**1. Introduction**

Sustainable urban development motivates investments in environment-friendly and user-centered Public Transport (PT) services. Three trends towards next generation PT systems are observed, namely 1) introduction of greener vehicles such as electric/hybrid busses (e-busses), 2) focus on high service quality (e.g. increased ride comfort via mitigation of stop-and-go driving) and 3) reduction of emissions and operating costs related to fuel/energy consumption and equipment wear and tear. These trends however bring new challenges. The first challenge is posed by different operational characteristics and constraints of e-busses, e.g. they need to periodically recharge batteries at e-charging stations placed in selected stops and terminals. This brings additional constraints into PT operations and its cost dynamics. The existing approaches lack the required degree of modeling detail necessary to capture the complex interactions emerging between bus operations and charging infrastructure. The second challenge is how to guarantee comfort- and cost-effective operations without negatively impacting general traffic performance. Relying solely on strategies such as Transit Signal Priority (TSP), which prioritize PT vehicles at signalized intersections, might cause congestion effects that could backfire on the PT system itself.

The main contribution of this work is that we jointly address constraints and control capabilities of all entities of the PT ecosystem, which consists of signal control, (e-)busses, and e-bus charging infrastructure. The developed methods combine cooperation and negotiation between all actors thanks to connectivity, in order to effectively achieve mutual goals. Thanks to bus real-time positioning systems (Automatic Vehicle Location, AVL) and vehicle-to-infrastructure communication (Signal Phase and Timing, SPaT), multi-objective optimization is employed to determine bus dispatching time, operating speeds, dwell time plans, e-bus charging schedules, and TSP requirements. Regarding the interaction between busses and e-charging infrastructure, the objective is to minimize electricity costs and adhere to the planned bus dispatching times. From the online/operational perspective, the problem is to model and optimize a connected and cooperative system with a set of heuristic tools and actions, such that real-time system disturbances can be addressed, in order to maximize the adherence to the offline plans. For example, busses can use information on upcoming green times to adapt their speeds or hold at a stop in order to avoid stopping at signals. Consequently, stop-and-go is mitigated in an efficient and non-invasive way.

This chapter is structured as follows. Section 2 provides an overview of the e-bus eco-system, and the integrated design approach we developed in this work. Section 3 focuses on the integrated scheduling and charging problem at the planning phase, in particular considering a hybrid fleet of electric and hybrid busses. Section 4 deals with the operational phase, and in particular it shows the benefits of the cooperative ITS-based control strategies. Finally, section 5 provides an outline and the potential future research directions for this research.

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

*Optimal Management of Electrified and Cooperative Bus Systems*

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

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

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

operation costs [3].

*Integrated design of bus systems.*

**Figure 1.**

policies such as zero-emission zones [10].

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