Analysis of the Correlation Between Electric Bus Charging Strategies and Carbon Emissions from Electricity Production

Abstract

Reducing carbon dioxide emissions in transportation has become a priority for achieving emission targets. Transitioning to electric vehicles significantly decreases global CO₂ emissions and reduces urban noise and air pollution. The selection of efficient charging strategies for electric bus fleets substantially influences their environmental impact. This study analyzes the charging strategy for electric bus fleets based on real operational data from Győr, Hungary. It evaluates the impact of different charging times and strategies on CO₂ emissions, considering the energy mixes of Hungary, Poland, Germany, and Sweden. A methodology has been developed for defining sustainable and environmentally friendly charging strategies by incorporating operational conditions as well as daily, monthly, and seasonal fluctuations in emission factors. Results indicate substantial potential for emission reduction through the recommended alternative charging strategies, although further studies regarding battery lifespan and economic feasibility of infrastructure investments are recommended. The novelty of this work lies in integrating real charging data with hourly country-specific emission intensity values to assess environmental impacts dynamically. A comparative framework of four charging strategies provides quantifiable insights into emission reduction potential under diverse national energy mixes.

1. Introduction

The effects of global climate change have led the European Union to focus increasing attention in recent years on reducing emissions of pollutants and greenhouse gases. The EU has constantly made rules to protect the environment [1]. Transport is responsible for 23% of global emissions, 70% of which are due to road transport [2]. Transport is responsible for 26% of CO₂ emissions in the EU [3]; road transport accounts for 71.7% [4], highlighting the importance of regulations in the sector. The emission values should be calculated for the whole life cycle of the vehicle, including the consequences of production, operation, and waste management [5]. In Hungary, 13% of total greenhouse gas emissions in 2021 were from the transport sector, which, although more favorable than the EU average, still required further reductions [6]. Another important global change in recent decades is increasing urbanization. In cities with growing populations and increasingly sized agglomerations, public transport has become a key element in meeting mobility demands [7]. The energy necessary to operate vehicles is generated by burning fuel in the case of internal combustion engine vehicles, which contribute directly to urban air pollution and greenhouse gas emissions [8]. Replacing conventional internal combustion engine vehicles with electric vehicles (EVs) can significantly reduce emissions of pollutants and carbon dioxide within cities. In China, the reduction of carbon emissions from transport in 80 cities with a population of over 1 million was analyzed between 2009 and 2014. Thanks to the spread of electric vehicles, emissions from urban transport have fallen by more than 16%, translating into an annual reduction of approximately 29 kg of CO₂ per citizen [9]. This effect can be increased if electricity is supplied using fewer carbon and hydrocarbon-based materials and based on renewable sources. In this context, electric vehicles can contribute to lowering urban and global air pollution and greenhouse gas emissions and minimizing the carbon footprint of cars [10].

Electric micro-transport devices such as scooters, electric bicycles, and motorcycles support the development of goods transport and personal mobility in urban areas. Lithium-based battery technologies are an important factor in the uptake of these devices [11,12]. Micromobility devices can typically replace cars in the United States, but they often replace public transport or walking in Europe. Depending on the local transport culture, these vehicles can be applied to reduce emissions [13]. Due to their small size, they can also improve the use of parking spaces in the urban environment [14].

Public transport plays an important role in urban transport. As part of the Smart Cities concept, modernizing public transport is also planned to create a more flexible system that can respond to passenger needs and compete with vehicle-sharing services [15,16]. Passengers expect convenient transfers, comfort, and punctuality when using public transport. These needs strongly influence the popularity of public transport [17,18].

An analysis of the energy consumption and emissions of rail and public road transport is needed to further develop these modes of transport for sustainability [19].

Buses are essential vehicles for urban mobility, but diesel buses produce significant emissions of pollutants such as NOx and soot, as well as local CO₂ emissions, which are present even in modern Euro 6 buses [20,21]. In a comparison of transport modes in Madrid, train transport produced the lowest emissions, followed by other track-based vehicles, such as metro and suburban trains. Buses were found to produce more pollution. Motorcycles and cars could have the highest environmental impact per passenger [22].

In this context, the acquisition and deployment of electric buses has been a priority in several countries [20]. Among urban public transport vehicles, the electrification of buses could achieve the most significant results in terms of CO₂ emissions [23]. Electric buses also have the advantage of low noise emissions compared to buses with conventional powertrains. This characteristic also benefits the roadside environment [24,25]. In the case of electric-only vehicles, the emissions from electric buses may be lower than those from trams and trolleybuses. The environmental impact of public transport solutions may be lower than that of electric cars [26].

A study on the lifetime CO₂ emissions of Shenzen’s electric buses highlights the importance of the electricity generation method in well-to-wheel emission assessment [27]. Researchers studying the city bus network in Liuzhou, China, have found that when diesel and electric buses are used together, electric buses are preferred in the city center’s high-passenger-volume areas, and diesel buses are preferred in the suburbs or on routes with lower passenger volumes. This would reduce the emissions of the entire fleet [28].

When assessing the emissions of electric vehicles, the source of the electricity needed to charge the batteries should also be considered [29]. Electricity production structure varies by country and is called the energy mix [30]. The most important criterion for assessing the energy mix is the CO₂ emissions required to produce 1 kWh of electricity [31].

The natural characteristics of each country strongly influence the composition of the energy mix [32], such as hydroelectric power plants that generate electricity in a more environmentally friendly form than [33] Poland or China, where the largest energy source comes from burning coal. In countries that produce electricity in this way, the carbon footprint of electricity is significant, and reducing it could be necessary [34,35]. Nuclear power plants operate with low CO₂ emissions, but managing and storing the nuclear waste generated requires great attention [36]. Renewable energy sources, such as hydro, solar, and wind, are the most beneficial, but they are not always permanently available, and in some countries, infrastructure, economic, and administrative problems make it difficult to integrate them into the electricity system [37]. The life cycle emission study of combustion engines and electric vehicles in China demonstrates the importance of the energy mix. If the sources of electricity generation are not environmentally friendly, the emissions of some pollutants from electric vehicle use may exceed emissions from internal combustion engine vehicles. However, this problem could be eliminated by modernizing electricity generation [38].

When charging infrastructure is planned or expanded, the availability of buses and the cost of electricity should be major considerations. By carefully designing the location of charging stations and charging times, the overall cost to the operator can be lowered by higher construction and vehicle procurement costs, as electricity can be purchased at higher costs during peak hours [39].

The availability of public transport in urban areas can be reduced by the length of charging times for electric buses. The length of charging times can be influenced by several factors, such as the power of the chargers, the state of charge, the size of the battery, the external temperature, the BMS (Battery Management System) of the vehicle, and the daily consumption values, which also depend on the drivers’ capabilities [40].

