Local emission of especially particulate matter (PM) and nitrogen oxide (NOx) are haz- ardous for people living in close proximity. The municipality of Rotterdam can not yet comply with national regulation regarding N Ox and the current levels of N Ox pose a health risk for society (Gemeente Rotterdam). The Port of Rotterdam and local government have set ambitious goals to reduce emissions and encourage the switch to sustainable alternatives. Companies transporting over water in the Rotterdam port area need to respond to this wider sentiment of zero emissions. The switch can be made by either transforming or replacing current fossil fuel vessels with battery electric vessels.Social relevance and the urge for more sustainable shipping have not yet resulted in large developments regarding full electric vessels. After the review of literature, a knowledge gap is formulated: ’The urge for full electric shipping and linkage to the adoption of a network of decentralised batteries.’ This research sheds light on Battery Exchange networks.Battery Exchange, the physical exchange of a discharged battery for a charged battery. Battery Exchange on vessels gives the opportunity to cope with range and charging limitations of the more commonly used stationary batteries. In this research the logistic operation of implementing a novel Battery Exchange network is elaborated upon. Research takes place at Skoon. Electrification of vessels using decentralised batteries provides major opportunities for Skoon, as the company specialises in managing battery networks. The ultimate aim of this research is to develop a model that can calculate the optimal battery exchange network configuration. The following research question is formulated:How can battery networks be implemented to supply ships with power?The Sargent model (Sargent, 2010) is one of the methodologies used to structure the research. It consists of a paradigm between the problem, conceptual model and comput- erised model. In order to create a conceptual model the framework by Robinson (2008) for conceptual modelling is used. Following the conceptual model, a functional model and consequently a computer model is developed to study the implementation of a battery pack exchange network.In order to test the model, a case study has to be executed. There are three main requirements to conduct research on modular battery electric vessels. The first requirement is the willingness of companies to cooperate in this research. The second requirement is the availability of data. Third, the energy demand of ships needs to be in the functional limit of batteries. Existing vessels can either be replaced entirely by an electric vessel or adapted to be battery compatible. An aim is to map and provide answers to the logistical challenges associated with the implementation of a new battery exchange network and investigate the financial feasibility. Interviews with various transportation companies in the Rotterdam port area were conducted to gather data and become familiarised with potential challenges. Companies include: the Watertaxi, VT group, Unibarge, Waterbus, Koninklijke Roeiers Vereeninging Eendracht, Bek & Verburg and Spido. After scrutiniza- tion, the Waterbus, KRVE and Bek& Verburg are deemed the most interesting candidates for electrification. The waterbus is the best candidate for electrification due to limited trip duration, frequent returns to fixed locations, a predictable schedule, potential for positive network externalities, Skoon brand visibility and data availability. Increased demand for sustainable transport, thus solidification of the waterbus as a daily mode of transport and an alternative to road transport has multiple advantages. First it can decrease road congestion. Second, the use of bicycles is promoted. Third, the need for road related infrastructure investments is lowered. Fourth it improves the connectivity to less accessible villages.The general functional model is turned into a computer model. Data on the Waterbus is incorporated in the model. The used Matlab model allows to minimise the costs, moreover the influence of variables can accurately be monitored. Battery Pack (BP) and charging infrastructure configurations can easily be switched and compared on both Capital expenditures and Operational expenditures using the model. In the model a genetic algorithm is used for optimisation, through sequential progression in generations the model tries to find the best configurations. Model input consists of the parameters and variables. Daily schedule, energy consumption, routes and exchange locations are fixed parameters. Moreover a variety of model alternations are tested: the battery pack capacity and installed charging power are altered. The model uses the various input variables and parameters to calculate the total amount of used BPs in model. Two exchange methods are tested. One approach is to exchange the BPs throughout the whole system. While another method is restricted BP exchange. Restricted exchange entails that the BPs are only used on one specific route. The former method is likely to require less total BP capacity while the latter methods constantly guarantees an optimal distribution of BPs. In this research we find a method to calculate and monitor the influence of variables and how it changes the optimal system configuration.Multiple experiments were executed on: price fluctuations, influence of charging strategies, change in energy consumption and different charging rates were monitored. First, price fluctuations were observed to have no influence on the total system composition. Second, also the influence of the charging strategies was limited, as only small changes on the amount of shore charging power were observed. With another division of BPs and relatively less charging power, charging strategies could have had more impact. Third, alternations in the energy consumption did have significant effect, the optimal BP capacity changed while the number of BPs stayed the same. In the last experiment the c-rate was varied with both a constant Battery Pack capacity and the solution algorithm searching for the most optimal solution. The hypothesis that: ’using BPs interchangeably throughout the network increases system efficiency’ is accepted for c-rates between one and two. However, the differences between the two exchange methods are marginal and route specific battery exchange does guarantee a steady distribution of battery packs.The most optimal network configuration given current suppliers is provided. The amount of batteries, effective Battery Pack capacity and required shore power on three different locations are depicted in the table, for a maximum c-rate of one. The research has a variety of limitations. First, the research is especially suitable for scheduled transport over water, application of the model for on-demand traffic would require alternations and incorporation of margins for uncertainty. Second, accessible data on energy consumption of vessels is a hard to find, the reliability of the data is an un- certainty. To cope with the uncertainty and test the influence of variability, fluctuations are incorporated in the model tests. Third, there are charging strategies which have been disregarded due to practical restrictions on the amount of model characteristics. Fourth, the impact of schedule changes has not been assessed.It is concluded that in establishing sustainable transportation over water, the model can help overcome range and charging time related limitations, while minimising required resources. Model optimisation and insight in the necessary resources the following advantages. Parameters can easily be adapted if changes occur in for example: the schedule, level of consumption or charging rate. Additionally, insight into the relevant information makes the operation of a battery exchange network more comprehensible for managers. Moreover, optimisation can make the implementation of a battery network and full electric propulsion financially more attractive. The research provides insights for Skoon in assessing the possibilities of battery exchange networks and serves as guidance in scrutinising other emerging markets.The main recommendations, for both Skoon and future research are enumerated. First, map battery lifetime, more specifically in-dept scrutiny on the relation between fast charging and battery degradation could provide new insights. Second, partner up for the development of a battery exchange method. Third, extend the model with extra vessels, linking additional customers to exchange Battery Packs with different clients in one network. Fourth, the integration of a visual display, to more efficiently communicate the model performance. Fifth, include more elaborate energy consumption measurements of vessels incorporated in the model.