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BatPaC – A Spreadsheet Tool to Design a Lithium Ion Battery and Estimate Its Production Cost

Lithium ion batteries are ubiquitous in consumer electronics today. Increasing demand for better performance – safer, lighter and smaller but with more capacity, with a longer life and lower cost – has prompted very significant investments in their development. The resulting progress has in turn spurred the deployment of this technology in transportation and stationary energy storage applications. Almost all car manufacturers have introduced commercial vehicles that use lithium ion batteries as their primary power source; some have announced timelines for discontinuing one or more product lines that use internal combustion engines and replacing them with battery electric vehicles.

The Battery Performance and Cost (BatPaC) model is a calculation method based on Microsoft® Office Excel spreadsheets that has been developed at Argonne for estimating the performance and manufacturing cost of lithium-ion batteries for electric-drive vehicles including hybrid-electrics (HEV), plug-in hybrids (PHEV) and pure electrics. The effort is being funded by the Vehicle Technology Office (VTO), which is part of the Energy Efficiency and Renewable Energy (EERE) office of the U.S. Department of Energy (USDOE). BatPaC was first developed in 2007, was subsequently peer reviewed, and it has served Argonne researchers and the greater battery community in studying the impact of material properties on performance at the pack level. With further developments the model now allows the design of cells and battery packs for automotive applications, to meet performance requirements (power, energy, recharge time), and estimates the cost of manufacturing the designed batteries. Since the cost depends on the materials, the design, and the manufacturing process, this bottom-up model/tool enables the user to study their effects. Designed for the lithium ion cell and battery researcher, BatPaC helps answer many what-if” questions by being

  1. Transparent in the assumptions made and the method of calculation
  2. Capable of designing a battery specifically for the requirements of an application
  3. Constrained by the physical limitations that govern battery performance
  4. A bottom-up calculation approach to account for every cost factor

The tool enables access to a broad range of users and to facilitate transparency about the many input parameters. Some of the input parameters in the spreadsheet use values and equations that are derived from external sources such as experiments or correlations from experimental/published data, transport or process models, and industry feedback on costs and manufacturing procedures.

The cell design in BatPaC assumes rectangular stiff pouches enclosed in modules that are further packaged within a pack enclosure. The assigned dimensions result in a more compact design than in vehicle batteries now being produced, but this high degree of compactness is anticipated to be achievable in future commercial batteries. The price of materials is based on our best estimate at the time of a version release. The cost factors of installed capital equipment, labor, and manufacturing floor space (loaded with overhead costs) for each step in the manufacturing process are based on those in a baseline plant with a capacity of 8 GWh per year. The economies of scale or projections for the future (when larger plants will be in production) are calculated for each step in the process with equations[1] of the form,

Eq (1)  Cost for A = Cost for B [(Throughput in step for Plant A) / (Throughput in step for Plant B)] p  

The cost of each of the items for each step in the process is calculated by comparing the cost to that of the baseline plant (B) and allowing for the difference in the throughput for that step in the process. The value of the exponent, p, varies with each cost item and for each processing step.

Validation of the input material and capital costs are difficult to achieve as few values are publicly available. We therefore rely, to a large extent, on private communications[2] from equipment manufacturers, materials suppliers, cell manufacturers, and original equipment manufacturers (OEM). Variation does exist amongst the communicated values and after making our best estimate of the correct values, we have assigned accuracy limits to the final cost estimates. While the largest uncertainty in calculated values will exist in point cost estimates, the most instructive information may be gained by examining ranges in parameter values and relative changes between material properties.

