Integration of a Multimodal Sensor System for Health Monitoring of Battery Cells

The energy transition is increasing demand for high-performance batteries while raising requirements for safety, reliability, and environmental compatibility. Limited raw materials also make it essential to extend battery lifetime and assess second-life applications reliably. This requires accurate information on state of health (SOH) and remaining useful life to optimize charging, maintenance, and reuse. Current battery monitoring mainly relies on external measurements such as current, voltage, and temperature. These methods provide only limited insight into internal aging processes, often detecting degradation too late and preventing direct, state-dependent operation. This dissertation therefore investigates whether a sensor can be integrated into battery cells to measure aging-relevant parameters without significantly affecting cell chemistry. A multimodal sensor system is developed and validated to record internal aging parameters for future smart battery management systems. It combines a distributed fiber-optic sensor based on optical frequency domain reflectometry for in-situ temperature and strain measurements with an electrically conductive fiber coating that serves as a reference electrode for measuring electrode potentials. This enables simultaneous observation of the cell’s thermo-mechanical condition and electrochemical potentials. To separate the coupled thermal and mechanical signals of fiber-optic sensors, a decoupling model using two fibers with different core dopings is developed. Calibration parameters derived from their spectral shifts allow temperature and strain to be calculated independently. Several demonstrator cells with integrated sensors were tested. The in-situ temperature was measured with an uncertainty of about 2 °C. Long-term cycling showed a capacity loss of 5.6% over 30 cycles, while lithiation of the conductive coating enabled stable reference-potential measurements. Post-mortem analyses revealed an influence of the sensor fiber on cell performance, which was substantially reduced through optimized cell design. The results demonstrate the feasibility of multimodal in-cell sensing under laboratory conditions and identify the key steps required to reduce sensor impact and scale the technology.