Minimizing Variability in EV Battery Manufacturing

Jig assembly for laser welding battery cell leads that compensates for height deviations and improves welding quality. The jig has a tilting mechanism with a hinge and spring that allows the welding contact member to tilt and maintain contact with the cell lead surface even if it is not flat or uniform. This compensates for height deviations and ensures continuous contact between the cell lead and laser for consistent welding quality.

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Autonomous optimization of battery manufacturing processes using machine learning. The method involves monitoring battery manufacturing parameters and generating deviation events when thresholds are exceeded. These deviation events are compared to historical events using similarity analysis. If similar, the historical adjustments are used for the new event. If dissimilar, machine learning models predict outcomes for adjustment candidates. Thresholds on confidence and risk determine autonomous vs supervised adjustment.

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Process control system for manufacturing non-aqueous electrolyte secondary batteries that prevents quality degradation without waiting for completed battery evaluation. The system calculates predicted cell characteristics based on material properties and manufacturing conditions. It then adjusts process targets at each step to bring the predicted characteristics closer to ideal values. This proactive correction aims to prevent quality issues during the battery manufacturing process itself, rather than waiting for finished battery evaluation.

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Battery cell sorting method and device that accurately determine good or defective cells at each step of production, monitoring, and handling to improve yield, reduce waste, and optimize cell reuse. The method involves creating a model to distinguish good from bad cells based on extracted regularities from historical cell data at each step. This model is used to determine cell quality at each step. For example, after initial testing, a model may identify singular points between good and bad cells based on charge/discharge characteristics. This model is then used to determine if a cell is good or bad after charging/discharging. This allows more efficient sorting compared to full tests at each step. The method also involves selectively aging cells that are initially classified as good but have high self-discharge rates.

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Predicting and planning battery production to improve yield and stability. It involves using monitoring data from previous batteries to forecast the production quality of the next battery. The forecast is compared against threshold values to determine if the next battery will be normal, abnormal, or require correction. This allows proactive adjustments to avoid issues and improve yield. If abnormal, it also generates early warning to address issues.

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Optimizing battery production to reduce defect rates by dynamically adjusting process parameters based on real-time measurements and cell quality. Sensors monitor production parameters like temperature, humidity, and voltage. Cell quality metrics like self-discharge rate, capacity, and internal resistance are measured. By correlating these measurements, controllable process parameters are identified that impact cell quality. The production process is then adjusted in real-time to optimize cell quality. This adaptive process control reduces defect rates compared to fixed parameter settings.

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Predicting lithium-ion battery performance inconsistency caused by manufacturing process variations to enable better control of battery design and consistency. The method involves analyzing the relationship between battery performance and process parameters using statistical models. By collecting design parameters and process deviations, consistency is analyzed using contour maps and probability cloud images. This provides intuitive visualization of how process variations affect performance and allows calculation of the probability distribution of performance consistency. It enables predicting battery performance inconsistency levels based on process variability and guiding measures to reduce deviations.

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Predicting the inconsistency of lithium-ion battery performance due to manufacturing variations using a statistical model. The method involves breaking down battery performance parameters into local parameters at multiple levels. It then calculates the inconsistency distribution of each local parameter using statistical models based on the manufacturing process variables. By combining these local inconsistencies, it predicts the overall inconsistency of battery performance parameters. The models consider independence, matching, and correlation between process variables to accurately represent inconsistency propagation.

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Optimizing battery manufacturing processes using machine learning and optimization techniques to improve battery performance and yield. The method involves generating predictive models of control index values based on material and manufacturing conditions using machine learning. It then uses optimization to determine the optimal values for the controllable parameters that minimize variations in the control index values. This allows automated optimization of critical parameters during battery production without needing precise control of non-controllable conditions.

