A review on electric vehicle battery modelling: from lithium-ion toward lithium–sulphur

Accurate prediction of range of an electric vehicle (EV) is a significant issue and a key market qualifier. EV range forecasting can be made practicable through the application of advanced modelling and estimation techniques. Battery modelling and state-of-charge estimation methods play a vital role in this area. In addition, battery modelling is essential for safe charging/discharging and optimal usage of batteries. Much existing work has been carried out on incumbent Lithium-ion (Li-ion) technologies, but these are reaching their theoretical limits and modern research is also exploring promising next-generation technologies such as Lithium–Sulphur (Li–S). This study reviews and discusses various battery modelling approaches including mathematical models, electrochemical models and electrical equivalent circuit models. After a general survey, the study explores the specific application of battery models in EV battery management systems, where models may have low fidelity to be fast enough to run in real-time applications. Two main categories are considered: reduced-order electrochemical models and equivalent circuit models. The particular challenges associated with Li–S batteries are explored, and it is concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed

Dual extended Kalman filter for state of charge estimation of lithium–sulfur batteries

Lithium-Sulfur is a promising technology for the next generation of batteries and research efforts for early-stage prototype implementation increased in recent years. For the development of a suitable Battery Management System, a state estimator is required; however, lithium-sulfur behavior presents a large non-observable region that may difficult the convergence of the state estimation algorithm leading to large errors or even instability. A dual Extended Kalman Filter is proposed to circumvent the non-observability region. This objective is achieved by combining a parameter estimation algorithm with a cell model that includes non-linear behavior such as self-discharge and cell degradation. The resulting dual Kalman filter is applied to lithium–sulfur batteries to estimate their State-of-Charge incorporating the effects of degradation, temperature, and self-discharge deviations.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.b - Per a 2030, ampliar la infraestructura i millorar la tecnologia per tal d’oferir serveis d’energia moderns i sos­tenibles per a tots els països en desenvolupament, en particular els països menys avançats, els petits estats insulars en desenvolupament i els països en desenvolupament sense litoral, d’acord amb els programes de suport respectiusObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Understanding and modelling the thermal behaviour of incumbent and future lithium ion batteries

The thesis begins with a literature review on the thermal behaviours for an incumbent and a future lithium ion battery, which are Lithium iron phosphate (LFP) prismatic batteries and Lithium sulfur (Li-S) pouch batteries, respectively. Research gaps were identified for both types of batteries, requiring the development of novel experimental techniques and/or modelling approaches for each type. Lithium sulfur batteries are an important next generation high energy density battery technology. However, the phenomenon known as the polysulfide shuttle was identified as one of the most important challenges needing to be overcome. It causes accelerated degradation, reduced Coulombic efficiency and increased heat generation, particularly towards the end of charge. Research was conducted on how to track, quantify and therefore prevent the shuttle effect, in order to improve the safety and increase cycle life of Li-S batteries in real applications. This required the real-time detection of the onset of shuttle during charge. The diagnostic technique Differential Thermal Voltammetry (DTV) was used to track the shuttle effect during charging for the first time, and quantitative interpretations of the experimental DTV curves were performed by thermally-coupling a zero-dimensional Li-S model. The DTV technique, together with the model, is a promising tool for real-time detection of shuttle in applications, to inform control algorithms for deciding the end of charging, thus preventing excessive degradation and charge inefficiency. Lithium iron phosphate prismatic batteries are widely used in both sustainable transportation and stationary energy storage. However, system level thermal management for large format prismatic cells is rarely considered in the literature. Equivalent circuit models (ECM) were shortlisted, due to their ease of implementation and low complexity. The accuracy of an ECM is critical to the functionality and usefulness of the battery management system (BMS). However, their accuracy is limited by how easy they are parameterised, and therefore different experimental techniques and model parameter identification methods (PIM) have been widely studied. Yet, how to account for significant changes in time constants between operation under load and during relaxation has not been resolved. In this work a novel PIM and a modified ECM is presented that increases accuracy by 77.4% during drive cycle validation and 87.6% during constant current load validation for a large format LFP prismatic cell. The modified ECM uses switching RC network values for each phase, which is significant for this cell and particularly at low state-of-charge for all lithium ion batteries. Different characterisation tests and the corresponding experimental data have been trained together across a complete State-of-Charge (SoC) and temperature range, which enables a smooth transition between identified parameters. Ultimately, the model created using parameters captured by the proposed PIM shows an improved model accuracy in comparison with conventional PIM techniques. Large format prismatic cell’s thermal management is challenging due to the large internal heat generation rate, longer distance for internal battery core away from the heat exchange cooling interface and therefore larger thermal gradient across the cell. The standardised surface Cell Cooling Coefficient (CCC) can be used to quantify the degree of difficulty of a target cell to be thermally managed. Here, in this thesis, the novel metric surface CCC is introduced and implemented onto a large format LFP prismatic cell, with aluminium alloy prismatic casing. Further, based on developed PIM, a parameterised and discretised 3-dimentional Electro-Thermal Equivalent Circuit Model is developed. The developed model is validated using the experimental data through embedding corresponding boundary conditions, including drive cycle noisy load and constant current CCC square wave load, electrically and thermally at the same time. The study offers a quantitative guide of the trade-off between cell energy density and surface CCC, and also a casing selection analysis is conducted. The CCC metric together with proposed model enable the cell manufacturer and Original Equipment Makers (OEMs) to customise the cell design based on the casing material, single cell energy density, cell thickness and CCC/capability to be thermally managed. In the future cell design process, this study offers a cost-effective, time-efficient, convenient and quantitative way, in order to achieve a better and safe battery design (high capacity, power and longer lifetime) for wider application needs. Finally, it is concluded that, for both incumbent and future lithium ion batteries, understanding the thermal behaviour is the key for a safer, lighter, longer lifetime, longer range application. By using engineering customised experimental techniques together with empirical and/or physical simulations, enhanced understanding with quantitative battery optimisation and thermal management are achieved in this thesis. The findings in thesis are beneficial for wide range of communities including research community, industry OEMs, application engineers, battery management system developers, control engineers and electric vehicle end users.Open Acces

