InCharge Bidirectional Chargers Empower Fleet Owners to Save Big on EV Transition

David Shultz

David Shultz reports on clean technology and electric vehicles, among other industries, for dot.LA. His writing has appeared in The Atlantic, Outside, Nautilus and many other publications.

InCharge Bidirectional Chargers Empower Fleet Owners to Save Big on EV Transition

Last week at the Advanced Clean Transportation Expo, Santa Monica-based InCharge unveiled a new family of bidirectional electric vehicle chargers.

While a new charger might not have been the most jaw dropping tech unveiled at the expo, bidirectional charging–especially right now–offers the kind of cost saving solutions that should be extremely attractive to anybody that owns electric buses, delivery vans, or even tractor trailers in significant numbers. Basically, any fleet owner looking to save some money during their transition from diesel to battery would benefit from these chargers.

Bidirectional charging, also called V2X technology, does what its name suggests. Instead of electricity always flowing from the grid into the vehicle, InCharge’s newest products also allow electrons to flow from the vehicle’s battery back into the grid–or anywhere else. This means that EVs basically become large, roving batteries that can be used to power virtually anything from the electricity in the depot, the grid, or other vehicles.

InCharge’s new product comes in three different sizes, 22kW, 44kW, and 66kW. All of which are considered relatively slow charging speeds compared to many direct current fast chargers that you might see on the side of the highway that are intended to charge your EV back to full capacity as quickly as possible. But speed is not the name of the game in bidirectional charging and isn’t much of a concern at depots, where vehicles usually sit idle overnight and have plenty of time to charge.

Instead, the technology is intended to help fleet owners save money. Especially right now, with the transition to electrification still in its relative infancy, the country’s energy grid in places like California is often saturated with renewable energy during the middle of the day when the sun is brightest and solar production is at maximum. During those hours energy is cheap and clean, but in the evening, when demand spikes and solar production begins to wane, electricity becomes dramatically more expensive and more reliant on fossil fuels.

According to InCharge CEO Terry O’Day the fleets his company is selling to are using the new tech for three different but closely-related applications.

The first is shaving the peak off of the demand curve. By enabling fleets to use electricity stored in their vehicle batteries to charge when energy demand is at its highest and most expensive, fleet owners can simple avoid charging when rates are at their highest. In the same vein, fleet owners can also hold onto their electrons until demand is high, and then sell the energy back to the grid for a profit. This is the same principle underlying the new residential rooftop solar rules outlined in NEM 3.0, which basically requires new solar installs to come with a battery in order to be profitable. But in the case of fleets, the scale is vastly magnified due to the size and number of the batteries in the system.

Finally, the tech can also be used to help fleet owners avoid drawing too much energy from the grid all at once: Right now, in California and many other places, grid operators charge a tariff for companies that use too much energy at any one time. Electricity may cost 30 cents per kilowatt hour, as long as you’re drawing less than 200 kWs at a time, for instance. But as soon as you exceed that level of power, companies may start charging more. Bidirectional charging can add the flexibility needed to stay below certain tariff levels–a concept known as tariff shifting.

All of this equates to cost savings for fleet owners. And while these savings will likely pale in comparison to the cost of buying a new fleet of EVs and installing the charging tech, the savings scale with how large the fleet is and can significantly ease the pain. O’Day can’t publicly divulge yet who the major customers have been for the new chargers, but he says InCharge has a pipeline of order numbering in the thousands, spanning from delivery companies to school districts.

Like much of the electrification industry, one of the biggest bottlenecks for InCharge is waiting for utility companies to install grid upgrades that allow the chargers to actually connect to the larger grid. “It's taking as much as 24 months to get utility upgrades at a lot of sites,” says O’Day. Against that background, planning remains a major challenge for fleet owners, and despite progress in standardizing the tech, interoperability between charger and vehicle can remain an issue. InCharge is O’Day’s fifth EV startup. “Each time I start one of these companies, I think it's you know, we're gonna be making cookies. Turns out, we're making snowflakes pretty much.”

While InCharge offers a turnkey solution and will work with clients to understand the needs and requirements of every custom install, the market remains somewhat disjointed. “Different providers in the value chain are all trying to come together and make their stuff work together. They may choose you for a slice of it, your brother for another slice of it, your sister for a different one, and then all the siblings have to work together,” O’Day says. “That can get complicated.”

The industry has already seen that drama play out in the light duty public charging sector, where every charger brand has its own apps, its own payment procedure, and its own charger standards. All of this has led to an unreliable charging experience for EV owners—a study from April 2022, for instance, found that less than three quarters of the chargers in its survey were actually operational.

For fleets, where vehicle uptime equals revenue, this is simply not an option, and the commercial transportation industry is eager to avoid the same pitfalls.

