Pack-level current-split estimation for health monitoring in Li-ion batteries

Due to the complicated structural hierarchy and electrochemical processes, lithium-ion battery packs are often monitored by numerous number of sensors. The performance of a pack is highly dependent on the health of these sensors. A faulty current sensor in particular, may affect the estimation accuracy of the state-of-charge and state-of-health. This may cause the battery to suffer from charging and aging issues. Therefore, a scheme to monitor health of these sensors has been proposed. The first step is to estimate the current-split among parallel connected cells, followed by diagnosing the health of sensors to improve the overall performance of battery at pack-level. A median-expectation based covariance intersection diagnosis approach (MCIA) is proposed. MCIA evaluates the median of a possible set of values by calculating the covariance of the interconnected cell structure to estimate the current-split. Performance evaluations have been conducted by analyzing sets of real-time measurements collected from Li-ion battery pack used in electric vehicles (EV). Results show that the proposed filter accurately estimated the battery parameters in the presence of temporary and permanent faults.

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