The algorithm for the design of the battery pack is based on meeting the requirements for power and energy storage, and the recharging time in the case of all-electric vehicles (EV) or plug-in hybrid vehicles. The energy storage requirement determines the amount of electrode materials required, based on the specific capacity of the two electrode materials and the cell voltage. The cell area determines the cell resistance and therefore the power achievable. The model solves the equations to determine the smallest cell area (or maximum electrode thickness) that can satisfy the power and charge time requirement. The amount of inactive materials, such as current collectors, separators, cell containment, etc. are then calculated based on the cell area. The results from the material needs are then used to calculate the cost of production in a virtual plant with 16 stations (electrode mixing, coating, calendaring, etc.), where the costs of capital, labor, and floor space are calculated from the estimates made for the baseline plant

The cell design calculations are based on the selection of electrode couples from provided options with cathode active materials (NCA, NMCxyz, etc.) and anode active materials (graphite, lithium titanate). Default values of properties such as the density, porosity, cell voltage, impedance, etc., for these material combinations are automatically selected for an electrode couple; however, the user is able to select alternate values as appropriate. Similarly, the default cost contributors to the manufacturing plant are embedded in the appropriate worksheets and can be adjusted as needed.

The results generated from a set of calculations include the dimensions, mass, volume, and cost of the cell, module, and pack; with various breakdowns of the cost items.

Experts from all aspects of battery development have reviewed the model both privately and as part of a formal peer-review process. The model was formally reviewed by a committee set up by the U.S. Environmental Protection Agency (USEPA) in 2011, and its continued development is periodically reviewed at the VTO Annual Merit Review meetings. The BatPaC model has been shared publicly via direct download or by request since 2012.

Contributors: Paul A. Nelson, Kevin G. Gallagher, Shabbir Ahmed, Dennis W. Dees, Naresh Susarla, Ira D. Bloom, Joseph J. Kubal, Juhyun Song,  Zhe Liu


  • Modulating electrode utilization in lithium-ion cells with silicon-bearing anodes, submitted to J. Power Sources
  • Impact of U.S. DOE R&D on Potential Future Battery Material Demand, EVS33, June 2020
  • Estimating the cost and energy demand of producing LMO for Li-ion batteries, Report ANL/CSE-20/1
  • Modeling the performance and cost of lithium-ion batteries for electric-drive vehicles, Report No. ANL/CSE-19/2 (2019)
  • Estimating cost and energy demand in producing lithium hexafluorophosphate for Li-ion battery electrolyte, Industrial & Engineering Chemistry Research, 2019, 58, 37543766
  • Cost of automotive lithium-ion batteries operating at high upper cutoff voltages, Journal of Power Sources 403 (2018) 5665
  • Modeling and analysis of solvent removal during Li-ion battery electrode drying, Journal of Power Sources, 378 (2018) 660670
  • Enabling fast charging: A battery technology gap assessment, Journal of Power Sources 367 (2017) 250-262
  • Technical and economic analysis of solvent-based lithium-ion electrode drying with water and NMP, Drying Technology, 36 (2), 234-244, 2018
  • Cost and energy demand of producing nickel manganese cobalt cathode material for lithium ion batteries, Journal of Power Sources 342 (2017) 733-740
  • Study of a dry room in a battery manufacturing plant using a process model, Journal of Power Sources 326 (2016) 490-497
  • Energy impact of cathode drying and solvent recovery during lithium-ion battery manufacturing, Journal of Power Sources 322 (2016) 169-178
  • Cost savings for manufacturing lithium batteries in a flexible plant, Journal of Power Sources  283 (2015) 506-516


[1] Perry’s Chemical Engineers’ Handbook, 9th Edition, edited by D.W. Green and M.Z. Southard, McGraw Hill Education, 2019.

[2] It is hoped that model users will be responsive by sharing their estimates of input values so that these can be aggregated and incorporated into BatPaC.



Support from the Vehicle Technologies Office at the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, is gratefully acknowledged. The authors gratefully acknowledge the guidance from David Howell, Tien Duong, Brian Cunningham, Peter Faguy, Samuel Gillard, and Steven Boyd. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated


This report and model were prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor UChicago Argonne, LLC, nor any of their employees or officers, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of document authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, Argonne National Laboratory, or UChicago Argonne, LLC.