Up to this point, Tesla is the only non commercial charging company that has managed to deliver a solid product. The EV giant is famous for the quality of its supercharger network, and to O’Day, the success isn’t particularly surprising. “For Tesla is it's an integrated, fully interoperable charger and vehicle where Tesla builds the software, they own the sites and they [control the payment processing.]”

While O’Day doesn’t want to compare InCharge to Tesla, he says that sort of unified turnkey approach will be vital for the commercial transportation industry as it works to eliminate diesel completely by 2036, as per the California Air Resources Board’s recent ruling. Getting there will be a Herculean effort, but bidirectional charging is almost guaranteed to be crucial in making the transition economically viable.

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Creandum’s Carl Fritjofsson on the Differences Between the Startup Ecosystem in Europe and the U.S.

Decerry Donato

Decerry Donato is a reporter at dot.LA. Prior to that, she was an editorial fellow at the company. Decerry received her bachelor's degree in literary journalism from the University of California, Irvine. She continues to write stories to inform the community about issues or events that take place in the L.A. area. On the weekends, she can be found hiking in the Angeles National forest or sifting through racks at your local thrift store.

Carl Fritjofsson
Carl Fritjofsson

On this episode of the LA Venture podcast, Creandum General Partner Carl Fritjofsson talks about his venture journey, why Generative-AI represents an opportunity to rethink products from the ground up, and why Q4 2023 and Q1 2024 could be "pretty bloody" for startups.

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AI Is Rapidly Advancing, but the Question Is, Can We Keep Up?

Lon Harris
Lon Harris is a contributor to dot.LA. His work has also appeared on ScreenJunkies, RottenTomatoes and Inside Streaming.
AI Is Rapidly Advancing, but the Question Is, Can We Keep Up?
Evan Xie

One way to measure just how white-hot AI development has become: the world is running out of the advanced graphics chips necessary to power AI programs. While Intel central processing units were once the most sought-after industry leaders, advanced graphics chips like Nvidia’s are designed to run multiple computations simultaneously, a baseline necessity for many AI models.

An early version of ChatGPT required around 10,000 graphics chips to run. By some estimates, newer updates require 3-5 times that amount of processing power. As a result of this skyrocketing demand, shares of Nvidia have jumped 165% so far this year.

Building on this momentum, this week, Nvidia revealed a line-up of new AI-related projects including an Israeli supercomputer project and a platform utilizing AI to help video game developers. For smaller companies and startups, however, getting access to the vital underlying technology that powers AI development is already becoming less about meritocracy and more about “who you know.” According to the Wall Street Journal, Elon Musk scooped up a valuable share of server space from Oracle this year before anyone else got a crack at it for his new OpenAI rival, X.AI.

The massive demand for Nvidia-style chips has also created a lucrative secondary market, where smaller companies and startups are often outbid by larger and more established rivals. One startup founder compares the fevered crush of the current chip marketplace to toilet paper in the early days of the pandemic. For those companies that don’t get access to the most powerful chips or enough server space in the cloud, often the only remaining option is to simplify their AI models, so they can run more efficiently.

Beyond just the design of new AI products, we’re also at a key moment for users and consumers, who are still figuring out what sorts of applications are ideal for AI and which ones are less effective, or potentially even unethical or dangerous. There’s now mounting evidence that the hype around some of these AI tools is reaching a lot further than the warnings about its drawbacks.

JP Morgan Chase is training a new AI chatbot to help customers choose financial securities and stocks, known as IndexGPT. For now, they insist that it’s purely supplemental, designed to advise and not replace money managers, but it may just be a matter of time before job losses begin to hit financial planners along with everyone else.

A lawyer in New York just this week was busted by a judge for using ChatGPT as part of his background research. When questioned by the judge, lawyer Peter LoDuco revealed that he’d farmed out some research to a colleague, Steven A. Schwartz, who had consulted with ChatGPT on the case. Schwartz was apparently unaware that the AI chatbot was able to lie – transcripts even show him questioning ChatGPT’s responses and the bot assuring him that these were, in fact, real cases and citations.

New research by Marucie Jakesch, a doctoral student from Cornell University, suggests that even users who are more aware than Schwartz about how AI works and its limitations may still be impacted in subtle and subconscious ways by its output.

Not to mention, according to data from, high school and college students already – on the whole – prefer utilizing ChatGPT for help with schoolwork over a human tutor. The survey also notes that advanced students tend to report getting more out of using ChatGPT-type programs than beginners, likely because they have more baseline knowledge and can construct better and more informative prompts.

But therein lies the big drawback to using ChatGPT and other AI tools for education. At least so far, they’re reliant on the end user writing good prompts and having some sense about how to organize a lesson plan for themselves. Human tutors, on the other hand, have a lot of personal experience in these kinds of areas. Someone who instructs others in foreign languages professionally probably has a good inherent sense of when you need to focus on expanding your vocabulary vs. drilling certain kinds of verb and tense conjugations. They’ve helped many other students prepare for tests, quizzes, and real-world challenges, while computer software can only guess at what kinds of scenarios its proteges will face.

