real-world examples that make a difference for your business.
Dealing in theory is always harder to understand. For that reason, we put together several real-world use cases to let you see just how Motorq can help you.
use case: vehicle pre-delivery tracking
track your vehicle being shipped.
You can track just about any package, even a $2 pack of gum, but not your multi-thousand-dollar vehicle. Motorq is changing that. By working with OEMs, we can track your vehicle from the factory to your hands, so you know where it is and when it will arrive.
Our inputs let you know when it’s on the factory lot, in transit on rail, on car carrier and more. Then, we’ll let you know based on historical data, and adjusted for any weather days, exactly when your vehicle will arrive.
use case: trip analysis
are your vehicle and driver getting where they need to go?
Before, your driver would start the day with five stops in eight hours. How long he or she took at each stop, or the route they took, was an unknown. But now you can you track their entire trip. Motorq gives you consistent trip information to enhance not only your operations, but the safety of your drivers and vehicles.
From trip start to trip stop you can monitor in real-time your vehicles.
Receive real-time behavior event information. Know when you your driver is braking hard, accelerating too fast, etc. to be able to identify and react to any sort of patterns or events.
Receive time and location points along the trip, encoded for size optimization.
use case: fueling misuse exception reporting
don’t pay for what you don’t use.
In the past, Fleets have paid billions in fuel that didn’t go into their vehicles each year. Without knowing where your vehicle is, and when the gas tank was filling up, employees could skirt the system. But now, just a couple dollars a month can let you track what’s going on with your fueling, virtually eliminating fuel fraud.
Is your vehicle near a gas station? If it’s not, it’s highly unlikely the right vehicle is being fueled up.
gas tank levels
We get the data on the amount of fuel in a vehicle. So, we know exactly how much needs to get filled and when. So, if someone is fueling a full tank, but they only need a half, you can be notified and take swift action.
powerful tools and reports
Lastly, we look at all of the data over time, identify any anomalies and summarize everything for you. So, when you have to have those sometimes uncomfortable conversations, you have the data to back you up.
use case: ev charging and distance optimizing
predict your battery range.
Motorq is running a long-term analyses of EV battery performance. This allows us to tell you how your battery should be performing, using varied inputs.
Charting factors such as daily range vs. battery usage, battery efficiency vs. vehicle speed, and battery efficiency vs. ambient temperature allows us to be able to determine the best range of your vehicle. We can then cross-reference that with charging locations, optimizing your distance, charging locations and your charge frequency, so you’ll get the most out of your battery.
use case: geo fencing
monitor where your vehicles are.
By putting a polygon around the area where a vehicle should or should not be, you can be notified if they were to leave that area. You’ll get inputs such as time in, time out, amount of time spent outside. Then, we’ll look at past history to identify patterns so you can take swift action in dealing with the situation.
use case: insurance driver safety scoring
monitor your drivers to improve safety.
Motorq sorts through multiple data inputs, giving you the most accurate summary of how safe your drivers are on the road. By monitoring their behavior consistently and using our Machine Learning, we can help you improve your safety, lower insurance premiums and even predict when a pattern of behavior could lead to accidents.
receive varied inputs
We receive inputs like, hard acceleration, hard braking, speeding, seatbelt violations and more and aggregate them from a variety of inputs.
let the algorithm do the work
Then our algorithm assesses each input, giving you a trip-level assessment, event-level assessment and weighted composite score for on a rolling distance basis. It even lets you customize it to your needs by allowing you to configure the thresholds.
summarize for you
We then create a summary, highlighting percentages of each event and giving that driver a specific score. And we continue tracking it over time, so you can see where things improve and where things might need to be addressed.
use case: predictive battery failure detection
predict when a battery may fail.
Downtime is a four-letter word in your business. That’s why we leverage our Machine Learning to help you predict when your battery may fail, to reduce downtime as much as possible.
By observing a vehicle’s battery voltage, ignition, speed, utilization, ambient temperature and more, our Machine Learning models have been able to predict battery failure of tens of thousands of batteries to within days of the actual event.
This allows you to plan ahead and schedule maintenance on your terms, instead of leaving your drivers and customers high and dry. It’s one thing when it’s one battery, but multiply that by the hundreds of thousands of batteries you may change each year and those predictions could save you millions.