The charging options can be broken down into three broad areas. Conductive charging, like the plug-in charging used for overnight depot charging and pantograph charging, provides a direct connection between the vehicle and the electrical grid. A third option is battery swapping, which can take a few minutes to restore the vehicle’s range. Batteries can also be charged using slow charging, which improves battery life. The most advantageous option is the possibility of using renewable natural resources. The infrastructure needed to implement it could be expensive [41,42]. Wireless charging would also allow charging at bus stops and parking areas. Smaller battery packs would be enough to install in the vehicles, allowing the design of lighter buses. BEB could, therefore, be less energy-consuming with increased efficiency and reduced emissions [43]. The disadvantages of this method are higher installation costs and efficiency issues of energy transfer through the air gap [44].

Charging electric buses with high-capacity chargers is recommended to shorten recharge times due to the high battery capacity. However, fast charging generates more heat, which negatively affects the charging rate [45]. High-power charging systems are often limited, as high power requirements can lead to grid problems, so they cannot always provide their rated maximum power [46]. By reducing the size of the battery, the uptake of electric buses could be helped, reducing their current high purchase price. The ultra-fast charger has the advantage of being able to provide 400–500 kW of charging power, which it can operate at 97% efficiency [47]. Repetitive ultra-fast charging can degrade the useful capacity of the battery [48].

It is helpful for operators to compare bus batteries’ overnight charging and opportunity charging costs. Bus purchase costs are higher for overnight charging as they require batteries with higher capacity and weight. Night charging can be achieved with slow-charging equipment, which has a lower purchase cost. An opportunity charging system allows the purchase of a bus with smaller-sized and lighter-weight batteries, but fast charging requires higher-cost infrastructure and can accelerate battery wear. In terms of life cycle costs, night charging typically has a lower overall cost due to the lower price of night electricity in dynamic pricing [49]. Integrating solar panels into the charging system can also reduce the peak load on the electricity grid and the cost of electricity supplied by charging during peak periods. The deployment of the system may require the integration of additional energy storage due to the weather-dependent properties of solar energy [50].

Further developments in charging technologies are expected, and new charging methods may become possible. V2V wireless charging could allow BEBs to transfer energy to each other as they move, but further improvements in the efficiency of wireless power transfer are necessary to make this method more attractive. To replace stationary charging, Mobile Charging Vehicles (MCVs) could also provide charging to buses when needed, but at a higher cost and with lower efficiency than currently used systems [51].

Charging infrastructure can be grouped according to different standards. In Europe, the IEC 61851 standard distinguishes between four charging modes, the first three being AC charging. Mode 1 is not recommended for EV charging; Mode 2 is single-phase, and Mode 3 is three-phase charging. Mode 4, which operates on DC, is capable of 400 kW of power according to the standard. The SAE J1772 standard divides DC charging into two parts: below 80 kW and between 80 kW and 400 kW. The couplings vary by type of current and by region of the Earth [52].

The charging temperature of lithium-ion batteries has a significant impact on their efficiency. At temperatures below 15 °C, the batteries require heating to allow appropriate amounts of lithium ions to move through the electrolyte at sufficient speed during charging, and at low temperatures, the risk of dendrite formation increases. At temperatures above 40 °C, the heat generated during charging and usage can lead to the risk of fire, explosion, and short circuits [53].

Increasing the charging performance may negatively affect the lifetime of batteries as the heat generated during fast charging could lead to internal polarization, resulting in higher self-discharge, which could cause an increase in energy consumption of up to 20%. Due to their faster degradation, fast-charged batteries may need to be replaced multiple times during the bus’s lifetime, potentially at considerable cost [54].

Battery failures can be divided into five primary causes. Mechanical failures include connection faults and deformations caused by vibration, bumping, or manufacturing defects. Electrical faults comprise short circuits, overcharging, over-discharging, and insulation problems. Thermal faults include excessive temperature rise, cooling system, and thermal runaway. Inconsistency faults are mismatches between battery cells that may cause faster aging and loss of capacity. Ageing faults are performance degradation as the battery naturally wears out [55]. In the case of battery systems used in vehicles, cell-by-cell diagnostics can be used to detect faulty cells during real-life operation of the vehicle [56]. In the case of bus fleets, the loss of driving range due to battery degradation can affect bus scheduling [57].

A literature review has shown that the number of electric buses is currently growing globally. The production of electricity for charging buses varies by country. These differences can be identified by analyzing the energy mix. The energy mix determines the emissions from the operation of electric buses. Various charging strategies can have different benefits in other countries. This research aims to compare charging strategies based on charging data from a real fleet of vehicles. The analysis will consider the charging options best suited to the fleet’s schedule. This study focuses on finding the bus fleet charging strategy with the best GHG emission values based on the energy mix of a specific country. Section 2 presents the real data used for the analysis and the energy mix of four European countries in 2024. Section 3 analyses the impact of four charging strategies for the four countries. Section 4 focuses on the adaptability of charging strategies. Section 5 summarizes the results of the analysis.

2. Materials and Methods

The energy data used to charge the buses come from 2024 in Győr, where the fleet includes 13 BYD K9UD e-buses in new condition. The charging point for the vehicles is located at the public transport company. The buses will arrive at operating hours and start charging; it is important to note that the driver changeover will occur at the site during the day. Charging is mainly achieved at night when approximately 100% of the buses are loaded and ready for the morning departure. In addition, a shorter charge is broken for the afternoon driver change. Charge-related data were collected using the FleetLink data communication system. For the analysis of energy consumption, data lines containing values with charging powers exceeding the nominal charging power of the charging stations were filtered out. These typically concerned shorter periods where power outages could have caused interruptions in charging.

Figure 1 shows the average daily charging energy consumption in kWh of the BEB (battery electric bus):

Based on the data, a bus’s average daily charging energy demand is 210.41 kWh, which is used as a comparison. The energy consumption of BEBs ranges between 189 and 231 kWh. There are several reasons for the different daily charging energy, e.g., buses have different schedules and routes, drivers have different driving styles, etc. Figure 2 shows the electric buses’ monthly aggregated charging energy in kWh in 2024. The consumption values in Figure 1 were divided by the days when the buses were charged. Due to weekend operation, the actual days of bus use may not correspond to this. For these reasons, all days in the year 2024 have been considered for the values defined later, as the days of charging and the days of operation of the buses are not necessarily equivalent.

