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The integration of a composite of porous medium with phase change material (PCM) offers significant advantages in thermal management systems, enhancing heat transfer efficiency and addressing various thermal regulation challenges. This approach utilizes the PCM's latent heat absorption and the enhanced thermal conductivity provided by the porous medium, resulting in optimized system performance. Its applicability spans across electronics cooling and building insulation systems. However, predicting the thermal behavior of this composite material is challenging, necessitating computational tools to anticipate its response under different conditions and evaluate its influence on cooling strategies. The objective of this study is to create a computational tool specifically tailored to evaluate constitutive parameters of this composite material, thereby providing a comprehensive description of its thermal behavior. To achieve this goal, the multiscale homogenization principle is employed to assess the composite's effective thermophysical material properties using the representative volume element approach. The repeating unit cell of the aluminum lattice is incorporated into the PCM to define a representative volume element. The finite element method (FEM) is utilized to solve the three-dimensional homogenization problem, yielding an orthotropic effective thermal conductivity due to the inherent symmetry of the repeating material cell. Moreover, the study leverages the apparent heat capacity method to effectively manage the phase transitions within the PCM domain, utilizing smooth and temperature-dependent functions to accurately describe the thermophysical properties of the PCM. Integrating the composite into battery pack thermal management, this study thoroughly examines thermal dynamics by comparing outcomes with and without PCM integration. The transient thermal problem is accurately tackled using the FEM, employing the evaluated effective constitutive parameters of the homogenized composite to minimize computational effort. The results indicate a notable decline in the highest temperatures of the battery pack, leading to a reduction of about 14 °C at the specific moment when the phase change material fully transitions into its liquid form. The obtained results emphasize the effectiveness and practical feasibility of the proposed thermal management strategy. The modeling approach presented provides a robust tool with significant efficiency in reducing computational time for analyzing the thermal behavior of large models, as the utilization of the homogenization technique notably decreases the computational time. [ABSTRACT FROM AUTHOR]

The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption. [ABSTRACT FROM AUTHOR]

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [ABSTRACT FROM AUTHOR]

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [ABSTRACT FROM AUTHOR]

This paper examines the electrification of transportation as a response to environmental challenges caused by fossil fuels, exploring the potential of battery electric vehicles and hydrogen fuel cell vehicles as alternative solutions. However, a significant barrier to their widespread adoption is the limited availability of charging infrastructure. Therefore, this study proposes the development of comprehensive charging stations capable of accommodating both battery and hydrogen vehicles to address this challenge. The energy is purchased from the day-ahead and intraday auction-based electricity markets, where the electricity price is subject to uncertainty. Therefore, a two-stage stochastic programming model is formulated while the price scenarios are generated utilizing a k-means clustering algorithm. Given the complexity of the proposed model, an efficient solution approach is developed through the hybridization of the Benders decomposition algorithm and stochastic dual dynamic programming. In the Benders master problem, day-ahead bidding variables are determined, whereas the Benders sub-problem addresses intraday bidding and charging station scheduling variables, employing stochastic dual dynamic programming to tackle its intractability. Additionally, we transform the mixed integer linear program model of the second stage problem into a linear program, confirming its validity through KKT conditions. Our model provides practical insights for making informed decisions in electricity markets based on sequential auctions. While the bidding curves submitted to the day-ahead market remain unaffected by scenarios, those submitted to the intra-day market show dependence on fluctuations in day-ahead market prices. • Designing a decision framework for a hybrid electricity/hydrogen station. • Participating in day-ahead and intraday electricity auction markets effectively. • Modeling price uncertainties using scenarios and k-means clustering. • Applying Benders decomposition for day-ahead and intraday bidding decisions. • Using stochastic dual dynamic programming for the Benders sub-problem. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEVs) are gaining recognition as a pivotal innovation in the quest for sustainable transportation, seamlessly blending the advantages of fuel cells with electric vehicle technology. As global initiatives to curtail greenhouse gas emissions and mitigate climate change gain momentum, FCHEVs offer a promising alternative to conventional internal combustion engine vehicles and battery electric vehicles (BEVs). This review aims to examine recent technological progress in enhancing FCHEV fuel efficiency and performance, focusing particularly on cost reduction, long-distance operational capabilities, and the incorporation of intelligent autonomous systems. A key innovation highlighted in this study is the exploration of cost-effective hydrogen storage techniques, including metal hydrides and chemical storage solutions, which have demonstrated potential to significantly increase storage capacity and reduce refueling times. Furthermore, advancements in energy management, particularly the integration of ultracapacitors and high-capacity batteries, offer new avenues for extending vehicle range and boosting overall efficiency. The emergence of autonomous FCHEVs represents another major leap forward, setting the stage for more efficient, self-driving vehicles in the near future. However, persistent challenges remain, including reducing hydrogen production costs, improving fuel cell durability, and developing a widespread hydrogen infrastructure. Addressing these issues will require ongoing innovation in fuel cell materials, hydrogen storage systems, and energy management strategies. This review underscores the transformative role FCHEVs can play in reducing emissions and enhancing energy efficiency in the transport sector. Collaboration between industry, researchers, and policymakers will be vital to unlocking the full potential of FCHEVs in creating a more sustainable future. • This review work explores the diverse advancements aimed at enhancing the fuel economy and performance of FCHEVs. • A primary focus of recent developments is the reduction of FCHEV costs. • Merging the benefits of hydrogen fuel cells with advanced electric vehicle technology. • The work also looks Fuel Cell Hybrid Electric Vehicles (FCHEVs), offering a promising path achieving sustainable transportation. [ABSTRACT FROM AUTHOR]

