Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte

The importance of global sensitivity analysis (GSA) has been well established in many scientific areas. However, despite its critical role in evaluating a model’s plausibility and relevance, most lithium ion battery models are published without any sensitivity analysis. In order to improve the lifetime performance of battery packs, researchers are investigating the application of physics based electrochemical models, such as the single particle model with electrolyte (SPMe). This is a challenging research area from both the parameter estimation and modelling perspective. One key challenge is the number of unknown parameters: the SPMe contains 31 parameters, many of which are themselves non-linear functions of other parameters. As such, relatively few authors have tackled this parameter estimation problem. This is exacerbated because there are no GSAs of the SPMe which have been published previously. This article addresses this gap in the literature and identifies the most sensitive parameter, preventing time being wasted on refining parameters which the output is insensitive to

Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles

Within the academic and industrial communities there has been an increasing desire to better understand the sustainability of producing vehicles that contain embedded electrochemical energy storage. Underpinning a number of studies that evaluate different circular economy strategies for the electric vehicle (EV) or Hybrid electric vehicle (HEV) battery system are implicit assumptions about the retained capacity or State of Health (SOH) of the battery. International standards and bestpractice guides exist that address the performance evaluation of both EV and HEV battery systems. However, a common theme is that the test duration can be excessive and last for a number of hours. The aim of this research is to assess whether energy capacity measurements of Li-ion cells can be accelerated; reducing the test duration to a value that may facilitate further EOL options. Experimental results are presented that highlight it is possible to significantly reduce the duration of the battery characterisation test by 70% - 90% while still retaining levels of measurement accuracy for retained energy capacity in the order of 1% for cell temperatures equal to 250C. Even at elevated temperatures of 400C, the peak measurement error was found to be only 3%. Based on these experimental results, a simple cost-function is formulated to highlight the flexibility of the proposed test framework. This approach would allow different organizations to prioritize the relative importance of test accuracy verses experimental test time when grading used Li-ion cells for different end-of-life (EOL) applications

Cycle life of lithium ion batteries after flash cryogenic freezing

Growing global sales of electric vehicles (EVs) are raising concerns about the reverse logistics challenge of transporting damaged, defective and waste lithium ion battery (LIB) packs. The European Union Battery Directive stipulates that 50% of LIBs must be recycled and EV manufacturers are responsible for collection, treatment and recycling. The International Carriage of Dangerous Goods by Road requirement to transport damaged or defective LIB packs in approved explosion proof steel containers imposes expensive certification. Further, the physical weight and volume of LIB packaging increases transport costs of damaged or defective packs as part of a complete recycling or repurposing strategy. Cryogenic flash freezing (CFF) removes the possibility of thermal runaway in LIBs even in extreme abuse conditions. Meaning damaged or defective LIBs may be transported safely whilst cryogenically frozen. Herein, LIBs are cycled until 20% capacity fade to establish that CFF does not affect electrical performance (energy capacity and impedance) during ageing. This is demonstrated on two different cell chemistries and form factors. The potential to remanufacture or reuse cells/modules subjected to CFF supports circular economy principles through extending useful life and reducing raw material usage. Thereby improving the environmental sustainability of transitioning from internal combustion engines to EVs

Thermal modeling of lithium ion batteries for temperature rise predictions in hybrid vehicle application

In order to develop a hybrid vehicle with lithium ion battery packs, it is necessary to understand the thermal behaviour of the lithium ion batteries used. This paper focuses on predicting the temperature rise of lithium ion batteries during a drive cycle in hybrid two wheeler applications. To predict the rise in temperature, a model is developed in Simulink, parameterized using the empirical parameters. The model is based on the Joule heating effect and heat capacity equation while considering the variation of internal resistance with respect to ambient temperature of operation, state of charge and C rate of operation. The internal resistance is measured by parameter evaluation testing through the pulse power characterisation method. To validate the Simulink model, the lithium ion batteries are tested on standard drive cycles and constant current discharges, and the rise in temperature is measured. The accuracy of the Simulink model was found to be ± 2.2°C, which is acceptable for this study and comparable to the other available models in the literature

Improved sizing calculator for rapid optimisation of pack configuration at early-stage automotive product development

The specifications that define a new automotive product development are established at an early stage in the product life cycle and define the direction of such a development, and changing these decisions becomes costlier the further the project evolves towards introduction into the market. Simulations and predictions underpin the crucial decisions made at the inception stage of the new product development life cycle, since these tools inform prototype development, production strategies, and improve profitability. The tool presented facilitates the decisions required when embarking on the new product development of a vehicle that incorporates electric - drive technologies and the vital choices made regarding the battery pack powering by such a vehicle. The tool functions can be split into two parts, firstly it incorporates a sizing model for determining the number of cells and the configuration required to meet a specified battery requirement. Secondly, a 1 - D model is implemented to determine some of the basic thermal and power characteristics that can then be utilised to inform other parts of the design specification. Improvements are proposed that improve previous model accuracy from 8 - 9% to 6% for thermal predictions and down to 3% for electrical simulations. When integrated with a database containing cell characteristics, the tool can identify candidate cells that meet the proposed requirements. In addition, the tool’s rapid execution time allows fact comparison between cell choices, at a level comprehensible by all project stakeholders in the decision making process

