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Dr. Song-Yul Choe Professor Auburn University

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High Resolution Modeling of Lithium Ion Battery and its Applications Auburn University Mechanical engineering Advanced Propulsion Research Lab. Song-Yul (Ben) Choe

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Trend of advanced propulsion systems

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Energy storage with battery Nominal Capacity (Amp-hours):15.7 Nominal Cell Voltage3.73 Cell Dimensions (mm)5.27 Cell Dimensions w/ terminals (mm)164.2 X 249.6 Maximum Cell Temperature (°C)75 PositiveLithium Metal Oxide NegativeCarbon ElectrolyteOrganic Material SeparatorSRS

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Typical models for battery Models base on equivalent electrical circuit Statics: resistors in series to a voltage source Dynamics: a capacitor connected in parallel to a resistor Models base on equivalent electrical circuit Statics: resistors in series to a voltage source Dynamics: a capacitor connected in parallel to a resistor Ignored effects : 1.Electrical behavior of the terminal as a function of SOC, T and material degradation, and O CV as a function of hysteresis and SOC. 2.Battery calendar life as a function of cycles and load profile 3.Heat generation as a function of SOC, change of entropy and I (charge and discharge), heat tr ansfer 4.Various temperature effects caused by gradients of ion concentrations and side reactions Ignored effects : 1.Electrical behavior of the terminal as a function of SOC, T and material degradation, and O CV as a function of hysteresis and SOC. 2.Battery calendar life as a function of cycles and load profile 3.Heat generation as a function of SOC, change of entropy and I (charge and discharge), heat tr ansfer 4.Various temperature effects caused by gradients of ion concentrations and side reactions

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Modeling of LiPB cell T T c e c e Φ e Φ e Φ s Φ s η SEI η SEI c s c s Separato r Li x C 6 Li y MO 2 Current collector (Cu) Current collector (Al) Electrolyte Positive electrode area Negative electrode area Electrode particle L X Y

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-+ Principle: Current, Concentration and State of Charge current = + Ion current Electron current -+-+-+ -+-+ Low SOCMedium SOC charging High SOC Current in micro cell SOC (state of charge) and c s (concentration in solid) Credit: huangqing

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Electrochemical Thermal Mechanical Model Charging and discharging processes: Heat generation, Elasticity and Degradation Multi scale and Multi-physics coupled problems

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Overview of model Micro cell model Energy conservation Heat transfer Charge conservation Energy conservation Heat transfer Charge conservation Temperature distribution Parameters: Battery geometry Maximum capacity Concentration Activity coefficient Diffusion coefficient Change of enthalpy Conductivity etc. Parameters: Battery geometry Maximum capacity Concentration Activity coefficient Diffusion coefficient Change of enthalpy Conductivity etc. Cell voltage Temp. distribution SOC Overpotentials Reaction rate Concentration Efficiency Cell voltage Temp. distribution SOC Overpotentials Reaction rate Concentration Efficiency Butler-Volmer Mass balance In electrolyte In solid Mass balance In electrolyte In solid Reaction rate Current Overpotential Concentration Nernst equ. Standard potential Micro cell model Initial conditions: Initial SOC Load profile Initial temperature distribution Ambient temperature Initial conditions: Initial SOC Load profile Initial temperature distribution Ambient temperature Single cell model Potential distribution Heat source

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Static and dynamic behavior of the battery Characteristics at different current rates (T=300K and SOC=100%)

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Transient behavior of ion concentration (Discharging behavior at a step current of 10C) At 1sec At 20 sec As lithium ion leaves from negative electrode and deposited in positive electrode, concentration at the interface of the negative electrode drops rapidly when compared with that of inners, while opposite phenomena occurs in the positive electrode. At 80 sec At 180 sec

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Potentials/Current density at positive and negative current collector

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Validation of a single pouch cell at 1C/2C/5C discharge/charge

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Thermal validation – 5C cycle 2D

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Heat generation using the model and calorimeter

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Measurement of Thickness The change of battery thickness caused by the volume change of electrodes is calculated by the model. In experiment, thickness of the battery is measured by measuring both sides of the battery during cycling.

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Mechanical stress of cells at 0.5C, 1C and 2C cycling

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Ben Choe, choe@auburn.edu, Research on HEV and Fuel cell 18 Copying of this presentation is strictly forbidden. Maximum Stress as a Function of Position during discharge (at one instant) Separator Cathode current collector Anode current collector The plotted stress at each position is the maximum value of the stress in the local electrode particle. The highest stress is found in the electrode near the separator.

