Modeling of Lithium-ion Battery Considering Temperature and Aging Uncertainties

This dissertation provides a systematic methodology for analyzing and solving the temperature and aging uncertainties in Li-ion battery modeling and states estimation in the electric vehicle applications. This topic is motivated by the needs of enhancing the performance and adaptability of battery management systems. In particular, temperature and aging are the most crucial factors that influence battery performance, modeling, and control. First, the basic theoretical knowledge of Li-ion battery modeling and State of Charge (SoC) estimation are introduced. The thesis presents an equivalent circuit battery model based SoC estimation using Adaptive Extended Kalman Filter (AEKF) algorithm to solve the initial SoC problem and provide good estimation result. Second, the thesis focuses on the understanding of the temperature-dependent performance of Li-ion battery. The temperature influence is investigated through Electrochemical Impedance Spectroscopy (EIS) tests to enhance the theoretical basis understanding and to derive model compensation functions for better model adaptability at different temperatures. Third, the battery aging mechanisms are revisited first and then a series of aging tests are conducted to understand the degradation path of Lithium-ion battery. Moreover, the incremental capacity analysis (ICA) based State of Health (SoH) estimation method xiv are applied to track battery aging level and develop the bias correction modeling method for aged battery. In the final phase, the study of parallel-connected battery packs is presented. The inconsistency problem due to different battery aging levels and its influence to parallel-connected packs are discussed. Based on simulation and experimental test results, it shows that the current difference in parallel connected cells is increased significantly at low SoC, despite the battery aging levels and the number of cells in parallel. In total, this dissertation utilizes physics-based battery modeling and states estimation method to optimize battery management under temperature and aging uncertainties in electric vehicle applications. The unique contributions include developing analytical compensation functions to improve equivalent circuit battery model adaptability under temperature uncertainty and developing ICA based SoH estimation and battery modeling method to overcome aging uncertainty.Ph.D.CECS Automotive Systems EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/134041/1/Gong Dissertation Final.pdfDescription of Gong Dissertation Final.pdf : Dissertatio

Efficient electrochemical model for lithium-ion cells

Lithium-ion batteries are used to store energy in electric vehicles. Physical models based on electro-chemistry accurately predict the cell dynamics, in particular the state of charge. However, these models are nonlinear partial differential equations coupled to algebraic equations, and they are computationally intensive. Furthermore, a variable solid-state diffusivity model is recommended for cells with a lithium ion phosphate positive electrode to provide more accuracy. This variable structure adds more complexities to the model. However, a low-order model is required to represent the lithium-ion cells' dynamics for real-time applications. In this paper, a simplification of the electrochemical equations with variable solid-state diffusivity that preserves the key cells' dynamics is derived. The simplified model is transformed into a numerically efficient fully dynamical form. It is proved that the simplified model is well-posed and can be approximated by a low-order finite-dimensional model. Simulations are very quick and show good agreement with experimental data

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

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

Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework

Universal Chemomechanical Design Rules for Solid-Ion Conductors to Prevent Dendrite Formation in Lithium Metal Batteries

Dendrite formation during electrodeposition while charging lithium metal batteries compromises their safety. While high shear modulus solid-ion conductors (SICs) have been prioritized to resolve pressure-driven instabilities that lead to dendrite propagation and cell shorting, it is unclear whether these or alternatives are needed to guide uniform lithium electrodeposition, which is intrinsically density-driven. Here, we show that SICs can be designed within a universal chemomechanical paradigm to access either pressure-driven dendrite-blocking or density-driven dendrite-suppressing properties, but not both. This dichotomy reflects the competing influence of the SICs mechanical properties and partial molar volume of Li+ relative to those of the lithium anode on plating outcomes. Within this paradigm, we explore SICs in a previously unrecognized dendrite-suppressing regime that are concomitantly soft, as is typical of polymer electrolytes, but feature atypically low Li+ partial molar volume, more reminiscent of hard ceramics. Li plating mediated by these SICs is uniform, as revealed using synchrotron hard x-ray microtomography. As a result, cell cycle-life is extended, even when assembled with thin Li anodes and high-voltage NMC-622 cathodes, where 20 percent of the Li inventory is reversibly cycled