  Accurate and reliable state of charge (SoC) estimation becomes increasingly
important to provide a stable and efficient environment for Lithium-ion
batteries (LiBs) powered devices. Most data-driven SoC models are built for a
fixed ambient temperature, which neglect the high sensitivity of LiBs to
temperature and may cause severe prediction errors. Nevertheless, a systematic
evaluation of the impact of temperature on SoC estimation and ways for a prompt
adjustment of the estimation model to new temperatures using limited data have
been hardly discussed. To solve these challenges, a novel SoC estimation method
is proposed by exploiting temporal dynamics of measurements and transferring
consistent estimation ability among different temperatures. First, temporal
dynamics, which is presented by correlations between the past fluctuation and
the future motion, is extracted using canonical variate analysis. Next, two
models, including a reference SoC estimation model and an estimation ability
monitoring model, are developed with temporal dynamics. The monitoring model
provides a path to quantitatively evaluate the influences of temperature on SoC
estimation ability. After that, once the inability of the reference SoC
estimation model is detected, consistent temporal dynamics between temperatures
are selected for transfer learning. Finally, the efficacy of the proposed
method is verified through a benchmark. Our proposed method not only reduces
prediction errors at fixed temperatures (e.g., reduced by 24.35% at -20{\deg}C,
49.82% at 25{\deg}C) but also improves prediction accuracies at new
temperatures.
