Connected Enterprise Solutions

Leaving the end-consumer side of the connected car for a moment, we would like to turn now to the enterprise side of things. Many enterprises are also looking at ways to leverage the IoT and connected vehicles for enterprise solutions. Fleet management and telematics have been important topics for over 20 years now, and this whole area is getting increasingly sophisticated. Leveraging the new connectivity to learn about the performance of car components in the field is another interesting area, especially for car component manufacturers.

Fleet Management

Companies that operate fleets of cars, trucks, ships, rail cars, and aircraft are often required to manage thousands of assets. For many fleet operators, the ability to have real-time data from these assets integrated into their fleet management systems is a very attractive proposition. Airlines and logistics companies rely on this data to manage their transportation networks efficiently. Leasing companies can use real-time fleet data to optimize financial forecasting and planning.

In the early days of online fleet management, vehicle trackers and similar hardware devices were deployed within the vehicles as independent units; for example, GPS units for vehicle positioning. Communication between the vehicle and the backend can be terrestrial or satellite-based. On-board bus systems like the CAN bus [CAN1] can be used by modern fleet management systems to access many different sources of data relating to the vehicle or asset; for example, engine state, fuel level, driving behavior, etc. This data can be used to provide functionally rich, connected fleet management solutions. The figure below provides an overview of the main features of an advanced connected vehicle fleet management solution [BSI1]. The section on field assets and devices also looks at other forms of fleet management, such as container tracking (see PurFresh case study) and mobile work equipment (see Kärcher case study).

Fleet management features (Source: Bosch)

Fleet management features (Source: Bosch)

Basic fleet management functionality typically includes fleet master data management as well as reporting and controlling features. Typically, a connected fleet management solution will need to integrate with other backend systems, such as an ERP or CRM system.

Vehicle state data can also be used for a variety of fleet management features. For example, mileage data can be used for proactive maintenance and to display the next service date to the driver. In this case, the system can also proactively guide the driver towards a suitable service station. Mileage reporting features can be used to compare projected final mileage against leasing contract data.  Accident notification features notify the fleet manager when an accident is detected. Vehicle diagnostics features can store error codes from the vehicle and make them available in plain text format. And finally, there are car theft warning features, which don’t require an explanation.

The next set of fleet management features that we will look at relate to driving behavior. Fuel management features replace the requirement to manually record fueling operations and fuel levels. Driver behavior features allow the recording of data about the motorist’s driving patterns such as speed, braking, and acceleration. Accident recorder features allow the storage of acceleration values together with the status of relevant vehicle sensors both before and after an accident. When it comes to reconstructing an accident and determining the root cause, this information is invaluable. There are also features available for recording key data about routes traveled. This information is mainly used for tax purposes.

Fleet productivity features also rely on data analytics and related technologies. For example, journey analysis can help optimize transportation routes. Accident/breakdown assistance features provide support for drivers. Geofencing can be used to control vehicle usage and prevent theft. Intra-fleet benchmarking allows the optimization of fleet costs and availability by comparing parameters such as mileage, fuel consumption, driving behavior, and service intervals.

Some of the connected fleet management features described here also feed into other use cases such as the usage-based insurance model described above.

Systematic Field Data

It’s not just fleet operators who stand to benefit significantly from the ability to access real-time data from their vehicles. OEMs and car component manufacturers can benefit too, even mid-development lifecycle. Instead of designing car components on the basis of theoretical assumptions and tests, manufacturers can get access to detailed data on how their components are performing in the field. This provides an invaluable insight for product designers and engineers. Of course, the data required in this case is considerably more detailed than the data required for the fleet management solutions discussed before.

For example, the Systematic Field Data Collection and Analysis project (sFDA) led by Bosch`s Corporate Department Automotive Systems Integration (C/AI) involves the deployment of data collection hardware units in millions of cars that connect up to various on-board car components such as car brakes, power steering units, etc. The system can capture detailed usage patterns, including temperature, voltage curves, and so on. Getting this data back from the individual vehicles and into the central system can be achieved in a number of ways. In some cases, cars are connected to the home Wi-Fi, which is then used to transfer the field data back to the central sFDA system. However, the more common scenario is for the data to be downloaded whenever the car visits a participating car repair shop.

Data obtained in this way is invaluable for component developers. Two examples of the type of analytics results that can be obtained from field data are shown in the figure below. The first example analyzes the usage patterns of individual pumps, the second shows driving behavior clusters. Naturally, data privacy plays an important role here too. In this particular case, the central system does not store the vehicle ID (VID), and instead uses an anonymized “hash value.” This value cannot be traced back to the VID, but is helpful when it comes to linking all of the data for a specific car over time.

Two examples of field data analytics at work (Source: Bosch Software Innovations)

Two examples of field data analytics at work (Source: Bosch Software Innovations)