furthermore, it is regarded as the logistical hub for the country’s commercial activities. It is located at an altitude of 2600 m, in a hollow formed by plateaus, settled on plains of volcanic deposits where the urban sprawl is growing rapidly, generating a dynamic of population displacement from rural to urban. Internal migration from the country to the city happened due to its commercial and manufacturing vocation that generates greater development of the territory [10]. Insights into energy consumption on slopes, including increased demand uphill and energy regeneration downhill, were applied to Ambato’s topographical challenges for more accurate energy assessments [11].

The morphology of Ambato has irregular slopes, which prevent urbanization and the generation of road infrastructure easily, as well as the passage of the Ambato River that crosses the city transversely, sectoring it into different platforms [10]. Figure 2 shows the distribution of slopes and existing urban transport routes.

In relation to urban mobility, the sustainable urban mobility plan mentions that 48% of Ambato’s trips are concentrated on public transport (buses, taxis, school transport, taxi routes, and informal vehicles), while 52% correspond to private transport (car, motorcycle, bicycle, walk, and other). Additionally, non-motorized or sustainable trips cover only 13% of participation (walking and cycling), while motorized or non-sustainable trips represent 87% of the total [7].

2.4. Calculation of Force Acting on the Wheel

The energy consumption of an electric vehicle is directly dependent on the force required to move its mass over a given distance within a specific time frame. As a result, vehicle dynamics play a crucial role in estimating the energy requirements for such vehicle force exerted on the wheel, which is determined by the sum of four key opposing forces: drag resistance, inertia resistance, rolling resistance, and slope resistance. These forces are essential components in the overall calculation of the vehicle’s energy and power requirements. Thus, the computation of both energy and power is intrinsically linked to the force exerted on the vehicle [12,13].

The calculation of the wheel force is the result of the sum of four forces that oppose movement: drag resistance, inertia resistance, rolling resistance, and slope resistance, which are part of the energy and power calculation of the vehicle. Consequently, energy and power calculation are part of the force of a vehicle [13,14].

It is possible to perform an energy demand analysis to find out what energy is required for a car to travel a given distance at a given speed at each time.

Table 3. Vehicular dynamics equations for propulsive force calculation [12,13].

Fx = Propulsive force [N]
${\mathit{F}}_{\mathit{x}}={\mathit{F}}_{\mathit{d}}+{\mathit{R}}_{\mathit{x}}+{\mathit{R}}_{\mathit{i}}+{\mathit{R}}_{\mathit{g}}$
Fd = Dragforce [N]
${\mathit{F}}_{\mathit{d}}={\displaystyle \frac{1}{2}}{\mathit{C}}_{\mathit{d}}{\mathit{\rho }}_{\mathit{a}}\mathit{A}{\mathit{V}}^2$	Cd = Coefficient of drag [-] ρa = Air density [kg/m3] A = Frontal area of the vehicle [m2] V = Velocity [m/s]
Rx = Rolling resistance [N]
${\mathit{R}}_{\mathit{x}}={\mathit{f}}_{\mathit{r}}\mathit{M}\mathit{g}\mathit{C}\mathit{o}\mathit{s}\mathit{\theta }$	fr = Rolling resistance coefficient [-] M = Mass [kg] g = gravity [m/s2] ϴ = Road slope [rad]
Ri = Resistance due to inertia
${\mathit{R}}_{\mathit{i}}=\mathit{M}\mathit{a}$	M = Mass [kg] a = Acceleration [m/s2]
Rg = resistance to slope [N]
${\mathit{R}}_{\mathit{g}}=\mathit{M}\mathit{g}\mathit{S}\mathit{e}\mathit{n}\mathit{\theta }$	M = Mass [kg] g = gravity [m/s2] ϴ = Road slope [rad]

details the equations involved in the wheel force analysis with all its considerations.

To calculate the positive energy required for the battery, the initial consideration is the torque demanded by the motor. Torque is a critical factor in determining the amount of energy necessary to propel the vehicle. Mathematically, torque can be expressed as a function of the longitudinal force acting on the vehicle, the rolling resistance force—accounting for the friction between the tires and the road surface—and the loaded radius of the wheels, which represents the effective radius under load conditions. These factors are combined in the following equation:

$${\tau }_x=F_x\times R_d$$

(1)

where the torque provided by the motor is τ_x

• force on the wheel is → F_x
• dynamic radius is → R_d

Once the torque demanded by the motor is obtained, the power output of the electric motor can be obtained as follows [7]:

$$P_m=F_x\times V$$

(2)

where P_m is the delivered power of the motor, F is the force on the wheel, and V is the vehicle speed.

