Visitors to the Niagara Falls will soon be able to experience one of the USAs top landmarks emission-free. Two new Maid of the Mist passenger vessels due to commence operation later this year will be powered entirely by high-capacity battery packs, becoming the first all-electric vessels to be built in the USA. Maid of the Mist, one of North Americas oldest tourist attractions, operates from April through to the first week of November, with boats departing for the base of Niagara Falls every 30 minutes, with an estimated 1.6 million guests on board annually. Each of the vessels will be powered by a pair of battery packs with a total capacity of 316kWh, split evenly between two catamaran hulls. The vessels will charge between every trip while passengers disembark and board. Shoreside charging will take seven minutes, allowing the batteries to power electric propulsion motors capable of a total 400kW (563hp) output. The power setup will be controlled by ABBs integrated Power and Energy Management System (PEMS), which will optimize the energy use on board. The batteries will be charged using hydropower the largest single renewable energy source for electricity generation in the USA, accounting for 7% of the countrys total utility-scale electricity generation. The use of locally produced renewable power will make the energy cycle for the operation of the new Maid of the Mist vessels entirely emission-free. We are thrilled to be the USAs first vessel owner to add all-electric ferries to our fleet, said Christopher M Glynn, president of Maid of the Mist Corp. We have chosen ABB to support us in our journey toward more sustainable operation based on their unparalleled experience in marine system integration, as well as efficient and innovative technologies for sustainable transportation. In addition to integrating the ship-to-shore battery charging connection, ABB will supply the Maid of the Mist new-building project with switchboards, drives and integrated control system, as well as the ABB Ability Marine Remote Diagnostic System for remote equipment monitoring and predictive maintenance.