Transportation is one of the largest single sources of air pollution in urban areas. This paper analyzes a model of solarpowered vehicle sharing system using buildingintegrated photovoltaics (BIPV), resulting in a zeroemission and zeroenergy mobility system for lastmile employee transportation. As a case study, an electric bicycle sharing system between a public transportation hub and a work center is modeled mathematically and optimized in order to minimize the number of pickup trips to satisfy the demand, while minimizing the total energy consumption of the system. The whole mobility system is fully powered with BIPVgenerated energy. Results show a positive energy balance in ebike batteries and pickup vehicle batteries in the worst day of the year regarding solar radiation. Even in this worstcase scenario, we achieve reuse rates of 3.8 people per bike, using actual data. The proposed system manages PV energy using only the batteries from the electric vehicles, without requiring supportive energy storage devices. Energy requirements and PV generation have been analyzed in detail to ensure the feasibility of this approach.
Most countries around the world are trying to reduce their total fossilfuel consumption with the main objective of reducing their greenhouse gas (GHG) emissions, which are mainly responsible for global warming, climate change, and deterioration of air quality in cities [
Southeast view of building 42 before and after rehabilitation of the shell. (BIPV) © CIEMAT.
Nowadays, 50% of people are living in cities (reaching 69% in the European Union) and it is estimated that over 60% of the total world’s population will live in urban areas by 2030 [
ebike sharing system operators in Madrid (Spain).
Bike sharing can be used in pointtopoint trips, or it can be used combined with other transportation modes in cities, increasing the flexibility of public transport infrastructures. For example, one of the weakest points in a public transport system is the access (i.e., to reach public transport stations) and egress trips (i.e., from public transport stations to the final destination). Bikes can be used to cover these specific trips, reducing the doortodoor total travel time, making the combination of bicyclepublic transport more competitive compared to private motor cars [
Previous works demonstrated the feasibility and the economic relevance of introducing electric vehicles in lastmile urban logistics operations [
Some ebike sharing programs have emerged in different urban environments: that is, university campuses, such as the University of Tennessee (UTK), Knoxville Campus in the US [
The remainder of this paper is organized as follows: Section
As a case study, we analyze the feasibility of an electric bicycle sharing system between a public transportation hub and a research center in Madrid, Spain. This section provides a detailed analysis of this case, its constraints, and the estimation models for PV generation and total energy consumption of the mobility system.
The Center for Advanced Research in Energy, Environment and Technology (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas—CIEMAT) is a public research institution located at the very end of the Moncloa Campus in Madrid (Spain) and occupying 71,000 m^{2}. This integrated campus is shared with othereducational and research centers like Complutense University of Madrid, Technical University of Madrid, and some other partner institutions [
Global irradiation and solar electricity potential on horizontally mounted PV modules in Spain [
The Moncloa Campus is situated in the west side of Madrid, covering an area of 2 square kilometers, and it is linked to the rest of the city public transport network by a single underground station called
Moncloa Campus, Ciudad Universitaria underground station, and CIEMAT location.
To estimate PV generation, we use the online calculator from Photovoltaic Geographical Information System (PVGIS) [
CIEMAT research centers are distributed in more than 70 different buildings in Moncloa Campus. Among them, CIEMAT building 42, which is the headquarters of the Renewable Energies Division, has recently installed a peak power of 27.2 kWp of photovoltaic (PV) cells in the BIPV facades, occupying a total surface area of 176 m^{2} [
Schematic diagram of the BIPV modules installed in the south and the east facades.
In order to estimate the solar electricity production of this PV, in a similar way to [
Table
Average daily and monthly expected electricity production (kWh) from the PV modules installed on the two considered facades, obtained from the PVGIS software [
East facade  South facade  

Month  Ed  Em  Ed  Em 
Jan  24.10  748  13.80  429 
Feb  28.00  785  20.00  560 
Mar  27.90  864  28.30  878 
Apr  21.20  635  31.00  929 
May  16.40  507  34.70  1080 
Jun  14.00  419  37.70  1130 
Jul  15.00  466  38.40  1190 
Aug  20.60  639  35.00  1090 
Sep  26.50  795  29.10  872 
Oct  28.00  867  21.50  667 
Nov  24.90  748  14.80  445 
Dec  24.10  749  12.60  392 
Yearly average  22.50  685  26.50  805 
Total for year  8220  9660  
Total PV installation for a year  
17880 
The worst months for PV generation, due to the low daily irradiance, are December and January [
Average monthly expected electricity production.
Estimated solar energy generated at December day.
Most of the 1200 members of the CIEMAT staff at Moncloa Campus have a 40hour working week, between 8:00 and 17:00, Monday to Friday, but they usually have a flexible work schedule. From different surveys [
Analyzing the underground timetable for line 6, the interval between two consecutive trains from 7:00 to 10:00 is 4 minutes. Taking into account the arrival time information extracted from those surveys, it is assumed that CIEMAT employees will arrive according to the probability density distribution shown in Figure
Temporal distribution of CIEMAT staff members’ arrivals per time slot during the morning period.
As aforementioned, the distance between the underground station and CIEMAT headquarters is 1.1 km. ebikes are initially locked in the bike station located outside the underground station. During the morning trips, subscribers will release the ebikes from this dock station and will return it over an empty dock station located at the CIEMAT.
Assuming an average speed of 15–20 km/hour per ebike [
As soon as the underground bike station empties and the CIEMAT bike station fills up, an electric pickup truck will tow back these ebikes to the initial station. The estimated time for this shuttle and swapping operation is around 3.75 minutes (see Figure
ebike sharing system mobility description.
ebike specifications.
Battery tech.  Nominal voltage  Nominal capacity  Weight  Motor  Nominal power  Bike weight 

