| Summary: | The lead–acid battery is still widely used today and will continue to be one of the benchmarks for diverse scenarios and applications. Although their energy densities are lower than more modern chemistries, the economic advantages of lead–acid batteries outweigh the disadvantages for numerous applications in transportation and stationary markets, uninterruptible power supply, and energy storage for wind or solar-based renewable systems. Nevertheless, optimizing their utility requires the continuous assessment of the state of health (SoH). The existing SoH estimation techniques use extrinsic quantities such as current, voltage, impedance, and temperature obtained through invasive (i.e., probing) methods. However, probing methods do not facilitate seamless cell-level charging to balance the SoH of the entire battery. Our study explored the non-invasive estimation of the battery SoH using acoustic energy. This method used sound both as an emitted and received signal propagating across the internal structure of a battery cell element unit (CEU) during its operation. Information from the received signals is analyzed relative to the aging degree of the lead–acid battery. The patterns of the received sound contain information on the CEU structure, which a data-driven algorithm subsequently encoded into a binary classifier of SoH levels: above 80% and below 80%. Results show that the binary classifier can distinguish between the two classes. A non-invasive SoH estimation technique can support the seamless operation of cell-level charging for lead–acid batteries. Balancing the SoH among all cells is needed to sustain the use life of the entire battery.
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