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Ben Choe, choe@auburn.edu, Research on HEV and Fuel cell 19 Copying of this presentation is strictly forbidden. Fracture is Observed near the Separator Other researchers took SEM images at the cross-section of cell, where fractures are found in the electrode near the separator [7] [8]. Our simulation shows that the highest stress locates at the electrode near the separator, where fractures are most likely to happen. Q. Horn and K. White, 2007 [8] J. Christensen, 2010 [7]

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Block Diagram for battery management system (BMS) Battery Pack/Module Current Voltage Temperature TRAY Temp. Predefined Map Ri(Charging) Ri(SOC) Voc(Charging) Voc(SOC) I,V(SOC) Imean Vaverage & Temp. Compensation Search IVSOC by IV Voltage MAP Temperature Charging/Discharging power Health monitoring & Protection Voltage Imbalance detection Diagnosis HCU User Interface Aging Coefficient & SOC Calculation Accumulated SOC Error Comp. Charging/Discharging Control Cooling Control Thermal Management SENSOR

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Review of models for Battery Empirical Model: Peukert’s equation Full order of Electrochemical, thermal and mechanical Model (ETMM: FOM): Electrochemical kinetics, Potential theory, energy and mass balance, and charge conservation, Ohm’s law, Empirical OCV and elasticity Electric equivalent circuit Model (EECM): Randles models with the 1 st, 2 nd and 3rd order Reduced order of Electrochemical thermal Model (ETM: ROM ): Empirical OCV Polynomial, State space, Páde approx., POD, Galerkin Reformulation and others Comp. time Accuracy Improvement of cell designs BMS Functionalities High Intermediate Low Low Moderate High

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Reduced order of the model (ROM) for real time applications Parameters: Cell geometry Kinetic and transp ort properties Initial conditions: Terminal voltage Load profile Ambient temperat ure Cell voltage Temperature SOC Ion concentration in electrolyte C e Ion concentration in electrodes C s Potentials Φ SOC estimation Input : Output : Battery : StepsApproachesResults Order reduction C e State space approach C s Polynomial approach Φ Parameters simplification Higher accuracy with less computational time Implicit method to solve PDEs Optimization of the ROM for real time applications

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Validation of the ROM 2. Test condition: Mode: Depleting Cycle #: 2 Temperature: 25ºC Current: 1C, 2C, 5C Initial SOC: 0% 1. Test condition: Mode: Depleting Cycle #: 5 Temperature: 0ºC, 25ºC, 45ºC Current: 1C, 2C, 5C Initial SOC: 0%

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Xueyan Li, xzl0017@tigermail.auburn.edu, Nov.,2012 24 Copying this presentation is strictly forbidden. SOC estimation using Extended Kalman Filter Output: Battery Measurement update Time update with the ROM SOC calculation Input: Error of SOC 7-10% Initial errors of BMS Feedback controls and real time model

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Xueyan Li, xzl0017@tigermail.auburn.edu, Nov.,2012 25 Copying this presentation is strictly forbidden. Results of the estimation based on ROM + EKF Test condition: Mode: JS Temperature: 25ºC Initial SOC: 100% Initial error: 0.2V (30% SOC) Test condition: Mode: Depleting Temperature: 25ºC Initial SOC: 0% Initial error: 0.5V (6.5% SOC) Current: Voltage: SOC: Current: Voltage: Error of SOC:

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Health monitoring of battery SOH SOH Q SOH P Capacity fade Power fade Other mechanism Research interests for SOH SOH Current i Battery pack ROM Model & SOH detection algorithm Output states value ( V T ) States estimation (V t SOC T ) Compare Aging parameters estimation ( a s ɛ s ) error

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Estimation of SOH Q The simulation of Q max is calculated by the semi-empirical model whose aging parameters are obtained from curve fitting.

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Fast charging: limiting factors Effects of Lithium plating: Capacity Irreversible loss of active Lithium Safety Dendrites can cause shorting within the electrodes Heat generation A mat of dead lithium and dendrites can increase the chances minor shorts will lead to thermal runway The reaction on the negative electrode is described as: When operated improperly, Li-ions are deposited on the anode surface instead of intercalating during charging: Cause of Lithium plating: Large current rate during charging, especially at high Li ion concentration Low temperature Reference: C. J. Mikolajczak, J. Harmon, From Lithium plating to Lithium –ion cell runaway Exponent [E x (40)]annual report, 2009 Observed Li plating

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Comparison of simulation results and experimental results: Charging Test condition: Temperature=25°C Initial V t = 2.9V Charge current: 1C/2C/5C rate Terminal Voltage (V) Surface Concentration (mol/cm 3 )

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New Charging method Battery i(t) Positive Terminal Estimated concentrations, SOC and temperature + - ROM Model Ambient Temperature; T Terminal Voltage: V T Charging/Discharging current Negative Terminal Reference: Maximum allowed concentrations and temperature, and desired SOC Pulse generator Two level or Three level

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Experimental Data for Charging at 4C Q max by CC and CV charging and the proposed charging method

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Fast Charging Algorithm Cell No.12 Ambient temperature (°C)25 Charging methodCC/CVPulse Charging current (C)44 Discharging current (C) 22 Rest time (min) 10 Cycles100 Benefits: 1.Less capacity losses after 100 cycles; 0.34Ah by the CC and CV. 0.24Ah losses by the proposing method Estimate losses at 500 cycles: 0.5 Ah 2.Less temperature rise 3.Reduction of charging time Test conditions: If there is no significant degradation, Q max = Cycle*P1 + P2

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Summary ModelingDesign Diagnosis and Prognosis Health monitoring (Growth of SEI, Change of conductivities, Losses of active materials and others) Power fade Capacity fade Multi-scale and Multi-physics high resolution electrochemical, thermal and mechanical modeling considering degradations of materials. 1.Cell design 2.System design Series and parallel connection 3.Cooling systems 4.Controls 1.SOC estimation 2.Temperature controls 5.Rapid charging and discharging

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Dr. Song-Yul Choe Professor Auburn University

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