Artificial intelligence offers fleet managers new tools to monitor, study and optimise company vehicle fleets. The integration of smart sensors, machine learning algorithms and network services is proving increasingly useful for predictive maintenance, traffic and route analysis, and for improving people’s safety. Variables that have a positive impact on vehicle wear and tear and the reduction of downtime and resources.
The systems facilitate fluid driving, i.e. without abrupt braking or acceleration, while also enabling lower fuel consumption: one of the heaviest costs in fleet management. Innovations also cover electric vehicles and charging systems, useful for favouring and supporting the renewal of the vehicle fleet. According to the European Environment Agency (EEA), in the EU passenger cars and light commercial vehicles such as vans are still responsible for around 16% and 3% respectively of total emissions of carbon dioxide, one of the main greenhouse gasses.

Technologies based on artificial intelligence are revolutionising the maintenance of company fleets, in particular through predictive systems. The installation of smart sensors and the use of machine learning algorithms ensure daily monitoring of the vehicle’s status, analysing data such as tyre pressure, engine oil levels or brake wear and tear. For example, the ThingWorx platform by PTC is a next-generation example of Internet of Things (IoT) and artificial intelligence (AI) integration. ThingWorx uses sensors connected to the assets, either vehicles or machinery, to collect data in real time.

Artificial intelligence is equally useful for optimising routes. We have come a long way since the days when we could only intervene with policies and directions for navigation. Some years ago, the case of the American company UPS became renowned. As Professor Graham Kendall of the University of Nottingham explained on The Conversation, in the United States the company’s drivers did not always pick the shortest route to arrive at their destination. Among the various guidelines, UPS suggested, when possible, to always turn right at crossroads. The reason? To avoid turning into oncoming traffic. Now there is AI-base software such as Routific that reduces mileage, fuel and delivery times. According to the company, the savings can reach up to 25%.  

According to the Fleet manager software market report survey, carried out by Fortune Business Insights, in 2022, the global market of fleet management software was valued at 20.58 billion dollars. The sector is expected to grow by 23.67 billion dollars in 2023 and to 79.82 billion dollars by 2030, with a compound annual rate of +19% during the forecast period. What also drives this business are the innovations for safety and comfort. Mobileye software uses, for example, artificial intelligence and predictive analysis to reveal potential dangers on the road, advising the driver in real time of possible collisions, veering out of the lane or other dangerous situations. Finally, fleet management systems such as Geotab use AI to monitor compliance with required driving and rest times, ensuring that the company adheres to current regulations and minimising the risk of fines.

This type of fleet management technology is going through a dynamic period that will be fully highlighted at upcoming international events. The most prominent of these events are "We make Future", held in Bologna from 13 to 15 June, and "Power2Drive Europe", in Munich from 19 to 21 June. At the same time, "Electric & Hybrid" will take place in Stuttgart from the 18 to 20 June.

The increased uptake of electric vehicles for company fleets also introduces new dynamics to the management of energy and charging infrastructure. Artificial Intelligence enables improvements in energy efficiency, reducing running costs. AI can analyse complex data to determine the best time and place for charging electric vehicles, taking into account various factors such as times of lower energy demand, electricity costs, availability of charging stations and the operational requirements of the fleet. By anticipating peaks in the fleet’s energy demand, AI-enabled proactive management avoid overloads on the distribution network. This is particularly useful for large fleets which can put significant pressure on the local distribution network during peak periods of demand. Another important issue for an electric fleet is the integration of renewable energy into the charging process. Using AI systems, companies can manage vehicle charging with optimal efficiency, making use of energy produced from renewable sources such as solar or wind power.