Due to the wide range of operating conditions under which a lithium-ion battery operates, it is essential to develop models that are able to replicate experimental behavior in a variety of temperature and current-rate scenarios. However, there are some complex effects in the range of moderate to high current rates that are difficult to justify with current formulations of both equivalent circuit and electrochemical models, due to model shortcomings or a challenging parameterization process. For this reason, in this article we present a discretely distributed, multi-particle equivalent circuit model capable of addressing said limitations. By obtaining quasi-static characteristics from thermodynamic tests and making informed assumptions about particle size distributions, the model is only dependent on three parameters that are directly correlated to those determined from experimental impedance data. The proposed model is able to provide accurate results in a current range from C/10 to 2C (RMS≤12 mV at 40 °C) as well as dynamic operation (RMS≤7 mV at 40 °C), and ensures consistent behavior at ambient temperatures in the range from 10 °C to 40 °C. For all the reasons above, the proposed model constitutes a suitable alternative for modeling complex behavior in lithium-ion batteries with a reduced computational cost and a well-defined parameterization process.

Electric vehicles (EVs) will be the dominant technology for the automobile industry due to efficiency and environmental reasons. Lithium-ion batteries lead the energy supply business for the most recent group of EVs and many other electronic consumer devices. One of the most important pieces of information for EV users is the state-of-charge of the battery, also known as SOC. The SOC works like the fuel gauge for the battery. Information about remaining battery capacity is essential to avoid running out of battery power. Battery remaining charge is not easy to estimate, due to non-linear phenomenon inside the battery. This work is concerned with SOC prediction using machine learning techniques. Three machine learning tools, called Artificial Bee Colony-Multilayer Perceptron (ABC/MLP), Artificial Bee Colony gradient boosting regression tree (ABC/GBRT) and Least Absolute Selection and Shrinkage Operator (LASSO) have been used to build models that enable the prediction of the SOC of a storage cell. The predictive results confirm the enhanced performance of the ABC/GBRT-based model over the other methods for SOC prediction. SOC errors remain below 1%, 10% and 17% for ABC/GBRT, ABC/MLP and LASSO, respectively. The goodness of fit, calculated using R2, was 0.99, 0.95 and 0.81 for the three methods, respectively. A comparison of the results obtained using all the methods has also been carried out.

Equivalent circuit models (ECMs) remain the most popular choice for online applications in lithium-ion batteries because of their simpler parameterization and lower computational requirements in comparison to electrochemical models. Nevertheless, standard ECMs lack physical insight and fail to accurately reproduce cell behavior under a wide range of operating conditions. For this reason, the development of physics-informed ECMs becomes essential so as to provide a better description of the physical processes while maintaining a reduced computational complexity. In this article, we propose a novel physics-based ECM derived directly from an electrochemical model, so that there is a clear correlation between circuit states and internal battery states, as well as circuit and physical parameters. The proposed model yields an RMS error below 1.46 mV for cell voltage, 0.28% for the surface concentration in the active material particles, 0.6% for the electrode-averaged electrolyte concentration and 0.32 mV for the charge-transfer overpotentials. Another key feature of this model is the relationship between circuit parameters and those identified in frequency-domain tests, which allows us to characterize and validate the model experimentally. We understand that the presented model constitutes an alternative to standard ECMs as well as electrochemical models as it combines advantageous characteristics from both of them.

Online temperature estimates are essential to the thermal monitoring and control of battery cells for battery management systems (BMSs). Due to hardware limitations, there has been a surge in interest in sensorless approaches for both surface and core temperatures. On this account, several methods have been proposed in the literature for the coestimation of state of charge (SOC) and temperature via RC-based electrical and thermal models and Extended Kalman Filters (EKFs). However, the stability and reliability of these schemes over the complete cell lifetime, when the effects of battery aging become apparent, have not been addressed thoroughly. In this article, a dual state-parameter estimation is carried out on an enhanced equivalent circuit model to coestimate the SOC and SOH on a commercial nickel-rich, silicon-graphite cell throughout its entire lifetime. A thermal model has been characterized based on the previous electrical model for the estimation of surface and core temperature of the cell. The continuous updating and correction of electrical parameters prove to be critical for temperature estimations to remain accurate in the long run, yielding a root mean square error (RMSE) in surface temperature below 1.2 degrees C for as long as 800 cycles.

Given the rising importance of energy storage for the technological future, the development of new storage technologies becomes critical. Lithium-ion batteries are a promising technology, in particular for electric mobility, because of their high energy density and specific energy. Among the different lithium technologies, the use of nickel-rich NMC positive electrodes and silicon-graphite negative electrodes (NMC/Si-Gr) allows for notably high specific energy values. However, this battery technology can have a short cycle life under certain conditions. As cycle life is a key issue for automotive applications, it is important to evaluate which factors can lead to an earlier degradation of NMC/Si-Gr batteries. This paper provides a long term cycling study of a commercial NMC/Si-Gr battery, including different types of cycling conditions, both static and dynamic, with the aim to identify which factors can shorten the cycle life of the technology. The state of health of the cells is measured and analyzed via capacity measurements and electrochemical impedance spectroscopy.