To achieve efficient cooling capabilities in electric vehicle (EV) batteries, battery thermal management systems with higher power density have garnered significant attention. This work introduces a novel computational analysis method to assess the temperature distribution within the designed multiple EV battery cooling module's, investigating the flow of both water and silver-based nanofluids as coolants. The EV battery module under study comprises ten cylindrical lithium-ion batteries of model 18,650 types. This comprehensive simulation considered various factors, such as the coolant's flow path, flow rate and type, appear to have significantly influencing temperature distribution. The analysis identifies "Case IV" as the most effective cooling configuration utilizing water as the coolant and demonstrating superior cooling capabilities compared to the conventional cooling module ("Case I"). This finding marks a critical step toward optimizing battery cooling methods and achieving efficient thermal management. The incorporation of silver nanoparticles in the base fluid (water) enhances the nanofluid's thermal conductivity and heat transfer efficiency, showcasing improved cooling capabilities beyond that of water alone. The performance of different nanofluid concentrations including 0.25% and 0.50% by volume was evaluated to demonstrate their impact on battery cooling efficiency. To contextualize the results, the cooling performance of silver nanofluids has been compared with that of TiO2 nanofluids reported in previous studies. The outcomes of this research underscore the potential of the computational analysis technique to advance temperature management systems for EV batteries, improving EVs performance and reliability and contributing to a more sustainable and efficient future of transportation. [ABSTRACT FROM AUTHOR]

With the widespread use of lithium‐ion batteries in electric vehicles, energy storage, and mobile terminals, there is an urgent need to develop cathode materials with specific properties. However, existing material control synthesis routes based on repetitive experiments are often costly and inefficient, which is unsuitable for the broader application of novel materials. The development of machine learning and its combination with materials design offers a potential pathway for optimizing materials. Here, we present a design synthesis paradigm for developing high energy Ni‐rich cathodes with thermal/kinetic simulation and propose a coupled image‐morphology machine learning model. The paradigm can accurately predict the reaction conditions required for synthesizing cathode precursors with specific morphologies, helping to shorten the experimental duration and costs. After the model‐guided design synthesis, cathode materials with different morphological characteristics can be obtained, and the best shows a high discharge capacity of 206 mAh g−1 at 0.1C and 83% capacity retention after 200 cycles. This work provides guidance for designing cathode materials for lithium‐ion batteries, which may point the way to a fast and cost‐effective direction for controlling the morphology of all types of particles. [ABSTRACT FROM AUTHOR]