Finally, it is possible to determine the energy required from the battery for the motor to operate against the route requirements by means of Equation (3):

$$E_x=P_x\times ∆t$$

(3)

where E_x is the energy required, P_m is the delivered power of the motor, and ∆t is the instantaneous time.

3. Analysis of Established Routes

In the analysis of the 13 established routes, the number of buses assigned to each route, as well as their respective operating frequencies, were taken into account, as shown in

Table 4. Evaluate routes with number of buses and circulation frequency.

Route	Buses [No.]	Laps Bus [No.]	Laps Total Route [No.]
1	11	8	88
2	22	2	44
3	11	9	99
4	11	9	99
5	10	5	50
6	21	4	84
7	18	7	126
8	29	6	174
9	26	7	182
10	25	5	125
11	16	7	112
12	16	7	112
13	15	5	75

. The analysis includes the number of daily trips each bus completes and the total number of cycles per day on the evaluated routes. This data allows for the precise determination of the energy required to complete the daily operational demands for each route.

The data regarding routes and operating frequencies were obtained from the transit and transportation administration of the city of Ambato, based on their contracts with private urban bus companies. This information is detailed in Table 4 and illustrated through the public transport route density map in Figure 3 [15].

All evaluated routes exhibit similar geographical and topographical characteristics, with both urban and rural transportation systems converging in the city’s central area and extending toward its peripheral regions. The altitude profiles of each route, derived from GPS data, were considered to account for the varying elevations across the city’s transport network.

Once the data were collected, the distances traveled along the evaluated routes were analyzed based on their operating frequency. Position, altitude, and instantaneous speed data were gathered to inform the mathematical model used to evaluate the energy demand for each of the analyzed routes.

The individual assessment of the routes provides insights into the maximum elevation points as well as the total distances the buses must travel to complete each circuit, as presented in

Table 5. Characteristics of evaluated routes.

Route	Altitude Max.	Altitude Min.	Distance Lap [Km.]	Distance Bus [Km.]	Distance Total [Km.]
1	2665.90	2522.20	38.20	305.60	3361.60
2	2853.50	2518.70	25.90	51.80	1139.60
3	2892.40	2470.10	38.20	343.80	3781.80
4	2800.60	2468.50	38.20	343.80	3781.80
5	2746.00	2479.50	38.20	191.00	1910.00
6	2707.30	2521.40	40.20	160.80	3376.80
7	2920.00	2553.70	31.00	217.00	3906.00
8	2844.70	2470.10	62.00	372.00	10,788.00
9	3025.10	2479.70	62.00	434.00	11,284.00
10	2729.80	2522.70	38.20	191.00	4775.00
11	2716.40	2469.50	18.20	127.40	2038.40
12	2862.10	2522.30	28.00	196.00	3136.00
13	2745.20	2561.30	35.40	177.00	2655.00

.

4. Results

The results indicate the energy demand for the evaluated routes, along with their respective performance metrics expressed in kilometers traveled per kilowatt-hour consumed. This analysis takes into account the distance per lap, the distance per bus per day, and the total distance covered by the buses assigned to each route, as illustrated in

Table 6. Distance covered and energy demand.

Parameter	Value	Unit
Distance covered (Lap)	493.7	[km]
Distance covered (Bus)	3111.2	[km]
Distance covered (Route)	55,934	[km]
Energy demand (Lap)	0.67	[GWh]
Energy demand (Bus)	4.23	[GWh]
Energy demand (Daily)	72.14	[GWh]
Performance (Average)	5.43	[km/kWh]

. Specifically, the cumulative distance traveled across all routes totaled 55,934 km, necessitating an energy consumption of 72.14 GWh per day. While the average energy efficiency of 5.43 km/kWh was notable, it revealed significant variations across the routes, primarily attributable to the steep gradients and challenging topography encountered.

Figure 4 presents the results derived from the analysis of the 13 evaluated routes, detailing the distance traveled on each route along with its corresponding performance metrics. The maximum distance recorded for a single route was 62.00 km, while the minimum distance was 18.20 km, resulting in an average distance of 37.98 km across all routes. The average energy efficiency of the buses was measured at 5.43 km per kilowatt-hour. However, this value exhibited considerable variation due to the differing slope characteristics inherent to each route.

