Fraunhofer IISB - Battery Systems...Current profile as input Dissipation calculated with R s is strongly temperature dependent Thermal model Dissipation as input Calculate resulting

Page 1 Dr.-Ing. Vincent LORENTZ Division Power Electronics - Group Battery Systems © Fraunhofer IISB

Fraunhofer IISB - Battery Systems

Li-lon Batteries for Stationary Energy Storages

Your R&D Partner for Innovative Solutions in Advanced Battery Systems


Presentation Outline

1. Introduction ; Motivation ; Cost Analys is

2. Competences and Development Flow

3. Battery Modelling and State Estimation (SOx)

4. Battery Management System (BMS)

5. Temperature Sensor & Gas Sensor for Safety

6. Antifuse for Enhanced Reliability and Availability

7. Conclusion and Outlook


DC-Grid: Integration of a Stationary Battery System

100 kW

Bidirectional

DC/DC-Converter

100 kW

20 kWh

Li-Ion Battery


Modular Battery Management System

Fraunhofer IISB

Fraunhofer IISB Fraunhofer IISB

Universal Battery Junction Box

Modular Battery System

System Topology LTO chemistry 20Ah prismatic cells 14 daisy-chained

modules 15s2p Modules Electrical Specifications 20kWh total energy 100kW maximum

continuous power 320A continuous charge

and discharge currents 315..567V voltage range 1.2mV precise voltage

monitoring at cell level High electronic

reliability achieved through redundant design for 24/7 grid applications

Air cooled system

Full-Custom High-Performance Stationary Battery System


Cost Analysis: Lead-Acid versus Lithium-Ion

Based on the estimation made by: http://www.powertechsystems.eu/en/technics/lithium-ion-vs-lead-acid-cost-analysis

Specifications Value

Energy that must be stored (usable) 50kWh

Discharge power 10kW (i.e., 5 hours runtime at C/5)

Cycling frequency 1 discharge/charge cycle per day

Average ambient temperature 23°C

Expected lifespan 5475 cycles (~15 years at 1 cycle per day)

Chemistry Lead-Acid AGM Lithium-Ion (LFP) Lithium-Ion (LTO)

Installed capacity 100kWh 62.5kWh 50kWh

Usable capacity 50kWh 50kWh 50kWh

Lifespan 3000 cycles @ 50% DOD 3000 cycles @ 80% DOD 6000 cycles @ 100% DOD

Battery cost 15,000€ (150€/kWh) (x2) 18,750€ (300€/kWh) (x2) 75,000 (738€/kWh) (x1)

Installation cost 2,000€ (x2) 2,000€ (x2) 2,000€ (x1)

Transportation cost 2,800€ (28€/kWh) (x2) 625€ (10€/kWh) (x2) 700€ (14€/kWh) (x1)

Total cost 19,800€ (x2) 40,125€ (x2) 77,700 (x1)

Cost per installed kWh (over 15 years)

0.79€/kWh 0.86€/kWh 0.79€/kWh

For the considered battery cell chemistries, a use-case can be found so that the considered chemistry offers a cost advantage. The central question is: does this use-case make sense in a commercial application?

http://www.powertechsystems.eu/en/technics/lithium-ion-vs-lead-acid-cost-analysis














Required Competences for Designing Battery System Solutions

Development and assembly of battery packs with

their thermal management system

Development of electrical, mechanical and thermal

battery models at cell, module and pack level

Development and assembly of battery monitoring

and battery management system (BMS) hardware

Development of battery state estimation algorithms

(e.g., SOC, SOH, SOF)

Development of safety sensors (e.g., temperature

sensors) for enhanced safety in battery systems

Development of actuators (e.g., power antifuse) for

enhanced reliability and availability in battery

systems

W

L

R

R

R

Temperature Sensors on

Lithium-Ion Battery Cells

Battery Monitoring and

Management Software

Battery Monitoring and

Management Hardware

Power Antifuses

Battery Modeling

Smart Power Integrated

Driver Circuit


Development Flow for Battery System Solutions

Mechanical Design

• Battery Cell Assembly Design

• Battery Module Assembly Design

• Battery Pack Assembly Design

• Battery System Mechanical Design

• System Integration

Electrical Design

• Battery Junction Box Design

• Battery Monitoring Design

• Battery Management Hardware Design

• Battery Management Software Design

• Cell Voltage Equalization Design

Thermal Design

• Coupled Electro-thermal Modelling

• Coupled Electro-thermal Simulations

• Thermal Layout of the Battery System

• Thermal Management (Liquid, Air)

• Cooling & Heating

System Design

• System Specifications (e.g., Energy, Power, Size)

• Safety and Reliability/Availability Requirements

• Cells Selection (Based on our Internal Database)

• Cells Modeling (Electrical, Mechanical, Thermal)