Next‐generation Li‐ion batteries are expected to exhibit superior energy and power density, along with extended cycle life. Ni‐rich high‐capacity layered nickel manganese cobalt oxide electrode materials (NMC) hold promise in achieving these objectives, despite facing challenges such as capacity fade due to various degradation modes. Crack formation within NMC‐based cathode secondary particles, leading to parasitic reactions and the formation of inactive crystal structures, is a critical degradation mechanism. Mechanical and chemical degradation further deteriorate capacity and lifetime. To mitigate these issues, an artificial cathode electrolyte interphase can be applied to the active material before battery cycling. While atomic layer deposition (ALD) has been extensively explored for active material coatings, molecular layer deposition (MLD) offers a complementary approach. When combined with ALD, MLD enables the deposition of flexible hybrid coatings that can accommodate electrode material volume changes during battery operation. This study focuses on depositing TiO2‐titanium terephthalate thin films on a LiNi0.8Mn0.1Co0.1O2 electrode via ALD‐MLD. The electrochemical evaluation demonstrates favorable lithium‐ion kinetics and reduced electrolyte decomposition. Overall, the films deposited through ALD‐MLD exhibit promising features as flexible and protective coatings for high‐energy lithium‐ion battery electrodes, offering potential contributions to the enhancement of advanced battery technologies and supporting the growth of the EV and stationary battery industries. [ABSTRACT FROM AUTHOR]

In this study, we evaluate a proactive battery EV safety assessment method using abnormal feature detection and neural networks. Four sophisticated algorithms --Isolation Timberland, One-Class SVM, Autoencoder and also LSTM-- were performed to assess their applicability in detecting anomalous battery behavior. The Isolation Woodland algorithm showed a balanced accuracy recall trade-off of the values 0.85 and 0.92 respectively One class SVM demonstrated highly sharp results with an accuracy and recall values of 0.78 and 0.8, respectively. The autoencoder, that used a large amount of learning and won with 0.92 accuracy score and an F1-score - 0.89 The LSTM structure, programmed for sequential information, indicated a great execution with a 0.94 review and the F1-score of 0. A comparative study has shown that these algorithms can provide alot flexibility in sending based on the clear requirements. [ABSTRACT FROM AUTHOR]

The battery management system (BMS) in electric vehicles (EVs) relies on State of Health (SOH) assessment to monitor battery health and ensure performance. BMS can detect and prevent cell balance, thermal runaway, and extend its lifespan. Methodical charging and discharging under different circumstances reduce battery degradation. Grid Partitioning (GP), Subtractive Clustering (SC), and Fuzzy C-Means (FCM) from the Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to predict battery SOH in this work. This study examines charge-discharge cycles of 8 lithium-ion pouch cells from the Oxford Battery Degradation Dataset. Charging cycle data extracts the five vital health indicators (VHIs), whereas discharging cycle data calculates cell capacity. For analyzing the effects of VHIs on SOH, a correlation matrix is formed. Three instances are defined depending on the individual VHIs impacts. Instance 1 utilizes VHI 1, VHI 2, and VHI 5, instance 2 utilizes VHI 3 and VHI 4, and instance 3 uses all VHIs as inputs. Due to enormous data points, only even-numbered cell data is utilized for training the ANFIS model, while odd-numbered cell data is used for validation and testing. In the best-case scenario, instance 1 gives the lowest root mean square error (RMSE) from all the methods. [ABSTRACT FROM AUTHOR]

This article presented a robust plan for an off-grid charging station (OGCS) for electric vehicles (EVs) and hydrogen vehicles (HVs) based on a photovoltaic (PV) system and a hydrogen storage system (HSS). This OGCS simultaneously supplies HVs and EVs continuously throughout the day. Also, HSS and fuel cell (FC) systems have been allocated in the OGCS to be used when we do not have access to the power of the PV system. In addition, a diesel generator (DG) is also designed to prevent in cases where we have extreme uncertainty, including the lack of energy in the PV system and the high load of the system, which may lead to load interruption. Uncertainties of electric and hydrogen loads of EVs and HVs in addition to PV production power are simulated using scenario-based stochastic optimization technique (SOT). Finally, a new framework based on stochastic p-robust optimization technique (SPROT) is applied to optimize the maximum relative regret (MRR) in the worst scenario in order to achieve robust planning in the uncertain environment. The obtained results from the proposed SPROT are compared with SOT. The compared results indicate a 4.51% raise in the average cost in SPROT and a 45.73% decrease in MRR that leads to robust planning. Finally, installed capacity of PV system will decrease from 1688 to 1685 kW, while installed capacity of DG will increase from 78 to 123 kW. • Off-grid charging station for electric and hydrogen vehicles is studied. • PV system, fuel cell and hydrogen storage system are considered. • Diesel generator is designed to prevent in cases where we have extreme uncertainty. • Uncertainties of electric and hydrogen loads, photovoltaic power are considered. • Stochastic p-robust optimization technique is proposed to minimize MRR. [ABSTRACT FROM AUTHOR]

