Dynamic thermal prediction of Li-Ion batteries using physics based modeling and neural network techniques. February 24, 2010

Transcription

1 Dynamic thermal prediction of Li-Ion batteries using physics based modeling and neural network techniques Prasad Shingne Tejas Chafekar

2 Agenda Motivation Background Model Equations and Boundary Conditions A revisit to Finite Difference Method Neural Network basics and implementation Results Conclusions

3 Agenda Motivation Background Model Equations and Boundary Conditions A revisit to Finite Difference Method Neural Network basics and implementation Results Conclusions

4 Why is the temperature study of a battery important? Batteries, specially Li-Ion batteries are being used for numerous applications like portable electronics, EVs and HEVs Usually a large scale battery of cells connected in series and parallel is required for high power applications This is when the substantial temperatures developed in batteries become a safety issue High temperatures of a battery detrimentally affect the performance of the batteries which could lead to accelerated degradation and shortened life span Effective thermal management/cooling systems are required to maintain the battery performance and preserve longevity Thus we need predictive models which can be inputs to the thermal management systems

5 Agenda Motivation Background Model Equations and Boundary Conditions A revisit to Finite Difference Method Neural Network basics and implementation Results Conclusions

6 Battery Energy Balance Battery temperature is affected by: Reactions Heat Capacity of the System Phase changes Mixing Electrical work Heat transfer with the surroundings First Law of Thermodynamics Equation simplifies to

7 Model Equations and Boundary Conditions 2-D, Transient heat transfer equation Boundary and Initial Conditions At the center [2] At the surface at time = 0

8 A revisit to Finite Difference Method Let Also

9 A revisit to Finite Difference Method T i+1,j A T i,j G B The model was implemented with the electrochemical model developed in [4] to get the SOC prediction

10 Agenda Motivation Background Model Equations and Boundary Conditions A revisit to Finite Difference Method Neural Network basics and implementation Results Conclusions

11 Neural Networks Basics: Wi = Weights B = Baises n = (Wi*Pi) a = f(wp+b) Inputs :V(t), V(t-1), V(t-2), I(t), I(t-1), I(t-2), T(t) Outputs: (a) SOC Func (f) : sigmoid function

12 Implementation Algorithm Used Minimize Error (a i -T i ) = SOC calculated -SOC desired Back Propagation Algorithm Updates weights and biases in the direction in which the performance function decreases rapidly X k+1 = X k -a k g k Levenberg Marquardt Algorithm X k+1 = X k - [J*J T +µ*i] -1 *J T E µ(k+1) = 10*µ(k) if error increases = 0.1*µ(k) if error decreases

13 Implementation Implementation in MATLAB T = [a,b,c.,n] %(desired SOC values from experimental data) P = [V(t), V(t-1), V(t-2), V(t-3), I(t), I(t-1), I(t-2), T(t)] %vector of inputs net = newff(minmax(p),[3,1],{ logsig }, trainlm) %creating a network [net,tr] = train(net,p,t) %Training a network Where: minmax(p) specifies the range that each input can take [3,1] are the number of layers logsig = the type of function used (sigmoid in this case) trainlm = specifying the algorithm (LM Algorithm in this case)

14 Agenda Motivation Background Model Equations and Boundary Conditions A revisit to Finite Difference Method Neural Network basics and implementation Results Conclusions

15 Results Temperature histories at center of the cell at different stages of discharge with different discharge currents [2] Temperatures are lower for forced convection cooling Temperatures are higher for higher discharge currents and increase with the state of discharge Measured and calculated surface temperatures under natural and forced convection [2]

16 Results Measured surface temperature profiles at different positions on battery fitted with an aluminum fin and under forced convection cooling [2] Calculated and measured surface temperature profiles of battery fitted with fins and under different cooling conditions [2]

17 Agenda Motivation Background Model Equations and Boundary Conditions A revisit to Finite Difference Method Neural Network basics and implementation Results Conclusions

18 Conclusions Thermodynamic treatment applied in [1] is fairly complicated and we can simplify it by ignoring the phase change effects, mixing effects and simultaneous reactions The treatment in [2] helps us formulate a physics model which can be run along with the model [4], which we used in class, to predict temperature evolution under different cooling conditions and by changing a few parameters if the battery is changed A Neural Network trained with data for a battery model can also be used for prediction of temperature but even if there is a small change in any parameter, output will contain error Physics based model is preferred as it gives us modularity of adding extra models (e.g. ageing) and flexibility of changing physical parameters

19 References 1. Bernardi D., Pawlikowski E., and Newman J., A General Energy Balance for Battery Systems, J. of Electrochem. Soc., Vol. 132, No. 1, (1985) 5 2. Wu M. S., Liu K. H., Wang Y. Y., and Wan C. C., Heat dissipation design for lithium-ion batteries, Journal of Power Sources 109 (2002) Park C., Jaura A. K., Dynamic Thermal Model of Li-Ion Battery for Predictive Behavior in Hybrid and Fuel Cell Vehicles, SAE Paper Speltino C., Domenico D. D., Fiengo F., and Stefanopoulou A., Experimental Validation of a Lithium-Ion Battery State of Charge Estimation with an Extended Kalman Filter 5. Y. Chen, J.W. Evans, J. Electrochem. Soc. 140 (1993) Crompton T. R., Battery Reference, 3 rd Edition, Newnes. 7. Linda O., William E. J. III, Huff M., Manic M., Gupta V., Nance J., Hess H., Rufus F., Thakker A., Govar J., Intelligent Neural Network Implementation for SOCI Development of Li/CFx Batteries, /09, 2009 IEEE

20 Questions?