Efficiency and cost-saving in battery pack level optimisation using Inverse Design approach

Battery plays a vital role in determining the price and performance of electric vehicles. EVs will be cost-competitive with ICE when battery prices reach below USD 100/kWh, says a Bloomberg NEF report.  Battery life and warranty are critical aspects for the total cost of EV ownership. Delivering target field performance with safety and warranty at a competitive price are key challenges for the cell, battery and EV companies.

Battery performance and cost are guided by cell chemistry, packaging design, and manufacturing technology. While cell chemistry is continuously evolving with higher energy density and cycle life, ‘cell, pack and stack design & engineering’ is critical to developing an optimal battery system that can deliver the performance and cost targets for all intended applications.

For EV OEMs in India, design innovations will be the primary focus in the next few years to drive the battery cost down till local Li-ion cell production projects are commercialised. There are significant cost-saving opportunities that can be realised in pack level optimisation.

Design process for EV battery packs

The design cycle for Li-ion battery packs involves a series of interconnected steps like material & cell selection, packaging & cooling system, detailed electrochemical-thermal-structural analyses, and testing for safety, range, and life. Optimal design involves iterations and systematic testing of several options before the final one is selected. The design analysis and packaging process require the use of multiple simulation and software tools involving principles of thermodynamic, electrochemistry and electrochemical kinetics, thermal heat management, and structural mechanics.

The eco-system for the battery packaging involves several modules such as battery design; simulation analysis, cooling systems & manifold; casing, adhesives, seals, and gaskets; electrical terminal, connectors, and busbar, as demonstrated in the figure below.

Overview of battery design & packaging workspace

Major challenges in the design and packaging of the battery are:

– Building a structural battery pack to reinforce the strength under dynamic loads and vibration.

– Meeting the constraints of space and volume

– Meeting the safety and performance requirements

The concept of structural battery packing is also moving toward the newer concept of mounting battery pack directly to the vehicle powertrain or body. Design acceleration is critical for OEMs to be ahead of the competition. This entire cycle is however time consuming and can take typically from couple of months to 6 months.

The design options keep expanding considering commercially available cells and their specifications, cell design and pack design concepts (4680 cylindrical cells, cooling systems, enclosure materials, Cell-to Pack), evolving material chemistry, structural batteries etc.

Can we include all these options, safety & warranty considerations as a part of the design evolution? Such a framework once suitably developed & deployed will allow the engineers to find the best design solution efficiently.

Inverse Design approach for battery packs

The above challenges lead to the concept of Inverse Design, i.e. an optimisation driven design methodology that can enable designers to accelerate the development of battery systems considering all options and constraints highlighted above.

An inverse design framework has recently been proposed for cell design [Battery 2030+ Roadmap, European Union Research & Innovation Program, 2020]. We believe this needs to be extended to pack design for EV OEMs.

A broad overview of the framework is outlined in the figure below:

Incorporating Inverse Design Framework

1. All major CAE softwares offer a range of simulation tools, analytic features and allow integration with external commercial codes, open-source tools, and databases. EV engineers are extensively using these softwares for battery design & development. The inverse design framework can be built leveraging such CAE platforms. It would be ideal to build the platform starting with the basic design and arrive at the final one through stage-wise optimisation using workflow automation. This will allow the engineers to adopt the platform for the entire design cycle starting from the battery specification and improve the design efficiency. Also, the platform should be flexible enough to allow easy incorporation of any new developments like materials chemistry and related changes, by the design engineers. 

2. Contrary to adopting a single platform approach as highlighted above, a two-pronged strategy involving a customised tool for design evolution and a CAE software for high fidelity analysis of battery is prudent for adopting the Inverse Design framework at the industrial level.

To fast track the development, the design tools should adopt inverse design framework including AI/ML technologies as outlined in the schematic above and function as a “One Stop Solution Centre” for EV engineers to create portfolio of optimal designs by fast screening all design options and optimise considering performance targets & constraints.

For example, design will be evolved using thermal runway as a constraint rather than post design analysis as conventionally done; such approach will lead to fast-track design evolution. Along with extensive materials, engineering, vendor databases & knowhow, key analysis features will be embedded so that fast design screening guided by AI/ML algorithms is possible. Also, these tools should be seamlessly connected with all CAE & CAD software for easy & cost-effective adoption by the industries. Being a standalone tool and customised for the design engineers, this will bring a great deal of agility & visibility in the design iteration process and can be easily deployed & adopted across the enterprise.

Potential tangible benefits of Inverse Design approach

The inverse design platform as proposed above, can be developed by integrating comprehensive materials and engineering knowledge-base with a suite of first principal & AI/ML models seamlessly and deployed through cloud computing or on-premises hybrid cloud in a user-friendly smart interactive framework.

The design platform, thus developed, would play a significant role to support the EV growth through faster & novel design innovations and can potentially reduce the design cycle time of pack significantly by about 25%. 

Furthermore, the platform will also provide a foundation for ‘Battery Digital Twin‘ and can be deployed for cloud-based intelligent battery management system as next generation solution for superior performance & warranty of electric vehicles.

About the Authors

This article has been written by Dr Biswajit Basu, Fellow INAE, India and Dr Pradip Majumder, Fellow ASME, USA. They are co-founders of Ebensto, which builds an intelligent battery design software platform (Ebliion®) and provides technology services for accelerated and agile development of batteries. Authors can be contacted at contact@ebensto.com.

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