All-electric Vessels In Service At Niagara Falls

What are being called the first all-electric vessels to be built in the US are now in service as tour boat at Niagara Falls.

The zero-emission ferries will be powered by high-capacity battery pack supplied and integrated by ABB. In addition to batteries, ABB has supplied an integrated power and propulsion solution for the newbuild vessels, including an offshore charging system, enabling sustainable operation with maximum reliability.

“Maid of the Mist has always evolved with the technology, and we are thrilled to open a new page in our company’s history, moving our fleet to zero-emission operation,” said Christopher M. Glynn, President of Maid of the Mist Corp., which operates the ferries. “Close collaboration with ABB has been instrumental in making this project a success, and we are proud of what we were able to accomplish together.”

As well as allowing passengers to enjoy the spectacular experience of Niagara Falls and safeguarding the environment, the vessels confirm growing acceptance of all-electric vessel propulsion, said Juha Koskela, ABB Marine & Ports Division president.

The vessels are each powered by a pair of battery packs providing 316 kWh total capacity divided across two catamaran hulls, offering a level of redundancy that helps to safeguard operations. The batteries are designed to allow the electric propulsion motors to reach an output of up to 400 kW, with the power setup controlled by ABB’s Power and Energy Management System. They are charged using locally produced hydroelectricity – ensuring that the energy cycle for the operation of the Maid of the Mist ferries is entirely emissions-free – in a process that takes just seven minutes during disembarkation and boarding.

Having installed the ship-to-shore battery charging connection, ABB also supplied the vessels with a comprehensive scope of electric, digital and connected solutions including switchboards, drives and the integrated control system, in addition to the ABB Ability Marine Remote Diagnostic System for monitoring and predictive maintenance.