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SOTI 2016

The Impact of the Connected Vehicle


Telematics is changing the makeup of the modern vehicle. As vehicles continue to evolve and this technology grows, the maintenance of vehicles could be grouped into one of three categories: corrective, preventive and predictive.

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The aftermarket is just beginning to understand the impact that connected vehicles can have on the future of the auto service business. This article describes that impact in terms of the maintenance options available to customers today and how those options will likely evolve in the future.

Maintenance Planning & Correction

The fact is that vehicle maintenance is planned and carried out in different ways today. Rune Prytz of Volvo’s Uptime & Aftermarket Solutions’ Advanced Technology & Research team, suggests that both existing and future maintenance options can be grouped into three broad categories:


• Corrective

• Preventive

• Predictive

Corrective maintenance, commonly referred to as a repair, is performed after failure occurs and the vehicle is out of operation. Unfortunately, we all know that is an all too common approach for corrective action, especially when a repair is infrequent or expensive, or a vehicle is reaching end of life.

Preventive maintenance is a common practice used to avoid   unplanned corrective maintenance. In this case, components are replaced or overhauled at specified intervals despite any indication of an impending failure.


In his recent researchi on machine learning, Prytz defines a new approach called predictive maintenance – the combination of monitoring along with predictive modeling. The objective of predictive maintenance is to determine the condition of vehicle components in operation, predict which component is likely to fail and when that failure will most likely occur. Predictive systems work by examining the current physical relationship of inputs and outputs for one or more operational processes and comparing those with an existing model of the faultless process.

This is the basis for model-based diagnostics (MBD), condition-based maintenance (CBM) and other fault-detection methods used to identify and predict upcoming failures. Such fault detection and isolation (FDI) approaches are known as “knowledge-based” fault detection and diagnosis, and have traditionally required interpretation by human technicians with the requisite experience and expertise, such as ASE certified advanced engine performance specialists.


Predictive maintenance takes this one step further by predicting future failures and recommending preventative action through the use of on-board fault detection systems. But collecting and storing FDI data until it’s transmitted through the telematics gateway has been problematic at best. Downloads are expensive and usually limited to small chunks of information.

Storing the data collected from thousands of components on the vehicle component area network (CAN) isn’t practical because it requires large amounts of storage, which is costly to embed within systems.

Off-Board Storage

Instead of attempting to retain and analyze FDI data on-board the vehicle, manufacturers have been accumulating large amounts of FDI data and storing it in off-board databases. This should come as no surprise since they have a vested interest in the performance and life expectancy of their vehicle components. But the public has recently learned that these databases may contain much more than simply FDI data used to monitor component level performance, bringing up privacy concerns and other issues.

Details from Data

Vehicle manufacturers collect a wealth of data that can not only be used to remotely determine the health of a vehicle, but can also be used to develop usage profiles that include the driving behavior of vehicle owners and operators.

Vehicle manufacturers collect a wealth of data that can not only be used to remotely determine the health of a vehicle, but can also be used to develop usage profiles that include the driving behavior of vehicle owners and operators.

So, exactly what data can vehicle manufacturers receive via telematics today? In a June 2016 article in EETimes Europeii entitled, “Which data do OEMs collect from connected cars,” writer Christopher Hammerschmidt provides an enticing glimpse of some of the specific information beyond FDI data that’s currently collected by the manufacturers of four production vehicles: the Mercedes Benz B Series; BMW 320d; BMW i3, and the Renault Zoe. The German motor club, ADAC, provides these details.  Their investigation shows these EU-based vehicle manufacturers collect a wealth of data that can not only be used to remotely determine the health of a vehicle, but can also be used to develop usage profiles that include the driving behavior of vehicle owners and operators.

According to the information provided by ADAC, the Mercedes-Benz B series transmits mileage, fuel level, coolant level and tire pressure every two minutes. Admittedly the analysis of this particular data set might be used to assess vehicle health. But the benefits derived from analysis of other data transmitted along with this information, such as how many times the seat belts were tightened as the result of emergency braking and the corresponding GPS position, seem questionable.


BMW acquires a lot of data on part wear, particularly drivetrain components, which is expected; however, the value in capturing the maximum engine RPM along with the associated mileage, the amount of time driven in each of the different operating modes of the automatic transmission or the frequency and position of seat adjustments is less obvious.

Perhaps more disturbing is the BMW I3 Electric Vehicle (EV) “Last State Call” message, automatically transmitted to the manufacturer every time the driver switches off the car and locks the door. This data packet includes:


• The content of the error buffer;

• Battery details including cell temperatures and charge level;

• Intermodal connection points where the driver changes for another means of transport;

• Driving mode operational data of the range extender;

• Mileage at various driving operations;

• Quality of the charging point along with its location, and

• The last 100 parking positions.

As for Renault, it can access each Zoe remotely via mobile connection to collect any desired data from the Component Area Network (CAN) bus! Whenever remote diagnostics are enabled, the car typically transmits a data package containing several serial numbers, time stamp, GPS position, temperature and state of charge of the high voltage traction battery, a minimum of every 30 minutes. In addition, the manufacturer can request this data packet at any time and/or change the composition of this data contained in this packet on the fly. Renault can even inhibit battery charging, which might be considered useful in case a lease or vehicle payment is past due. While the remote diagnostics feature is deactivated by default in this EV, it can be activated remotely at any time.


The ADAC automobile club criticized the OEMs for not documenting the information discovered during their investigation. They have demanded manufacturers disclose a complete list of functions and parameters being retrieved and stored in their databases. In addition, they insist independent repair dealers should get full access to that list.

What About the U.S. Manufacturers?

A recent Frost & Sullivan study, “The Future of Parts and Service Retailing in the Automotive Aftermarket,”iii  reports General Motors has already rolled out prognostic capabilities as part of their OnStar telematics service. The study suggests it may not be long before GM offers the ability to send owners notification of services recommended through predictive analysis, based on the FDI data and extensive vehicle usage statistics they collect. The connectivity doesn’t end there – drivers may even be able to pay for post warranty services while still on the road.


Where does this leave the independent repair shop?

In the end it will likely be up to the collective automotive aftermarket to ensure consumers retain their right to choose what data the manufacturer collects and who has access this data, especially driver specific usage statistics. With future of the OBDII port in question, we must offer the driving public a viable choice for diagnostic services through vehicle telematics.

    i Machine learning methods for vehicle predictive maintenance using off-board and on-board data – Rune Prytz – ISBN: 978-91-



   i i Which data do OEMs collect from connected cars?


   i i i Future of Parts and Service Retailing in the Automotive Aftermarket – Research Code : NE4B-01-00-00-00, SKU: AU00984-GL-



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