According to Figure 2, the average annual charging energy in 2024 amounted to 54,959 kWh. Charging energy consumption shows several variations over the year. In the spring months (especially April and May), the charging energy is relatively lower, possibly due to fewer electric buses in service (possibly due to maintenance or a reduced number of routes). After July, a significant increase is observed, reaching a peak in August and December. The reason for the increased energy demand is, on the one hand, that more BEBs were in use and, on the other hand, that air conditioning and increased urban traffic in summer increase the consumption of buses. In winter, heating systems can increase consumption. The bus fleet follows an overnight charging strategy, a widespread operating practice for electric vehicles. As the buses are not in service at this time of the day, the electricity grid is less loaded, and electricity is cheaper at night; the night depot charging method is currently more economically advantageous for the operating company.

Based on annual data, Figure 3 shows the number of charges per minute of the day averaged over a bus. The primary charging period is between 22:00 and 5:00 when the use of charging units is highest. Buses return to the site at the end of the evening shift and typically finish charging in the morning. Charging activity increases around 22:00 and peaks between midnight and 1:00. The second peak in charging occurs between 12:00 and 14:00 when a small but distinct midday charging surge is observed. This occurs from shift routes (driver change) for buses that may need additional charging during the day to complete the afternoon commute comfortably. The driver’s driving style on the morning shift also influences the need for and the amount of charging during the day. There can also be significant differences in consumption between individual drivers [40]. This can be important on longer routes or during periods of higher energy consumption (e.g., in winter). During the rest of the day, charging activity is significantly lower, as this is when most buses are in service. The nighttime peak is similar each month. In winter months (December, January, February), higher night charging activity is observed, probably due to higher energy demand for heating. Furthermore, it is also observed that very minimal charging times are seen outside of night and shift changes; these are likely to be on days when servicing or repairs are being carried out. The other conclusion is that there is no charge for bus drivers during the mandatory 1 h rest period after 4.5 h. The energy needed to charge the buses per hour was also analyzed using fleet data. For the analysis of the energy consumption of the charges, outliers (very short values, spikes) were eliminated from the charge data. Based on the filtered database, the average values of the battery charging power in different months were observed.

Figure 4 shows that the charging initially starts at around 80–90 kW, with relatively stable charging power for the first 2.5 h and only a small drop. After 2.5–3 h, the power drops sharply. The duration of the charges is maximized at 5 h. The data show that some overnight charges lasted longer than this, but the battery has a very low power demand. A further influencing factor may be that the grid load is not the same every month. In winter, the electricity grid may be under higher load (due to heating demand), which may affect the available charging capacity. In summer, a lower load on the grid may allow higher charging performance. Accordingly, in winter, the charging power starts at a lower initial value, especially in January, when it remains below 80 kW. The lower winter performance may be due to the increased load on the electricity grid, which is caused by the use of electricity for home heating and the heating needs of the batteries during charging in cold temperatures. The charging power is highest in summer, especially in the first 2 h.

The data used to calculate electricity emissions are from electricitymaps.com. Their data sources are freely available databases provided by governments, international organizations, electricity grid operators, and utility companies worldwide. The source for the European country data presented is ENTSO-E, the European Network of Transmission System Operators for Electricity. The association has 40 members from 36 countries. It is responsible for ensuring the interconnected operation of the European electricity grid. These data are used to determine the composition of electricity generation in each country, with CO₂ values determined primarily using the principles of the IPCC (2014) Fifth Assessment Report, but also working with emission values from other sources for some energy sources. Data treated in these terms are available to subscribers of this website. The calculation method used was a real-time, consumption-based carbon accounting system based on the flow tracing technique [58]. The method traces the physical flow of electricity from the generator to the consumer and determines the proportion of a certain generator’s output that reaches different consumers on the European electricity grid, taking account of imports and exports between countries [59]. It assigns a specific emission value (gCO₂eq/kWh) to each production technology and country, calculated based on the IPCC 2014 greenhouse gas emission factors [60]. The mix of energy consumed in each country is modelled based on local production and imports, from which the actual carbon intensity of consumption is determined. This method allows for a more accurate and time-sensitive CO₂ accounting, particularly due to cross-border energy flows.

For the analysis, the hourly CO₂ equivalents of the energy mix of different countries were used. Later, these data were also associated with the charging times of buses. Figure 5 shows the monthly average carbon intensity (gCO₂/kWh) of Hungary’s electricity generation in 2024, broken down by hour [61].

The values show significant daily fluctuations, indicating variable use of different energy sources. Emissions are stagnant between 0:00 and 5:00 in the morning, and there is a start to increase in the morning hours, with a spike between 7:00 and 9:00, which is associated with an increase in residential and industrial energy consumption. A slight decrease is observed in the early afternoon between 12:00 and 14:00, indicating the impact of solar panel production. During the afternoon peak, the highest emission values occur between 16:00 and 20:00, which is related to the increase in peak period energy consumption. During the night (22:00–5:00), emissions fall as energy demand decreases and the load on fossil power plants is lower. The change in carbon intensity during the day reflects the impact of renewable energy sources, particularly the increasing role of solar energy. Carbon intensity is higher in winter, especially in the morning and evening. This is due to higher heating demand and the limited availability of weather-dependent renewable energy sources. More fossil energy sources are used to compensate for the lack of capacity. Emissions are lower in the summer months, especially during daylight hours, which may be due to higher solar power generation, which reduces the use of fossil fuels. However, peak air conditioning loads (in the afternoon) may increase emissions slightly. An intermediate situation is observed in spring and autumn. The decrease during the day is more moderate than in summer, but the evening peak is still significant. The reduction in CO₂ intensity during the day suggests that solar energy plays an important role in Hungary’s energy mix. The minor role of wind energy is indicated by the fact that emissions do not show a clear daily trend that is adjusted to wind patterns. During the evening and morning peak hours, greater involvement of fossil power plants is needed to provide electricity, which is provided by gas and coal plants. Furthermore, the fact that Hungary imports more electricity, mainly from neighboring countries, is an influencing factor; if the imported energy comes from fossil sources, it may increase the CO₂ intensity at certain hours.

The seasonal differences can be seen in the carbon intensity of electricity generation illustrated in Figure 5. In summer (June–August), CO₂ intensity is lower, especially at midday, which can be explained by the increased use of solar energy. Conversely, higher emissions are observed in winter (December–February), especially in the morning and evening peak periods. This is due to increased heating demand for electricity and limited solar energy production. More balanced values close to the average were observed in spring and autumn.

Figure 6 also shows the carbon intensity of Hungary’s electricity generation (gCO₂/kWh) in 2024, broken down by hour [61]. However, the following figure shows CO₂ emission values based on LCA (Life Cycle Assessment). Direct emissions consider only operational emissions. In contrast, the Life Cycle Assessment includes greenhouse gas emissions related to the production of energy resources and the construction and maintenance of hydroelectric, solar, and nuclear power plants [62].

