ZEVi (Zero-Emission Vehicle Intelligence) is an EV fleet management tool that utilizes machine learning to optimize EV fleet operations, ensuring businesses don’t overspend on power and operations teams can track their hardware with complete confidence.
My Role
Build a tool that helps fleet teams manage EV vehicles for their fleet.
Time
2022-2024
My Team
- Product Designer
- Data Scientist
- Product Managers (x3)
- Software Delveloper
Notable Achivements
Turned a complex Alert system for Hardware and Operations teams into a manageable interface.
Fully redesigned the platform to be scalable with any hardware, and created its first Design system with variables.
The tool was eventually sold to Greenlane, one of the first public charging networks for commercial vehicles.
Problem
The Zevi could not scale to the complexities of EV fleets in its current iteration. It needed to represent multiple types of chargers, ports, vehicles, and bring convenience to operations teams, electrifying their fleets.
User Insight
Range anxiety, fear of hardware failure, and a lack of understanding of utility rates and change.
Business Goal
Create a Fleet management tool to support EV fleet operations, enabling them to adapt to EV vehicles while maintaining low costs, powered by machine learning.
Approach
Reseach
We partnered with an EV journalist who connected us to multiple school districts around the country, 15 in total, and talked to large Supply Fleet managers such as AB InBev, and US Foods to get an idea of their current thoughts, feels, and operation strategies.
Interviewed
Directors: 4
Superintendents: 2
Fleet Managers: 4
Ev Bus Companies: 1
Operational Managers: 4
Quotes
"We don’t have the headcount to have someone manually plug and unplug vehicles... ” - Fleet Manager
“Will we be able to know what Vehicles are over charging?”
- Head of Electrificartin
“How do we address chargers that lose connection?” -Superintendents
KeyTake aways for Core Themes
1. Lack of overhead to manage the charging.
2. Lack of trust in the hardware, telematics, vehicles, and charger.
3. Afraid of the complexity of Demand Charge rates & Tariffs.
How do we Address these themes, and create confidence?
The Plan
Create features that directly address initial hesitations and the feasibility of using electrified fleets. Decrease feelings of anxiety, and inform and educate when we can to bridge the knowledge gap.
Solution 1
Enhance operational fleet visibility and simplify hardware issues.
Solution 2
Make it easy to see the state of all the chargers & ports in your depot.
Solution 3
Create an easy way to optimize energy usage, understand and forecast future costs.
Each of these solutions is designed to address a specific theme of pain points. The idea was that if we could address the core fears associated with electrifying a fleet, then we would be one step closer to normalizing its practice.
Fleet Page
View all your vehicles and their charging status, including whether they are charging, idle, paused, or affected by a faulty charger. Set target charge times.
Reacting to broken charger
In this example, a fleet manager receives a message showing a broken charger and notices the connected vehicle to that charger is in an error state. They can communicate with ground operations, allowing them to switch out a charged vehicle for another one that needs charging.
Charger Page
Allows Operations teams to manage their charging schedules. See real energy time, output charger type, model, and number of ports. New fleet managers could easily see and scan the chargers at their depots.
Energy Page
Allows Operations teams to scan their energy usage quickly. Businesses no longer need to be in the dark about how they're being charged. The system can adjust to the different utility characteristics of every state. From different tariffs to demand charges, all of these have been optimized to improve any operations.
This was a brief idea of my experience. Lets chat about the details! ✉️
