Batteries are really hard to predict and manage:
- We don’t know for how long they would operate for
- How long would it take for them to fully charge
- When we should buy new ones to replace the old ones
Seems familiar, no? Remember that time your phone died at 10%? Yes, it is a problem that we all face with our smartphones. But, imagine, just for a moment, the problems faced by companies owning a fleet of electric vehicles: EV manufacturers and fleet operators.
Billions of dollars are being spent on battery tech. Why don’t we have a reliable and consistent technology yet?
Batteries are made up of chemicals (we are not getting in to the details) that are really affected by 2 things:
- Usage habits: the way we use the batteries (discharge them) and the way we charge them (yes, you can’t leave your smartphone charging overnight. It will kill your battery)
- Environmental factors: temperature, humidity, and pressure affect battery behavior and lifespan
Currently, EV manufacturers rely on
Believe or not, there is no way to cover all the possible usage habits and environmental factors in a lab environment. For that reason, companies end up:
- Changing batteries periodically which leads to battery waste (environment and cost)
- Changing batteries once they fail which might lead to sudden failures during critical missions (tight delivery schedule, an electric ambulance rushing to the hospital, etc.
Again, changing batteries once they fail isn’t only unreliable but also:
- The company will need to order a new battery to replace the failed one. It will take more or less 1 month for delivery, resulting in downtime
- The company will keep an inventory of batteries (exchange directly batteries when they are dead). However:
- Inventory space costs money
- Batteries die slowly when they are not being used (degradation)
For the above reasons, we believe that battery tech is one of the key factors that are limiting the growth of electric mobility worldwide.
And this is why dox was born…
At dox, we believe that the only solution would be to actively monitor the battery and predict its behavior based on users’ driving habits and environmental factors. With our proprietary machine learning algorithm, we predict key battery metrics based on the above factors and companies will be able to:
- Know exactly when to order and replace their batteries which will reduce inventory costs and battery waste
- Get notified before something wrong happens with the battery which will improve reliability
- Automate team management, reporting,
andanalytics which will help companies reduce operational and maintenance costs.