1. Introduction
Annual direct global greenhouse gas (GHG) emissions from transportation, measured in metric tons of carbon dioxide equivalent, are estimated to represent about 15% of total emissions across all sectors. Two-thirds of total transportation GHG emissions are associated with road vehicles [
1,
2,
3]. This vehicle category mainly includes light-, medium- and heavy-duty trucks and passenger cars. Thus, the electrification of vehicles is continuously developing to minimize their effect on the climate crisis.
In 2022, the global EV fleet’s total electricity consumption was approximately 110 TWh, equivalent to the Netherlands’ total electricity demand [
4]. Recent projections by the International Energy Agency (IEA) estimate that electricity demand for EVs will range between 950 and 1150 TWh by 2030. The first scenario is based on the stated policies and the second on the announced pledges. Under these scenarios, EV electricity demand is expected to represent 3.2% to 3.8% of final global electricity consumption. This translates to GHG emissions resulting from the electricity generation mix and the accelerated aging of distribution transformers due to overloading. Hence, the concept of self-consumption in road vehicles could play a critical role in the energy transition within the transport sector. Solar energy is the most convenient energy source for this role considering technical feasibility. The subject of this study is the examination and evaluation of different options for EVs’ power supply using PV technology.
Over the last few years, several studies have been conducted on the synergy between PV and EV systems. Many studies focus on the powertrain topology. The hybrid solar vehicle (HSV) concept, discussed in [
5], offers a solution for reducing emissions compared to hybrid electric vehicles (HEVs), which heavily rely on the electricity grid’s energy mix. It is also noted that simulations are valuable for assessing the economic impact of installing PV panels on vehicles. In [
6], an extensive review of possible configurations and standards of the connection between EVs and PV systems and related power converters is conducted. In 2017, Coffman et al. [
7] substantiated that Hawaii was not yet mature enough to adopt commercially available EVs at the time as a GHG-abatement strategy based on the life-cycle cost and greenhouse gas emissions associated with market prices and the status of the electricity generation mix of this year. However, they demonstrated that this issue could be resolved just by utilizing the solar energy retrieved during weekends for EV charging. They also suggested that policy measures are necessary to match EV charging with spatially and temporally varying RES.
Growing interest in the topic has expanded research into additional areas. Jiang et al. developed a solar-oriented route planning algorithm to balance energy harvesting and consumption in urban areas where available solar irradiance is unpredictable due to variable shading within the city [
8]. In [
9], three scenarios representing different methods of absorbing solar power for charging the electric buses of a university campus are compared. In [
10], PV-powered EV charging systems (EVCSs) are reviewed comprehensively, involving all possible modes of operation of a solar charging station, solar rooftops, vehicle-integrated PV (ViPV) systems, and solar parking lots. Mouli et al. studied a 10 kW solar-powered bidirectional EV charger compatible with two charging standards, namely, “Charge de Move” (CHAdeMO) and combined charging standard (CCS) [
11]. Focus is given to the power converters and an experimental prototype is developed and tested with a V2G-enabled Nissan Leaf and a CHAdeMO controller. Peak efficiency ranges between 95.2% and 96.4%, with power density three times higher than existing solutions at the time. Reference [
12] reviews the economic, environmental, and social benefits of solar-powered EV-charging networks, and suggests implementing a demand management price (DMP) rate beneficial to both utility services and EV drivers. Ding et al. [
13] proposed a reconfigurable PV array structure for an EV onboard system. Experimental testing of the proposed algorithms, through measurements of solar irradiance at five points on the vehicle, showed a 149% increase in system efficiency. Other research has studied optimal configurations and approaches for effectively charging EV batteries with energy recovered by PV panels [
14,
15]. Several applications of exploiting solar energy for vehicle charging have also been recently studied in the aviation [
16] and shipping [
17] sectors.