ABSTRACT Recently, numerous bidirectional wireless power transfer (WPT) systems with enhanced features for electric vehicle (EV) battery charging have been proposed. The converters in these WPT systems are generally configured with two synchronized full‐bridge–controlled converters operating at fixed duty cycle (two‐level operation) to enable WPT between the transmitter and receiver magnetic charge couplers. However, these require additional circuits to be employed in a wide range of load conditions, which lead to high costs and reduced power density. As a substitute, this article introduces a new wireless EV charging system using bidirectional dual T‐type converter (BDTC) configuration with PWM control. A wide range of load variations can be obtained without any additional circuits/switches with a proposed charging system based on a BDTC. Due to the reduced switching loss, reduced reactive circulating current, low cost, and increased efficiency, along with simple control, the proposed architecture offers significant advantages over traditional WPT systems. The proposed wireless EV charging system operates with series resonance in both directions. A mathematical model of the BDTC converter is developed, and the operating principle is detailed. Experimental results were acquired from the 3.3‐kW laboratory model, which exhibits nearly flat efficiency characteristics under various loading conditions; hence, the feasibility of the BDTC topology is validated. [ABSTRACT FROM AUTHOR]

In recent years, lithium-ion batteries (LIB) have become the de facto energy storage means for electric vehicles (EVs) due to their high energy density. However, LIBs require state of charge (SOC) monitoring to ensure safe operating conditions and for an enhanced lifespan. Since SOC cannot be directly measured, various estimation methods have been proposed in recent literature, most notably the recent rise in popularity of long short-term memory-recurrent neural networks (LSTM-RNN). Current research in the use of LSTM-RNNs typically applies a single strong data-driven model that can produce accurate predictions at the expense of lengthy model training times. As LSTM-RNNs must be retrained as LIBs age to maintain reasonable estimation accuracies, this poses a problem for EV battery management system processors. To address this research gap, this work proposes a homogeneous ensemble learning model based on several LSTM-RNN base models, as a solution to reduce the training time. The LSTM base models are fused by a meta-learner, to overcome the shortcomings of traditional ensemble fusion methods. Data diversification methods for homogeneous ensembles are also reviewed and benchmarked in this paper. The proposed method achieves a low model training time by 2.6–3.5 times while maintaining a similar mean absolute error (MAE) of 1.4% when compared to conventional shallow and deep LSTM-RNN models. The proposed model was also successfully validated with battery discharge data collected from a custom build battery tester. It is anticipated that the proposed LIB SOC estimation approach can contribute to increased feasibility of using artificial intelligence in EVs in general and improve EV battery management. [ABSTRACT FROM AUTHOR]

It has been widely suggested that the transition to battery electric vehicles will require 30% fewer assembly workers than those needed for internal combustion engine vehicles. Here, we use publicly available datasets on vehicle production and employment to show that labor intensity has increased at U.S. vehicle assembly plants that have fully transitioned to assembling battery electric vehicles. During the production ramp-up period, labor intensity increases by more than ten-fold compared to historic combustion vehicle assembly labor intensity. For one assembly site studied, labor intensity and total employment remained three times higher after a decade of electric vehicle production. Our study suggests that it may take longer than 15 years for electric vehicle assembly sites to achieve labor intensity parity with internal combustion vehicle assembly. Thus, rapid widespread loss of employment at vehicle assembly plants is a smaller risk than many fear. Moreover, our study calls for more regionally focused analyses of the transition's effects on labor using data-driven and macro-level surveying approaches. This study finds that U.S. vehicle assembly plants that have fully transitioned from gas engine to electric vehicle assembly have required higher labor intensity. It suggests that more, not fewer, workers are needed for electric vehicle assembly. [ABSTRACT FROM AUTHOR]

