Accurate and Intelligent State-of-X estimation – Including Sensing Techniques

The integration of smart functionalities plays an essential role to increase safety, reliability and cycle life of lithium ion batteries. The scope of this vacancy is to address this vision by developing spatially resolved thermal, mechanical and acoustic/ultrasonic sensing arrays to monitor the critical cell parameters in-operando and to help localizing emerging defects at an early stage to reduce premature battery aging. The intended sensing functionalities are non-invasive and affordable in order to keep the levelized cost of energy (LCOE) low.

The implemented sensor components are either commercially available or of a sufficiently high TRL level, so that the lab-scale validation and demonstration of the potential for smart sensors and resulting optimized battery operation is a realistic target. The sensing data is to be correlated with battery performance and implemented in models, helping to create a smart battery system with advanced management systems on cell and battery level in order to establish smart batteries with an enhanced operation window and prolonged cycle life. Additionally, the need for enhanced sensing techniques is to be critically evaluated for current and future battery technologies. For example, is there a need to monitor each cell individually, or is the monitoring of selected cells sufficient to tailor aging models and cell management systems.

Hence, the goal is to gain full supervision and control of the battery system. In order to increase its quality, reliability and life (QRL), it is necessary to monitor and control in-operando the system’s performance and to ensure its operation within a predefined safe operation area (SoA). In this regard, the states of batteries such as the state of charge (SoC), state of health (SoH) in terms of available state of energy (SoE) and state of power (SoP), and state of safety (SoS) ought to be systematically extracted. The challenge is to incorporate smart functionalities into the battery cell for following in time and space, different relevant cell component parameters such as temperature variations, dynamics among interfaces, structural changes by the integration and development of various sensing technologies so as to facilitate control of individual cells status within the battery system.

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