*6.1. General Description and TCO Model*

According to Figure 2, the TEAM uses the simulation data outputted by the COM, as well as the fleet loan payment, insurance, registration, maintenance and similar costs provided by DMM, in order to calculate the fleet TCO (Figure 17). The TCO corresponds to what is in financial terminology called Net Present Value (NPV) of an investment, which is an index that valorises the investment while considering the time value of money. Rates at which the money value decreases or increases over time are in this case modelled by the inflation and discount rates, respectively (where the latter corresponds to the profit that today's money can generate in the future through investments or bank savings). Calculation of future value of money is called compounding, while the opposite approach, in which the NPV of future money is calculated, is referred as discounting. The TCO is calculated by discounting all future expenses, which the investment is expected to generate, to the present time, as shown in Figure 17.

The TCO model components (Figure 17) are divided into three groups depending on the time basis on which the input expenses data are sampled [29], and the corresponding individual costs are given in Table 6 (with no VAT included). The bus service life is considered to be 12 years, the inflation rate 3% and the discount rate 7%. The annually sampled data include registration, maintenance and insurance (RMI) cost, which have been determined for the CONV fleet based on the (past) data provided by the city bus transport operator, and discounted to prices in 2019 according to inflation data [30]. The RMI cost for the HEV, PHEV and BEV fleets are assumed to be 15%, 20% and 40% lower, respectively, when compared to the CONV fleet, because of the significantly reduced CO2 emissions and simplified maintenance of e-buses [31–33].

The monthly expenses relate to loan payment for purchase of new vehicles and charging infrastructure, including the cost of replacing the e-bus batteries. A general-purpose bank loan is assumed, which is taken over a period of seven years, with a continuous interest rate of 5% and equal monthly annuities. The daily sampled data relate to operating cost, i.e., the fuel and electricity expenses, which are calculated by multiplying fuel and/or electricity consumptions obtained by COM simulations with fuel and/or electricity prices. As in the case of the annually sampled data, the operational cost is adjusted for inflation. Irregular maintenance cost is modelled by a fixed rate occurring every two years. The TEAM also provides the possibility of sensitivity analysis, which allows for the investigation of to what extent variations of a particular parameter affect the TCO. This helps to determine the TCO

model reliability, e.g., parameters that cause higher TCO sensitivity should be more reliably estimated. The sensitivity analysis is not considered in this paper.

**Figure 17.** Flowchart of Techno-Economic Analysis Module (TEAM).

**Table 6.** Input parameters used for TCO calculation for different bus types (no VAT is included).


<sup>1</sup> Includes incentives (1000 EUR for HEV, 2500 EUR for PHEV and 5000 EUR for BEV) estimated based on [34]. <sup>2</sup> Costs for transformer substation (TS) and charging station (CS) are estimated based on the data provided by local electric utility company and [35], respectively. <sup>3</sup> The battery replacement costs are estimated based on [35] and the replacement is assumed to occur every 6 years because the average bus battery life is 5−12 years [36]. <sup>4</sup> Winter time: 7 a.m. to 9 p.m. (HT), 9 p.m. to 7 a.m. (LT); Daylight saving time: 8 a.m. to 10 p.m. (HT), 10 p.m. to 8 a.m. (LT).

## *6.2. Simulation Results*

The TCO results are given in Figure 18 for different types of city bus fleets and charging configurations selected in Section 5. Different charging scenarios are considered, starting from optimistic Scenario 1 to conservative Scenario 6. In the basic case (Scenario 1), all the e-bus fleets turn out to be competitive with the CONV fleet, which is explained by the influence of high share of fuel cost (see Figure 19) for the particular case of relatively large fleet utilisation (250 km/bus/day in average; see also Figure 5). For the same reason, the e-bus fleets have relatively comparable TCO values. Similar results are obtained if the fuel and electricity prices are randomly sampled (Scenario 2), rather than being constant as given in Table 6. Scenario 3 accounts for the need to use reserve buses in the case of BEV fleet, as found by COM simulations (Section 5). Since in the considered case, BEV 2, the use of two reserve buses results in a marginal increase of electricity consumption (Table 4) and a low number of bus swaps, the use of second reserve bus is very marginal, and is thus excluded from the TCO analysis. Due to the cost of reserve bus, the BEV fleet TCO increases above that of PHEV fleet, but it is still competitive to CONV fleet. When accounting for the e-buses' battery replacement cost (Scenario 4), the TCO of BEV fleet, which has the largest and costliest battery, becomes around 10% higher than that of CONV fleet. If the PHEV- and BEV-type bus electricity consumption is increased by the factor of 40% (Scenario 5) or 100% (Scenario 6) to account for modelling errors (e.g., those related to heating system in winter), the PHEV fleet becomes marginally competitive or uncompetitive, respectively, while the BEV vs. CONV fleet TCO excess tops 23%. This TCO excess in the ultimate BEV case may be compensated for by larger incentives, higher ticket prices (which would reflect better passenger experience), future increase in fuel prices, future decrease of battery prices and similar factors.

**Figure 18.** Comparative TCO values for different bus fleet types and electrification scenarios.

The comparative TCO time profiles for different types of bus fleet are shown in Figure 20 for Scenario 4, which is deemed to be most realistic scenario involving the battery replacement and reserve bus cost. The corresponding time profiles of individual TCO costs are shown in Figure 21. For the PHEV, and particularly the BEV fleet, the TCO rapidly rises during the first 7 years due to loan expenses related to the purchase of these expensive buses and corresponding charging infrastructure (Figure 20). Once the loan is paid off, the energy cost becomes dominant, where the efficiency of e-buses and low cost of electricity become beneficial and bring significant savings, as opposed to the CONV case, where the fuel expenses dominate (Figure 21).

*Energies* **2020**, *13*, 3410

**Figure 19.** Cost shares for different type of bus fleets and Scenario 4 from Figure 18.

**Figure 20.** TCO time profile for different bus fleet types and Scenario 4 from Figure 18.

**Figure 21.** Time profiles of individual TCO costs for different type of bus fleets and Scenario 4 from Figure 18.

Figure 19 shows the percentage shares of individual costs for different types of bus fleets. As the electrification evolves from HEV, via PHEV to BEV buses, the energy (fuel and electricity) cost share monotonically and significantly reduces, but the bus and charging infrastructure cost share increases with similar trends. The PHEV and particularly BEV fleets have lower RMI cost, but this saving is not large enough to compensate for the battery replacement cost.