The escalating demand for electric vehicles (EVs) arises from apprehensions regarding fossil fuel depletion and the ecological repercussions of conventional combustion engines, propelling the transition towards environmentally sustainable alternatives. EVs are conventionally comprised of a powertrain, battery management system, and communication infrastructure, with the battery playing a pivotal role. Achieving an efficient EV battery charger necessitates the implementation of a proficient charging algorithm and a high-power converter capable of adeptly regulating battery parameters. Among the array of available options, a bi-directional DC/DC converter is often employed for this purpose. This study predominantly focuses on the Bi-Directional Dual Active Bridge Converter with Single-Phase Shift Control. It is renowned for attaining a broad voltage range through transformer turn ratio adjustments. Its controllable parameters, such as phase shift ratio and duty cycle, bolster its versatility. Moreover, due to its input–output isolation and minimal passive elements, this converter exhibits superior efficiency due to its soft-switching capabilities. The analytical framework encompasses steady-state and dynamic assessments, small-signal modeling, and system transfer function analysis, all of which contribute to formulating an optimized controller design. Additionally, the study conducts simulations of various battery charging techniques, including constant current (CC), constant voltage (CV), and CCCV mode, employing a 1.5 kW Dual Active Bridge DAB-based charger through MATLAB/SIMULINK. Furthermore, voltage mode control simulations for a 5 kW DAB converter augment the study's insights into effective EV charging strategies. [ABSTRACT FROM AUTHOR]

This article explores the progress and challenges of the graphene industry 20 years after its discovery. Graphene, a thin sheet of carbon, was initially praised for its unique properties but faced difficulties in mass production. However, companies are now finding success by exploring different forms of graphene and improving production methods. The article highlights the Graphene Engineering Innovation Centre in Manchester, which supports companies working with graphene. It also discusses the various applications of graphene in fields such as electronics, coatings, and medical devices. Despite challenges, there is optimism about graphene's future impact and the emergence of other 2D materials. [Extracted from the article]

ABSTRACT This work deals with the design and development of a dual source based propulsion architecture for electric vehicles. The converter utilized for the propulsion system is derived from a conventional CuK converter, which operates in a bidirectional power flow mode. The proposed propulsion system has two energy sources: a battery and a supercapacitor (SC). The proposed system has continuous input and output currents at low and high voltage sides, which improves the cycle life of hybrid energy storage (SC and battery) and the DC‐link capacitor. Further, the proposed converter has magnetic reduction compared to a conventional two‐input bidirectional CuK converter. Moreover, an effective and simpler control strategy (only one switch is pulse width modulated (PWM) operated at a time) has been developed for energy management between sources during charging (regenerative braking) and discharging (propulsion), which is effectively verified by simulation and experimental results. Further, a frequency domain (bode plot)‐based technique is used for controller design and stability analysis of the proposed system. [ABSTRACT FROM AUTHOR]

This article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi‐scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large‐scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33‐bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid. [ABSTRACT FROM AUTHOR]

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

A method based on strong tracking unscented Kalman filter with multiple suboptimal fading factors (MSTUKF) was proposed to accurately estimate the state of charge (SOC) of power batteries of electric vehicles online. Taking a certain lithium-ion battery as the research object, a second-order RC equivalent circuit model of the battery was established based on its external characteristics and related mechanism. Then the recursive least squares method with forgetting factor was adopted to identify the model parameters, and the MSTUKF nonlinear state space equation of the battery was established according to the equivalent circuit model. Finally, the SOC estimation algorithm was verified by simulation experiments under ECE15 and UDDS conditions. The results show that the error of MSTUKF in SOC estimation of lithium-ion battery is kept within 1.5%, so this method can estimate battery SOC accurately. [ABSTRACT FROM AUTHOR]

This study aimed to determine a method to suppress thermal runaway in electric vehicles by passing water directly inside the battery case. A scaled-down model experiment was conducted using a lithium-ion battery pack consisting of six 18650 cells, which is equal to about one-thousandth of an electric vehicle's charging capacity. The heat generation rate, heat transfer coefficient, and time constant for cooling were measured using a simple model for the cooling methods, thermal runaway stages, and state of charge. When thermal runaway occurred during natural cooling, the battery temperature rose to 630 °C at a rate of 116 °C/s. Through water injection, the thermal runaway was quickly suppressed with a time constant of about 3 s and a heat transfer coefficient of 3400 W/m2·K. The water effectively prevented chain explosions and kept harmful gases emitted from the batteries. It was found that it is difficult to completely suppress thermal runaway using the latent heat of the stagnant water in the spaces between cylindrical batteries. If the experimental results of this study were to be applied to an actual vehicle, it is expected that thermal runaway could be suppressed with a time constant of about 170 s and 1 ton of water. [ABSTRACT FROM AUTHOR]

