Xinfan Lin

Online Parameterization of Lumped Thermal Dynamics in Cylindrical Lithium Ion Batteries for Core Temperature Estimation and Health Monitoring

Lithium ion batteries should always be prevented from overheating and, hence, thermal monitoring is indispensable. Since only the surface temperature of the battery can be measured, a thermal model is needed to estimate the core temperature of the battery, which can be higher and more critical. In this paper, an online parameter identification scheme is designed for a cylindrical lithium ion battery. An adaptive observer of the core temperature is then designed based on the online parameterization methodology and the surface temperature measurement. A battery thermal model with constant internal resistance is explored first. The identification algorithm and the adaptive observer is validated with experiments on a 2.3Ah 26650 lithium iron phosphate/graphite battery. The methodology is later extended to address temperature-dependent internal resistance with nonuniform forgetting factors. The ability of the methodology to track the long-term variation of the internal resistance is beneficial for battery health monitoring.

Neutron Imaging of Lithium Concentration in LFP Pouch Cell Battery

This paper shows how neutron radiography can be used for in situ quantification of the lithium concentration across battery electrodes, a critical physical system state. The change in lithium concentration between the charged and discharged states of the battery causes a change in number of detected neutrons after passing through the battery. Electrode swelling is also observed during battery charging. The experimental setup and the observations from testing a pouch cell with LFP cathode and graphite anode are reported here. The bulk Li concentration across the electrode and folds of the pouch cell is quantified at various states of charge. To interpret the measurements, the optics of the neutron beam (geometric unsharpness) and detector resolution are considered in order to quantify the lithium concentration from the images due to the thinness of the electrode layers. The experimental methodology provides a basis for comprehensive in situ metrology of bulk lithium concentration.

Parameterization and Validation of an Integrated Electro-Thermal Cylindrical LFP Battery Model

In this paper, for the first time, an equivalent circuit electrical model is integrated with a two-state thermal model to form an electro-thermal model for cylindrical lithium ion batteries. The parameterization of such model for an A123 26650 LiFePO4 cylindrical battery is presented. The resistances and capacitances of the equivalent circuit model are identified at different temperatures and states of charge (SOC), for charging and discharging. Functions are chosen to characterize the fitted parameters. A two-state thermal model is used to approximate the core and surface temperatures of the battery. The electrical model is coupled with the thermal model through heat generation and the thermal states are in turn feeding a radially averaged cell temperature affecting the parameters of the electrical model. Parameters of the thermal model are identified using a least squares algorithm. The electro-thermal model is then validated against voltage and surface temperature measurements from a realistic drive cycle experiment.Copyright

State of Charge Imbalance Estimation for Battery Strings Under Reduced Voltage Sensing

Reducing voltage sensing in a battery pack is beneficial for cutting the cost of the battery management system. In this paper, a methodology is designed to estimate the states of charge (SOC) of two cells connected in series when only their total voltage is measured. First, the feasibility is analyzed based on the nonlinear observability of the two-cell string under reduced voltage sensing. Furthermore, observability analysis is performed to different battery chemistries to determine their respective observable SOC ranges based on the voltage-SOC relationship. A trajectory-based nonlinear observer, the Newton observer, is then designed for SOC estimation and has been validated by experiments. The limitation of extending the method to strings with more than two cells is also discussed. The methodology is initially designed under the assumption of equal and known capacity and resistance among cells. The robustness of estimation is finally investigated when the above assumptions do not hold.

Quadruple adaptive observer of the core temperature in cylindrical Li-ion batteries and their health monitoring

Temperature monitoring is a critical issue for lithium ion batteries. Since only the surface temperature of the battery can be measured, a thermal model is needed to estimate the core temperature, which can be higher and hence more critical. In this paper, an on-line parameter identification scheme is designed for a cylindrical lithium ion battery thermal model, by which the parameters of the thermal model can be identified automatically. An adaptive observer is designed based on the on-line parameterization methodology and the closed loop architecture. A linear battery thermal model is explored first, where the internal resistance is assumed to be constant. The methodology is later extended to address temperature dependent internal resistance with non-uniform forgetting factors. The capability of the methodology to track the long term variation of the internal resistance is beneficial for battery health monitoring.

