**3. Data Post-Processing Module (DPPM)**

#### *3.1. General Description*

According to the DPPM flowchart shown in Figure 3, the recorded driving data are first processed and stored in a database. Next, the driving cycles are extracted by using the DMM-based data related to geographical coordinates of end stations and depot (Section 3.2). At the same time, the fleet statistics are calculated for the entire fleet and individual buses (Section 3.3). Finally, the module outputs including driving cycles and statistical features can be plotted in different formats, and they are saved into the database.

#### *3.2. Extraction of Driving Cycles*

A single driving cycle is defined by the velocity vs. time and road slope vs. travelled distance profiles between two consecutive end stations, including depot (see Figure 1). The corresponding time profiles of cumulative fuel consumption are also extracted.

**Figure 3.** Flowchart of Data Post-Processing Module (DPPM).

The driving cycle segmentation process resulted in a total of 122,727 extracted driving cycles. An example of recorded driving cycle is shown in Figure 4. Note that the vehicle velocity is directly measured, while the road grade is reconstructed from the rate of change of altitude when expressed with respect to the travelled distance. In order to reduce the noise in the reconstructed road slope profile, the altitude signal is pre-filtered by a low-pass double-sided Butterworth filter [16].

**Figure 4.** Sample of reconstructed driving cycle for city bus route Hotel Palace–Pile and afternoon hours: velocity vs. time profile (**a**) and road slope vs. distance travelled given along altitude source profile (**b**).

#### *3.3. Calculation of Vehicle Fleet Statistics*

A rich set of statistically significant driving features is calculated for the purpose of actual/conventional city-bus transport system characterisation and in support of transport electrification (e.g., locating charging stations, Section 5). The features related to individual buses, all given per-day-basis, include: the total fuel consumption and distance travelled; average fuel consumption in L/100 km; the total time the bus is dwelling at depot, individual end stations or any other locations (typically bus stops); total driving time; mean velocity; number of bus stops per kilometre; number of bus visits to depot and end stations. The results are stored in a two-dimensional (2D) matrix (one per bus), whose rows and columns represent individual days and the statistical features, respectively.

Once the individual statistics are stored, they are further used to calculate the same features for the entire fleet on the basis of individual day, week, month or year. The results can be presented in different ways, e.g., instead of individual dwelling times, one can get the information about percentage share of time the buses or entire fleet are resting at depot, end stations and other locations (see Figure 5). Specific fleet features requiring denser sampling (30 min, herein) are also calculated. An example of the daily average fleet velocity profile is shown in Figure 6. Other features related to entire fleet include: average number of buses being parked at depot on 24-hour time basis (Figure 6), clusters of buses parking durations in relation to geographical coordinates, count of transitions between individual end stations/depot, etc.

**Figure 5.** Percentage time share of buses being operated and parked in depot, end stations and other locations.

**Figure 6.** Time profiles of fleet average velocity and average number of buses resting at the depot, both given on daily basis and averaged over the considered five-month period.

Figure 6 indicates that the average bus velocity when operating is around 30 km/h and it is higher in early morning and night hours, as well as over weekends. The low-velocity gap between 2 a.m. and 5 a.m. corresponds to the interval when most of the buses are parked in the depot. The buses rarely visit the depot in other time intervals, particularly over the work days (when the average number of buses is lower than 1).

Figure 5 confirms that the share of total time of buses being parked in the depot is relatively small (approx. 30%) and comparable to the share of end-station parking time (approx. 25%). The rest of the time the buses spend in driving (40%), while only for a small portion of time (approx. 5%), they rest elsewhere, typically at bus stops.

Figure 7a indicates that there are significant differences in bus resting time at different end stations. The average resting durations for most pronounced end stations are between 10 and 20 min, thus making them good candidates for installation of fast chargers. When selecting the best candidates, a charging station utilisation factor should also be considered (Figure 7b). The final end stations targeted

for electrification are then obtained by taking the cross-section of these two criteria (end stations underlined in red in Figure 7).

**Figure 7.** Boxplots of time duration of buses being parked at different end stations (**a**) and end stations charging utilisation (**b**).

#### **4. E-Bus Simulation Module (EBSM)**

#### *4.1. General Description*

As illustrated in flowchart in Figure 8, the EBSM simulates different types of city buses (CONV, HEV, PHEV and BEV) over the recorded driving cycles extracted by the DPPM (Section 3). The simulation first involves loading of vehicle-related parameters from the database, which need to be previously defined in the DMM. Next, the vehicle is simulated over the selected driving cycles by using the numerically-efficient backward-looking model (Section 4.2). Note that the vehicle model includes a control strategy that manages the gear ratio in the CONV and BEV cases, and the internal combustion engine (ICE) torque in the HEV and PHEV cases (Section 4.3). The emphasis has been on transforming the previously developed control strategy [17,18] to a form of off-line optimised maps, instead of using an on-line optimisation algorithm. The EBSM outputs time responses of key powertrain variables, such as cumulative fuel and electricity consumption, CO2 emissions and transmission gear ratio.

**Figure 8.** Flowchart of E-Bus Simulation Module (EBSM).

#### *4.2. Vehicle Modelling*

#### 4.2.1. Considered City Buses

The MAN Lion's City buses with the length of 12 m and the capacity of up to 126 passengers are represented in virtual simulation by the Volvo 7900 bus model. The Volvo 7900 platform was chosen because it includes all three e-bus variants considered (HEV, PHEV and BEV; Table 2).


