Optimizing vehicle fleets: how artificial intelligence is transforming charging and maintenance?
Case study: Alstom and Cleo
The rapid electrification of transport is profoundly transforming how operators manage their vehicle fleets. Managing electric charging and ensuring preventive maintenance: these are challenges that require greater responsiveness and precision. In this context, artificial intelligence is establishing itself as a concrete lever of efficiency. Far from the abstract image sometimes attributed to it, it acts here as a decision‑support tool capable of analyzing, predicting and optimizing daily operations.
Polara and Alstom particularly well illustrate this transformation: Polara integrated the Cleo platform following an acquisition in order to automate and optimize charging management; Alstom uses intelligent systems to anticipate maintenance needs and improve the reliability of public rail transport. Together, they demonstrate how AI can improve performance while supporting the transition to zero‑emission mobility.
Polara / Cleo — Automating charging to free operators
The electrification of vehicle fleets brings its share of challenges, notably regarding charging. Scheduling times, avoiding consumption peaks, ensuring all vehicles are ready on time: these tasks, once simpler to manage with fossil fuels, quickly become complex in an electric context. To address this reality, Polara relies on Cleo, an intelligent charging management software platform originally developed within Hydro‑Québec, then integrated into Polara following an acquisition. Powered by artificial intelligence, Cleo automates and optimizes the entire end‑to‑end charging process.
Cleo's solution gradually learns vehicles' habits through telematics data and environmental information such as weather. It anticipates the energy needs of each vehicle based on its actual use and orchestrates charging so as to avoid overloads and costs related to power calls or unnecessarily fast charging. Concretely, the operator no longer has to manually schedule charging hours: they simply plug in the vehicles, and the system automatically determines when and how to charge them at the best cost. This adaptive intelligence ensures that vehicles are always ready to hit the road, while reducing energy costs and avoiding grid overload.
AI also allows the platform to adjust charging plans in real time when an unforeseen event occurs, such as a route change or a modification of departure times. By simplifying an operation that was once heavy and time‑consuming, Cleo frees up managers' time and facilitates the adoption of electric vehicles in professional vehicle fleets.
Alstom — Making rail maintenance predictive
In the rail sector, equipment reliability and passenger safety are absolute priorities. Alstom incorporates artificial intelligence into its strategy as one lever among others to improve operational performance and limit service interruptions. Each train and each network component now generate thousands of data points from sensors, scanners and onboard monitoring systems. This information, processed continuously by Alstom's intelligent platforms, makes it possible to monitor train health in real time.
Tools such as HealthHub™, TrainScanner or InfraScanner analyze more than two hundred indicators, ranging from temperature to brake wear, including vibrations and noise. Thanks to these predictive analyses, it becomes possible to identify early signs of failure and intervene before an incident occurs. This predictive maintenance approach reduces downtime, improves safety and ensures better service regularity.
AI does not only serve to maintain trains: it also helps improve the passenger experience, by helping to understand their habits, anticipate periods of crowding and enhance onboard safety. The combination of technology and human approach thus helps Alstom achieve its objectives regarding safety, efficiency and sustainability.
Concrete and measurable results
These initiatives illustrate the tangible impact of artificial intelligence on fleet performance. Polara, with Cleo, succeeded in completely automating electric vehicle charging, reducing energy costs while eliminating errors and delays related to manual scheduling. For its part, Alstom has revised its way of managing rail maintenance thanks to proactive anomaly detection, offering more reliable and safer networks for users.
In each case, artificial intelligence is not an end in itself: it is put at the service of a concrete need, with constant attention to ease of use and operational value.
Lessons and perspectives
The combined experience of Polara and Alstom shows that the success of an AI project relies above all on the quality of the data. An AI cannot learn and produce reliable results unless it is based on clean, consistent and well‑structured information. This preparation step, often underestimated, is nevertheless decisive.
These companies also remind us that technology does not replace humans: it assists them. Data interpretation, final decision‑making and system supervision remain the operators' role. It is this alliance between field expertise and the proper use of artificial intelligence that produces the best results.
Finally, simplicity of adoption is essential. The more intuitive and integrated a solution is with existing tools, the more it is used and the more its benefits become visible. Cleo is a good example: by making charging automatic and invisible, the platform has removed a major barrier to fleet electrification.
Conclusion — Towards an intelligent and sustainable fleet
Artificial intelligence is no longer a distant promise: it is already transforming transport operations today. On a bus or a train, it observes, learns and anticipates to make each journey more efficient. Thanks to it, managers can charge their vehicles at the right time and avoid breakdowns before they occur.
These innovations, driven by Polara and Alstom, show that it is possible to reconcile operational performance and sustainability. Tomorrow's fleets will be connected, adaptive and predictive: systems capable of understanding their environment, adjusting their behavior and acting autonomously. Artificial intelligence thus becomes the silent engine of cleaner, more reliable and more human mobility.
AI in the service of ethics
However, artificial intelligence cannot be deployed without reflection on its responsible use.
Like any powerful tool, it must be used with discernment:
- respecting data protection ;
- ensuring transparency of decisions ;
- and keeping humans at the heart of processes.
It is equally important to ensure that the use of AI is aimed at increasing collective well‑being. Furthermore, considering its significant energy use, it must be used in ways that reduce its ecological impact as much as possible and only when it can have a real positive impact.
AI ethics is not a constraint, but an essential condition for its use.
There are resources available to support transport stakeholders towards an ethical, reliable and sustainable AI.
- The Montreal Declaration is addressed to policymakers as well as to any person, any civil society organization and any company wishing to participate in the development of AI in a responsible manner. It lists principles that serve as the directions of an ethical compass for guiding the development of artificial intelligence toward morally and socially desirable ends.
- Here is also a resource offered by the Order of Engineers of Quebec. This document presents a summary of the 6 axes of vigilance of the Professional Practice Guide, highlighting the recommendations of the Order of Engineers of Quebec on the responsible use of artificial intelligence.
We encourage you to adopt a thoughtful and responsible approach in your use of artificial intelligence. The resources we offer below are not exhaustive; we therefore invite you to deepen your knowledge and to remain attentive to best practices in ethical AI.