Liion polymer  36 V  360 Wh  4.6 kg  Brushless  250 W  20.4 kg 
To estimate the ebike average consumption, two different tests were performed. Firstly, different users rode an ebike during 3 months around the CIEMAT headquarters, measuring travelled distance, average vehicle speed, travel time, and energy consumed. From this data, energy consumption per kilometer was evaluated. Table
Electric consumption data from CIEMAT headquarters tests.
Distance  Time  Average speed  Energy consumed  Consumption 

1450 km  108.5 h  13.6 km/h  11.62 kWh  8.01 Wh/km 
A second test was performed over the real route between the underground station and CIEMAT headquarters. Two different users, with very different weight (122 kg versus 75 kg), travelled across this route at the same time, riding two ebikes. Table
Electric consumption from two different users over the same specific route.
Distance  Time  Average speed  

1.1 km  5 min  15 km/h  


Energy consumed  Energy consumed/km  Energy consumed/km/kg  
User 122 kg  12.15 Wh  9.34 Wh/km  0.076 Wh/km/kg 
User 75 kg  8.9 Wh  6.6 Wh/km  0.088 Wh/km/kg 
The objective of the designed mobility system is to satisfy the demand of CIEMAT employees between the underground station and work site in the morning and afternoon, minimizing the number of trips performed by the pickup EV trailer, defined by (
Optimization model parameters.
Symbol  Description  Value 


Time interval index, from 7:00–19:00  [1,720] min 

Charging time interval for ebikes, from 10:00–16:00  [180,540] min 

Passenger demand from A (metro station) to B (CIEMAT)  [ 

Passenger demand from B (CIEMAT) to A (metro station)  [ 

Pickup EV trailer capacity (number of ebikes which can be transported)  5 

Travel time from A to B on ebikes  6 min 

Travel time from B to A on ebikes  5 min 

Travel time from A to B of pickup EV  4 min 

Travel time from B to A of pickup EV  4 min 

Total number of ebikes available in the system  25 

Initial number of ebikes in the docking station A at 7:00 am  25 

Initial number of ebikes located at B at 7:00 am  0 

Total number of pickup EV available in the system  1 

Number of pickup EV located at A at 7:00 am  0 

Number of pickup EV located at B at 7:00 am  1 

ebikes parking slots in A  25 

ebikes parking slots in B  25 
Integer variables of the optimization model.
Symbol  Description  Value 


Number ebikes at A  (0,…,25) 

Number ebikes at B  (0,…,25) 

Number ebikes on pickup EV 
(0,…,5) 

Number ebikes on pickup EV 
(0,…,5) 

Number of pickup EV trailers at A  (0,1) 

Number of pickup EV trailers at B  (0,1) 

Number of pickup EV trailers traveling 
(0,1) 

Number of pickup EV trailers traveling 
(0,1) 

Number ebikes traveling from A to B  (0,…,25) 