A recent Forbes editorial by academic Thomas Davenport suggests that, while AI is getting all the hype right now, other forms of computing or machine learning are still going to be more effective for a lot of basic tasks. From a marketing perspective in 2023, it’s helpful for a tech company to throw the “AI” brand around, but it’s not magically going to be the answer for every problem.

Davenport points to a similar (if smaller) whirlwind of excitement around IBM’s “Watson” in the early 2010s, when it was famously able to take out human “Jeopardy!’ champions. It turns out, Watson was a general knowledge engine, really best suited for jobs like playing “Jeopardy.” But after the software gained celebrity status, people tried to use it for all sorts of advanced applications, like designing cancer drugs or providing investment advice. Today, few people turn to Watson for these kinds of solutions. It’s just the wrong tool for the job. In that same way, Davenport suggests that generative AI is in danger of being misapplied.

While the industry and end users both race to solve the AI puzzle in real time, governments are also feeling pressure to step in and potentially regulate the AI industry. This is much easier said than done, though, as politicians face the same kinds of questions and uncertainty as everyone else.

OpenAI CEO Sam Altman has been calling for governments to begin regulating AI, but just this week, he suggested that the company might pull out of the European Union entirely if the regulations were too onerous. Specifically, Altman worries that attempts to narrow what kinds of data can be used to train AI systems – specifically blocking copyrighted material – might well prove impossible. “If we can comply, we will, and if we can’t, we’ll cease operating,” Altman told Time. “We will try, but there are technical limits to what’s possible.” (Altman has already started walking this threat back, suggesting he has no immediate plans to exit the EU.)

In the US, The White House has been working on a “Blueprint for an AI Bill of Rights,” but it’s non-binding, just a collection of largely vague suggestions. It’s one thing to agree “consumers shouldn’t face discrimination from an algorithm” and “everyone should be protected from abusive data practices and have agency over how their data is used.” But enforcement is an entirely different animal. A lot of these issues already exist in tech, and are much larger than AI, and the US government already doesn’t do much about them.

Additionally, it’s possible AI regulations won’t work well at all if they aren’t global. Even if you set some policies and get an entire nation’s government to agree, how to set similar worldwide protocols? What if US and Europe agree but India doesn’t? Everyone around the world accesses roughly the same internet, so without any kind of international standard, it’s going to be much harder for individual nations to enforce specific rules. As with so many other AI developments, there’s inherent danger in patchwork regulations; it could allow some companies, or regions, or players to move forward while others are unfairly or ineffectively stymied or held back.

The same kinds of socio-economic concerns around AI that we have nationally – some sectors of the work force left behind, the wealthiest and most established players coming in to the new market with massive advantages, the rapid spread of misinformation – are all, in actuality, global concerns. Just as the hegemony of Microsoft and Google threaten the ability of new players to enter the AI space, the West’s early dominance of AI tech threatens to push out companies and innovations from emerging markets like Southeast Asia, Subsaharan Africa, and Central America. Left unfettered, AI could potentially deepen social, economic, and digital divisions both within and between all of these societies.

Undaunted, some governments aren’t waiting around for these tools to develop any further before they start attempting to regulate them. New York City has already set up some rules about how AI can be used during the hiring process while will take effect in July. The law requires any company using AI software in hiring to notify candidates that it’s being used, and to have independent auditors check the system annually for bias.

This sort of piecemeal figure-it-out-as-we-go approach is probably what’s going to be necessary, at least short-term, as AI development shows zero signs of slowing down or stopping any time soon. Though there’s some disagreement among experts, most analysts agree with Wharton professor and economist Jeremy Siegel, who told CNBC this week that AI is not yet a bubble. He pointed to the Nvidia earnings as a sign the market remains healthy and not overly frothy. So, at least for now, the feverish excitement around AI is not going to burst like a late ‘90s startup stock. The world needs to prepare as if this technology is going to be with us for a while.

Rivian CEO Teases R2, New Features in Instagram AMA

David Shultz

David Shultz reports on clean technology and electric vehicles, among other industries, for dot.LA. His writing has appeared in The Atlantic, Outside, Nautilus and many other publications.

Rivian CEO Teases R2, New Features in Instagram AMA

Rivian CEO RJ Scaringe took to Instagram last weekend to answer questions from the public about his company and its future. Topics covered included new colors, sustainability, production ramp, new products and features. Speaking of which, viewers also got a first look at the company’s much-anticipated R2 platform, albeit made of clay and covered by a sheet, but hey, that’s…something. If you don’t want to watch the whole 33 minute video, which is now also on Youtube, we’ve got the highlights for you.

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