The emerging nickel-rich/silicon-graphite lithium-ion technology is showing a notable increase in the specific energy, a main requirement for portable devices and electric vehicles. These applications also demand short charging times, while actual charging methods for this technology imply long time or a significant reduction in cycling life. This study analyses the factors that affect the charge behavior for 18,650 commercial nickel-rich/silicon-graphite batteries. For that, long-term cycling tests have been carried out, including electric vehicle standard tests. It can be concluded that this technology has two key issues to develop an efficient charge method: high charge rates should be avoided, mainly below 15% state of charge, and the charge should be finished at 95% of actual cell capacity. This allows that, regardless of application and cell degradation level, cells can be recharged in 2 h without a negative impact on cycling life. For faster charge applications, a new method has been developed to minimize charging time without compromising the cycle life as much as the high current manufacturer method. The proposed fast charge method has proven to be notably faster, recharging in an average 1.3 h (48% less than the high current method and 68% less than the standard method).

The fast charging of Lithium-Ion Batteries (LIBs) is an active ongoing area of research over three decades in industry and academics. The objective is to design optimal charging strategies that minimize charging time while maintaining battery performance, safety, and charger practicality. The main problem is that the LIB technology depends on multi-disciplinary engineering factors that form rapidly varying intrinsic states in the cell during the charging process. These factors take the form of interdependent electrochemical, structural, and thermo-kinetic perspectives. Here, the list can grow as electrochemical changes; charge transfer, ionic conductivity, structural transformations; mass/particle transfer, migration, diffusion, and thermo-kinetic exchanges; phase transitions, heat effects, and collectively their inter-dependencies. Fast charging intensifies this varying nature making it very difficult to achieve an optimal process. In fact, many charging strategies fail to adhere to such rapid variations and are based on predefined/fixed parameters such as voltage, current, and temperature, individually or collectively, that enforce and aggregate stress on the LIBs. Consequently, fast charging accelerates battery degradation and reduces battery life. In order to facilitate the design of optimal fast charging strategies, this paper analyzes the literature around the influences of intrinsic factors on the LIB charging process under electrochemical, structural, and thermo-kinetic perspectives. Then, it examines the existing charging strategies with a new categorical analogy of; 1) memory-based, 2) memory-less, and 3) short-cache, showing their efforts to achieve the optimal charging targets and challenges in adapting to the demanded intrinsic variations. Accordingly, a potential paradigm shift for the next generation of LIBs' fast charging strategies has been identified in the new area of short-cache-based natural current-absorption-driven charging strategies. Importantly, this new approach is competent in bringing the practical intelligence necessary to adapt the control of LIB fast charging over rapid intrinsic variations.

An intelligent model of the incremental capacity (IC) curve of an automotive lithium-ferrophosphate battery is presented. The relative heights of the two major peaks of the IC curve can be acquired from high-current discharges, thus enabling the state of health estimation of the battery while the vehicle is being operated and in certain cases, aging mechanisms can be suggested. Our model has been validated using a large dataset (number of batteries) representing different degradation scenarios, obtained from a recently available open-source database.

A new battery charger, based on a multiphase resonant converter, for a high-capacity 48 V LiFePO4 lithium-ion battery is presented. LiFePO4 batteries are among the most widely used today and offer high energy efficiency, high safety performance, very good temperature behavior, and a long cycle life. An accurate control of the charging current is necessary to preserve the battery health. The design of the charger is presented in a tight correlation with a battery model based on experimental data obtained at the laboratory. With the aim of reducing conduction losses, the general analysis of the inverter stage obtained from the parallel connection of N class D LCpCs resonant inverters is carried out. The study provides criteria for proper selection of the transistors and diodes as well as the value of the DC-link voltage. The effect of the leakage inductance of the transformer on the resonant circuit is also evaluated, and a design solution to cancel it is proposed. The output stage is based on a multi-winding current-doubler rectifier. The converter is designed to operate in open-loop operation as an input voltage-dependent current source, but in closed-loop operation, it behaves as a voltage source with an inherent maximum output current limitation, which provides high reliability throughout the whole charging process. The curve of efficiency of the proposed charger exhibits a wide flat zone that includes light load conditions.

