Thermal Model Parameter Identification of a Lithium Battery

The temperature of a Lithium battery cell is important for its performance, efficiency, safety, and capacity and is influenced by the environmental temperature and by the charging and discharging process itself. Battery Management Systems (BMS) take into account this effect. As the temperature at the battery cell is difficult to measure, often the temperature is measured on or nearby the poles of the cell, although the accuracy of predicting the cell temperature with those quantities is limited. Therefore a thermal model of the battery is used in order to calculate and estimate the cell temperature. This paper uses a simple RC-network representation for the thermal model and shows how the thermal parameters are identified using input/output measurements only, where the load current of the battery represents the input while the temperatures at the poles represent the outputs of the measurement. With a single measurement the eight model parameters (thermal resistances, electric contact resistances, and heat capacities) can be determined using the method of least-square. Experimental results show that the simple model with the identified parameters fits very accurately to the measurements

Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context

Battery management systems may rely on mathematical models to provide higher performance than standard charging protocols. Electrochemical models allow us to capture the phenomena occurring inside a lithium-ion cell and therefore, could be the best model choice. However, to be of practical value, they require reliable model parameters. Uncertainty quantification and optimal experimental design concepts are essential tools for identifying systems and estimating parameters precisely. Approximation errors in uncertainty quantification result in sub-optimal experimental designs and consequently, less-informative data, and higher parameter unreliability. In this work, we propose a highly efficient design of experiment method based on global parameter sensitivities. This novel concept is applied to the single-particle model with electrolyte and thermal dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The proposed method avoids the simplifying assumption of output-parameter linearization (i.e., local parameter sensitivities) used in conventional Fisher information matrix-based experimental design strategies. Thus, the optimized current input profile results in experimental data of higher information content and in turn, in more precise parameter estimates.Comment: Accepted for 21st IFAC World Congres

Identifiability and parameter estimation of the single particle lithium-ion battery model

This paper investigates the identifiability and estimation of the parameters of the single particle model (SPM) for lithium-ion battery simulation. Identifiability is addressed both in principle and in practice. The approach begins by grouping parameters and partially non-dimensionalising the SPM to determine the maximum expected degrees of freedom in the problem. We discover that, excluding open circuit voltage, there are only six independent parameters. We then examine the structural identifiability by considering whether the transfer function of the linearised SPM is unique. It is found that the model is unique provided that the electrode open circuit voltage functions have a known non-zero gradient, the parameters are ordered, and the electrode kinetics are lumped into a single charge transfer resistance parameter. We then demonstrate the practical estimation of model parameters from measured frequency-domain experimental electrochemical impedance spectroscopy (EIS) data, and show additionally that the parametrised model provides good predictive capabilities in the time domain, exhibiting a maximum voltage error of 20 mV between model and experiment over a 10 minute dynamic discharge.Comment: 16 pages, 9 figures, pre-print submitted to the IEEE Transactions on Control Systems Technolog

Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement

This study presents a method of estimating battery cell core and surface temperature using a thermal model coupled with electrical impedance measurement, rather than using direct surface temperature measurements. This is advantageous over previous methods of estimating temperature from impedance, which only estimate the average internal temperature. The performance of the method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate cell fitted with surface and core thermocouples for validation. An extended Kalman filter, consisting of a reduced order thermal model coupled with current, voltage and impedance measurements, is shown to accurately predict core and surface temperatures for a current excitation profile based on a vehicle drive cycle. A dual extended Kalman filter (DEKF) based on the same thermal model and impedance measurement input is capable of estimating the convection coefficient at the cell surface when the latter is unknown. The performance of the DEKF using impedance as the measurement input is comparable to an equivalent dual Kalman filter using a conventional surface temperature sensor as measurement input.Comment: 10 pages, 9 figures, accepted for publication in IEEE Transactions on Sustainable Energy, 201

Model Reduction for Multiscale Lithium-Ion Battery Simulation

In this contribution we are concerned with efficient model reduction for multiscale problems arising in lithium-ion battery modeling with spatially resolved porous electrodes. We present new results on the application of the reduced basis method to the resulting instationary 3D battery model that involves strong non-linearities due to Buttler-Volmer kinetics. Empirical operator interpolation is used to efficiently deal with this issue. Furthermore, we present the localized reduced basis multiscale method for parabolic problems applied to a thermal model of batteries with resolved porous electrodes. Numerical experiments are given that demonstrate the reduction capabilities of the presented approaches for these real world applications

Identification and characterization of the dominant thermal resistance in lithium-ion batteries using operando 3-omega sensors

Poor thermal transport within lithium-ion batteries fundamentally limits their performance, safety, and lifetime, in spite of external thermal management systems. All prior efforts to understand the origin of batteries' mysteriously high thermal resistance have been confined to ex situ measurements without understanding the impact of battery operation. Here, we develop a frequency-domain technique that employs sensors capable of measuring spatially resolved intrinsic thermal transport properties within a live battery while it is undergoing cycling. Our results reveal that the poor battery thermal transport is due to high thermal contact resistance between the separator and both electrode layers and worsens as a result of formation cycling, degrading total battery thermal transport by up to 70%. We develop a thermal model of these contact resistances to explain their origin. These contacts account for up to 65% of the total thermal resistance inside the battery, leading to far-reaching consequences for the thermal design of batteries. Our technique unlocks new thermal measurement capabilities for future battery research

Experimental and analytical study on heat generation characteristics of a lithium-ion power battery

This document is the Accepted Manuscript version of the following article: Yongqi Xie, Shang Shi, Jincheng Tang, Hongwei Wu, and Jianzu Yu, ‘Experimental and analytical study on heat generation characteristics of a lithium-ion power battery’, International Journal of Heat and Mass Transfer, Vol. 122: 884-894, July 2018. Under embargo until 20 February 2019. The final, definitive version is available online via: https://doi.org/10.1016/j.ijheatmasstransfer.2018.02.038A combined experimental and analytical study has been performed to investigate the transient heat generation characteristics of a lithium-ion power battery in the present work. Experimental apparatus is newly built and the investigations on the charge/discharge characteristics and temperature rise behavior are carried out at ambient temperatures of 28 °C, 35 °C and 42 °C over the period of 1 C, 2 C, 3 C and 4 C rates. The thermal conductivity of a single battery cell is experimentally measured to be 5.22 W/(m K). A new transient model of heat generation rate based on the battery air cooling system is proposed. Comparison of the battery temperature between simulated results and experimental data is performed and good agreement is achieved. The impacts of the ambient temperature and charge/discharge rate on the heat generation rate are further analyzed. It is found that both ambient temperature and charge/discharge rate have significant influences on the voltage change and temperature rise as well as the heat generation rate. During charge/discharge process, the higher the current rate, the higher the heat generation rate. The effect of the ambient temperature on the heat generation demonstrates a remarkable difference at different charge states.Peer reviewe

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source