LCA-based emission values are higher in all periods, especially during peak periods. Hourly emission patterns remain similar, but LCA-based values are more indicative of long-term environmental impacts. Nighttime emissions are lower than in the previous graph, as the LCA method considers energy sources’ total production and life cycle emissions. Seasonal variations are still observed but are smaller, as the LCA method is less sensitive to daily and seasonal fluctuations than the operational data. Emissions were also analyzed for different countries, comparing the energy mix of a total of four countries in the European Union:

Figure 7 shows the average hourly carbon intensity (gCO₂/kWh) of electricity generation in Hungary, Poland, Germany, and Sweden in 2024 [61,63,64,65]. It is important to note that the figure shows the annual average of hourly values, but more detailed monthly (and hourly) data have been used in further calculations. Based on the values, it can be observed that Poland’s electricity generation is the most polluting, averaging between 522 and 723 gCO₂/kWh. The emission curve decreases to around 520 gCO₂/kWh during the day but can be above 700 gCO₂/kWh at night. The reason for the high emissions is that a large part of energy production is coal-based, with minimal renewable energy and no nuclear energy in the energy mix. In Germany, on average, 1 kWh of electricity production generates 200–330 g of CO₂ emissions, which is significantly lower than in Poland. Emissions are lower during the day due to high solar generation, but higher in the afternoon and evening when fossil fuels must be used to cover peak load. Hungary’s electricity generation averages between 140 and 240 gCO₂/kWh, which is lower than Germany’s but still significant. During the morning and afternoon peaks, emissions are higher due to the start-up of fossil fuel-based power plants. The lowest values occur during the day when solar energy is more important. Hungary’s energy production is mainly based on nuclear and natural gas, and renewables are increasing. The average direct emissions from Sweden’s electricity generation are 1–2 gCO₂/kWh, the lowest of all the countries studied. Sweden mainly uses hydropower, nuclear power, and wind power. Emissions are consistently low throughout the day, with no significant increase or decrease during peak periods. The LCA-based carbon intensity values for the four countries mentioned above are shown in Figure 8:

The LCA methodology considers the full life cycle emissions of energy sources and shows higher CO₂ intensities than direct emissions in all countries studied. Based on these data, Poland also has the highest emissions, ranging from 578 to 780 gCO₂/kWh. This indicates that coal-based power generation is highly polluting during operation and its life cycle. According to LCA data, Germany’s electricity generation emissions range from 255 to 384 gCO₂/kWh, significantly more than the values calculated on an operational basis. This is because emissions from producing and installing renewable energy sources (wind, solar) are not negligible. For Hungary, the previous direct emission values were 140–240 gCO₂/kWh, while the LCA values were 185–275 gCO₂/kWh, i.e., on average, 40–50 gCO₂/kWh higher. This shows that the country’s nuclear and gas power plants have significant emissions at the life cycle level. In Sweden, the LCA indicates around 14–16 gCO₂/kWh; although nuclear and hydropower have almost zero emissions during operation, the production and infrastructure of the power plants still produce emissions.

An earlier study on the same electric bus fleet in Győr found that the fleet’s energy consumption is influenced by a number of factors, the most important being the driving style, the climatic conditions, and the use of air conditioning. A maximum difference of 31.85% was observed between the most and least efficient drivers, with a standard deviation of ±7.21% for the average consumption of the drivers studied, confirming that driving style impacts energy demand. Looking at the temperature effects, it was found that the lowest consumption was in the 15–20 °C range. The energy consumption measured in the −5–0 °C range was +30.8% higher, while the 0–5 °C band was +19.3% higher, and the 10–15 °C zone also showed an 8.6% higher consumption. Constant use of air conditioning caused a 9.34% increase in energy consumption during the coldest January period. [40]

In cold weather conditions, there is an approximately linear relationship between the temperature decrease and the increase in consumption caused by heating the passenger compartment and batteries. The fleet data from Győr support the correlation presented earlier, and an analysis in Finland, where cold winters can cause an average increase in consumption of up to 45% [66]. In an Italian case, a summer air conditioner could cause a 20% increase in consumption in a warmer climate [67]. In Qatar’s desert climate, where temperatures above 40 °C may occur, the energy demand of electric buses could increase by up to 50% due to air conditioning [68]. Extreme climates could cause a significantly higher increase in energy consumption.

The bus fleet in Győr operates on urban routes and some agglomeration routes. The type and length of routes did not show a clear correlation with the energy demand of the routes. Important to note that these measurements are only for Győr; other cities, climatic conditions, and buses may have different values [40]. Figure 9 illustrates the factors affecting the emissions of the battery electric bus fleet.

3. Results

The consumption of electric buses is based on the values for Győr, as described in detail in the previous section. The emission values for each country and the corresponding calculation methods are also based on those defined in Materials and Methods. Figure 10 shows the CO₂ emissions related to the production of electricity consumed by the electric bus fleet for the energy mix of four different countries, Hungary, Poland, Germany, and Sweden:

The CO₂ emissions from operations in each country are presented monthly, and CO₂ emissions are given in tons. Poland (black line) has the highest CO₂ emissions in all months, mainly due to coal-based power generation. In August and November, peak CO₂ emissions from fleet energy use are above 50 tons for Polish use. The CO₂ emissions from fleet electricity use in Germany are medium, with visible seasonal variations. Lower emissions are observed in the summer months due to higher use of renewable energy sources. For the Hungarian operation (blue line), CO₂ emissions are relatively balanced throughout the year. In Sweden, the CO₂ emissions of the electricity used to operate the fleet are minimal in all months of the year, which means a clean energy mix. The figure shows that the green status of a fleet of identical electric buses is relative and that the environmental impact can vary significantly from country to country depending on the composition of the energy mix.

The order of the CO₂ emissions from fleet operations between countries remains the same when analyzed using the LCA method, as illustrated in Figure 11. This shows that the basic characteristics of the energy mix of countries continue to determine the fleet emission values. Although the values increased due to the LCA analysis, the seasonal trends and peaks remained, so the seasonal energy source distribution did not change significantly even when the life cycle analysis was considered. The energy mix values for Sweden also represent the lowest emissions when LCA analysis is used. LCA analysis is essential to accurately determine the carbon footprint of electric vehicles, as infrastructure and electricity generation emissions associated with energy production are significant factors.

The four different strategies presented in Figure 12 define the charging curves of the electric bus fleet. The main objective of determining the charging strategies was to minimize the potential impacts on the current bus schedules.

Charging Strategy #1 was created by summarizing current charging data. It primarily uses night depot charging. During night hours, the load on the electricity grid is lower, and electricity costs are more affordable. In addition, occasional daytime charging has also been implemented. This strategy has the advantage of lower energy costs and fewer times to connect to the charger. The flexibility of using buses during the day is limited as the range of the buses is mainly determined by nighttime charging.

