**1. Introduction**

Due to environmental concerns, there is a strong tendency of electrifying road transport systems by means of introducing different types of electric vehicles [1]. Apart from reducing pollutant and CO2 emissions, electric vehicles (EV) are characterised by substantially reduced noise pollution, lower operating cost (including energy and maintenance cost) and generally better driving characteristics. On the other hand, higher investment cost, slow battery charging and limited driving range inhibit their faster proliferation [2]. This is why the transition to fully electric vehicles (FEV or BEV) is characterised by application of hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV) [3].

City bus transport is a natural candidate for electrification, aiming at improvement in city air quality and reduction of noise. Since the city bus routes are known in advance and the operation is intermittent, the range- and charging-related issues are of lesser significance than with passenger cars. These issues can be tackled by the following two basic approaches [4]: (i) the buses are equipped with large enough battery packs to sustain half a day or even full day of operation, and the buses are efficiently recharged by using slow charging (typically overnight); and (ii) the battery size is minimised and superfast charging is employed at bus stops (typically end stations). Therefore, it is generally of interest to find optimal locations of charging stations, as considered in [5–11] with a focus on passenger cars and urban areas.

In [12], e-buses and corresponding charging systems are analysed from the total cost perspective by using data related to route specifications, timetables and other local conditions. Additionally, the authors have developed a user-friendly tool which enables the user to investigate and quantify trade-offs between EV battery size, charging infrastructure cost and vehicle fleet operational costs. However, all routes are modelled as straight lines between the bus stops and elevation is collected only at stops, thus hiding some relevant topographical features along the routes and affecting the analysis accuracy. In [13], a stochastic integer program was developed to jointly optimise charging station locations and bus fleet size, while considering stochastic bus charging demand and time-of-use electricity tariffs for the case of real-world network in Melbourne. However, this study does not consider charging scheduling for individual buses. The aforementioned city bus transport optimisation study [4] relies on an artificial urban bus driving cycle when conducting bus fleet simulations, thus neglecting influential effects of city traffic congestion, road slopes, often stops and so on. The authors of [14] indicated that lowering the fuel cost for electric buses can balance the high investment costs related to building charging infrastructure, while additionally achieving a significant reduction in pollutant emissions. To that extent, detailed techno-economic analyses comparing the total cost of ownership (TCO) of conventional and EV fleets should be conducted [15].

To the best of the authors' knowledge, studies dealing with extensive virtual simulations of different e-bus-type fleets based on real-life driving cycles and concerning spatially-distributed charging management and related TCO analyses have not been considered in the literature thus far. To fill the gap, a unique simulation tool for planning of city bus transport electrification, which contains all of the above functionalities, has been developed, which is described in this paper, including its application to a pilot study for the City of Dubrovnik. The tool consists of four modules aimed at (i) post-processing and statistical analysis of a large set of recorded driving cycles, (ii) simulation of conventional (CONV) and different types of e-buses (HEV, PHEV and BEV), (iii) virtual simulation of e-bus fleets over recorded driving cycles including user-defined setting of charging station locations and charging management itself and (iv) techno-economic analyses. The main contributions of the paper include: (i) creating a unique and flexible/transferable simulation tool resulting in realistic, data-driven transport electrification analyses; (ii) building a static map-based form of HEV/PHEV-type bus model including its control strategy, which drastically boost computational efficiency of large-scale fleet simulations; (iii) performing a detailed techno-economic analysis based on realistic virtual bus fleet simulation and actual technical data provided by city bus transport companies.

The paper is organised as follows. Section 2 describes the methodology of recording driving cycle data and overviews the structure of developed simulation tool. Section 3 outlines the Data Post-Processing Module (DPPM) and presents corresponding results of conventional city-bus transport system characterisation. Section 4 deals with E-Bus Simulation Module (EBSM) and discusses belonging simulation results for the four considered types of city buses. Section 5 describes the Charging Optimisation Module (COM) and overviews the results related to obtaining near-optimal charging infrastructure configuration for the cases of PHEV- and BEV-type bus fleets. The Techno-Economic Analysis Module (TEAM) is briefly described in Section 6, and the corresponding TCO results are discussed for various e-bus fleet scenarios. Concluding remarks are given in Section 7.

#### **2. Pilot Data and Simulation Tool Structure**

#### *2.1. Recording of Driving Cycle Data*

The driving cycle data have been collected on a sub-fleet of 10 MAN Lyon's City NL323 buses operating in the city of Dubrovnik. They are considered a good representative of the overall fleet, as they cover all major bus routes (Figure 1) and represent around 1/3 of regularly used city buses. The driving data recording was performed by utilising a commercial GPS/GPRS vehicle tracking device installed in the selected buses for the purpose of this study. The data, collected from a built-in GPS device and vehicle CAN bus, are summarised in Table 1. Recording was conducted continuously for a period of five months, and the data sampling time was 1 second.

**Figure 1.** City bus routes in Dubrovnik along with end-station and depot locations.


**Table 1.** List of available city bus tracking data.