Structural identifiability of equivalent circuit models for Li-Ion batteries

Structural identifiability is a critical aspect of modelling that has been overlooked in the vast majority of Li-ion battery modelling studies. It considers whether it is possible to obtain a unique solution for the unknown model parameters from experimental data. This is a fundamental prerequisite of the modelling process, especially when the parameters represent physical battery attributes and the proposed model is utilised to estimate them. Numerical estimates for unidentifiable parameters are effectively meaningless since unidentifiable parameters have an infinite number of possible numerical solutions. It is demonstrated that the physical phenomena assignment to a two-RC (resistor–capacitor) network equivalent circuit model (ECM) is not possible without additional information. Established methods to ascertain structural identifiability are applied to 12 ECMs covering the majority of model templates used previously. Seven ECMs are shown not to be uniquely identifiable, reducing the confidence in the accuracy of the parameter values obtained and highlighting the relevance of structural identifiability even for relatively simple models. Suggestions are proposed to make the models identifiable and, therefore, more valuable in battery management system applications. The detailed analyses illustrate the importance of structural identifiability prior to performing parameter estimation experiments, and the algebraic complications encountered even for simple models. View Full-Tex

Accelerated internal resistance measurements of lithium-ion cells to support future end-of-life strategies for electric vehicles

Industrial and academic communities have embarked on investigating the sustainability of vehicles that contain embedded electrochemical energy storage systems. Circular economy strategies for electric vehicle (EV) or hybrid electric vehicle (HEV) battery systems are underpinned by implicit assumptions about the state of health (SOH) of the battery. The internal resistance of battery systems is the essential property for determining available power, energy efficiency, and heat generation. Consequently, precise measurement is crucial to estimate the SOH; however, the international standards and best practice guides that exist to define the measurements include long preconditioning and rest times that make the test duration prohibitive. The aim of this research is to critically evaluate whether test duration times for internal resistance measurements can be reduced to values that may facilitate further end-of-life (EOL) options. Results reveal a newly developed technique using pulse-multisines is two to four times faster to perform when compared to the standard protocol whilst maintaining accuracy for battery electric vehicle (BEV) and HEV cells, respectively. This novel method allows different stakeholders to rank the relative importance of test accuracy verses experimental test time when categorising used Li-ion cells for different EOL applications. View Full-Tex

Understanding non-linearity in electrochemical systems using multisine-based non-linear characterization

Background: With the development of advanced characterization techniques, lithium-ion battery non-linearities have recently gained increased attention which can benefit battery health diagnosis and ageing mechanism identification. In comparison to conventional single sine wave-based methods, the multisine-based non-linear characterization method has the advantage of capturing the dynamic voltage response within a short testing duration, and therefore has further development potential for on-board applications. However, understanding lithium-ion battery electrochemical processes that contribute to battery non-linearities is still unclear. Methods: In this paper, the sensitivity of the Doyle–Fuller–Newman model parameters are analysed in the frequency domain to investigate the electrochemical processes that contribute to the non-linear dynamics of the voltage response. To begin with, the non-linearities of the Doyle–Fuller–Newman model with validated parameters are characterized and compared to experimental data from a commercial cell. This demonstrated a significant difference between the mathematical model and the non-linearities determined experimentally. Then, a global sensitivity analysis is applied to determine the most sensitive parameter contributing to battery non-linearities. Finally, the appropriate value of the most sensitive parameter which results in the closest non-linear response to the commercial battery is estimated through minimizing the root mean square error. Results: The results show that the charge transfer coefficient is the most sensitive parameter contributing to battery non-linearities among the Doyle–Fuller–Newman model parameters. The non-linear response of the Doyle–Fuller–Newman model is validated with good agreement with the experimental results, when the Butler–Volmer kinetic is asymmetrical due to the unequal anodic and cathodic charge transfer coefficients

Current distribution and anode potential modelling in battery modules with a real-world busbar system

The performance of a lithium-ion battery pack is not only related to the behavior of the individual cells within the pack, but also presents a strong interdependency with the temperature distributions, interconnect resistance between cells, and the cell’s physical location within the complete battery pack. This paper develops representative busbar circuits with different fidelities to simulate the behavior of cells within a battery module and analyses the influence of cell-to-cell heat transfer and interconnect resistance on the distribution of cell current and anode potential in a battery module. This work investigates multi-physics interactions within the battery module, including cells, interconnect resistances, and temperature distributions, while analyzing the lithium plating problem at the module level. Specifically, the cell model used in this study is a validated thermally coupled single-particle model with electrolyte, and the battery module uses a commercially representative busbar design to include 30-cells in parallel. The effects of parameter changes within the battery pack on individual cells are simulated and analyzed. The study highlights that some cells in the battery module would present a higher risk of lithium plating during fast- charge conditions as they experience a lower anode potential during the charge events

Colossal Positive Magnetoresistance in a Doped Nearly Magnetic Semiconductor

We report on a positive colossal magnetoresistance (MR) induced by metallization of FeSb$_{2}$, a nearly magnetic or "Kondo" semiconductor with 3d ions. We discuss contribution of orbital MR and quantum interference to enhanced magnetic field response of electrical resistivity.Comment: 5 pages, 5 figure