The European Union is pushing forward its European Green Deal setting a climate goal of a 55% GHG decrease by 2030 and a net-zero emission economy by 2050. Sustainable mobility is one of the main areas of interest, with Battery Electric Vehicles and Hydrogen Electric vehicles (or Fuel Cell vehicles) as key technologies to reduce pollution and greenhouse gas emissions and, therefore, global warming. Each technology is better suited for specific applications and standardization plays a key role to ensure the deployment of a safe, cost effective, energy efficient, sustainable transport. Sustainable refueling stations where several technologies coexist are envisioned and multiple standards should be implemented in a coordinated manner. A review of current European legislation and standardization for Hydrogen and Battery Electric Buses and Heavy-Duty Trucks is presented in this paper. Some indications of areas needing to be further standardized such as higher capacity and flow charging rates for Hydrogen vehicles are also given.

As the electric vehicle market keeps growing up, the importance of achieving good technical characteristics is increasingly important. The most common technology for EV energy storage is the use of battery packs, in particular, lithium-ion batteries (LIBs). One of the most promising solutions is based on nickel-rich positive electrodes and silicon-graphite negative electrodes. This paper shows an evaluation of nickel-rich/silicon-graphite LIBs, using several cycling tests under different regimes for evaluating the impact of the different aspects that are important in a EV, such as high current charging. The batteries state of health is evaluated via capacity measurement, efficiency calculation and impedance measurements.

Prognostics in State of Health (SoH) of lithium-ion batteries (LIBs) holds high importance in ensuring the reliability and safe electrification of battery systems such as in mobile electronic devices, electric vehicles (EVs), and grid battery energy storage systems (BESS). However, due to reasons such as the SoH dependency on a chain of historical data, both internal and external to the battery systems, the SoH estimation is not directly measured. Indirectly, several electrochemical in-situ measurement methods are employed as non-destructive determination of SoH such as ICA, DVA, and EIS, yet forming more post-mortem type of analysis. Alternatively, the machine learning (ML) techniques in data mining for predictions shown high caliber in unambiguously identifying the multi-scale, multi-factorial and complex degradation patterns of LIBs. This work investigates a couple of ML techniques, FNN and LSTM models, to train on a dataset on LIB degradation profiles and improve them for SoH predictions on new battery aging cycles. Here, a publicly available dataset at NASA website has used to train the models. Further, it is expected to explore the performance and the reliability of the LSTM for SoH predictions and plans to extend the solution for other types of batteries.

A fuzzy model is presented for detecting changes in the incremental capacity curve of an automotive lithium-ferrophosphate battery through analysis of the data collected while the vehicle is being operated. By means of the proposed model, the state of health of the energy storage system can be estimated on-vehicle. The fuzzy model is derived through distal learning and describes the instantaneous slope of the stored charge with respect to the battery voltage. The scheme has been validated in batteries with different levels of deterioration. It is concluded that the fuzzy model is able to anticipate the most frequent deteriorations of the battery and therefore it can be used to prevent its premature wear.

The European Union is pushing forward its European Green Deal where sustainable mobility is one of the main areas of interest, especially after the COVID-19 crisis. The role of battery electric buses (BEBs) in public transit is key to achieve sustainable mobility and to reduce city pollution and greenhouse gas emissions and, therefore, global warming. Standardization plays a key role to ensure the deployment of a safe, cost effective, energy efficient, sustainable BEBs fleet. Standardization of components, interoperability, generalized recharge possibility, and even the possibility of using the BEBs as controllable loads, with energy storage that can be returned to the grid, must be done. This paper presents the most relevant legislation at European level, the International and European standardization outline in the field of BEBs, and the most important standardization activities going on. It gives references to deal with the very high number of standards relevant for the BEBs and the related activities that are in constant update, from different standardization entities.

Due to their high energy density, lithium-ion batteries with blended silicon-graphite (Si-Gr) anodes and nickel-rich (NMC) cathodes have been regarded as one of the most promising technologies for next-generation consumer electronics and electric vehicles. However, there are still several technical challenges to overcome for successful wide-spread adoption; in particular, deciphering the degradation phenomena remains complex and challenging, as the blended nature of the electrode creates a new paradigm, with the Si/Gr ratio likely changing with aging. Although ex-situ techniques have been used, a set of in-operando tools that enable diagnosis and prognosis on this technology has yet to be developed. Herein, we present a mechanistic investigation that generates a complete degradation mapping coupled with proposed aging features of interest, to attain accurate diagnosis and prognosis. The mechanistic model allows analyzing aging modes that display incubation periods as a potential prelude to thermodynamic plating, and the identification via incremental capacity of unique silicon features that change predictably as it degrades. A comprehensive look-up table summarizing key features is provided to provide support both to scientists and engineers on designing next-generation battery management systems for this technology.

