2. Electric Vehicles: History and Evolution
The Hungarian inventor Anyos Jedlik István is credited with inventing a system in 1828 similar to a skateboard powered by an electric motor [
25]. It was not until 1835 that the first electrically powered carriage-type vehicle, attributed to the English inventor Robert Anderson, was presented at an industry conference. It used a disposable battery powered by crude oil. The same year, Professor Sibrandus Stratingh (The Netherlands) together with his assistant Christopher Becker invented a three-wheeled EV [
26]. In the same year, the American inventor Thomas Davenport invented the first model EVs. These were primitive vehicles that barely reached 12 km/h, and are considered the first EVs [
27]. In 1837, the Scottish inventor Robert Davidson developed his own invented electric motor, performing tests on a model locomotive, and in 1841–1842 testing his motor in real vehicles [
28].
In 1860, the first rechargeable lead–acid battery was invented by the French physicist Gaston Plante [
29]. In 1882, William E. Ayrton and John Perry (England) developed a three-wheeled EV integrating two batteries, with the possibility of switching between them to control the vehicle’s speed [
30]. The first EV production is credited to Thomas Parker in 1884 [
31], coming before Karl Benz and his Motorwagen petrol-powered vehicle in 1886 [
32]. William Morrison (US) developed a six-passenger EV capable of reaching 23 km/h, in 1895 according to [
33] or between 1887 and 1890 according to [
34]. In 1897 the first electric taxi service was launched in London [
35]. In 1898, Ferdinand Porsche invented an EV called the P1, which is considered the world’s first hybrid EV, running on both electricity and gas [
36]. In 1899, the EV ‘La jamais contente’ exceeded 100 km/h [
37]. By the end of the 19th century, about 40% of vehicles in the US were battery-powered electric, with the rest powered by steam or gasoline. EVs were clean and comfortable, but batteries were inefficient and expensive, allowing for a distance of only a few kilometers and using a battery exchange model in which discharged batteries were removed and charged at service stations. Before the end of that century, Borland Electric’s EV travelled 100 miles from Chicago to Milwaukee, charging the batteries overnight and repeating the reverse trip next day [
38].
At the beginning of the 20th century, speed and range performance were similar in EVs and gasoline vehicles. In 1900, New York City came to have a fleet of electric taxis; between 1900 and 1910 there were 38% EVs, which worked without vibrations, although they had complex recharging systems. In comparison, 40% were steam cars, which had to wait almost 45 min to produce steam and constantly poured water, while 22% were powered by gasoline and were difficult to start, producing smoke and vibrations. There were also electric buses for public transportation; between 1909 and 1914, the Fritchle company sold almost 200 vehicles per year, guaranteeing 100 miles on a single charge. The sales peak was reached in 1912 [
39], with EVs arousing so much interest that Henry Ford and Thomas Edison partnered to study opportunities around a possible low-cost EV in 1914. However, due to the cumbersome electrical charging systems and parallel discovery of large oil wells, the consequent cheaper gasoline as well as the development of better ICEVs, promoted by Henry Ford in 1908 with his Model T, tipped the balance towards thermal propellants. In 1912, Ford’s mass production of vehicles meant that a gasoline car cost almost three times less than an EV [
40]. In the same year, the first electric starter was invented by Charles Kettering, eliminating the cumbersome hand crank when driving ICEVs. This combination of factors led to EVs disappearing in the US over the following decade [
41]. In Europe, Germany used EVs during the 1930–1940s. The EV concept was not rethought until the oil crisis in the 1970s, during which fuel costs increased dramatically [
42]. In 1969–1970, General Motors developed the GM XP 512E, an EV prototype for cities [
43].
In 1971, NASA used its Lunar Rover EV, with a range of 90 km and a speed of 13 km/h, for the Apollo 15 mission on the Moon’s surface [
39]. Another example of an EV from the same era was the 1974 Citicar [
44]. In 1976, Chevrolet offered the Electrovette [
45], and in the same year Volkswagen announced an Elektro-Golf, which had an external appearance similar to the original Golf GTi [
46].
In 1979, Chrysler presented the ETV-1 Electric Car [
47] and the Comuta EV [
48]. All these models were limited in both top speed (72 km/h) and autonomy (64 km). In the 1980s, various environmental studies determined that one of the most important causes of pollution in large cities was ICEVs [
49]. Electronics industries were called upon to improve battery capacity, with Nickel Metal Hydride (Ni-MH) batteries already on the market at the end of 1980s, and Lithium batteries with a much higher energy density very close to becoming a reality (1991) [
50]. Due to the pollution levels reached in these years, the California Air Resources Board made the decision that by 1998 2% of the cars sold should not produce emissions and by 2003 this should increase to 10% [
51].
