New Energy Battery Industry

New Energy Battery Industry

In the new energy battery industry, AIMS leverages advanced technologies such as artificial intelligence, big data, and 5G industrial dedicated networks to develop and apply intelligent control systems for production manufacturing. These systems upgrade traditional production lines into intelligent ones that can "perceive comprehensively, make autonomous decisions, execute precisely, and diagnose themselves." The process capability index (CPK) has been increased from 1.2 to over 2.2, achieving unmanned control of key processes.

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Business Pain Points

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Complex Parameter Adjustment

The current process requires multiple adjustments of equipment parameters, heavily relying on manual experience. The proficiency and focus of the operators directly affect product quality.

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High Control Precision Requirements

The control precision of this process directly impacts product consistency, with execution mechanisms requiring repeatable precision at the micrometer level.

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Significant Labor Demand

With the rapid increase in new energy battery business and the continuous expansion of production lines, there is a severe shortage of labor.

Solution

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Automatic Parameter Adjustment

Based on the process mechanism and data, an online adaptive digital twin model has been constructed. Model predictive control and virtual sensing technologies have been developed to form an autonomous decision-making system. This enables automatic optimization of the initial piece adjustment parameters and real-time optimization of the production process for key processes.

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High-Precision Control

The industry's first high-precision adjustment hardware has been developed, combined with a feedback control system, to achieve high-precision control at the 5-micron level.

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Unmanned Operation

An intelligent cockpit system for key processes in new energy batteries has been independently developed. This system enables real-time monitoring, intelligent parameter adjustment, precise process execution, and equipment fault diagnosis. It can centrally monitor multiple devices and achieve 100% unmanned operation.

Customer Value

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Reduced Quality Fluctuations

The Control CPK (Process Capability Index) has been improved from 1.2 to 2.2, reaching an internationally leading level.

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Enhanced Control Precision

A reconfigurable fully automatic adjustment mechanism has been developed with micrometer-level precision and millisecond-level response time.

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Improved Operational Efficiency

By utilizing digital twin models and intelligent control algorithms, changeover and debugging time has been reduced by 85%, from an expected 4 hours to 0.5 hours.

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Unmanned Workshop

Integrated remote centralized monitoring, automatic anomaly diagnosis, and over-the-air control program updates have been developed, reducing the number of operators per machine from 2 to 0.5, achieving unmanned centralized workshop control.

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One-Click Startup for First Piece

Complex multidimensional parameters are solidified in the algorithm model, enabling automatic first-piece parameter calculation and optimization during the debugging process.

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Anomaly Analysis and Localization

All related data is uniformly recorded and analyzed to identify root causes of anomalies and locate faults, constructing a failure mode case library.