Number ebikes traveling from B to A  (0,…,25) 
Equation (
Equations (
During the morning, the employees’ demand to travel, using their ebikes from CIEMAT to the underground station,
Equations (
Restrictions (
IBM ILOG CPLEX Optimization Studio v. 12.5.1.0 was used for solving the defined optimization problem, and MATLAB® was later used for analyzing and plotting the results.
It is assumed that a single pickup EV was used in this system. The optimal number of ebikes in this fleet is then evaluated running the optimization model and checking its convergence. If a feasible solution is not obtained, the number of ebikes is increased in one until convergence is reached.
With this procedure, the minimum number of ebikes in the fleet that can fulfill the mobility requirements was fixed to 25, as it is presented in Table
Once the minimum number of pickup EV and ebikes were set in the sharing system, the optimization algorithm was run to determine the minimum number of trips from pickup EV required to balance the ebike fleet. This value was 28 during each demand period (28 in the morning trips and 28 more in the afternoon).
The optimization model also determines the optimal moment to transport the ebikes from CIEMAT dock station to the underground station, avoiding ebike scarcity in this last dock station, and the exact number of bikes moved in each trips (it is an integer variable between 1 and 5). It is important to notice that at the end of the morning, all ebikes and the pickup EV are located at the CIEMAT in order to be recharged before the afternoon trips.
In a similar way, controlling charging and discharging of leadacid batteries is critical to extend the lifetime of microgrid systems [
From the previous mobility analysis, it is observed that during the morning period (from 7:00 to 10:00), the total number of ebike trips is 95. The energy required per bike during this period is 8.459 Wh/ebike, and the energy demanded by the ebike fleet during the morning period will be 803.605 Wh. The total energy demanded by the ebike fleet for the full day will be 1607.21 Wh.
The total number of trips by pick up EV trailer during the morning period is 28, consuming 5.544 kWh. The total energy demanded by the EV trailer for the full day will be 11.088 kWh, and the total amount of energy required by the proposed ebike sharing system (ebikes plus EV trailer) will be 12.7 kWh/day. Table
Energy demanded by the proposed ebike sharing system (ebikes plus pick up EV trailer).
Description  Value  Units 

Pickup EV trailer consumption  0.18  kWh/km 
Number of trips by EV trailer/day  56  
Total daily pickup EV trailer consumption  11.088  kWh 
ebike consumption  0.0077  kWh/km 
Number of trips by ebikes/day  190  
Total daily ebike fleet consumption  1.607  kWh 
Total daily ebike sharing system consumption 

kWh 
There is a charging station located outside CIEMAT building 42, which is connected to the PV panels through three 10 kW singlephase MPPT inverters. This charging station is composed by an AC level 2 charging point, which operates at 3.7 kW (230 V/240 V16 A) to charge the pickup EV trailer and 25 Schuko plugs type F (also known as CEE 7/4) protected by 5 singlepole 6A 230/240 V, 50 Hz, circuit breaker to charge the ebikes.
The complete ebike sharing system will be recharged during the midday period (10:00 to 16:00), when all CIEMAT employees are working and there is no demand for trips. At the beginning of the day, all ebikes are parked at the underground docking station and the EV trailer is empty and parked at CIEMAT. It is assumed that the initial capacity of the ebikes is 1 kWh (11.11% of the total ebike capacity), and the initial capacity of the EV trailer is 8 kWh (33.33% of the EV trailer battery capacity). The solar resource is not available early in the morning; therefore, it is necessary to have energy in the batteries of the pickup and ebikes to be able to perform the first morning trips. With these assumptions, we prove that, even in the worst day of the year, the remaining energy at the end of the day is even higher.
As soon as the employees start to arrive in the morning, they pick up an ebike from the docking station, returning it to the EV trailer in the CIEMAT headquarters. When the EV trailer is full, this vehicle will carry back the ebikes to the docking station located near the metro station. During these trips, all vehicles involved in this sharing system (EV trailer and ebikes) will spend energy. Figure
ebike sharing system hourly energy consumption.
The worst months for PV generation, due to the daily low irradiance are December and January [
Total ebike sharing system energy demand versus solar energy available for charging.
Figure
Charging process of the ebike sharing system.
Figure
EV charging and discharging process and SOC evolution.
Figure
ebike charging and discharging process and SOC evolution.
This study concludes that photovoltaics have a huge potential to satisfy the energy demand of mobility systems for lastmile employee transportation. The proposed system manages PV energy using only the batteries from the electric vehicles, without requiring any supportive energy storage device.
Taking advantage of the existence of a buildingintegrated PV system currently available at the workplace, we have analyzed and optimized an ebike sharing system fully powered by solar energy, providing a zero CO_{2} emission and zero grid electricity consumption system. To determine the total daily electric demand of the ebike sharing system, different tests were performed over real ebike models, evaluating the ebike consumption under several conditions. From the daily mobility requirements of several employees (traveling from the nearest public underground station to their common workplace), an optimization model was designed to size this ebike sharing system, determining the optimal number of ebikes and the minimum number of trips required by the pickup EV to keep the balance of ebikes in both dock stations. In addition, the pickup EV consumption was estimated based on real consumption information.
Related to PV generation, monthly and daily PV electricity production was estimated based on accurate solar radiation data, tilt and orientation data of the PV modules installed in each facade, and the inverter and solar module datasheets.
With all this information, it has been demonstrated that it is completely feasible to design a zeroemission ebike sharing system to solve the lastmile problem, completing the public transport system. This healthy solution will also allow reducing the GHG emissions in urban areas.
The authors declare that they have no competing interests.
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/20072013) under Grant Agreement no. 270833.