  Lithium-ion batteries (LiBs) degrade slightly until the knee onset, after
which the deterioration accelerates to end of life (EOL). The knee onset, which
marks the initiation of the accelerated degradation rate, is crucial in
providing an early warning of the battery's performance changes. However, there
is only limited literature on online knee onset identification. Furthermore, it
is good to perform such identification using easily collected measurements. To
solve these challenges, an online knee onset identification method is developed
by exploiting the temporal information within the discharge data. First, the
temporal dynamics embedded in the discharge voltage cycles from the slight
degradation stage are extracted by the dynamic time warping. Second, the
anomaly is exposed by Matrix Profile during subsequence similarity search. The
knee onset is detected when the temporal dynamics of the new cycle exceed the
control limit and the profile index indicates a change in regime. Finally, the
identified knee onset is utilized to categorize the battery into long-range or
short-range categories by its strong correlation with the battery's EOL cycles.
With the support of the battery categorization and the training data acquired
under the same statistic distribution, the proposed SOH estimation model
achieves enhanced estimation results with a root mean squared error as low as
0.22%.
