Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.publishedVersio

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation. Document type: Articl

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Wind energy is one of the most important sources of energy in the world. In recent decades, wind as one of the massive marine energy resources in the ocean to produce electricity has been used. This chapter introduces a comprehensive overview of the efficient ocean wind energy technologies, and the global wind energies in both offshore and onshore sides are discussed. Also, the classification of global ocean wind energy resources is presented. Moreover, different components of a wind farm offshore as well as the technologies used in them are investigated. Possible layouts regarding the foundation of an offshore wind turbine, floating offshore, as well as the operation of wind farms in the shallow and deep location of the ocean are studied. Finally, the offshore wind power plant challenges are described

An Experimental Study on Thermal Performance of Graphite-Based Phase-Change Materials for High-Power Batteries

High-power lithium-ion capacitors (LiC) are hybrid energy storage systems (EES) with the combined benefits of lithium-ion batteries (LiB) and supercapacitors, such as high specific energy, high specific power, and a long lifetime. Such advanced technology can be used in high-power applications when high charging and discharging are demanded. Nevertheless, their performance and lifetime highly depend on temperature. In this context, this paper presents an optimal passive thermal management system (TMS) employing phase-change materials (PCM) combined with graphite to maintain the LiC maximum temperature. To evaluate the thermal response of the PCM and the PCM/G, experimental tests have been performed. The results exhibit that when the cell is under natural convection, the maximum temperature exceeds 55 °C, which is very harmful for the cell’s lifetime. Using the pure paraffin PCM, the maximum temperature of the LiC was reduced from 55.3 °C to 40.2 °C, which shows a 27.3% temperature reduction compared to natural convection. Using the PCM/G composite, the maximum temperature was reduced from 55.3 °C (natural convection) to 38.5 °C, a 30.4% temperature reduction compared to natural convection. The main reason for this temperature reduction is the PCM’s high latent heat fusion, as well as the graphite thermal conductivity. Moreover, different PCM/G thicknesses were investigated for which the maximum temperature of the LiC reached 38.02 °C, 38.57 °C, 41.18 °C, 43.61 °C, and 46.98 °C for the thicknesses of 15 mm, 10 mm, 7 mm, 5 mm, and 2 mm, respectively. In this context, a thickness of 10 mm is the optimum thickness to reduce the cost, weight, volume, and temperature

A Comprehensive Review of Lithium-Ion Capacitor Technology: Theory, Development, Modeling, Thermal Management Systems, and Applications

This review paper aims to provide the background and literature review of a hybrid energy storage system (ESS) called a lithium-ion capacitor (LiC). Since the LiC structure is formed based on the anode of lithium-ion batteries (LiB) and cathode of electric double-layer capacitors (EDLCs), a short overview of LiBs and EDLCs is presented following the motivation of hybrid ESSs. Then, the used materials in LiC technology are elaborated. Later, a discussion regarding the current knowledge and recent development related to electro-thermal and lifetime modeling for the LiCs is given. As the performance and lifetime of LiCs highly depends on the operating temperature, heat transfer modeling and heat generation mechanisms of the LiC technology have been introduced, and the published papers considering the thermal management of LiCs have been listed and discussed. In the last section, the applications of LiCs have been elaborated

Equivalent Circuit Model for High-Power Lithium-Ion Batteries under High Current Rates, Wide Temperature Range, and Various State of Charges

The most employed technique to mimic the behavior of lithium-ion cells to monitor and control them is the equivalent circuit model (ECM). This modeling tool should be precise enough to ensure the system’s reliability. Two significant parameters that affect the accuracy of the ECM are the applied current rate and operating temperature. Without a thorough understating of the influence of these parameters on the ECM, parameter estimation should be carried out manually within the calibration, which is not favorable. In this work, an enhanced ECM was developed for high-power lithium-ion capacitors (LiC) for a wide temperature range from the freezing temperature of −30 °C to the hot temperature of +60 °C with the applied rates from 10 A to 500 A. In this context, experimental tests were carried out to mimic the behavior of the LiC by modeling an ECM with two RC branches. In these branches, two resistance and capacitance (RC) are required to maintain the precision of the model. The validation results proved that the semi-empirical second-order ECM can estimate the electrical and thermal parameters of the LiC with high accuracy. In this context, when the current rate was less than 150 A, the error of the developed ECM was lower than 3%. Additionally, when the demanded power was high, in current rates above 150 A, the simulation error was lower than 5%

Advanced Thermal Management Systems for High-Power Lithium-Ion Capacitors: A Comprehensive Review

The acceleration demand from the driver in electric vehicles (EVs) should be supported by high-power energy storage systems (ESSs). In order to satisfy the driver’s request, the employed ESS should have high power densities. On the other hand, high energy densities are required at the same time for EVs’ traction to minimize the range anxiety. In this context, a novel ESS has emerged that can provide high power and energy densities at the same time. Such technology is called lithium-ion capacitor (LiC), which employs Li-doped carbon as negative electrode and activated carbon as positive electrode. However, high heat generation in high current applications is an issue that should be managed to extend the LiCs life span. Hence, a proper thermal management system (TMS) is mandatory for such a hybrid technology. Since this ESS is novel, there are only several TMSs addressed for LiCs. In this review article, a literature study regarding the developed TMSs for LiCs is presented. Since LiCs use Li-doped carbon in their negative electrodes, lithium-titanate oxide (LTO) batteries are the most similar lithium-ion batteries (LiBs) to LiCs. Therefore, the proposed TMSs for lithium-ion batteries, especially LTO batteries, have been explained as well. The investigated TMSs are active, passive, and hybrid cooling methods The proposed TMSs have been classified in three different sections, including active methods, passive methods, and hybrid methods

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns

Features extraction for battery SOH estimation from battery pulsed charging operation

Pulse charging is recognized as a charging technique for maximizing the life of lithium-ion batteries. In this paper, 10 features are extracted from the battery PC operations for battery state of health prediction. By permuting, combining and comparing features, the prediction performance is improved when using two features as input