Charging Strategy #2 also operates nightly depot charging and defines regular, pre-scheduled charges during shift changes. As observed in Figure 3, during shift changes, it is possible to charge the buses for approximately two hours. This period corresponds to the most favorable time for solar power generation, playing a significant role in the Hungarian energy mix. Charging Strategy #2 was designed to benefit from this. Charging in this case was assumed to occur between 12:00 and 14:00. This approach provides greater flexibility in daily operations, as charging during shift changes provides a longer daily range than Charging Strategy #1. Regular charging results in a predictable energy demand, which can lead to improved energy management. The disadvantage of this method is that charging during the day can be more expensive. Furthermore, synchronizing charging during shift changes may be a logistical challenge.

Charging Strategy #3 will further increase the range and safe operation of buses by introducing short, regular charges during in-shift breaks in addition to shift changeover times. Currently, drivers take a mandatory one-hour break during their shift, as required by law, after approximately 4 h of work, and then resume their shift. During these rest periods, buses could be charged regularly, during sunshine hours for most of the year, helping to use electricity from solar panels and reduce the carbon footprint of the bus fleet. This was the rationale behind Charging Strategy #3, setting the times for additional charging during shifts between 8:00 and 10:00 and 18:00 and 20:00. The Charging Strategy #3 method also requires more logistically complex and precise scheduling planning. To implement it, it may be necessary to build charging points at stops that are currently not provided.

For Charging Strategy #4, an idealized situation has been assumed, where the battery packs could be changed in a few minutes to allow charging of the removed packs at any time of the day; these were planned for the periods with the lowest CO₂ emissions using fast charging based on the Hungarian energy mix data. Charging Strategy #4 aimed to demonstrate the maximum CO₂ reduction potential of renewable energy sources in Hungary. The method minimizes operational downtime as battery swapping is faster than conventional charging. It also shifts energy use over time, significantly reducing environmental impact. However, the system requires significant investment costs, as it needs swappable batteries and the proper logistics infrastructure.

The different strategies have different charging timings, taking into account daily operational needs. The main objective of the analysis is to determine the most carbon-efficient method. The four strategies have different advantages and disadvantages regarding cost efficiency, operational flexibility, and environmental protection. The current Charging Strategy #1 is cost-effective, but the buses’ daily range relies too much on overnight charging and does not minimize carbon emissions. Charging Strategy #2 is predictable but may involve higher costs due to daytime charging. Charging Strategy #3 allows for greater operational flexibility due to more frequent charging but may result in faster battery degradation over a longer time period.

Figure 13 shows the CO₂ emissions of the electric bus fleet in Győr based on four different charging strategies, calculated using two different methods: the direct CO₂ emissions of electricity charged to the BEB batteries, shown in the left diagram, and emissions calculated using LCA methods, shown in the diagram.

The four charging strategies are as follows: Strategy #1 (red)—night depot charging with on-demand daytime charging; Strategy #2 (yellow)—night depot charging and charging between the morning and afternoon shifts at all times. Strategy #3 (blue)—night depot charging and charging between the AM and PM shifts at all times, and charging during breaks during shifts. Strategy #4 (green)—battery swapping and charging of removed batteries during the lowest CO₂ emitting hours.

Direct CO₂ emissions from electricity generation vary seasonally, being lower in the summer and higher in the winter. The share of renewables in the energy mix also varies with the seasons. Strategy #4 produces the lowest monthly emissions, charging during the hours with the lowest CO₂ emissions. Strategy #1 produces the highest emissions, especially in summer and autumn, when the effects of renewables cannot be felt due to charging at night. LCA emissions are higher than direct emissions and show similar seasonal trends.

Figure 14 shows the CO₂ emissions of the electric bus fleet in Poland based on four different charging strategies, calculated using two different methods: direct emissions (left diagram) and LCA emissions (right diagram).

In Poland, CO₂ emissions are significantly higher than in Hungary for all charging strategies for direct and LCA-based calculations. Strategy #4 (green) results in the lowest monthly emissions, but the difference is smaller in proportions than in Hungary.

Figure 15 shows the CO₂ emissions from operating an electric bus fleet in Germany.

In Germany, CO₂ emissions are significantly lower than in Poland but higher than in Hungary. Significant seasonal variations are observed, especially in summer and autumn. Strategy #4 results in the lowest emissions each month. This approach utilizes the renewable energy potential of the German energy mix in the most efficient way, as charging occurs when the share of renewables is higher during low-emission hours.

Figure 16 shows the CO₂ emissions of the electric bus fleet in Sweden based on four different charging strategies.

In Sweden, the CO₂ emissions of electricity used by the fleet in operation are the lowest on both a direct and LCA basis of the four countries studied. Direct emissions show minimum CO₂ emission values in all months and for all strategies. In Sweden, there are no significant differences between charging strategies due to the clean energy mix, in contrast to the other countries studied, where the differences between strategies are much more important. It can be concluded that Sweden is a suitable location to operate an electric bus fleet due to its very clean energy mix.

A comparison of the four countries and the four charging strategies clearly shows that the CO₂ emissions of the electric bus fleet are highly dependent on the energy mix and the charging method. Although all countries can reduce emissions by implementing favorable charging strategies, the energy mix determines the fundamental differences. Strategy #4 is the most efficient from an environmental and bus operating time perspective, but it requires high initial investment costs and can only pay off in the long term. Implementing Charging Strategy #4 is most beneficial if the investment costs can be afforded and reducing carbon emissions is a priority in fleet management. If this is not feasible, Charging Strategy #3 may be an appropriate compromise regarding flexibility and efficiency, while Charging Strategy #2 could be a more cost-effective alternative to upgrading the current system.

4. Discussion

Section 3 concluded that Charging Strategy #4 is the most favorable option for all countries. The following tables show each country’s monthly CO₂ reduction potential in tons.

Table 1. CO₂ emission of the bus fleet in Hungary with different charging strategies.

