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Intelligent Manufacturing Technologies for Lithium-ion 6T End-of-Line Testing



OBJECTIVE: An advanced, intelligent Lithium-ion 6T manufacturing end-of-line tester and cell selector system to improve 6T battery quality & performance. 

DESCRIPTION: The 28-V Lithium-ion 6T drop-in replacement battery (Li-ion 6T) is a critical technology to enhance energy storage to improve warfighting performance across the Army, Marines, and Navy. The Li-ion 6T is a drop-in replacement for legacy Lead-Acid 6T batteries for starting, lighting, and ignition (SLI) and silent-watch applications, and provides the same form, fit, and expanded function, including increased silent watch time, significantly extended cycle life, and faster recharge time. Deficiencies in Li-ion 6T manufacturing inspection technologies and processes could result in three possible undesirable outcomes: (1) battery products with latent defects, in either the cells or BMS, which causes premature failure or safety issues in the field (such as an internal cell short); (2) battery products with deficient performance for their intended function as a result of poorly matched cells (such as poor cycle life); or (3) low yield resulting in increased production cost through waste. While manufacturing technologies exist for basic cell selection and end-of-line testing, these processes could benefit substantially from innovations in cell analysis techniques, hardware-in-the loop modeling & simulation, and machine learning. Technologies developed should be specifically for Li-ion battery pack production processes versus cell production processes and should be specifically focused on cell selection at the start of the pack production process and end-of-line testing of the final product at the end of the pack production process. A Li-ion 6T battery product’s performance is directly affected by the cells chosen for the battery. Currently, cells within the Li-ion 6T battery are matched in many cases simply by capacity and internal resistance and manufacturing cell selection equipment and processes are not designed specifically with Li-ion 6T in mind. Accordingly, innovative solutions must be developed and demonstrated which will allow for enhanced cell selection & sorting as well as for Li-ion 6T battery pack end-of-line testing, designed to ensure that the military-specific SLI and silent-watch missions can be met by the final 6T product. Cell selection solutions should take into account technologies such as internal resistance measurement, internal short detection, electro-impedance spectroscopy, calorimetry, and neural networks as well as other innovative analysis techniques. Cell selection and pack end-of-line test technologies shall be capable of integration into a high-volume 6T production process of at least 500 packs/month, and should be scalable to processes of up to 2000 packs/month. End-of-line test solutions must be able to account for the whole operational voltage and temperature range of the battery as well as be capable of simulating pulse events such as cold crank. The systems and solutions developed should be open-architecture to the greatest extent possible. Solutions developed should include real-time modeling & simulation to allow for analysis of the suitability of a produced battery to meet Army requirements, such as Silent Watch. Technology developed should be generally applicable and adaptable to all Li-ion 6T products as well as to all low-voltage commercial Li-ion battery packs. Innovative solutions developed for pack end-of-line testing shall include the ability to determine compliance to all MIL-PRF-32565 periodic production inspection (PPI) tests and have a secure way of reporting results of PPI testing to the Qualifying Activity (such as public-private key encryption). The solutions should also be capable of learning in an effort to help reduce future failures through correlation of PPI/end-of-line test data to cell selection, with the goal of preventing batteries that fail compliance from making it into the field. 

PHASE I: Identify and determine the engineering, technology, and hardware and software needed to develop this concept. End-of-line test technologies developed shall include all listed PPI testing in Table VII of the MIL-PRF-32565, including: Physical characteristics, Dimensions and weights, Terminal posts and threaded sockets, Full charge capacity, Cranking amps, Charging, Charge acceptance, Safety protections, Workmanship, and Defects. Additionally, technologies developed should allow for prediction and assessment of whether the following IPI tests will be met by the battery including: Deep cycle life, High temperature deep cycle life, Retention of charge, Battery storage life, Battery service life, Surges, spikes, and starting operation, Voltage surges, Voltage spikes, and Electromagnetic compatibility/interference. Battery Management System Hardware in the Loop Simulation to determine BMS quality and compliance should also be considered to verify CAN bus requirements, “Measured parameters” tolerances, state of charge estimation accuracy, state of health estimation accuracy, and power capability estimation accuracy. Solutions developed shall improve yield and reduce waste, and consequently improve production costs, by at least 5%. Automated PPI testing using technologies developed under this effort shall reduce the time required for completion of PPI by half. Drawings showing realistic designs based on engineering studies are expected deliverables. Additionally, modeling and simulation (M&S) tools needed to drive the end-of-line tester and cell selection technology is expected. A bill of materials and volume part costs for the Phase I designs should also be developed. This phase also needs to address the challenges identified in the above description. 

PHASE II: Develop and integrate prototype hardware and software into high-volume manufacturing equipment using the designs and technologies developed in Phase I. Deliverables shall include electrical drawings and technical specifications, software, M&S and test results, and at least one Li-ion 6T pack end-of-line tester and one cell selector capable of integration into a high-volume Li-ion 6T manufacturing process and production line. The end-of-line tester and cell selector shall be designed initially for processing only one type/size of Li-ion 6T cell and Li-ion 6T pack product, but the technology shall be designed such that it is generally applicable to all Li-ion 6T cells as well as to commercial cells, applications, and Li-ion pack products. Testing of the Phase II design shall include mock manufacturing runs using small production batches of Li-ion 6T cells and Li-ion 6T batteries. Integration of the technology developed and demonstration on an existing Li-ion 6T manufacturing process and production line line capable of at least 200 packs/month is expected in this phase. The scalability of the technology to high-volume Li-ion 6T production of up to 2000 packs/month should also be demonstrated based upon throughput and rate capabilities of the end-of-line tester and cell selector. A bill of materials and volume part costs for the Phase II design should also be developed. 

PHASE III: This phase will begin installation and integration of the solutions developed in Phase II into military Li-ion 6T and commercial Li-ion pack production processes and into low- to high-volume manufacturing lines. 


1: Lambert, Simon M., et al. "Rapid nondestructive-testing technique for in-line quality control of Li-ion batteries." IEEE Transactions on Industrial Electronics 64.5 (2017): 4017-4026.

2:  Wu, Yi, et al. "Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography." Energies 11.4 (2018): 925.

3:  Seo, Minhwan, et al. "Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell." Energies (19961073) 11.7 (2018).

4:  Wolter, M., et al. "End-of-line testing and formation process in Li-ion battery assembly lines." Systems, Signals and Devices (SSD), 2012 9th International Multi-Conference on. IEEE, 2012.

5:  Parthiban, Thirumalai, R. Ravi, and N. Kalaiselvi. "Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells." Electrochimica Acta 53.4 (2007): 1877-1882.

6:  Gogoana, Radu, et al. "Internal resistance matching for parallel-connected lithium-ion cells and impacts on battery pack cycle life." Journal of Power Sources 252 (2014): 8-13.

7:  "Performance Specification: Battery, Rechargeable, Sealed, 6T Lithium-ion," MIL-PRF-32565,

KEYWORDS: Manufacturing, Lithium-ion, 6T, End-of-line Testing, Modeling, Simulation, Batteries, Power, Energy 

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