• Predictive Maintenance
  • Transportation Efficiencies
  • Connected Vehicles
  • Vehicle Fleets

The Rise of Connected Vehicles Is Changing the Approach to Vehicle Maintenance

Jun 14, 2018

In late April, I attended the Advanced Clean Transportation Expo in Long Beach, California. One of the main themes at the Expo was how the penetration of Internet of Things (IoT) technologies enhances commercial vehicles. Currently, commercial vehicle maintenance is preventative; meaning maintenance is scheduled to occur after some interval of mileage or time, and whenever an engine light notifies a driver that maintenance is needed. However, with the increasing application and prevalence of connected sensors throughout vehicles, fleet owners are shifting away from the traditional approach to maintenance.

From Interval Maintenance to Maintenance On-Demand 

As more vehicles become connected and as more sensors are added to track more parts, there is a shift away from preventative maintenance toward new models.

Vehicles equipped with connected sensors are already enabling changes to the maintenance chain. OEMs have begun using telemetry data to offer remote diagnostics services for fleet managers who own vehicles with connected sensors. Remote diagnostics can enhance vehicle maintenance by providing real-time analysis of engine fault codes and other component issues to enable faster, more informed maintenance. However, these remote diagnostics tools are often reactionary in nature, and work alongside preventative maintenance strategies. And for fleet managers, the holy grail of connected vehicles is predictive maintenance.

Using connected vehicle sensors, predictive maintenance would enable fleet managers to stay on top of maintenance requests and fix parts before they fail. By aggregating telemetry data collected from a vehicle fleet and correlating it with component failure history, predictive models can be built that project the service life of components. As the real-time data of components, such as the starter battery or brakes, begins to resemble what happens leading up to a failure, the vehicle can be serviced before it needs unscheduled downtime. This enables fleets to reduce their vehicle downtime and reduce costs by avoiding catastrophic maintenance events. As fleets become more reliant on predictive maintenance and vehicles come equipped with more sensors to track most—if not all of—a vehicle’s components, there will be less need for preventative and scheduled maintenance to take place.

The Future of Predictive Maintenance and IoT

IoT technologies are also expanding into the connected vehicle space. Edge analytics of vehicle components, in particular, will be hugely impactful on fleet management. Currently, most of the telemetry data gathered for remote maintenance is not analyzed at the point of data collection (also known as the edge). Rather, much of the remote diagnostics data analysis happens in the cloud. As vehicle component sensors become more advanced and IoT-enabled, more of the data analysis used for remote diagnostics and predictive maintenance will occur at the edge by embedding the lifecycle models that were developed in the cloud from aggregate data. As the number of sensors on each vehicle grows and becomes more sophisticated in collecting data, so too will the volume of data grow. Moving large amounts of data will become a consideration in the costs of real-time analysis. A shift toward edge maintenance, where the analysis used to make maintenance decisions happens at the sensor, will reduce the amount of data needing to be sent to the cloud.

These changes will require the status quo of vehicle maintenance to change over the next 5-10 years as the technology continues to penetrate the vehicle population and as fleet managers realize the added value in such services. Stakeholders on the maintenance side and those upstream will need to adapt to new business models where predictive and edge maintenance replace current business models that revolve around scheduled and catastrophic maintenance. These new maintenance models may have to become more integrated with other platforms to remain competitive with their service delivery and parts availability.