CO2 Emission of the Bus Fleet in Hungary with Different Charging Strategies
Emission Type	Direct Emission				LCA Emission
Charging Strategy	C_S_#1	C_S_#2	C_S_#3	C_S_#4	C_S_#1	C_S_#2	C_S_#3	C_S_#4
Emission Property	Actual Emission	Emission Difference	Emission Difference	Emission Difference	Actual Emission	Emission Difference	Emission Difference	Emission Difference
Dimension	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]
January	11.47	−0.06	−0.29	−1.67	13.81	−0.05	−0.24	−1.63
February	9.88	−0.07	−0.22	−1.43	11.81	−0.06	−0.15	−1.38
March	11.93	−0.25	−0.72	−2.68	14.30	−0.23	−0.66	−2.67
April	4.46	−0.25	−0.49	−1.41	5.40	−0.26	−0.49	−1.43
May	5.20	−0.18	−0.48	−1.67	6.32	−0.18	−0.48	−1.65
June	5.69	−0.29	−0.62	−1.97	6.82	−0.29	−0.64	−1.98
July	10.17	−0.83	−1.56	−4.24	12.04	−0.83	−1.59	−4.30
August	16.91	−1.56	−3.49	−8.21	19.67	−1.60	−3.60	−8.49
September	12.13	−0.37	−1.23	−3.88	14.45	−0.38	−1.22	−3.98
October	12.38	−0.29	−0.82	−3.07	15.00	−0.28	−0.73	−3.09
November	18.70	−0.06	−0.52	−2.90	22.37	−0.02	−0.41	−2.86
December	17.10	0.10	0.09	−1.59	20.35	0.17	0.26	−1.41
Yearly SUM	136.03	−4.12	−10.36	−34.74	162.34	−4.01	−9.95	−34.87

They highlight the potential negative impacts of certain charging strategies in specific months.

shows the differences in the CO₂ emissions of the electric bus fleet in Győr when different charging strategies are applied. Regarding total annual emission reductions, the current direct emissions are 136.03 tons CO₂, while the LCA emissions are 162.34 tons CO₂. Selecting the more favorable charging strategy, Charging Strategy #4, would reduce the direct emissions from electricity generation by up to 34.74 tons and the emissions calculated based on LCA by 34.87 tons. For Charging Strategy #3, this value represents a reduction of 10.36 tons of direct CO₂ emissions and 9.95 tons of CO₂ LCA emissions. For Charging Strategy #2, this value is 4.12 tons and 4.01 tons, respectively.

For Charging Strategies #2 and #3, the monthly data for December show that some charging strategies can increase CO₂ emissions compared to the current situation. In December, Charging Strategy #2 would cause an increase of 0.10 t of direct emissions, 0.17 t of LCA emissions, and Strategy #3 would cause an increase of 0.09 t of direct emissions, +0.26 t of LCA emissions. In December, continuing with Charging Strategy #1 would be advisable. All alternative charging strategies reduce emissions during the rest of the year. Strategy #4 remains effective in December.

Based on the results in Hungary, Charging Strategy #3 would be appropriate for the first three strategies, which do not necessarily require additional investment. The mixed strategy would achieve a direct emission reduction of 10.44 t and an LCA reduction of 10.21 t annually.

An analysis of CO₂ emissions for the same fleet of electric buses operating in Poland is shown in

Table 2. CO₂ emission of the bus fleet in Poland with different charging strategies.

CO2 Emission of the Bus Fleet in Poland with Different Charging Strategies
Emission Type	Direct Emission				LCA Emission
Charging Strategy	C_S_#1	C_S_#2	C_S_#3	C_S_#4	C_S_#1	C_S_#2	C_S_#3	C_S_#4
Emission Property	Actual Emission	Emission Difference	Emission Difference	Emission Difference	Actual Emission	Emission Difference	Emission Difference	Emission Difference
Dimension	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]
January	36.36	−0.04	0.20	−0.84	39.67	−0.04	0.17	−0.91
February	31.33	−0.13	−0.54	−2.70	34.18	−0.13	−0.59	−2.83
March	38.77	−0.76	−2.15	−7.04	42.27	−0.80	−2.28	−7.31
April	17.36	−0.81	−1.64	−4.34	18.87	−0.84	−1.71	−4.49
May	21.85	−0.81	−2.78	−7.37	23.75	−0.83	−2.88	−7.59
June	23.62	−1.19	−3.13	−7.28	25.80	−1.24	−3.27	−7.57
July	38.99	−2.86	−6.37	−13.52	42.73	−2.98	−6.67	−14.10
August	50.02	−3.70	−7.77	−17.39	54.96	−3.87	−8.15	−18.17
September	38.36	−1.23	−3.80	−10.17	42.29	−1.28	−3.98	−10.60
October	43.44	−0.91	−2.94	−8.00	47.72	−0.95	−3.09	−8.36
November	50.83	0.21	0.31	−2.07	55.83	0.22	0.32	−2.17
December	47.40	0.17	0.45	−0.99	51.98	0.17	0.41	−1.08
Yearly SUM	438.33	−12.06	−30.17	−81.70	480.04	−12.59	−31.72	−85.20

They highlight the potential negative impacts of certain charging strategies in specific months.

. Based on the annual totals, the current direct emissions are 438.33 tons CO₂, while the LCA emissions are 480.04 tons CO₂. Implementing Charging Strategy #2 would result in 12.06 tons of CO₂ direct emissions and 12.59 tons of CO₂ LCA emissions. Charging Strategy #3 would reduce 30.17 tons of CO₂ direct emissions and 31.72 tons of CO₂ LCA emissions. Charging Strategy #4 would reduce 81.70 tons of CO₂ direct emissions and 85.20 tons of CO₂ LCA emissions. For Poland, the analysis of the impact of charging strategies shows that in the winter months, especially in November, December, and January, certain strategies may increase CO₂ emissions compared to the current situation. Charging Strategy #2 shows a direct CO₂ emission increase of 0.21 t in November and 0.17 t in December. A similar trend is observed for LCA emissions, with an additional 0.22 t in November and 0.17 t in December. Charging Strategy #3 results in 0.31 t of direct additional emissions in November, 0.45 t in December, and 0.20 t in January. There is also a significant increase in LCA emissions, with 0.32 t in November, 0.41 t in December, and 0.17 t in January. Summing up the additional emissions observed during the winter period, Charging Strategy #2 has an additional direct emission of 0.38 t and an additional LCA emission of 0.39 t. These values are higher for Charging Strategy #3: 0.96 t direct emissions and 0.90 t LCA emissions.

It can be concluded that for the bus fleet in Poland, the use of Charging Strategy #2 in November and December and Charging Strategy #3 in November, December, and January is not recommended as it would increase emissions compared to the current situation. Charging Strategy #4 would result in significant CO₂ emission reductions in all months. If Charging Strategy #4 is not feasible, Charging Strategy #3 could be used; however, by using Charging Strategy #1 from November to the end of January, this option would achieve an annual CO₂ emission reduction of 31.13 t direct and 32.62 t LCA.

The data show that, due to the current situation’s high CO₂ emissions, very significant results can be achieved by scheduling charging strategies for buses in Poland, even for a fleet of only 11 buses.

The results of the analysis of the CO₂ emissions from the production of electricity from the operation of the electric bus fleet in Germany, used during charging, are shown in

Table 3. CO₂ emission of the bus fleet in Germany with different charging strategies.

