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About Sendyne

OverviewFounded in 2010Private US companyServe OEM, Tier 1 and emerging customers23 patents (3 in China)Numerous patents pending Modeling

Software for Li-ion batteries, control and beyond.

SensingCurrent and voltage measurements and

insulation monitoring.

SimulationAdvanced software for desktop and embedded computing.

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Challenges in designing with batteries

For the battery pack designer:

• Choosing the best cell to meet performance requirements.• Deciding on the pack architecture (series vs parallel).• Pack thermal management and cooling.

For the system designer (BMS, control, …):

• Testing the implementation against realistic battery inputs.• Handling various types of load, rate, operating temperature.• What will happen when the cells start to age and/or degrade?

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Cell testing limitations

Cell testing required to cover many scenarios:

• Multiple charge/discharge rates.• Various types of load pattern (constant, driving profile, …).• Experiments repeated for various temperatures.

Limitations:

• Experiments have to be repeated to capture cell variability.• How to test for impact of cell aging and degradation?• Time consuming and costly.

Temperature Rate

Load pattern

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Can we do better?

• We need a software unit that can act as a virtual cell.

• For each cell to be tested a virtual counterpart would be created to speed up cell selection.

• These software units could be combined to simulate modules and packs.

Temperature + load

Cell voltage, current,cell temperature

Virtual Cell

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Virtual cell requirements

Accuracy and predictive power:

• Reproduce with high fidelity the dynamics of the cell.• Ability to predict the cell behavior under various loads and temperature.

Quick to setup:

• Can be derived from a few experiments only.• Quick parameters extraction process.

Ease of use:

• Usable in common simulation packages.• Abstract modeling and numerical methods details.

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Virtual cell applications

Virtual Cell

System simulation

Pack design

BMS development

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Virtual cellModeling

Electrochemical model:

• Accurate physics-based formulation.• Reasonable and physical behavior of the model under

varying load conditions.• Better predictive ability compared to empirical

formulations (e.g. equivalent circuits).• Pseudo-2D theoretical framework (Newman’s model).

Sibatov et al., 2019

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Virtual cellModeling

Compact model formulation:

• Modeling techniques developed by the semi-conductor industry.

• Effective parameters with physics-based meaning.• Improve computational efficiency for real time

simulations.• Reduce overall number of parameters.

Physical scalingsAccuracyComputational

efficiency

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Virtual cellParameters extraction

Efficient model optimization:

• Requires only a few quick experiments rather than extensive cell testing.

• Does not require cell design detailed knowledge (e.g. half-cell potentials, cell internal geometry).

• Leverage rescaling and dimensional analysis for improved convergence.

Temperature Rate

Load pattern

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Virtual cellFMI for Co-Simulation

Functional Mock-Up Interface:

• Standardized interface for simulation software units.• Standalone software unit for co-simulation (FMU).• Supported on many platforms: MathWorks Simulink, NI LabView,

Altair Activate, Excel, …

Cell Model+

Solver

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Virtual cellFMI for Co-Simulation

Co-Simulation paradigm:

• FMU can be used at all development stages: design, validation, …• FMU can be combined to simulate modules and packs.• Software in the loop for BMS development.

Cell FMU

Cell FMU

Cell FMU

Cell FMU

Cell FMU

Cell FMU

Cell FMU

Cell FMU

Cell FMU

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CellMod FMU

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CellMod FMU

Model features:

• Pseudo-2D electrochemical model.• Thermal model for cell surface temperature and core

temperature estimate.• Optimized for Panasonic NCR18650A.• Include parameters to simulate cell aging and capacity fade.

Simulation features:

• Support various load types (current, power, voltage, ohmic load).• Ability to switch load type.• Fast simulation for real time applications.

Features

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CellMod FMUModel performance

Model optimization:

• Target Panasonic NCR18650A lithium-ion cell.• Data used for optimization:

‣ 3 experiments at ambient temperature (low, medium, high rates).‣ 2 additional experiments at high rate at different temperature.

• Unknown cell design: no prior knowledge on internal geometry and half-cell potentials.

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CellMod FMUModel performance

Validation with constant power

Data not used for parameters extraction

Runtime prediction errorIn 18 experiments

2 simulations within statistical variationsof experimental data.

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CellMod FMUAging simulation

FMU inputs for aging simulation:

• Capacity loss.• SEI growth (increased internal

impedance).

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CellMod FMUAging simulation

Same cell, same experiments, 3 years apart

3A fresh cell experiment

3A aged cell experiment

6A aged cell experiment

3A fresh cell simulation

3A aged cell simulation

6A aged cell simulation

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CellMod FMUCell thermal model

FMU provides thermal input and outputs:

• Ambient temperature as input.• Simulates cell surface and core

temperature.• Can be used to simulate thermal

dynamics in packs.

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CellMod FMUCell thermal model

Cell surface temperature simulations vs experiments

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CellMod FMUAvailability

Product launch in late September (09/25/2019)

• FMUs will be distributed through a partnership with Altair.• Lite version with limited functionality already available upon request.

Webinar with Altair on 10/17/2019

For more information contact us at info@sendyne.com

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