State of charge estimation of cells in series connection by using only the total voltage measurement

The voltage of lithium ion batteries is usually monitored to prevent overcharge and overdischarge. For battery packs consisting of hundreds of cells, monitoring the voltage of every single cell adds significant cost and complexity to the battery management system (BMS). Reducing voltage sensing by only measuring the total voltage of multiple cells in series connection is desirable if the state of charge (SOC) of individual cells can be correctly estimated. Such goal cannot be achieved by an extended Kalman filter, because the cell SOCs are not observable in the linearized battery string model. In this paper, an observer based on solving simultaneously multiple nonlinear equations along the trajectory of SOC evolution is used for the estimation problem. Existence of the solution depends on the nonlinearity of the battery voltage-SOC relationship. The observer is applied to a LiFePO4/graphite battery string with 2 cells, where the individual cell SOCs are observable in low and high SOC ranges. Experimental results show good convergence of SOC and voltage estimation, indicating that this new methodology can be applied to, at least, halve the voltage sensing in a battery pack.

Modeling and Experimental Study of PEM Fuel Cell Transient Response for Automotive Applications

Abstract This paper presents an analysis of the dynamic response of a low pressure proton exchange membrane (PEM) fuel cell stack to step changes in load, which are characteristic of automotive fuel cell system applications. The goal is a better understanding of the electrical and electrochemical processes when accounting for the characteristic cell voltage response during transients. The analysis and experiment are based on a low pressure 5 kW proton exchange membrane fuel cell (PEMFC) stack, which is similar to those used in several of Tsinghuas fuel cell buses. The experimental results provide an effective improvement reference for the power train control scheme of the fuel cell buses in Olympic demonstration in Beijing 2008.

State of Charge Estimation Error due to Parameter Mismatch in a Generalized Explicit Lithium Ion Battery Model

Model-based state of charge (SOC) estimation with output feedback of the voltage error is steadily augmenting more traditional coulomb counting or voltage inversion techniques in hybrid electric vehicle applications. In this paper, the state (SOC) estimation error in the presence of model parameter mismatch is calculated for a general lithium ion battery model with linear diffusion or impedance-based state dynamics and nonlinear output voltage equations. The estimation error due to initial conditions and inputs is derived for linearized battery models and also verified by nonlinear simulations. It is shown that in some cases of parameter mismatch, the state, e.g. SOC, estimation error will be significant while the voltage estimation error is negligible.Copyright

Neutron imaging of lithium concentration in battery pouch cells

This paper shows how the principle of neutron radiography can be used to quantify the critical physical state of lithium concentration across battery electrodes at steady-state conditions (after a long relaxation time or small load) as a first step in this important effort to measure in-situ battery physical states and validate electrochemical battery models. A model of the expected loss in beam intensity after passing through the different layers of a battery pouch cell is constructed based on the material densities and dimensions. This model is augmented with simulation of the neutron transmission behavior, including optical effects due to the geometric unsharpness and the detector response. The resulting model provides the basis for a comprehensive simulation of the in-situ metrology of lithium concentration in Li-ion batteries, and comparison with experimental results. This work was also presented as a poster at the 27th Annual Army Science Conference [1].

On the analytic accuracy of battery SOC, capacity and resistance estimation

The Cramer-Rao bounds for battery state of charge, capacity and resistance estimation are derived in this paper. Independent of any specific form of observers, the bounds explore the quality of the data collected for estimation and indicate the best achievable accuracy of any (unbiased) estimator under measurement noise. The derivation is performed based on an equivalent circuit model. First, the Cramer-Rao bounds for standalone estimation, where only one state/parameter is estimated, is derived. The discussion is then extended to combined estimation where multiple state/parameters are estimated from the same data set. It is found that for current inputs that satisfy certain patterns, loss of accuracy in combined estimation can be avoided. The derived explicit analytic expressions are easy to use for improving the accuracy of both online and offline state/parameter estimation.