CO2 Emission of the Bus Fleet in Germany with Different Charging Strategies
Emission Type	Direct Emission				LCA Emission
Charging Strategy	C_S_#1	C_S_#2	C_S_#3	C_S_#4	C_S_#1	C_S_#2	C_S_#3	C_S_#4
Emission Property	Actual Emission	Emission Difference	Emission Difference	Emission Difference	Actual Emission	Emission Difference	Emission Difference	Emission Difference
Dimension	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]
January	16.51	0.13	0.58	−0.33	19.80	0.13	0.50	−0.43
February	12.60	0.10	0.61	−0.62	15.40	0.10	0.55	−0.69
March	16.45	−0.35	−0.86	−4.02	20.10	−0.39	−1.09	−4.34
April	6.14	−0.42	−0.78	−2.24	7.76	−0.48	−0.95	−2.47
May	7.78	−0.37	−1.15	−3.28	9.79	−0.41	−1.34	−3.58
June	8.98	−0.73	−1.88	−4.59	11.06	−0.79	−2.11	−4.92
July	14.82	−1.57	−3.32	−8.02	18.31	−1.72	−3.76	−8.66
August	21.59	−2.64	−5.49	−12.22	26.35	−2.86	−6.09	−13.23
September	16.31	−0.70	−1.98	−6.06	19.99	−0.77	−2.24	−6.40
October	20.31	−0.32	−0.78	−4.14	24.25	−0.36	−0.95	−4.43
November	27.97	−0.29	−1.21	−3.80	33.01	−0.30	−1.36	−4.05
December	21.68	−0.05	0.00	−1.07	26.10	−0.07	−0.14	−1.25
Yearly SUM	191.14	−7.23	−16.25	−50.38	231.92	−7.91	−18.99	−54.44

They highlight the potential negative impacts of certain charging strategies in specific months.

. The current annual direct emissions are 191.14 tons of CO₂, while the annual LCA emissions are 231.92 tons of CO₂. Implementing Charging Strategy #2 could reduce direct emissions by 7.23 tons CO₂ and LCA emissions by 7.91 tons CO₂. Charging Strategy #3 could result in a decrease of 16.25 tons of direct CO₂ emissions and 18.99 tons of CO₂ LCA emissions. Charging Strategy #4 would result in 50.38 tons of direct CO₂ emissions and 54.44 tons of LCA CO₂ emissions. Analysis of the charging strategies shows that some could increase CO₂ emissions during winter. Charging Strategy #2 shows a direct CO₂ emission increase of 0.13 t in January and 0.10 t in February. LCA emissions also provide the same values. For Charging Strategy #3, the additional emissions are minor in December but show direct emissions of 0.58 t in January and 0.61 t in February. There is also a notable increase in LCA emissions, with a surplus of 0.50 t in January and 0.55 t in February. Summing up these excess emissions, Charging Strategy #2 has both direct excess emissions and LCA excess emissions of 0.33 t. These values are much higher for Charging Strategy #3, with 1.19 t of direct emissions and 1.05 t of LCA emissions.

In Germany, Charging Strategies #2 and #3 are not recommended for January and February, as they would increase emissions compared to the current situation. Charging Strategy #4 could lead to significant CO₂ emission decreases each month. If Charging Strategy #4 is not implementable, Charging Strategy #3 is the most beneficial option, but Charging Strategy #1 can be used in January and February. This solution would achieve an annual CO₂ emission reduction of 17.44 t direct and 20.03 t LCA.

Table 4. CO₂ emission of the bus fleet in Sweden with different charging strategies.

CO2 Emission of the Bus Fleet in Sweden with Different Charging Strategies
Emission Type	Direct Emission				LCA Emission
Charging Strategy	C_S_#1	C_S_#2	C_S_#3	C_S_#4	C_S_#1	C_S_#2	C_S_#3	C_S_#4
Emission Property	Actual Emission	Emission Difference	Emission Difference	Emission Difference	Actual Emission	Emission Difference	Emission Difference	Emission Difference
Dimension	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]
January	0.09	−0.01	−0.02	−0.04	0.79	−0.01	−0.04	−0.07
Feburary	0.16	0.00	−0.03	−0.04	0.75	−0.01	−0.07	−0.08
March	0.15	−0.01	−0.04	−0.04	0.89	−0.01	−0.04	−0.02
April	0.03	0.00	−0.01	0.00	0.35	0.01	0.02	0.03
May	0.07	0.00	−0.01	−0.02	0.51	0.00	−0.03	−0.01
June	0.09	−0.01	−0.02	−0.04	0.55	−0.01	−0.04	−0.06
July	0.13	−0.02	−0.06	−0.07	0.94	−0.04	−0.09	−0.10
August	0.15	−0.04	−0.09	−0.11	1.16	−0.06	−0.17	−0.20
September	0.02	0.00	−0.01	−0.01	0.77	0.00	−0.03	−0.03
October	0.07	0.00	−0.01	−0.02	0.83	0.00	0.00	0.00
November	0.14	−0.01	−0.05	−0.08	1.09	−0.01	−0.07	−0.13
December	0.12	−0.01	−0.03	−0.03	1.02	−0.05	−0.10	−0.15
Yearly SUM	1.23	−0.11	−0.37	−0.50	9.65	−0.19	−0.66	−0.83

They highlight the potential negative impacts of certain charging strategies in specific months.

shows the analysis of the CO₂ emissions of the electric bus fleet in Sweden. Based on the annual totals, current direct emissions are only 1.23 tons CO₂, while LCA emissions are 9.65 tons CO₂. The application of Charging Strategy #2 reduces 0.11 tons of direct CO₂ emissions and 0.19 tons of CO₂ LCA emissions. Implementing Charging Strategy #3 would reduce 0.37 tons of direct CO₂ emissions and 0.66 tons of LCA CO₂ emissions. Charging Strategy #4 results in a reduction of 0.50 tons CO₂ direct emissions and 0.83 tons LCA CO₂ emissions.

In Sweden, the analysis shows that none of the strategies will increase direct emissions during the year. However, emissions under LCA in April for Charging Strategy #2, Charging Strategy #3, and Charging Strategy #4, and October for Charging Strategy #3 and Charging Strategy #4, show a slight increase. However, the differences observed in October are negligible. In April, Charging Strategy #2 would increase 0.01 t LCA emissions, Charging Strategy #3 would increase 0.02 t LCA emissions, and Charging Strategy #4 would result in an additional 0.03 t LCA emissions. For Sweden, there is no apparent trend that a charging strategy different from the current one would lead to additional emissions during the winter months. The minimal additional emissions in April and October mean these values are incidental and small, so the mixed charging strategies analyzed for previous countries would not be necessary for Sweden. Strategy #4 still results in the lowest annual LCA emissions. Applying Charging Strategy #2 may be advisable in any case; Charging Strategy #3 may be even more favorable if no additional investments are needed. Charging Strategy #4 would be inappropriate due to the small emission differences, as it would require additional batteries and infrastructure investments. Due to Sweden’s green energy mix, emissions are significantly lower than in the other three countries. In Sweden, the impact of different strategies is minimal, as the current charging method also generates very low emissions due to the energy mix. The difference achieved is more significant when analyzed in percentage terms and not so pronounced when analyzed in terms of the mass of pollutants emitted.