System Fabrication

• Component Selection (e.g., Breakers, Connectors)

• Component Fabrication (e.g., Packages, Bus Bars)

• System Assembly and Integration

• Final Tests and Characterization

• Delivery of the Battery System Prototype


Electro-thermal Simulation Needed for Thermal Management

Electrical model

Current profile as input

Dissipation calculated with Rs is strongly temperature dependent

Thermal model

Dissipation as input

Calculate resulting temperature distribution

Issue: long FEM simulation time

Simulation:

electrical model Current

Dissipation

Simulation:

reduced thermal

model

Average

temperature

Voc

L ZW Ri

Z1 Z2 Rsd


Thermal Parameters Rth and Cth

Electrical Parameters

CAD Model with λth and cth

Model Order Reduction

Parameter Optimization

Electrical Model without Parameters

Electrical Model with Parameters

Coupled Electro-thermal Simulations

Reduction/expansion matrix: Expanded Model with

Temperature Distribution

BMS SOC with Kalman Filter (EKF, AEKF,

UKF)

Dimensioned Battery System

Electrical

Thermal Electro-thermal

Coupled Electro-thermal Modelling Workflow


Thermal Modeling Using Model Order Reduction (MOR)

Aim: generate a low dimensional approximation of the system

Mathematical method (i.e., does not rely on intuition)

CADFEM Toolbox used here

n~10000-100000; r~100

Result for a pouch cell

Mean temperature on electrode stack

Error < 0.1°C

FEM: 2000s (4 CPUs)

MOR: 5s (1 CPU) 1600:1 ratio

Physics &

Geometry

System of

n equations

Reduced system of

r << n equations FEM MOR


Reconstruction of the Temperature Distribution Output of MOR: mean temperature of electrode stack

MOR method reduction uses transformation matrices

Transformation matrices enable inverse transformation

Reconstruction of the temperature gradients (at 500s)

Comparison FEM/MOR with reconstruction at one time step

Error < 0.03°


Electro-thermal Coupling: Simulation versus Experiment

Experiment: Real Cell Heated by Discharge Cycle Thermographic imaging

Electro-thermal coupled simulation with MOR

Comparison shows good accuracy in values and distribution: ΔT<1.5°C

Thermocouple position

Measurement TC [°C]

Simulation [°C]

ΔT [°C]

Below MINUS tab 37.9 38.8 0.8

Center below tabs 35.5 36.6 1.1

Below PLUS tab 37.3 38.0 0.7


Battery State Estimation: Workflow and Assessment

Model with Parameters

Kalman Filter (EKF, AEKF, UKF)

SOC SOx

Temperature Measurements

Voltage Measurements

Current Measurements

Measurement Profiles

Global Optimization Multi-objective Genetic Algorithm

Simultaneous Calibration Against Multiple Profiles

Selection of Champions

Local Optimization Intense Post-Optimization • Levenberg-Marquardt • Sequential Quadratic Programming • …

Final Solution

Constraints

Measurement Synchronization

Particle Filter (Research Topic)

Fraunhofer IISB Advanced S imulation Framework

Battery Model without

Parameters

Further Developments

Required

Cutting-Edge Technology Available


Battery Management and Monitoring: Electronic Architecture

Features:

Battery monitoring electronics based on Linear Technology LTC6804-1 state-of-the-art battery monitoring IC

16bit resolution of voltage and temperature measurements

Electronics based on proven designs for mobile and stationary applications

Software-less monitoring electronics eliminates software problems

Safety aspects :

Redundant monitoring electronics possible (main and backup path)

Ultra low current consumption during sleep state (4µA)

+

-

Monitoring IC

Module x

Differential communication bus

MCU Gateway

IC

BMS

+

-

Monitoring IC

Module 2

+

-

Monitoring IC

Module 1


DC/DC Converter

2 Isolated CAN

Transceivers

Relay Drivers

Monitoring ICs

Infineon

TriBoard

Companion IC

Infineon CIC61508

Add On Board II

Add On Board I

32 bit Microcontroller

Infineon TriCore TC1798

Infineon TriBoard

32bit TC1798 MCU

OSEK/AUTOSAR Automotive Operating System

JTAG Interface

Add On Board I

Galvanically Isolated Relay Drivers

Data Interface to Monitoring Circuits

Real Time Clock

SD Memory Card for Logging

Add On Board II

Galvanically Isolated Power Supply

Isolated CAN Interfaces (2x)