In 1996, General Motors began full-scale manufacturing of the all-electric GM EV1. After thousands of units had been sold, the zero-emissions requirement was abruptly changed to low emissions, leading to General Motors pulling all EVs from the market. Toyota left some of its RAV-4 type models on the market. Officially, users were told that this action was due to the end of the useful life of the batteries [
38]. The next step was the development of hybrid vehicles, which supported a gasoline engine using Ni-MH batteries. In 1997, Toyota launched the Prius in Japan. In 2000, its distribution was expanded worldwide with great success, becoming the most sold hybrid model in the world [
52]. In 2007, companies decided to manufacture EVs again due to increases in fuel prices. In 2008–2009, Tesla built a 100% EV Tesla Roaster with a lithium battery and 320 km of autonomy [
53]. In 2010, a Daihatsu Mira was converted into an EV by the Japan Electric Vehicle Club, with the range exceeding 1003 km one charge [
54]. In the same year, the EV ‘Venturi Jamais Contente’ reached a speed of 515 km/h [
55]. At the same time, the ‘Lekker Mobil’ traveled 605 km from Munich to Berlin on a single charge of 115 kWh in real cooling/heating and traffic conditions [
56].
The Chevrolet Volt extended-range electric vehicle (E-REV) was launched on the market in 2010. In this technology the power transmitted to the wheels is completely electrical and comes from two sources, the first stored in the vehicle batteries and the second produced by converting gasoline to electricity [
57].
In 2011, the Nissan Leaf was declared the best car by the European Car of the Year awards [
58].
The Opel Ampera, commercially launched in 2011, offered plug-in vehicle capabilities while being an E-REV [
59]. In 2012 it was declared the best car in the European Car of the Year awards [
60].
In 2013, the Drayson Racing Technologies B12/69EV reached 330 km/h [
61]. In 2014, Nissan’s ZEOD RC reached 300 km/h [
62]. In the same year, the e-Golf was introduced on the market with a 24.2 kWh battery. In 2017, its capacity was increased to 35.8 kWh [
63].
In 2017, the Rimac Concept reached 1088 hp, comparable to the famous Bugatti Veyron with 1001 hp [
64]. In 2021, the ‘e-Miles’ was presented; designed for city driving, it is driven with a joystick, 90% of its parts are 3D printed, and it can be controlled via smartphone, with autonomous driving planned as well. [
65]. In 2022, the high-end ‘Lucid Air’ was sold with a range of up to 520 miles, surpassing the 500-mile range anxiety barrier for customers regarding EVs compared to ICEVs [
66]. Advances in 2023 and 2024 have been fundamentally dedicated to increasing autonomy, safety, and battery reliability [
67], deploying a greater number of fast chargers accessible to citizens [
68], and reducing the price of EVs to facilitate user uptake [
69].
5. Methodological Review of Existing Literature
A main aim of this study is to carry out a chronological analysis of review articles related to the incorporation of EVs in distribution networks and the integration of Renewable Energy Sources (RES) and management of these systems over time. Review articles include a summary of the most important advances at the time they were written; a meta-analysis of these will enable us to understand the evolution of technological research on the subject we are dealing with and make extrapolations for the future.
To conduct the study, we worked with the IEEE Xplore, Scopus, Google Scholar, and Web of Science databases, planning different levels of searches. In the first place, the search period was determined, finding 1973 as the first year in which an EV article was published within what we consider the modern era, as opposed to the historical context discussed in
Section 2. An iterative searching process was carried out including more terms and conditions, revealing a few articles containing the characteristics that we sought. In a first analysis (
Figure 11), we evaluated the literary production containing “electric vehicle and electrical networks” terms, along with their possible variations in the search rules, for any type of scientific article, finding the first references in 1976 (series in blue). A part of this methodology is based on [
104]. The next analysis added the “renewables” term to the previous search, obtaining the yellow series.
Regarding the blue series, two periods were detected: the first up to 2007, with scant scientific output, and another from 2007 onwards showing continuously growth until 2022. Looking at the chart carefully, as many as eight different trend changes can be observed:
1973–1975: There are no scientific ‘EV’ plus ‘electrical networks’ publications.