The analysis shows that implementing Charging Strategies #2, #3, and #4 does not have a positive impact every month for every country. It is worth considering whether the current charging mode described in Charging Strategy #1 should be retained for some months, especially in winter. However, for a fleet, applying different practices would mean a more complex system for drivers to follow. In addition, it would be more appropriate to alternate charging strategies daily instead of monthly, because weather plays a key role in CO₂ emissions.

Table 5. CO₂ emission of the bus fleet in surveyed countries with different charging strategies.

CO2 Emission of the Bus Fleet in Surveyed Countries with Different Charging Strategies
Emission Type	Direct Emission				LCA Emission
Charging Strategy	C_S_#1	C_S_#2	C_S_#3	C_S_#4	C_S_#1	C_S_#2	C_S_#3	C_S_#4
Dimension	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]	CO2 [t]
Hungary	136.03	131.91	125.67	101.29	162.34	158.33	152.38	127.47
Poland	438.33	426.28	408.16	356.63	480.04	467.46	448.32	394.85
Germany	191.14	183.91	174.89	140.75	191.14	183.91	174.89	140.75
Sweden	17.36	−0.81	−1.64	−4.34	18.87	−0.84	−1.71	−4.49

displays the CO₂ emissions of the bus fleet in the countries surveyed under different charging strategies. In the case of Sweden, the difference between Charging Strategies #3 and #4 is not as large as for the other countries. In Poland, where coal-based electricity generation is dominant, the most significant emission reductions can be achieved with Strategy #4. Still, if this is not feasible, it is recommended to use Strategy #3, reverting to #1 in winter. In Hungary and Germany, Strategy #4 is also the most environmentally friendly, but requires significant infrastructure development. However, if this is not feasible, Strategy #3, which takes advantage of sunshine periods and shift changes, offers an efficient and cost-effective alternative. In the future, all countries are expected to move towards establishing a cleaner energy mix. Based on the Swedish example, the further a country advances in this development, the differences in the mass of CO₂ emitted by each charging strategy decrease. Countries can build hydropower plants by taking advantage of their geography. Depending on the climate of the area, wind turbines and solar power generation can be developed. In the absence of hydroelectric power, nuclear power can also provide a constant demand for electricity. These solutions can reduce emissions from fossil energy production. As a result, the battery swap strategy, which is the most advantageous for the other three countries but requires infrastructure investments and additional batteries, will lose its advantages in the future. Given the decades-long lifetime of buses, the technological and logistical background needed for implementation may already be questionable.

5. Conclusions

The research results show that charging strategies and a country’s energy mix significantly impact the environmental impacts of operating a BEB fleet. Based on an analysis of four different charging strategies, results show that Charging Strategy #4 provides the most significant emission reductions in all countries studied. However, its implementation requires high investment costs, and therefore, its economic efficiency needs further investigation. In Sweden, where the energy mix is dominated by hydro and nuclear, the charging strategy has less impact on emissions as electricity generation is associated with low carbon emissions. In Poland, where the power generation is coal-based, significant CO₂ emission reductions can be achieved by applying different charging strategies, but emission values remain high even when using these strategies. Germany and Hungary have a more favorable energy mix compared to Poland. In these countries, seasonal and diurnal variations have a stronger impact on the choice of favorable charging times as a result of the increased utilization of solar energy.

The current strategy of basing on overnight charging has higher CO₂ emissions. Charging Strategy #2, which uses charging adapted to shift changes in addition to night charging, increases operational flexibility while reducing CO₂ emissions and does not require additional investment. Charging combined with night charging using shift changes and in-shift rest periods (Charging Strategy #3) can effectively reduce environmental impacts, but its influence on battery life requires further analysis. Battery swapping has the lowest carbon footprint (Charging Strategy #4), but its applicability is limited due to high investment costs. Its potential benefits decrease as the energy mix improves. Battery swapping would be most feasible for three-shift bus operations due to higher bus utilization.

To operate electric bus fleets efficiently, the charging strategy needs to be chosen according to the energy mix of the respective country. As the size of the electric bus fleet increases, the impact of the right choice of charging strategy is becoming even more critical. In the case of Charging Strategy #4, the analysis approached battery charging by fast charging, which could have a detrimental effect on battery degradation. From November to February, when the difference between the CO₂ emissions of night and daytime currents is smaller, it may be preferable to charge the batteries at night using a slow charging method for this strategy.

However, it is important to note that the analysis considered the charging data of a fleet of 11 buses of the same model located in Győr, Hungary. As these buses represent only a part of the urban fleet, the average values could vary with the electrification of the entire bus fleet. Data on the charging values of the bus batteries were only available for the start and end times of the charging process, so the charging curves had to be constructed as mathematical models, which do not entirely match the real charging processes. The research was based on the Győr bus fleet’s real parameters, where the depot charging infrastructure is currently built at one location. For Charging Strategy #3, the value of CO₂ generated by the additional charging options that could be implemented at stops and the additional batteries and infrastructure improvements needed to implement Charging Strategy #4 were not analyzed. In each case, this would need to be considered in the context of local circumstances. As the analysis focused on emissions, no calculation of the financial impact of each charging strategy was made. This study aimed to determine the electric buses’ CO₂ emission reduction potential for different charging profiles. The charging modes did not consider the disadvantages of additional load during the peak hours of the electricity grid. Increasing the territorial distribution of the charging stations could reduce the load on the electricity network, but would require a major financial investment. A limitation of the research results is that fleet consumption data are taken from operating conditions in Hungary; the impact of extreme weather conditions in other surveyed countries on consumption was not examined.

Future research should analyze the long-term degradation of batteries to better account for the adverse effects of fast-charging strategies. Determining charging times requires considering the financial aspects in detail due to the dynamically varying electricity cost depending on the electricity grid load. Testing different charging strategies and tracking battery charges in real time could refine our results. The fleet currently under study operates with buses and charging stations of the same type, so it would be advisable to investigate fleets of different types or mixed brands in different cities. To obtain more accurate data on electric bus fleets operating outside Hungary, involving foreign researchers and transport companies in the analysis might be appropriate, to improve the consideration of climatic impacts and different operating conditions. Further research comparing other modes of public transport with electric buses could be considered.