After processing the data through the mathematical model of vehicle dynamics for the evaluated routes, the resulting consumption figures and their corresponding energy efficiencies were derived. The results obtained are presented in

Table 7. Daily Energy Demand (routes).

Route	Lap	Bus	Total	Performance
	[kWh]	[kWh]	[kWh]	[km/kWh]
1	37.78	302.24	3324.64	8.09
2	37.4	74.8	1645.6	1.39
3	66.2	595.8	6553.8	5.19
4	22.56	203.04	2233.44	15.24
5	78.94	394.7	3947	2.42
6	52.66	210.64	4423.44	3.05
7	68.06	476.42	8575.56	3.19
8	56.94	341.64	9907.56	6.53
9	47.82	334.74	8703.24	9.08
10	50.72	253.6	6340	3.77
11	76.68	536.76	8588.16	1.66
12	54.32	380.24	6083.84	3.61
13	24.12	120.6	1809	7.34
Total	0.67	4.23	72.14	[GWh]

.

These findings have critical implications for the design and deployment of charging infrastructure in Ambato. For example, routes with lower performance, such as Route 2 and Route 11, will require more frequent charging points to maintain operational continuity, increasing the energy demand and the strain on the city’s electric grid. Additionally, the projected daily energy requirement of 72.14 GWh highlights the need for robust energy planning and investment in renewable energy sources to sustainably support the transition to electric public transportation.

5. Conclusions

The variables analyzed in this research included instantaneous speed and slope, both of which were evaluated as functions of time and position, utilizing data obtained through GPS technology. These variables, in conjunction with the technical specifications of the transportation means and the ideal parameters for calculations, informed the vehicle dynamics equations used to ascertain the energy demand necessary for the operation of electric buses on the evaluated routes.

The data supplied by the city administration of Ambato, along with the comprehensive database generated during the study, facilitated an in-depth understanding of the public transport system’s characteristics, encompassing route distribution, frequency, and the geomorphology of the road network within the study area. The findings confirmed that altitude significantly influences energy demand, which is particularly notable given that the highest elevation recorded was 3025 m above sea level and the longest route extended 62 km.

This research aimed to assess the energy requirements for transitioning to electric buses within the unique topographical context of Ambato. The results corroborate the initial hypothesis that the city’s varied altitudes and slopes contribute to higher energy consumption compared to flatter terrains. Specifically, the findings indicate that uphill segments substantially increase energy demand, whereas downhill sections can facilitate energy regeneration, as evidenced by an average efficiency of 5.43 km/kWh. These results align with previous studies (e.g., Energy Efficiency and Emissions Reduction in Mountainous Regions, 2021), which demonstrated that the performance of electric vehicles in hilly regions is highly contingent upon elevation changes and road gradients.

In contrast to studies conducted in flatter regions, where energy consumption patterns are generally more uniform, the energy demands observed in Ambato are markedly higher on routes characterized by steeper inclines, such as Route 2, which recorded an efficiency of 1.39 km/kWh. This variation underscores the necessity for adapting energy and infrastructure planning to address the unique geographical challenges faced by cities like Ambato.

The findings of this study have substantial implications for policymakers and urban planners engaged in the advancement of electric mobility. The projected daily energy demand of 72.14 GWh for the city’s urban transport routes highlights the urgent need for robust energy infrastructure, particularly in areas with challenging topographies. Policymakers should prioritize investments in electric grid enhancements and the strategic placement of charging stations, especially in high-elevation zones where energy demands are amplified.

Moreover, the spatial dynamics of Ambato—where all routes converge in the city center—must be integrated into transport planning to optimize routing and minimize energy consumption. The model developed in this research provides a valuable tool for predicting energy demand in other cities across Ecuador, contingent upon the availability of similar data regarding transport routes and terrain. Additionally, planners should consider the advantages of incorporating renewable energy sources to satisfy the increased electricity demands without exacerbating carbon emissions.

This study contributes to a broader understanding of the energy requirements for electric public transport systems in cities facing challenging geographical conditions. It illustrates the necessity for tailored infrastructure solutions and adaptive planning strategies to ensure a successful transition to electric mobility in urban environments such as Ambato.