1976–1990: Small EV models were introduced into the market, with limited benefits because they used lead–acid batteries with low charge density. In this period, the effects on the power factor of the first EV chargers, which generated harmonics in the distribution network, began to be investigated [
105].
1991–1996: These were the years in which the GM EV1 was launched and the use of Ni-MH batteries with higher capacities and charge cycles became more widespread. There was interest studying the issue of charging peaks when many EVs were present in distribution networks, with some authors concluding that EV penetration greater than 20% could not be achieved due to the long battery charging time of 12 h [
106].
1997–2005: The launch and worldwide distribution of the Toyota Prius meant that Hybrid Electric Vehicles (HEV) began to take center stage. The study of BEVs and their impact on networks continued to advance, seeking to determine the impacts of different levels of EV penetration [
107] and fast chargers [
108] as well as how transformers are affected by EVs [
109].
2006–2010: Fuel price increases and the launch of the Li-ion battery Toyota Roaster BEV boosted the interest of researchers in EVs. The Vehicle-to-Grid (V2G) paradigm was defined for the first time [
110], which triggered profuse research on this subject. By the end of this period, almost all of the “classic” EV problems had been described.
2011–2016: BEVs along with hydrogen combustion engines and fuel cells, were considered a real solution for gradually replacing fossil fuel-powered combustion engines. Studies carried out at this point included “classic” EV problems, optimization of recharging [
111], cancellation of harmonics, and potential positive effects of EVs on the network [
112].
2017–2020: Topics related to network voltage stability through the automatic variation of battery charge current level were researched [
113] as well as the effects on EV generation and consumption in real time [
114]. The IEA report addressed EV results on charging at low demand times, the variable use of RES for reliability, and Demand-Side Response (DSR) aspects as a means of EV charging control [
115].
2021–2022: An acceleration in scientific output is observed during this period. Specific issues regarding how networks are affected by alternating EV charges with high-capacity batteries emerge as a new concept to be addressed [
116]. On the other hand, research on aspects such as the characterization of several EVs and their supraharmonic emissions [
117], which in previous years were not easy to predict, could be derived from observations of real technologies integrated at scale.
Regarding the yellow series, a logical reduction in the number of articles is observed, with gradual growth from 2006 until 2022, indicating that the three search terms have generated continually increasing interest in the scientific community.
Considering only review articles that already include consolidated progress from previous years in different aspects, the two series are shown in
Figure 12.
Both bar graphs correspond to Series 1 and 2 seen previously, but with only review articles included. For this study, we only selected the articles corresponding to the orange series, as it incorporates the criteria we searched for and the number of studies is manageable, allowing for selection of only the most appropriate. Our meta-analysis covered 2012 to 2022, comprising the years of greatest EV development and progression.
Up to four articles per year were selected proportionally to the number of articles published (except the first years), as indicated in
Table 2, based on quality, the content of the publications, the number of citations, and a reasonable geographical distribution reflecting the global research diversity, as can be seen from
Table A1,
Table A2,
Table A3,
Table A4,
Table A5,
Table A6 and in
Figure 13. The countries of origin of the selected review papers were USA, Canada, Colombia, China, Australia, India, Pakistan, Malaysia, Denmark, France, Sweden, Germany, Italy, and Spain representing an even distribution of knowledge from across the world.
Regarding the fields in which the authors of the review articles worked, 96.88% were researchers at their respective universities, while the remaining 3.12% worked in the electrical industry.
In this review, eight main categories were studied over time, as in our view they include the essential aspects of both EVs and their integration into electrical networks and RES effects. Certain categories were subdivided into other subcategories to collect concepts and details that may be interesting to follow and research. We show these elements in
Table 3, with the same colors subsequently used in the other graphs for data analysis.
Simulations: This category provides tools to predict future effects by simulating scenarios of interest, anticipating the best technical and economic solutions. Within it, four subcategories are considered: long term, which includes simulations to anticipate effects from several days to years; short term, for simulations ranging from milliseconds to hours or even a day; optimization algorithms, for the type of simulations that find optimal points of energy consumption, percentage of RES to include, and cost reduction strategies on the production and consumer’s side; and PHEV/EV penetration and electric system capacity. Simulations are carried out to determine how many EVs the electrical distribution system can accept according to the restrictions studied in each use case.