Comparison of the state of lithium-sulphur and lithium-ion batteries applied to electromobility

The market share in electric vehicles (EV) is increasing. This trend is likely to continue due to the increased interest in reducing CO2 emissions. The electric vehicle market evolution depends principally on the evolution of batteries capacity. As a consequence, automobile manufacturers focus their efforts on launching in the market EVs capable to compete with internal combustion engine vehicles (ICEV) in both performance and economic aspects. Although EVs are suitable for the day-to-day needs of the typical urban driver, their range is still lower than ICEV, because batteries are not able to store and supply enough energy to the vehicle and provide the same autonomy as ICEV. EV use mostly Lithium-ion (Li-ion) batteries but this technology is reaching its theoretical limit (200–250¿Wh/kg). Although the research to improve Li-ion batteries is very active, other researches began to investigate alternative electrochemical energy storage systems with higher energy density. At present, the most promising technology is the Lithium-Sulphur (Li-S) battery. This paper presents a review of the state of art of Li-Sulphur battery on EVs compared to Li-ion ones, considering technical, modelling, environmental and economic aspects with the aim of depicting the challenges this technology has to overcome to substitute Li-ion in the near future. This study shows how the main drawbacks for Li-S concern are durability, self-discharge and battery modelling. However, from an environmental and economic point of view, Li-S technology presents many advantages over Li-ion.Peer ReviewedPreprin

Concurrent real-time estimation of state of health and maximum available power in lithium-sulfur batteries

Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile

Powering the future: a comprehensive review of battery energy storage systems

Global society is significantly speeding up the adoption of renewable energy sources and their integration into the current existing grid in order to counteract growing environmental problems, particularly the increased carbon dioxide emission of the last century. Renewable energy sources have a tremendous potential to reduce carbon dioxide emissions because they practically never produce any carbon dioxide or other pollutants. On the other hand, these energy sources are usually influenced by geographical location, weather, and other factors that are of stochastic nature. The battery energy storage system can be applied to store the energy produced by RESs and then utilized regularly and within limits as necessary to lessen the impact of the intermittent nature of renewable energy sources. The main purpose of the review paper is to present the current state of the art of battery energy storage systems and identify their advantages and disadvantages. At the same time, this helps researchers and engineers in the field to find out the most appropriate configuration for a particular application. This study offers a thorough analysis of the battery energy storage system with regard to battery chemistries, power electronics, and management approaches. This paper also offers a detailed analysis of battery energy storage system applications and investigates the shortcomings of the current best battery energy storage system architectures to pinpoint areas that require further study.This publication is part of the project TED2021-132864A-I00, funded by MCIN/ AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR”.Peer ReviewedPostprint (published version

Advanced state of charge estimation for lithium-sulfur batteries.

Lithium-sulfur (Li-S) batteries have a high theoretical energy density, which could outperform classic Li-ion technology in weight, manufacturing costs, safety and environmental impact. The aim of this study is to extend the research around Li-S through practical applications, specifically to develop a Li-S battery state of charge (SoC) estimation in the environment of electrical vehicles. This thesis is written in paper based form and is organised into three main areas. Part I introduces general topic of vehicle electrification, the framework of the research project REVB, mechanisms of Li-S cells and techniques for SoC estimation. The major scientific contribution is given in Part II within three studies in paper-based form. In Paper 1, a simple and fast running equivalent circuit network discharge model for Li-S cells over different temperature levels is presented. Paper 2 uses the model as an observer for Kalman filter (KF) based SoC estimation, employing and comparing the extended Kalman filter, the unscented Kalman filter and the Particle filter. Generally, a robust Li-S cell SoC estimator could be realized for realistic scenarios. To improve the robustness of the SoC estimation with different current densities, in Paper 3 a fast running online parameter identification method is applied, which could be used to improve the battery model as well as the SoC estimation precision. In Part III, the results are discussed and future directions are given to improve the SoC estimation accuracy for a wider range of applications and conditions. The final conclusion of this work is that a robust Li-S cell SoC estimation can be achieved with Kalman filter types of algorithms. Amongst the approaches of this study, the online parameter identification approach could deliver the best results and also contains most potential for further improvement