**2. The electrified and cooperative bus system**

The quality and service level of bus systems often rely on the interaction of different lines, in order to provide optimal frequencies and hence acceptable waiting times for the users, and to offer sufficient capacity to accommodate the demand, measured in terms of passenger flows. These flows vary across the network due to the variability of the demand, which differs depending on the origin and the destination of the users, and in time. To match the demand with the supply, bus operators aim to manage efficiently their fleet of vehicles, identifying at any time the most opportune vehicle type and the number of vehicles to be assigned to a line, together with their dispatching times. This decision has consequences on the way lines run smoothly and provide a certain level of service quality to the passengers, as well as it impacts the operational costs (**Figure 1**). In this study we consider design decisions (node density, network density and line density) as given.

Allocating a small number of vehicles limits the service frequency, which affects the waiting time. However, increasing this number will have a negative impact on the operating costs, since more vehicles and drivers will need to be employed. Too many vehicles on the other hand may result in under-utilizing vehicle capacities, reducing the marginal profits for the operators. Hence, optimally allocating fleet resources in the network is a fundamental planning problem that impacts both operators' costs and passengers' experience.

#### **2.1 Electrification opportunities and challenges**

Emerging trends in green PT systems offer new benefits: e-busses reduce emissions, energy use, noise as well as offer smoother rides. There are three types of e-busses—hybrid electric, plug-in hybrid electric, and battery electric. The last two *Optimal Management of Electrified and Cooperative Bus Systems DOI: http://dx.doi.org/10.5772/intechopen.93892*

**Figure 1.** *Integrated design of bus systems.*

are able to recharge their batteries from an electric power grid via an *opportunity charging*—a bus periodically charges at bus stops or terminals. This allows to downsize battery and extend bus range to a desirable value. E-bus systems are currently moving from pilot projects to small-scale deployments with single line/operator with very few charging stations. The potentials and needs of large-scale e-bus systems have been investigated by the EU's Zero Emission Urban Bus System (ZeEUS) project [1] as well as Volvo's City Mobility Program [2]. More recent EU projects investigated the impact of fleet mix and configuration parameters to the operation costs [3].

When introducing e-busses, additional costs need in fact to be accounted for, since current battery-capacitated e-busses need to be recharged multiple times a day (e.g. a Volvo 6700 bus can perform a trip in full electric mode for around 30 km, and each vehicle can run distances of a few hundreds of km each day). Current opportunity charging technologies allow a bus to recharge up to 80% in a matter of 6-10 min, while novel flash charging technologies can recharge in less than a minute, but it extends the range of only few more kilometers. An example is the TOSA system in Geneva, a single line that uses both opportunity (3-4 min with low power) and at bus stops e-charging (15-second each 1-1.5 km with high power) [4]. Given the costs of fast and flash charging, bus operators charge their e-busses overnight, when the cost of electricity is lowest, and then use opportunity charging stations, typically located at line terminals, to recharge during the short resting times of the drivers. Flash charging are up to date very rarely implemented, given the very high costs of the relatively small gain in terms of range extension.

The charging infrastructure creates a strong link between infrastructure planning and bus operations [5]. The location and charging operations in fact influences the dispatching times of the vehicles, and in turn irregularities in the operations with recurrent phenomena of bus bunching may result in busses queuing at the charging station, with additional propagation of delays and overall degradation of service levels. Therefore, past research focused on developing a proper system design including strategic locations of e-charging stations [6, 7]. Energy efficiency was also addressed via energy management strategies for the engine [8], and regenerative breaking technologies [9], and taking into account environmental policies such as zero-emission zones [10].

In this study we contribute to this stream of research by focusing on the problem of integrating vehicle scheduling and dispatching times with recharging needs and operations of the e-bus fleet. In particular, we consider the problem of managing a

mixed fleet of vehicles, which will be likely to be the case for the next years to come, since full electrification will require heavy investments in the electrical grid and current batteries and chargers are considered a relatively immature technology to completely replace combustion engines. We show in Chapter 3 that optimally assigning vehicle types in the network will provide benefits for both service quality (mitigation of delays due to charging) and operating costs (more e-busses used in daily operations are likely to bring lower energy consumption costs).

allows to manage the interplay between PT ecosystem actors (vehicles, signals, and e-infrastructure). Secondly, it enables joint optimization and coordination of actions carried out by the different actors, in order to achieve system goals.

*Optimal Management of Electrified and Cooperative Bus Systems*

*DOI: http://dx.doi.org/10.5772/intechopen.93892*

**Figure 2** provides an overview of the eCoBus integrated system developed in this project. The core module consists of collecting *static* input, namely the location of charging stations, lines timetables, together with the characteristics of the fleet (number of e-busses and hybrid vehicles), the characteristics of the lines (trip lengths) and of the signal infrastructure. We also assume to collect in real time trip times through AVL technology, battery states from the busses, status of each charging (occupied, available) and to have a good estimate of the passenger arrivals at stops (via e.g. APC information). These are input to the *scheduling and charging optimization* module, which is presented in detail in Section 3, whereas the *driver advisory system* combining holding and C-ITS based control and TSP are used at the operational phase to manage the vehicles in real time. The integrated system is shown to provide significant benefits both for planning objectives (better use of the fleet and the charging infrastructure, lower operations costs), and management goals (lower trip time variability and passenger costs, less fuel or energy consumed, less use of TSP requests). These benefits will be showcased in simulations using

**3. Mixed Fleet vehicle scheduling and charging optimization**

Vehicle scheduling problems in public transportation have been approached as part of the "full operational planning process" [29]. From a modeling perspective, these problems are usually formulated as Mixed-Integer Linear Programs (MILP), under the name of Single/Multi-Depot Vehicle Scheduling Problem (SDVSP/ MDVSP) [30]. The impact of electrification on bus scheduling problems has been recently taken into consideration by researchers, e.g. [31, 32], in preparation and support towards widespread Public Transport electrification. In this Section we

realistic scenarios in the next sections.

**Figure 2.**

**99**

*The eCoBus integrated management ecosystem.*