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Manufacturing an electricity-storage module with improved sealing to prevent electrolyte leakage from the stack of bipolar electrodes. The module has a stacked body of bipolar electrodes with sealing portions at the edges. The sealing portions are surrounded by an injection-molded outer sealing that covers the ends. The outer sealing has flanges contacting the terminal sealing portions. The outer sealing is shaped so that at least one set of flange-terminal overlap from the sides. This prevents pressure deformation of just the terminal sealing portions and reduces the chance of electrolyte leakage.

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Intelligent manufacturing method and system for battery cells that enables automated adjustment of real-time process parameters during battery production to improve yield and quality. The method involves setting and saving process parameters based on cell model, then intelligently correcting real-time equipment parameters that drift outside the set range. This prevents manual parameter resetting when swapping cells and allows real-time parameter adjustment during production. The method uses a manufacturing execution system to monitor equipment parameters, determine if they're within limits, calculate correction values, and feed back adjustments to the equipment.

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Method of manufacturing an electrical connector, such as a bus bar for electric vehicles, that has improved electrical properties. The method involves plating the battery terminal connection part before shaping the connector to form a uniform plating layer also on the inner surface of the through hole where the battery electrode passes. This is done by masking and plating just the terminal connection part on a metal plate. After plating, the remaining parts of the connector are formed. This allows mass production without separate plating steps.

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Manufacturing method for all-solid batteries with enhanced insulation at the edges to prevent short circuits and improve safety. The battery manufacturing involves forming a cathode layer and an anode layer with a larger area. Insulating layers are added at the cathode edges. When the layers are pressed together, the added insulator deforms to form a membrane between the cathode, electrolyte, and outer insulator. This creates an insulating barrier at the cathode edges to prevent short circuits.

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A prismatic battery cell design that simplifies manufacturing and enables scalability of battery capacities while minimizing cost. The cell has a front cover with a cavity, a rear cover with a corresponding cavity depth, and an electrode group located in the cavities. The rear cover depth matches the electrode thickness. This allows using a standardized front cover and simply varying rear cover depths for different cell capacities.

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A square lithium-ion secondary battery with simplified manufacturing process and reduced contact resistance compared to prior art designs. The battery has a square shape and includes a battery can, lid, generating element, external terminals, and current collectors. The improvement is in the structure of the external terminals and current collector connections to the lid. The external terminals have separate bus bar and current collector joints that are directly mounted on the lid. The current collector joint inserts through a hole in the lid and seals it to prevent electrolyte leakage. This eliminates the need for additional parts like terminal bases and rivet connectors.

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Battery modules for electric vehicles and grid storage that are easier to assemble and have improved weld joint reliability. The module uses a zigzag bending pattern for the electrode tabs that connect the battery cells. This zigzag shape absorbs vibrations during ultrasonic welding, preventing damage to the cells.

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Reducing damage to battery cell current collectors during welding to improve overall cell performance and reliability. The method involves designing the battery cell terminal to have an extended portion with a thinner cross-section. When the current collector foils are laser welded to the terminal, the thinner section and protruding foils are fused. This reduces the volume of molten metal that solidifies and shrinks during cooling, avoiding stresses that can tear the foils.

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Monitoring the production process of crystalline silicon cells using data analysis to improve consistency and quality of finished cells. The method involves breaking down performance parameters of finished cells into subgroups, analyzing the subgroup statistics, and using that analysis to identify production factors impacting cell stability. This interrelated analysis allows monitoring of production processes that affect cell performance, unlike traditional SPC methods that only monitor process data.

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Predictive control system for optimizing manufacturing processes and product quality by avoiding accumulation of variations. The system predicts product characteristic values during manufacturing using mathematical models based on past data and current process values. If the predictions deviate from targets, it calculates optimum process conditions to correct the deviation. This allows active control to reduce product variation by compensating for accumulated errors. The models are periodically updated based on measured data to adapt to process changes.

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An energy storage arrangement for electric vehicles that is more cost-effective to produce than conventional arrangements. The arrangement uses busbars to connect the energy storage cells in series and parallel. The busbars are fastened on the end caps of the cells, rather than on the terminal connections. This eliminates the need for complex and error-prone bonding processes.

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