Geometry Optimization Approaches of Inductively Coupled Printed Spiral Coils for Remote Powering of Implantable Biomedical Sensors

Electronic biomedical implantable sensors need power to perform. Among the main reported approaches, inductive link is the most commonly used method for remote powering of such devices. Power efficiency is the most important characteristic to be considered when designing inductive links to transfer energy to implantable biomedical sensors. The maximum power efficiency is obtained for maximum coupling and quality factors of the coils and is generally limited as the coupling between the inductors is usually very small. This paper is dealing with geometry optimization of inductively coupled printed spiral coils for powering a given implantable sensor system. For this aim, Iterative Procedure (IP) and Genetic Algorithm (GA) analytic based optimization approaches are proposed. Both of these approaches implement simple mathematical models that approximate the coil parameters and the link efficiency values. Using numerical simulations based on Finite Element Method (FEM) and with experimental validation, the proposed analytic approaches are shown to have improved accurate performance results in comparison with the obtained performance of a reference design case. The analytical GA and IP optimization methods are also compared to a purely Finite Element Method based on numerical optimization approach (GA-FEM). Numerical and experimental validations confirmed the accuracy and the effectiveness of the analytical optimization approaches to design the optimal coil geometries for the best values of efficiency

Reinforcement learning for the control of battery electrothermal behaviours in electric vehicles

International audienceBattery lifetime is related, among other things, to the battery temperature and RMS battery current. This paper presents an improved energy management of battery / supercapacitors (SCs) hybrid energy storage system (HESS) in an electric vehicle (EV) aiming at reducing the RMS battery current and battery temperature. A reinforcement learning (RL) based real-time energy management framework is designed to ensure an optimal power flow distribution between battery and supercapacitors starting from historical observation of the RMS battery current. First, the battery and SCs storage devices are modeled. An electrical model is used for the SC and an electrothermal representation is adopted to follow the evolution of the battery temperature and its electrical parameters (current, voltage). Then the RL energy management problem is formulated satisfying the electrical HESS constraints. The proposed methodology generates in real time an optimal power sharing between battery and SCs without any prior knowledge of the load variations of the EV. In our work, we propose a novel approach combining the rule based controller Frequency sharing with RL learning to achieve the best solution optimality. This approach is effective to adapt the rule-based strategy to work in their efficiency region and introduce additional intelligence. Simulation results have confirmed the convergence of the RMS battery current to the minimum values and appreciable reductions of the battery temperature are obtained

Reinforcement learning-based power sharing between batteries and supercapacitors in electric vehicles

International audienceEnergy management of Battery/Supercapacitors (SCs) hybrid energy storage system (HESS) aims to reduce RMS battery current values and enhance the battery lifetime. This paper presents a reinforcement learning (RL) based energy management strategy for Electric Vehicles (EV). This approach allows for learning in real time the optimal power flow distribution between battery and supercapacitors starting from historic of the observation of RMS current of battery. The power management problem is presented with RL formulation verifying the electrical HESS constraints. The presented framework uses the RL technique to control the power flow distribution leading to the minimization of the RMS battery current. Particularly, we propose a methodology that generates optimal frequency sharing policy between battery and SCs taking into account the load variations of the EV dynamically in real time. Numerical simulations carried out on Matlab/Simulink confirmed the convergence of the RMS battery current to the optimal value without any prior knowledge of the driving conditions. The proposed framework aims to adapt automatically the power management policy to the optimal solution