Anode-cathode alignment is the intrinsic safety baseline of lithium batteries; an anode that does not fully cover the cathode readily triggers lithium plating. DaoAI reconstructs the cell's internal structure with 3D X-ray CT, then analyzes alignment layer by layer with AI, raising efficiency by about 90x.
Whether wound or stacked, the relative position of anode and cathode must satisfy the requirement that the anode fully covers the cathode. Once alignment is insufficient, exposed cathode-edge regions readily undergo lithium plating during charging, which accumulates over time to pierce the separator and trigger internal short circuits; electrode bending and layer dislocation also alter internal stress distribution. These defects are buried inside the cell and can only be observed non-destructively by means such as X-ray CT. The leading power-battery manufacturer had relied on engineers reading CT slices layer by layer manually, which was time-consuming and subjective per cell and could not support line-scale sampling volumes.
A cell often has dozens of layers, and manually reading alignment margins layer by layer is slow and fatiguing, making analysis throughput the bottleneck of internal quality assessment.
DaoAI Solution
DaoAI combined 3D X-ray CT with AI layer-by-layer analysis: after CT non-destructively reconstructs the cell's internal 3D structure, AI automatically locates anode and cathode edges layer by layer, quantifies alignment margins, identifies anomalies such as bending and dislocation, and automatically aggregates each cell's alignment distribution and extremes. Engineers shift from manual layer-by-layer measurement to reviewing and adjudicating AI results, greatly increasing analysis throughput.
- 3D X-ray CT non-destructively reconstructs the cell's internal 3D structure, covering both winding and stacking
- AI automatically locates anode and cathode edges layer by layer and quantifies alignment margins
- Automatically identifies internal anomalies such as bending and dislocation and aggregates extremes
- Engineers move from layer reading to result review, greatly raising analysis throughput
The bottleneck of alignment analysis was never the CT but the reading — so we let AI read.
After deployment, the analysis efficiency of anode-cathode alignment and bend inspection rose about 90x versus manual layer-by-layer reading, compressing per cell analysis from hours to minutes. CT sampling coverage expanded accordingly, and assessment of internal alignment quality shifted from experience-led to data-driven and traceable.
FAQ
What impacts will insufficient alignment between the anode and cathode have on lithium-ion batteries?
Insufficient alignment between the anode and cathode can lead to lithium plating on the exposed cathode edges during charging. Long - term accumulation may pierce the separator and cause internal short circuits. Bending and misalignment of the electrodes can change the internal stress distribution. DaoAI can improve the analysis efficiency to detect these issues.
How does DaoAI improve the analysis efficiency of anode-cathode alignment?
DaoAI combines 3D X-ray CT with AI layer - by - layer analysis. After the CT reconstructs the internal structure of the battery cell, the AI automatically locates the edges of the anode and cathode, quantifies the alignment margin, identifies abnormalities and summarizes the results. Engineers shift from manual reading to result review, with an efficiency improvement of about 90 times.
What problems did a leading power battery company have in the previous anode-cathode alignment analysis?
Previously, a leading power battery company relied on engineers to manually read CT slices layer by layer. Analyzing a single battery cell was time-consuming and subjective, and couldn't support the sampling scale of the production line. Analysis efficiency became a bottleneck for internal quality assessment. DaoAI can solve such problems.