Charger Control Interface

Companion IC (Infineon CIC61508) for Safety

On Board Temperature Measurement

Isolation Monitoring Interface

Precision Voltage Reference

Interface for

Isolation

Monitoring

Battery Management System: Hardware Overview


Provide

Safety

Safety For

Humans

Explosion

Fire

Electric Shock

Safety for Battery

Overtemperature

Overvoltage

Overcurrent/ Short circuit

Undervoltage

Undertemperature

Enhance

Battery Lifetime

Temperature Control

Heating Demand

Cooling Demand

Electric Control

Charge Management

Power/Current Derating

Battery State Estimation

BMS Self Diagnosis

Functional State

SOC

SOH

SOF

Battery Management System: Software Overview


Battery Monitoring Electronics Based on the LTC6804-1

Voltage measurement of the battery system (>100 Cells) in 290µs

Accuracy of voltage measurements

Single cell: ≤1.2mV

Module: ≤1%

Quasi synchronous voltage sampling

Robust daisy chain communication

Energy consumption at 25°C: 13mA (ON) ; <10µA (SLEEP)

Fully redundant design for ASIL-D and 24/7 applications: includes functions for error&failure detection and compensation

Evaluation of up to 16 external temperature sensors per module

Passive balancing with failure correction

Fully Redundant Battery Monitoring with Failure Compensation – No Software!


Battery Monitoring: Redundant Solution for 24/7 Applications

Backup IC 1 Main IC 1 MUX Balancing Ctrl

MUX Temperature Measurement

Daisy Chain Connector

Temperature Sensor Connectors

Passive Balancing Cooling Area Voltage Measurement Filters

Main IC 2


Printed Low-Cost Flexible Temperature Sensor

High thermal sensitivity: 3% per °C

Designed for low-cost and fast manufacturing

Highly flexible substrate

Overall thickness less than 300µm

100µm flexible substrate (PI, PET etc.)

50µm screen printed contacting

layer with silver compound

50µm screen printed resistive layer

100µm passivation layer

passivation layer

resistive layer

interdigitated

electrodes layer

plastic substrate


Gas Sensor for Enhanced Safety in Battery Systems

The idea is to detect dangerous operating

conditions of lithium-ion battery cells by means of

the cost-effective AppliedSensor iAQ-100 module

Accurate and reliable measurement of:

Temperature

Humidity

Carbon dioxide (CO2) levels

Volatile organic compounds (VOCs)

But: only reacts to differences in temperature or

composition of the gas mix

AppliedSensor iAQ-100


Gas Sensor: Battery Abuse Tests

NMC Li-ion 5Ah cell abuse test

12C overcharge current

Cell mechanically fixed

No shut down after gas detection

Measurements of:

Gas (VOC/CO2)

Cell voltage

Cell temperature

Cursor 1: Initial gas detection

Cursor 2: Estimated beginning of thermal runaway Δt: 40s


Antifuses: New Bypass Electronic Device

Battery systems for high-power applications require stacking of ~100 cells in series

Bypass feature against single cell failure

Cell can be shorted out from the circuit

Power Antifuse Symbols

Untriggered Antifuse

Triggered Antifuse


Realization of Antifuse Devices

Assembly and packaging

Press-pack design

Aluminum springs

Mold compound for 600°C: K-Therm® AS 600 M

„Demonstrator sample“: Polycarbonate top housing

Designed for 100A and 1mΩ


Dashboard of the Battery System

Dashboard showing the major control values for the battery system:

Value: Voltages, Temperatures, Current, Power

Status: Power and Pre-charge Contactors, Galvanic Isolation, Voltages, Temperatures, Current

Battery State Estimations: SOC, SOH, SOF, SOL


Conclusion and Outlook: Examples of Active Research Topics

High Availability Redundant Battery Electronics (without Software

Running Locally) Addressing Safety-Critical Applications (e.g., ASIL-D)

Parametric Model-Order-Reduction Applied to Electro-thermal Battery

System Models for Simulating Cycles at System Level (e.g., NEDC, WLTP)

Reduction of Temperature Gradients in Battery Systems for Homogenous

Ageing of all the Battery Cells, thus Reducing the Need for Balancing

Sensorless Battery Cell Temperature Estimation, thus Enabling Safe and

Cost-Efficient Accurate Battery Pack Temperature Monitoring

Integration of Gas Sensors in Battery Systems for Early Fault Detection

and Improved System Safety

Power Antifuse as Low-Cost Device for Bypassing Faulty Battery Cells

Printed Temperature Sensor for Low-Cost Temperature Sensing to Improve

the Safety in Automotive Battery Systems


Thank you for your attention For questions, do not hesistate to contact us:

Dr.-Ing. Vincent LORENTZ Group Manager Battery Systems;

Division Power Electronics

Fraunhofer IISB, Schottkystraße 10

D-91058 Erlangen, Germany

Telefon +49 9131 761-346

[email protected]

mailto:[email protected]

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Fraunhofer IISB - Battery Systems...Current profile as input Dissipation calculated with R s is strongly temperature dependent Thermal model Dissipation as input Calculate resulting