Technology: Includes technological aspects of EVs and their integration into the electrical network. Seven subcategories are considered: battery technology remanufacturing and recycling, which refers to batteries in general along with chemistry, autonomy, types, and recycling; chargers/charging stations, including research to make the charging network viable or to deal with speed, location, or harmonics aspects; fast chargers and related problems and solutions; wireless chargers and research into these systems; Vehicle-to-Grid/Home/Vehicle (V2GHV), Vehicle–Grid Integration (VGI), and Grid-to-Vehicle (G2V) research, which includes the aspects of bidirectional electricity transfer between the grid and EVs; electric motors, including constructive aspects; and finally electronics, including advances in power electronics related to inverters, converters, and control systems for the improvement of EVs.
Grid Impact: This includes papers that refer to the impact of EVs on the electricity grid. Five subcategories were selected: smart charging/charge management and location, including general design aspects of smart charger installation, topologies, and their best location; transformer congestion and line deterioration in distribution networks, including papers on power transformers and their potential loss of life due to the effects of EV connections; quality of electrical signal, which includes research related to this variable when EVs are incorporated into the network; stability of power systems and grids under increasing EV penetration; and demand response (DR), which collects knowledge related to interactions between users and their demand responses for different distributor strategies.
Cost, energy, and pollution savings of using EV instead of ICEVs: Several articles provide estimations or justifications around EVs in terms of energy savings and avoiding pollutants () and particles () compared to traditional ICEVs.
Emission reduction management in relation to ICEV without RES: This category includes articles in which conclusions are drawn about the reduction of CO2 emissions from incorporating EVs in cases without integration of RES in the system. Aspects such as better balancing of charging and storage capacity on the part of EVs in relation to ICEVs are included as well.
Smart Grid technologies: Articles referring to communication technologies, control strategies, and management of networks are included in this category.
EV and Wind Power: Contains five subcategories involving studies on the influence of RES and the relationship with EV penetration: increments in EV and wind power facilities, relating to both systems and their necessary joint growth; shortage and quality of production (RES penetration issues), determining classic challenges of RES penetration in grids and how EVs can help; energy efficiency vs. fossil fuel consumption, including studies analyzing EV energy efficiency with wind power models in comparison to fossil fuel vehicles; emission reduction management, including studies of how emissions are reduced by wind power usage; and operational cost management (users and companies), including economic studies on better management of EVs and wind power together.
EV and Solar Power: Includes studies considering the influence of SP and its relationship with EV penetration. Six subcategories are included, the first five being equivalent to those of the previous WP category but for photovoltaic energy (PV): increments in EV and solar facilities; shortages and quality of production (RES penetration issues); energy efficiency vs. fossil fuel consumption; emissions reduction management; operational cost management (users and companies); and an additional sixth category, energy management among EVs, PV, and HVAC, which considers studies incorporating both categories plus heat, ventilation, and air conditioning (HVAC) systems in buildings.
Our analysis of these categories is shown in
Table 4, with a tick indicating when a topic is discussed in the corresponding study.
Based on
Table 4, a study of the timeline was carried out to observe the evolution of the different categories related to EVs and study the scientific community’s interest in each of them. For each year, the percentages of total studies in each category are compared in
Figure 14.
S appears every year in the selected articles, showing variations between 8.33% and 30.00% (mean: = 17.60%, standard deviation: = 6.44%) with respect to those ones studied. Its behavior is oscillating, although present throughout the period. It can be concluded that the use of simulation for EV research in electrical networks has been persistent and continues to be so. Predicting system behaviors allows for their evolution and optimization, and is key to obtaining valuable results.
T is present every year, varying between 6.25% and 25.00% ( = 18.45% and = 7.00%) with oscillating behavior, although present throughout the period. The technological topics change during the years of study, with greater focus on electric motor technology in the first years and power electronics (inverters, converters, and control systems) for elimination of harmonics and better fast charger implementation in the later years.
GI is present throughout the period, oscillating between 12.50% and 25.00% ( = 20.42% and = 5.30%). All studies refer to this topic, whether in a simply introductory way or by developing it in more depth. The increase in network EV penetration and associated problems is a subject on which the scientific community agrees, appearing in many studies. The low level of confirms this fact, although the base search for articles was focused on this topic.
C&S presents intermittent behavior, with a range of values between 0% and 12.50% ( = 5.05% and = 4.99%). This topic can be segregated into two sets of research: studies seeking to justify the superiority of EVs compared to ICEVs in terms of energy and pollution in cases where the primary energy is not renewable; and studies in which it is assumed that EVs will eventually be used with a renewable mix.
