Control & optimization of fuel cell temperature
Proton exchange membrane fuel cells (PEMFC) represent a key technology for the development of clean and sustainable energy systems, particularly for heavy-duty transport applications where their energy density is very attractive. However, in order to represent a viable industrial alternative, a number of obstacles still need to be overcome, including operating costs and, above all, the durability of the systems under real-world conditions. Among the levers for action, optimizing operating conditions is a promising avenue for limiting the degradation phenomena occurring within the cell. The operating temperature is a particularly key parameter because it affects all aspects of the system, from the kinetics of degradation mechanisms to the thermal capacity that the system can dissipate, including the water balance within the fuel cell. Despite the influence of this parameter on durability, it is generally only optimized at the system level to achieve the best performance, the shortest possible response time and to limit the size of the thermal management system.
The aim of this thesis is to work on optimizing the temperature management of a fuel cell within a system, taking into account not only performance but also sustainability criteria. To do this, the impact of operating temperature on degradation mechanisms will be analyzed using various simulation tools already available at LITEN and the teams' fifteen years of experience in studying PEMFC fuel cell degradation. Various thermal architectures will be proposed and evaluated in conjunction with the work on temperature control optimization. The latter will be implemented on a real fuel cell system in order to demonstrate the relevance of the proposed solution using concrete experimental data.
Task planning under constraints
The autonomy of embedded systems, in particular robots, comes from their capability to plan their next actions. Although, it becomes critical to ensure the safety of the behaviour as the systems are exposed to human interaction more and more (eg. Autonomous cars, toy UAVs, cobots in manufacturing and so on).
The goal of the thesis is to study task planning under constraints: select the best sequence of actions while optimising several criteria like efficiency, safety and other domain specifics. The thesis involves two main axes, the first is to study how to model the systems constraints in a manner that can be understood both by the human experts and the planning algorithm (eg. Using Operational Design Domain or Dynamic Assurance Case to evaluate system’s safety). Ontologies and knowledge graphs would probably be adequate to model the constraints. The model would benefit from their expressivity and the already-existing tooling. The second main axis is the improvement of the planning algorithm to leverage those models. Those models shall have a generic structure since it is necessary to represent many natures of constraints: safety, efficiency/cost, social “confort”, shared resources on the critical path, type and quantity of interactions between the agents, geometric feasibility, ...
As the thesis is aimed at robotic autonomous systems, it will be important to demonstrate and evaluate the system on real-world use cases.
Multi-criteria Navigation of a Mobile Agent applied to nuclear investigation robotics
Mobile robots are increasingly deployed in hazardous or inaccessible environments to perform inspection, intervention, and data collection tasks. However, navigating such environments is far more complex than simple obstacle avoidance: robots must also deal with communication blackouts, contamination risks, limited onboard energy, and incomplete or evolving maps. A previous PhD project (2023–2026) introduced a multi-criteria navigation framework based on layered environmental mapping and weighted decision aggregation, demonstrating its feasibility in simulated, static scenarios.
The proposed thesis aims to extend this approach to dynamic and partially unknown environments, enabling real-time adaptive decision-making. The work will rely on tools from mobile robotics, data fusion, and autonomous planning, supported by experimental facilities that allow realistic validation. The objective is to bring navigation strategies closer to real operational conditions encountered in nuclear dismantling sites and other industrial environments where human intervention is risky. The doctoral candidate will benefit from an active research environment, multidisciplinary collaborations, and strong career opportunities in autonomous robotics and safety-critical intervention systems.