A few studies have focused on developing models of PV-powered EV powertrains, incorporating detailed physics modeling of the energy exchange between interacting components under varying external conditions. In [
18], a case study on utilizing PV panels to extend the range of EVs in Ukraine shows that 1.16% and 6.32% of the range of the New European Driving Cycle (NEDC) could be covered by solar energy during the winter and summer months, respectively. However, energy savings calculations are based on the available solar irradiance and the constant specific energy consumption per kilometer, as provided by the US Environmental Protection Agency (EPA) and NEDC, while the energy transfer between EV powertrain subsystems is neglected. Nivas et al. [
19] presented a simulation model of the energy processes of an EV charged by PV panels using MATLAB Simscape. Models of EV powertrain components are involved, including the PV array, the MPPT algorithm block, the power converters, the battery, and the motor. Although results show that AC and DC power, voltage, and current can be tracked at all system points, the data used indicate that these results do not correspond to any realistic solar EV application. In [
20], an onboard PV system for a plug-in EV is modeled, and the energy and economic impacts are evaluated among different weather conditions for five commercial vehicles that represent a range of design specifications. Results show that the daily driving range could be extended from 4.8 km to 100.6 km. Mobarak et al. [
21] conducted a detailed study on a solar-powered EV. Their research involved integrating a thin-film solar cell panel into an EV, considering the effects of panel tilt, vehicle direction, and partial shading on solar generation in urban environments. A solar EV (SEV) model was developed in MATLAB/Simulink. However, a simple single resistor circuit was used to model the EV battery neglecting its transient response. Simulations for Los Angeles and Detroit revealed that annual energy consumption was reduced by 21.5% and 17.5%, respectively. The daily solar range extension peaks at 47 km/day in Detroit.
This work aims to study and evaluate the use of solar energy for recharging EVs. Three cases are considered: a reference case of an EV charging directly by the national grid, a second case where EV can recharge by solar energy through mounted PVs, and a third case where solar power is supplied to the EV charger through residential PV panels. The analysis is conducted in Berlin and Los Angeles, cities with different weather conditions that are expected to significantly impact system performance. Moreover, these cities are selected because of the large share of EVs in their total vehicle fleet. Compared to the relevant literature, a dynamic SEV system model with an adequately accurate representation of each powertrain subsystem is developed. A customized driving cycle based on the worldwide harmonized light-duty test cycle (WLTC) is employed to simulate realistic EV operation. Dynamic modeling improves the accuracy of estimating EV energy consumption and state of charge (SoC). The energy consumption results are used to derive economic and environmental metrics and further evaluate the examined cases in both locations.
In summary, the key contributions of this work compared to previous studies are as follows:
Dynamic modeling of SEVs using accurate subsystem models and the advantages of the Modelica language;
Evaluation of system performance with a realistic customized driving cycle;
Analysis of system operation in Berlin and Los Angeles, two cities with high EV market share and different variation in solar irradiance and ambient temperature throughout the year;
Use of selected metrics to assess the examined solutions’ energy, environmental, and economic performance.
5. Conclusions
This study presents a comparative analysis and evaluation of technologically mature alternatives for recharging EVs using solar energy harnessed by PV systems. Three cases are examined. Case 1, where an EV is charged via a residential grid-connected charger; Case 2, which involves a solar EV (SEV) with mounted PVs; and Case 3, which utilizes a residential PV-battery installation to supply power to the charger. These scenarios are assessed in two cities with differing climates but high EV adoption rates: Berlin and Los Angeles.
The results indicate significant benefits for both cities when adopting Case 2 and Case 3 solutions. In Case 1, the annual grid energy consumption per 100 km is 23.9 kWh/100 km in Berlin and 23.8 kWh/100 km in Los Angeles. This value is reduced by 44% in Berlin and by 67% in Los Angeles for Case 2. In Case 3, the reduction is even more pronounced, with a 59% decrease in Berlin and a 98% decrease in Los Angeles. However, the reduction is limited in Berlin from November until February.
The use of an SEV also appears to reduce drivers’ range anxiety by providing on average an additional driving range of 20.3 km/day in Berlin and 30.4 km/day in Los Angeles. Environmental benefits are notable as well. In Case 1, the annual GHG emissions are 1522.9 kg CO2-eq in Berlin and 755.6 kg CO2-eq in Los Angeles. Case 2 reduces these emissions to 853.3 kg CO2-eq in Berlin and 624.5 kg CO2-eq in Los Angeles. With Case 3, the emissions are further reduced to a minimum of 624.5 kg CO2-eq in Berlin and 16.9 kg CO2-eq in Los Angeles.
Annual charging cost presents a higher reduction for Los Angeles, since the reference cost of USD 1186.8 for Case 1 is decreased to USD 394.3 for Case 2 and only USD 26.5 for Case 3. In Berlin, the charging cost of Case 1 equals USD 940, and lower costs of USD 526.7 and 385.5 USD are accomplished in Cases 2 and 3, respectively. While the initial charging cost in Berlin is lower than in Los Angeles, Cases 2 and 3 still incur higher costs in Berlin due to the lower availability of solar irradiance.
In all examined cases, the quantitative results of this study strongly depend on the use of the selected driving cycle. To overcome this limitation, future work could estimate the benefits or drawbacks of the examined cases for multiple EVs classified by their duty, distance, and behavior. Moreover, the study of SEVs with higher efficiency based on the specifications given for prototypes under development could accelerate their market entry. To assess the financial benefits of the examined cases, a long-term investment evaluation would be useful.