Optimal Design of Lithium Battery Energy Storage Systems (LiB-ESS)

Thermal Considerations

Jorge Reyes Marambio

Centro de Energía Universidad de Chile

PRESENTATION TOPICS

• Joint Venture

• Problem Description

• Methodology and Results

• Conclusion and next steps

Joint Venture

Problem Description

Thermal Management For Hybrid

Vehicles BEHR, 2009.A123 Systems Grid Solutions*NanophosphateTM Lithium Ion Enabling

New Possibilities for the Electric Grid

Battery Management

System

Thermal Management

SystemCell Module Pack

Recent Progresses of LG Chem’s Large-Format Li ion polymer batteriesMohamed Alamgir, Satish Ketkar and KwanghoYooLG Chem Power, Inc., Troy, Michigan, USA

Problem Description

22 kWh LPF Battery ESS forKombi EV Conversion. 2013

3.7 kWh NCM Battery ESS forEolian III. 2012

Battery Management

System

Thermal Management

SystemCell Module Pack

296 Wh NMC Battery for eSEEDElectric Bicycle

Problem Description

21˚C

45˚C

Power and Capacity v/s TemperatureShort-Term Evaluation

Cycle Number v/s TemperatureLong-Term Evaluation

Handbook of Battery. Third Edition. 2002

Problem Description

Chengke Zhou; Kejun Qian; Allan, M.; Wenjun Zhou, "Modeling of the Cost of EV Battery Wear Due to V2G Application in Power Systems," Energy Conversion, IEEE Transactions on , vol.26, no.4, pp.1041,1050, Dec. 2011

Thermal Management System

Goals

T max < 60˚C

25˚C < T optimal < 40˚C

T between cells < 5˚C

T min > 0˚C

T within cells < 5 - 10 ˚C

Problem Description

Thermal Goals

Min T max

Min ∆T max

Min Volumen

Trade-off between space and temperature in the cells arrangement problem

� Constant cooling air inlet

� Maximum area restriction

� Constant cell heat generation Q = R I^2

� Adiabatic Walls

Problem Description

Arrangement #1

Arrangement #2

39 ºC 74 ºC

59 ºC

44 ºC

Metodology

Multi-objetive problem

Due the no linear behavior of the problem,

the approach is to solve it using Genetic Algorithms

no

OkEND

Initialize

Population

Evaluate obj.

Function of each

individual

Multi-Objective

Genetic

Algorithm

yes

Metodology

Using ANSYS to evaluate each individual in each generation

Challenges

✓✓✓✓ Fast evaluation

✓✓✓✓ Good Precision

✓✓✓✓ Add more and more cells

Approach 1

no

OkEND

Initialize

PopulationANSYS

Multi-Objective

Genetic

Algorithm

yes

Preliminary Results

Approach 1

Metodology

Using ANSYS to fit parametric or black box models

Challenges

✓✓✓✓ Modeling Complex and Non- linear

phenomena

✓✓✓✓ Include all relevant variables

✓✓✓✓ Processing huge amount of data

Approach 2

no

OkEND

Initialize

Population

Parametric

Model

Multi-Objective

Genetic

Algorithm

yes

Preliminary Results

Approach 2

Conclusion and next steps

� Thermal management is a critical aspect in battery systems

� ANSYS enables the undestanding of thermal influence in battery systems.

� There exist a trade-off between thermal goals, space and power consumption of the cooling system

� Periodic boundary conditions will allow to predict the thermal behavior in longer and wider battery packs.

� CFD simulations to determine relationships between heat transfer patterns and packaging strategies.

Optimal Design of Lithium Battery Energy Storage Systems (LiB-ESS)

Thermal Considerations

Thanks !