E shows intermittent behavior, and does not appear every year. The range of values is between 0% and 13.04% ( = 5.45% and = 5.46%). Again, two types of research can be identified: studies investigating EVs as a solution to reduce emissions by themselves (i.e., without adding RES), and those directly focused on other aspects while assuming a future renewable mix.
SG oscillates between 0% and 25.00% ( = 11.58% and = 8.19%). This category is closely related to telecommunications and the Internet of Things (IoT) systems, and frequently involves the management of smart grids; therefore, the presence of high EV penetration shows important dependency on the GI category. Depending on the topics researched in each year’s reviews, authors may not specifically indicate this; nevertheless, any grid impact would be impossible without smart grids.
WP oscillates between 0% and 16.67% ( = 7.92% and = 5.96%). This essential category shows a two-way technical dependency between EV and wind power, with high penetration of both being possible.
SP oscillates between 0% and 25.00% ( = 13.55% and = 7.17%). This is an essential category for enabling high EV and solar power penetration.
To rank the categories and estimate their relative importance and consistency over time, we defined the following two key performance indicators (KPIs).
In both equations,
i is the corresponding category indicated in
Table 5 and
Table 6.
We ordered the previous categories based on these KPIs, with a higher value indicating greater relative presence on the part of a category and a lower the value indicating greater consistency and stability over time.
In
Table 5 and
Table 6, the first places (GI, T, and S) indicate the importance and temporal consistency for the subject and categories of study. Next, SP and SG have similar importance, with their order reversed by only one position based on each KPI. The next place corresponds to the WP category. Finally, the last positions are for C&S and E categories, which had the least impact on the studies.
Figure 15 presents the same type of analysis for the set of subcategories belonging to S, incorporating years and average frequency.
Table 7 orders the
S subcategories from greatest to least interest and consistency over time according to the KPIs described above. It can be concluded that the subcategory with the greatest research importance is that of optimization algorithms, which are the most interesting tool for researchers.
Figure 16 shows a similar analysis for the T subcategories. Using the previous method,
Table 8 shows the subcategories ordered from most to least important.
At a technological level, the most widely studied subcategories are ‘V2GHV or (VGI) or (G2V)’, with high research relevance. Well behind, the second subcategory of ‘Battery technology, remanufacturing, and recycling’ also accumulates a large amount of research in the study period. Solving technological problems related to both topics is essential for massive integration of EVs at the general level.
In the case of GI (
Figure 17), using the previous method,
Table 9 shows the subcategories ordered from most to least important.
Between the second and third rows of
Table 9, more weight was selected for transformer congestion and line deterioration in distribution networks versus stability of power systems/grid, as the relative variation of
(4.00%) was greater than that of
(3.84%). Due to these small differences, both subcategories are considered equivalent in importance. The most important variable is smart charging/charge management and location. This makes perfect sense, as most articles refer to controlled and scheduled charging strategies as well as optimal location of electrical infrastructure to optimize equipment, materials, and costs. This is arguably the most promising topic of study. The following two subcategories ensure that distribution networks can support high EV penetration and that the useful life of transformers will not be limited due to overloading.
We did not carry out this analysis on the categories that do not include any subcategories, as they were studied in the pie charts shown previously in
Figure 14.
In the case of WP (
Figure 18),
Table 10 is obtained by applying the same analysis to the data. The first two subcategories represent two different sides of the same reality, constituting the greatest challenges for this category, namely, that high penetration of EVs and wind power is not easy if carried out separately.
In the case of SP (
Figure 19),
Table 11 was obtained by applying the same analysis to the data.
As in the previous case, the most important variables belong to the first two rows, with both having considerable technological interdependence. While in the previous WP category both subcategories had practically similar values, in the SP category the shortage problem is less important compared to ‘Increments in EV and wind power facilities’, indicating that although combination of EVs and PV is necessary for high penetration of both technologies, the integration of PV itself is less reliant on EVs for higher penetration than vice versa. This seems reasonable, as in the case of homes PV production is normally lower than the needs of the home and when PV resources are available they are normally fully consumed, making it easier to decouple PV accumulation than in the case of wind energy.
With our analysis of the selected review articles completed, a meta-analysis of the technical content of the 32 selected articles was carried out, incorporating the results of each for the selected category where appropriate. In this paper, we only perform a meta-analysis of the grid impact category, leaving the rest of the categories for a future publication.