The demand for energy storage systems is experimenting an important growth in the current days. Applications such as electric vehicles and large-scale energy storage systems are expected to keep growing in the upcoming years. This creates the need for better energy storage systems. Lithium-ion batteries are one of the most widely used due to their advantages in terms of energy density and decreasing cost. However, current state-of-art materials still do not have enough energy density to achieve the desired performance levels in their main applications. To solve this problem, one of the most interesting technologies that has been developed in the recent years is the use of nickel-rich positive electrodes and silicon-graphite negative electrodes. These new technologies can achieve higher energy densities than the current lithium-ion technologies, but their long-term behavior has still not been researched enough. In this paper, two different commercial 18650 cells with nickel- rich positive electrodes and silicon-graphite negative electrodes are evaluated. A test schedule with several aging scenarios and the application of different techniques (incremental capacity analysis and electrochemical impedance spectroscopy) are proposed. The main objective is to evaluate this new battery technology in relation to the current state-ofart technology.

Fast charging lithium-ion batteries (LIBs) remain as a key factor to enhance in battery powered systems such as electric vehicles (EVs), grid battery energy storage systems (BESS) or portable electronics. Because of the inherent multidisciplinary engineering impacts of the LIB charging process, the design of an optimal fast charging method is challenging. Fast charging is mostly compromised by the deterioration of the health of the battery in terms of both capacity and power fading, and safety. Hence, it is essential to design better fast charging protocols to improve the acceptability, reliability and marketability of battery powered systems. In addition, emerging LIB technologies containing blended silicon-graphite (Si-Gr) with improved energy densities are expected to become widely used in next-generation systems. Hence the design of novel, technology-oriented charging schemes are required. This paper examines the suitability of different pulse charging patterns for fast charging, coupled with varying relaxation schemes applied on commercial, high-energy density Si.Gr cells. The Pulse relaxation is expected to reduce the overpotential stress driven by high-rate currents and constant current charging. By measuring the Internal Resistances (IR) distribution over the state of charge (SOC), a favorable SOC range for high C-rate charging is obtained. As a practical reference point, the fast charging schemes targeted the charging time from the manufacturer's standard 4 h-charge scheme to 1 h, while maintaining safe cell temperature and voltage. A table of comparison provides charging and power efficiencies, temperature rise and charge percentage as a summary of a set of interesting fast charging schemes. The presented results provide a set of insights and indicators to better understand and improve pulse charging design schemes on this specific Si-Gr based battery.

Next-generation Electric Vehicles (EVs) will demand higher range, longer lifespan and better reliability than current EVs. In order to achieve that, the improvement of the energy storage system is crucial. EVs generally use lithium-ion batteries (LIBs) as their energy storage system. The development of new electrode materials in the last few years, in particular the use of silicon-graphite negative electrodes, promises an increase in energy density and, therefore, potentially higher range and lifespan on EVs. The proper evaluation of this battery technology is fundamental to assure the desired levels of energy density without a notable performance loss. In this paper, we propose a characterization method for LIBs with silicon-graphite electrodes, on which the batteries are tested under various EV standards, such as the recently enforced Worldwide Harmonized Light Electric Vehicle Test Protocol (WLTP)

Lithium ion battery (LIB) degradation originates from complex mechanisms, usually interacting simultaneously, and in various degrees of intensity. Due to its complexity, to date, identifying battery aging mechanisms remains challenging. To resolve such issue, various techniques have been developed, including in-situ incremental capacity (IC) and peak area (PA) analysis. The use of these techniques has been proved to be valuable for identifying LIB degradation, both qualitatively and quantitatively. In addition, due to their in-situ and non-destructive nature, the implementation of these techniques is feasible for onboard, battery management systems (BMS). However, the understanding and direct applicability of IC and PA techniques is not straightforward, as it requires the understanding of electrochemical and material science principles. Unfortunately, BMS design teams rarely include battery scientists, and are mainly composed of electrical engineers. Aiming to bridge gaps in knowledge between electrical engineering and battery science, here we present a set of direct look-up tables generated from IC analysis, that provides a simple tool for the evaluation of LIB degradation modes. We begin with a brief overview of the basics of IC and PA techniques and their relation to battery degradation modes, to later present the look-up tables, and conclude with various real-life examples of cell degradation, to illustrate the use of the look-up tables. This study exemplifies the use of look-up tables for BMS applications, providing a simple, fast and accurate real-time estimation of LIB degradation modes.

High power lithium iron phosphate (LFP) batteries suitable for Electric Vehicles are tested in this work. An extended cycle-life testing is carried out, consisting in various types of experiments: standard cycling, optimized fast charge with high constant current discharge (4 C) and simulating driving dynamic stress protocols (DST). The fast charge/DST discharge tests are carried out with depth of discharge (DOD) dependency (100% DOD and partial 50% DOD discharge). A complete analysis of the cycling results is developed, showing an overall good performance of the tested batteries. In all of experiments, long term U.S. Advanced Battery Consortium goals are met: fast charging, cycle life and specific energy. Only the long term specific energy goal is not achieved, which is a drawback intrinsic in this technology. The results provide useful information for battery selection, BMS designs and other applications in EV industry.