The battery cell failure is the main reason to make lead-acid battery pack lose efficacy suddenly during the process, the traditional identification methods should depend on high-precision detection equipment and complicated battery mechanism model, which needs much production costs and limited usage. Considering the differences of internal parameters of equivalent resistance and equivalent capacitance between failure battery cell and normal one; due to float charging flow will show on the time scale by the voltage, a fault diagnostics method based on outlier detection is designed in this paper, it takes time series clustering analysis technology to make similar analysis of voltage time series that is produced by each battery cell, and positions the failure battery with judging the larger differences of outlier. In order to reduce the calculating explosion risk that produced by long span time series, it will take approximate representation of piecewise aggregate approximation to make dimension reduction operation for time series, and the speed of accelerating is also improved. This method has strong practicability that could apply for micro-controller directly.
