BluWave-ai has completed a pilot program for Canada’s first electric vehicle (EV) charging management software in Ottawa involving cars of different makes and backed by artificial intelligence (AI).

Using its EV Everywhere app, the Ottawa-based company's AI platform controlled participants’ EV charging. The system determined the best time for charging in order to minimize strain on the Hydro Ottawa grid and tap into renewable energy for the cleanest battery top-ups.

The company looks to address the problem that could arise from greater EV adoption: surging demand for electricity at peak times.

A report commissioned by the Canadian government found approximately 679,000 public charging ports will be needed in the country by 2040 to meet 2035 and 2040 sales mandates for zero-emission vehicles. Without enough electricity, the chargers may overload grids or take power from other needs.

“Some of the EV manufacturers, they do give you the ability to control charging with the app, but there isn’t anything that does everything: demand response events, multiple vehicles, being able to link to the utility and take into account their transformers, their distribution grid. What’s novel here is the combination of everything all together,” Alex Linchieh, BluWave-ai’s director of product management, said in an interview with Sustainable Biz Canada.

EV Everywhere in Ottawa

Linchieh said the collaboration with Hydro Ottawa started when the company asked the utility how many EVs it could handle before grid troubles would emerge. Research conducted jointly by BluWave-ai, Hydro Ottawa and the University of Waterloo found if a quarter of homes in some neighbourhoods had EV owners who plugged their vehicles into a Level 2 charger at peak hours, it would bottleneck the grid.

The testing started in 2023 and further study is ongoing, with hundreds of vehicles participating, Linchieh said. Over 100 EV models are compatible with EV Everywhere, the company said in a release, including manufacturers such as Tesla, Toyota, BMW, Hyundai, Jeep, Porsche and Volkswagen.

Hydro Ottawa recruited customers who reached out to the utility because of their interest in EV ownership and optimizing charging.

EV Everywhere subscribers took part in weekly demand response events, which informs users on the app when charging will be halted and resumed to alleviate strain on the grid. The charging was tweaked in line with an AI model that determined when it would create the least stress on the grid and would use the most wind and solar energy. The pilot also examined where a battery could be placed on the grid to handle possible bottlenecks.

It applies BluWave-ai’s BluScore, a figure generated by AI that calculates the best time to charge an EV at a certain location. In Ottawa’s case, it uses data from Ontario’s electricity market operator and other sources to predict the renewables mix, demand, cost and local strain on the grid, leading to demand response events that are coordinated with Hydro Ottawa.

While most of Ontario’s electricity is from nuclear and hydro, high demand will activate gas peaker plants that burn natural gas to create power. By avoiding peak hours, EVs will rely on cleaner power while reducing energy bills for drivers, Linchieh explained. Utilities could also plan system upgrades using the BluWave-ai data.

“This successful pilot showcases the potential of AI to improve grid reliability and offer tangible benefits to our customers,” Guillaume Paradis, chief operating officer of Hydro Ottawa, said in a release.

Lessons from the pilot

From the pilot, BluWave-ai developed a clearer idea when and why people plug in their EVs. Customers have the choice to opt out of a demand response event if they provide a reason, such as needing a charge to attend to an urgent matter, Linchieh said.

“We’re trying to get a better picture of what are the expected participation rates and then map that to when the grid is strained so that Hydro Ottawa has a better understanding of, ‘OK when there is grid strain, what are the options here in terms of curtailing vehicles and what do we think the compliance rate is going to be?’”

Over the next year, BluWave-ai will continue to refine its modelling for the grid, including targeting certain vehicles in specific neighbourhoods, he said.

The pilot demonstrates the capability of the company’s product, Linchieh said. Having it work in the production environment is “huge for us”, he added, and gives BluWave-ai the confidence it can be deployed globally.