Remaining capacity prediction of Li-ion batteries based on ultrasonic signals

ABSTRACT

The estimation of Li-ion battery degradation performance is the key to the safe and effective operation. The current estimation methods for Li-ion battery degradation performance are mainly based on indirect electrical characteristic parameters such as voltage and current, which lack the direct characterization of internal materials, which will affect the accuracy and real-time performance of the prediction. Ultrasonic wave can directly characterize the changes in internal material properties of Li-ion battery during charging-discharging cycles. At present, the correlation between ultrasonic signal and Li-ion battery degradation process has been proven, and the relevant ultrasonic characteristics have been analyzed and established. On the above basis, this paper designs a battery degradation performance detection platform based on ultrasonic, which obtains the ultrasonic response signal and further selects four characteristics of battery aging. Aiming at the situation that the prediction starting point cannot be determined due to the possible loss of process data during the experiment, this paper uses random sampling to establish a test set to simulate the unknown prediction starting point, and establishes a battery remaining capacity prediction method based on the combination of the main model and residual correction model. Finally, the experiment proves that the proposed model has high accuracy.

CO EDITOR-IN-CHIEF:

• Kuo, Cheng-Chien

ASSOCIATE EDITOR:

• Kuo, Cheng-Chien

KEYWORDS:

• Li-ion battery
• ultrasonic
• remaining capacity

Nomenclature

$E$	=	Young’s modulus of the material
$F$	=	the set of all available tree models
$f_k$	=	the shape of the $k$th tree
$g_i$	=	transfer the establishment information of the top $k-1$ trees to the $k$th tree
$h_i$	=	transfer the establishment information of the top $k-1$ trees to the $k$th tree
$I_j$	=	the set of samples of $j$ in the leaf node
$k$	=	the number of tree models
$K$	=	medium bulk modulus
$q_{\left(x_i\right)}$	=	the position in the leaf node of sample $x_i$
$T$	=	the number of leaf nodes of the $k$th tree
$V$	=	Poisson’s ratio
$V_{ultra}$	=	propagation velocity of ultrasonic waves
$x_i$	=	$i$th sample
$Z_{ultra}$	=	current acoustic impedance of the battery
$\gamma$	=	Coefficient of leaf nodes
$\lambda$	=	he leaf weight penalty canonical term
$\mu$	=	truncation factor
$\rho$	=	medium density
$\omega$	=	the leaf node value

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the [LGG21F010002].