Diagnostic Technique for Large-scale Battery System


Student thesis: Master's Thesis

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Award date29 Oct 2021


Batteries storage system provides energy for power generation and distribution. To ensure system functionality and robustness, state-of-charge (SOC) and state-of-health (SOH) of the battery must be monitored by the battery management system. Determination of the SOC and SOH of batteries is a black box problem because the two parameters cannot be accessed directly. In practice, battery model is adopted to extract the intrinsic states of a battery. By extracting the intrinsic parameters in the battery model, SOC and SOH can be estimated. This thesis presents the findings of research on a new diagnostic technique for large-scale battery systems.

The batteries in large-scale storage systems are constructed by a plurality of series- and/or parallel-connected batteries. They are typically placed in unattended or remote locations. It is sometimes difficult to detect unexpected premature battery failures. An experimental prototype that can conduct real-time estimation of the SOC and SOH of two battery sets is built. Each battery set can be a single battery unit or multiple series- and/or parallel-connected batteries. The idea is based on using a switched-inductor circuit to control the discharging profile of one battery set to charge the other battery set, and vice versa, so that the charging and discharging processes of the two battery sets are profiled with such energy recycling process. By sensing the voltage and current of the battery sets, the SOC and SOH of the battery sets can be extracted. The proposed technique is applied to an electric wheelchair to monitor the condition of two lead-acid batteries.