Control Oriented Modeling and State of Health Estimation for Lithium Ion Batteries
Open Access
- Graduate Program:
- Mechanical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- September 12, 2013
- Committee Members:
- Christopher Rahn, Dissertation Advisor
- Hosam Kadry Fathy, Committee Member
- Alok Sinha, Committee Member
- Constantino Manuel Lagoa, Committee Member
- Keywords:
- Lithium ion batteries
- modeling
- state of health
- estimation
- Abstract:
- Lithium ion (Li-ion) batteries are attracting signifi cant and growing interest due to their many applications, particularly in hybrid and electric vehicles. Their high energy and high power density render them an excellent option for energy storage in these vehicles. Sophisticated battery management systems (BMS) that ensure long battery life and efficient utilization are based on low order electrochemical models that can accurately capture the battery dynamics. This thesis develops reduced order, linear models of Li-ion batteries that can be used for model-based power train simulation, design, estimation, and control in hybrid and electric vehicles. First, a reduced order model is derived from the fundamental governing electrochemical charge and Li+ conservation equations, linearized at the operating state of charge and low current density. The equations are solved using analytical and numerical techniques to produce the transcendental impedance or transfer function from input current to output voltage. This model is then reduced to a low order state space model using a system identifi cation technique based on least squares optimization. Given the prescribed current, the model predicts voltage and other variables such as electrolyte and electrode surface concentration distributions. A second model is developed by neglecting electrolyte diff usion and modeling each electrode with a single active material particle. The transcendental particle transfer functions are discretized using a Pade Approximation. The explicit form of the single particle model impedance can be realized by an equivalent circuit with resistances and capacitances related to the cell parameters. Both models are then tuned to match experimental EIS and pulse current-voltage data. As Li-ion cells age, they experience power and energy fade associated with impedance rise and capacity loss, respectively. Identi fication of key aging parameters in lithium ion battery models can validate degradation hypotheses and provide a foundation for State of Health (SOH) estimation. This thesis develops and simplifies an electrochemical model that depends on three key aging parameters, cell resistance, solid phase diff usion time and the capacity factor. Offline linear least squares processing of voltage and current data from fresh and aged NCM and LFP cells produce estimates of these aging parameters. An adaptive gradient based recursive estimator is also designed that can estimate these aging parameters onboard a vehicle in real time. The estimated parameters vary monotonically with age, consistent with accepted degradation mechanisms such as solid electrolyte interface (SEI) layer growth and contact loss. Finally, a control oriented degradation model is developed for LFP cells by incorporating the aging mechanism of SEI layer growth in the negative electrode with a nonlinear single particle model. This is the major degradation mechanism in LFP cells because the positive electrode does not appreciably age due to its extreme stability. The model predicts the experimentally measured capacity loss and increase in film resistance.