Core Temperature Estimation for Cylindrical Cell Battery ... · Core Temperature Estimation for Cylindrical Cell Battery Module Subhajeet Rath TNO, The Netherlands . Workshop “Solutions

GV-06-2017 iModBatt and GHOST projects have received funding from the European Union’s Horizon2020 Programme for research and innovation under Grant Agreements No. 770054 & 770019

Hotel ARIMA, Paseo de Miramón, 162, 20014 San Sebastián, Spain, 18th October 2019

Workshop “Solutions to electromobility challenges” Hosted by CIDETEC Energy Storage with the support of CRF

Core Temperature Estimation for Cylindrical Cell Battery Module

Subhajeet Rath

TNO, The Netherlands

Workshop “Solutions to electromobility challenges”, 18th October 2019, San Sebastian (Spain) 2

HIFI-ELEMENTS Introduction

• Three-year EU project involving 16 partners

• Objective

– Develop, Validate and Publish a standardization of model

interfaces for common e-drive components

– Implementation of a model/data management tool and a co-

simulation tool for MiL and HiL environments

• End Goal

– Reduction of development and testing effort

– Decrease in vehicle energy consumption

– Increase in validation test coverage


HIFI-ELEMENTS Task 2.3

• Partners: RICARDO, CIDETEC, VUB and TNO

• Development and Validation of a scalable and flexible

battery pack model

• Battery Pack Model

– Electrical model

– Ageing model

– Thermal model

– BMS model



High Level Schematics



High Level Schematics


Thermal Model

• Temperature evolution for input operating condition

• Model

– Battery Model

– Cooling Model

• BMS Output

– Avg, Min and Max battery cell temperature (𝑇𝑎𝑣𝑔, 𝑇𝑚𝑎𝑥, 𝑇𝑚𝑖𝑛)

– Battery coolant pressure difference (∆𝑃𝑓)

– Battery heat loss (𝑄 𝑐𝑜𝑜𝑙𝑎𝑛𝑡)

– Battery coolant pressure drop coefficient (𝜁)


Thermal Model Battery Model

TNO Battery Module



TNO Battery Module

(Top View)



TNO Battery Module

(Top View)


Top View Cell 9 Side View Cell 9



• State

– 𝑇1𝑐 , 𝑇1

𝑠, ⋯ , 𝑇𝑛𝑐𝑒𝑙𝑙𝑐 , 𝑇𝑛𝑐𝑒𝑙𝑙

𝑠 , 𝑇𝑇𝑝, 𝑇𝐵

𝑝

• Input

– 𝑄 1, ⋯ , 𝑄 𝑛𝑐𝑒𝑙𝑙 , 𝑇𝑎, 𝑄 𝑇, 𝑄 𝐵

• Parameter

– 𝐶𝑐 , 𝐶𝑠, 𝐶𝑝, 𝑅𝑐𝑠, 𝑅𝑠𝑠 , 𝑅𝑠𝑎, 𝑅𝑠𝑝, 𝑅𝑇𝑎, 𝑅𝐵𝑎



Cooling Plate (Module) Cooling Plate (Model)

Thermal Model Cooling Model


• State

– 𝑄 𝑇, 𝑄 𝐵, ∆𝑃𝑓

• Input

– 𝑇𝑇𝑝, 𝑇𝐵

𝑝, 𝑇𝑖𝑛𝑙𝑒𝑡

𝑓, 𝑉

• Parameter

– 𝐴𝑜, 𝜌, 𝑐𝑝, 𝑅𝑝𝑓 , 𝜁

Thermal Model Coolant Model


Thermal Model

Battery Model

State: 𝑇𝑇𝑝, 𝑇𝐵

𝑝

Input: 𝑄 𝑇 , 𝑄 𝐵

Cooling Model

State: 𝑄 𝑇 , 𝑄 𝐵

Input: 𝑇𝑇𝑝, 𝑇𝐵

𝑝


• Parameters to identify:

𝐶𝑐 𝐶𝑠 𝐶𝑝 𝑅𝑐𝑠 𝑅𝑠𝑠 𝑅𝑠𝑎 𝑅𝑠𝑝 𝑅𝑇𝑎 𝑅𝐵𝑎 𝑅𝑝𝑓 𝜁

• Calibration Test

– Cell Calorimetry Test

– Cell Heating Test

– Pressure Drop Test

– 3D FEM Simulation

Model Calibration


• Test conducted at CIDETEC

• Accelerating Rate Calorimeter (ARC)

• Measure temperature rise for known heat addition

Model Calibration Cell Calorimetry Test


• Test conducted at CIDETEC

• Accelerating Rate Calorimeter (ARC)

• Measure temperature rise for known heat addition

• Identified Parameter

– 𝐶𝑐𝑒𝑙𝑙 = 60.5 J/K

– 𝐶𝑠 = 9.15 J/K (Material Properties)

– 𝐶𝑐 = 𝐶𝑐𝑒𝑙𝑙 − 𝐶𝑠 = 51.35 J/K

Model Calibration Cell Calorimetry Test


Model Calibration Cell Heating Test

• Battery heating with asymmetric current profile

• Cooling with constant flow rate




• Cooling with constant flow rate





– 𝐶𝑝 = 620.10 J/K

– 𝑅𝑠𝑠 = 181.02 K/W

– 𝑅𝑠𝑝 = 14.4 K/W

– 𝑅𝑠𝑎 = 106.16 K/W

– 𝑅𝑇𝑎 = 0.18 K/W

– 𝑅𝐵𝑎 = 1.57 K/W

– 𝑅𝑝𝑓 = 0.02 K/W


• Coolant circulation throught cooling plate at constant

flow rate

• Pressure drop recorded from Inlet to Outlet

Model Calibration Pressure Drop Test


• Coolant circulation throught cooling plate at constant

flow rate

• Pressure drop recorded from Inlet to Outlet


– 𝜁 = 37.92

Model Calibration Pressure Drop Test


• Parameters to identify: 𝑅𝑐𝑠

• Core Temperature measurement not available

• Virtual Modelling of a cell with known material

properties

Model Calibration


Model Calibration 3D FEM Simulation

• FEM Model of a single cell


Model Calibration 3D FEM Simulation

• FEM Model of a single cell


– 𝑅𝑐𝑠 = 0.6 K/W


• Module subjected to WLTP drive cycle

• Ambient

– 0°C

– 20°C

– 40°C

• Measurement and Model compared

– Cell 20

– Cell 27

Model Validation


Model Validation Ambient 0°C

Temperature Evolution Error Histogram

Error [° C]


Model Validation Results

Ambient Temperature Cell 20 Cell 27

Max Error Error Peak Max Error Error Peak

0°C 1.34° C 0.05° C 1.81° C 0.10° C

20°C 1.34° C 0.35° C 2.13° C 0.05° C

40°C 1.34° C 0.85° C 2.61° C 0.05° C


• Thermal State Observer (TSO) used in Thermal

Management System

– Safety

– Reliability

– Battery Life

• Kalman Filter

– Uses State-Space Thermal Model

– Predicts system states from measurements

– Real time capable

Thermal State Observer


• TSO Tuning:

– Measurement Noise: 0.5°C

– Process Noise: 0.05

• Measurement and Prediction compared

– Surface temperature compared as Core Temperature not

available

• Assumption: Core temperature can be predicted with

a known surface temperature

Thermal State Observer


TSO Schematics

Thermal State Observer Validation

Measured

Estimated


Temperature Evolution Error Histogram

Thermal State Observer Validation

Error [° C]


Thermal State Observer Results

Ambient Temperature Cell 20

Max Error Error Peak

40°C 1.35° C 0.15° C


• Battery module prototype build

– Will be used for future WP testing

• Scalable electro-thermal model

– Calibration

– Validation

• Kalman Filter based Thermal State Observer

– Core Temperature estimation

– Inner cell estimation from outer cell measurement

– Real-time capable

Conclusion

Thanks for your attention!

GV-06-2017 iModBatt and GHOST projects have received funding from the European Union’s Horizon2020 Programme for research and innovation under Grant Agreements No. 770054 & 770019

Documents

Core Temperature Estimation for Cylindrical Cell Battery ... · Core Temperature Estimation for Cylindrical Cell Battery Module Subhajeet Rath TNO, The Netherlands . Workshop “Solutions