**Table 2.** Basic parameters of modelled 12 m city buses [19].

Volvo e-buses use lithium iron phosphate (LFP) battery due to its high specific power required for propulsion and fast charging. The battery packs of Volvo 7900 HEV, PHEV and BEV bus variants have energy capacities of 4.8 kWh, 19 kWh and 76 kWh, respectively (Table 2).

#### 4.2.2. Modelling

In the backward-looking models, the powertrain variables are calculated in the direction from the wheels towards the engine and/or e-motor, starting from the wheel speed and torque being defined by the driving cycles [20]. In order to boost the computational efficiency, the powertrain dynamics is neglected, except for the battery state-of-charge (SoC) dynamics that are represented by a first-order model.

The considered parallel configuration of a HEV/PHEV-type bus is illustrated in Figure 9a [17]. The battery is represented by the equivalent battery circuit model shown in Figure 9b, which is described by the following state equation [20,21]:

$$\text{SoC}(t) = -\frac{I\_{\text{batt}}(t)}{Q\_{\text{max}}} = \frac{\sqrt{\text{l}I\_{\text{oc}}^2(\text{SoC}) - 4R(\text{SoC})P\_{\text{batt}}(t)} - \text{l}I\_{\text{oc}}(\text{SoC})}{2Q\_{\text{max}}R(\text{SoC})},\tag{1}$$

where *Uoc* is the open-circuit voltage, *R* is the internal resistance, *Ibatt* is the battery current, *Q*max is the maximal battery charge capacity and the SoC is defined as *SoC* = *Q*/*Q*max, with *Q* denoting the actual charge.

**Figure 9.** Functional scheme of considered parallel HEV/PHEV powertrain (**a**) and battery equivalent circuit model (**b**).

The model input *Pbatt* represents the battery output power defined as:

$$P\_{\text{but}} = \eta\_{\text{MG}}^k \cdot \tau\_{\text{MG}} \cdot \omega\_{\text{MG}} \,. \tag{2}$$

where τ*MG* and ω*MG* are the motor/generator (M/G) torque and speed, respectively, η*MG* is the M/G machine efficiency (represented by η*MG*(ω*MG*,τ*MG*) map; see Figure 10c and [17]) and the coefficient *k* is equal to 1 or −1 depending on whether the M/G machine operates as a generator or motor, respectively. The M/G machine speed and torque are given by the following kinematic equations:

$$
\hbar \omega\_{\rm MG} = i\_o h \omega\_w = i\_o h \frac{v\_{\rm v}}{r\_w} \,\tag{3}
$$

$$\tau\_{MG} = \frac{\frac{\tau\_w}{\eta\_{lr}(\tau\_w)} + \frac{P\_0(\omega\_w)}{a\_w}}{i\_o \eta\_r} - \tau\_{\varepsilon\_{sr}} \tag{4}$$

where *vv* is the vehicle velocity, *rw* is the tire effective radius, *h* and *i*<sup>0</sup> are the transmission and final drive ratios, respectively, τ*<sup>w</sup>* and *ww* are the total wheels torque and speed, respectively, η*tr*(τ*w*) and *P*0(ω*w*) are drivetrain efficiency and idle power loss maps [17] and τ*<sup>e</sup>* is the engine torque considered as a control variable (in addition to *h*). The wheel torque is determined according to vehicle longitudinal dynamics equation covering the vehicle acceleration torque and aerodynamic, road grade and rolling resistances [20].

**Figure 10.** Simulation results for PHEV bus over the driving cycle given in Figure 4 repeated 15 times, including: cumulative fuel consumption time response (**a**), battery SoC vs. distance travelled (**b**), M/G machine operating points (**c**), and engine operating points (**d**).

The fuel consumption at the driving cycle end time *tf* is determined as:

$$V\_f = \frac{1}{\rho\_{fuel}} \int\_0^{t\_f} \dot{m}\_f dt = \frac{1}{\rho\_{fuel}} \int\_0^{t\_f} \left( A\_{\varepsilon k} (\pi\_{\varepsilon\_\ell} \alpha\_\varepsilon) \frac{\pi\_\varepsilon \alpha\_\varepsilon}{3.6 \cdot 10^6} \right) dt\,,\tag{5}$$

where *Aek* is the engine specific fuel consumption given by the map shown in Figure 11, ρ*fuel* is the diesel fuel density (ρ*fuel* = 845 g/L) and ω*<sup>e</sup>* equals ω*MG* or 0 when the engine is switched on or off, respectively. Note that the integral Equation (5) is realised by using the Euler integration method with the common sample time of the backward model equal to 1 s.

**Figure 11.** Engine specific fuel consumption map including illustration of ECMS-based operating point search.

In the case of a conventional (CONV) bus, the battery and M/G machine are omitted from the functional scheme in Figure 9, while the AMT is replaced by a torque converter AT. The torque converter is represented by a backward-looking map ωι (ω*t*,τ*t*) derived offline from the well-known static torque converter model (see [22] and references therein) and the map τ *<sup>i</sup>* = τ *<sup>t</sup>* / *R*<sup>τ</sup> (ω *<sup>t</sup>*/ω *<sup>i</sup>*), where the subscripts *i* and *t* denote impeller/engine and turbine/transmission input variables, and *R*τ (.) is the static model torque ratio map. For the BEV-type bus, the engine is omitted and a two-speed AMT is used.

Vehicle auxiliary devices (HVAC system, servo steering, air compressor, engine cooling fan and alternator) are modelled based on the nominal power of each device and a binary power-modulating signal, whose duty cycle is made dependent on the driving and atmospheric conditions (urban driving conditions and ambient temperature dependence are assumed) [23].