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents' operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent's actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation. [ABSTRACT FROM AUTHOR]

The rapid evolution of electric vehicles (EVs) highlights the critical role of battery technology in promoting sustainable transportation. This review offers a comprehensive introduction to the diverse landscape of batteries for EVs. In particular, it examines the impressive array of available battery technologies, focusing on the predominance of lithium‐based batteries, such as lithium‐ion and lithium‐metal variants. Additionally, it explores battery technologies beyond lithium ("post‐lithium"), including aluminum, sodium, and magnesium batteries. The potential of solid‐state batteries is also discussed, along with the current status of various battery types in EV applications. The review further addresses end‐of‐life treatment strategies for EV batteries, including reuse, remanufacturing, and recycling, which are essential for mitigating the environmental impact of batteries and ensuring sustainable lifecycle management. Finally, market perspectives and potential future research directions for battery technologies in EVs are also discussed. [ABSTRACT FROM AUTHOR]

Lithium iron phosphate (LiFePO4, LFP) has long been a key player in the lithium battery industry for its exceptional stability, safety, and cost-effectiveness as a cathode material. Major car makers (e.g., Tesla, Volkswagen, Ford, Toyota) have either incorporated or are considering the use of LFP-based batteries in their latest electric vehicle (EV) models. Despite LFP's well-researched status as a cathode material, it is expected to fulfill additional demands in electric vehicle applications, such as fast-charging capabilities, wide temperature range adaptability, and higher energy density. This perspective examines the LFP supply chain, synthetic approaches, manufacturing processes, market trends, recent advancements, and evolving demands to better understand its future role in the EV market. [ABSTRACT FROM AUTHOR]

This paper presents a sustainable electric vehicle (EV) charging system that operates in three modes of operation to maximize the yield of photovoltaic (PV) system. The design and analysis of the EV charging system is customized based on the operational or office hours of a corporation. The proposed system incorporates a battery pack capable of providing at least one day of autonomy to overcome the weather conditions during early morning, shading times, or cloudy days. In this study, the perturb and observe (P&O) algorithm is modified and used to operate the PV system at maximum power point (MPP) when charging either the EV or the storage battery. The load current, in both cases, is regulated using proportional integral (PI) controllers and pulse width modulation (PWM) switching of DC-DC converter. The proposed system operation is switched between three modes (boost operation for direct charging of EV and discharging of storage battery, and buck operation for charging of storage battery) by a simple event-driven finite state machine (FSM). Simulation results showed excellent tracking behavior of the proposed system when supplying a 5 kW load with variation in solar irradiance between 1000 and 400 W/m2, battery state of charge (SOC) between 40% and 100%, and temperature between 15 to 39. [ABSTRACT FROM AUTHOR]

In addressing the critical challenge of developing sustainable energy solutions for electric vehicle (EV) battery charging, this study introduces an innovative direct current (DC) microgrid system optimized for areas with high solar irradiance, such as Ain El Ibel, Djelfa. The research confronts two primary difficulties: maximizing solar energy utilization in the microgrid system and ensuring system stability and response accuracy for reliable EV charging. To tackle these challenges, the study presents two original achievements. Firstly, it develops a neural network-enhanced Maximum Power Point Tracking (MPPT) controller, which is further optimized with Particle Swarm Optimization (PSO) to increase the efficiency of solar energy capture. Secondly, it refines the system's reliability through the advanced calibration of a Fractional Order Proportional-Integral (FOPI) controller using the Grey Wolf Optimization (GWO) technique, marking a notable improvement in microgrid system stability and response accuracy. The integration of a solar panel array, battery storage, and a supercapacitor, coupled with these advanced optimization techniques, exemplifies a significant leap forward in enhancing efficiency and reliability of EV battery charging through renewable energy sources. Comprehensive simulation and evaluation of the system underscore its superiority over conventional methods, demonstrating the effectiveness of combining neural network-based optimization with PSO and GWO. This breakthrough not only advances the field of renewable energy, particularly for solar-powered EV charging stations, but also aligns with global efforts towards sustainable transportation solutions. [ABSTRACT FROM AUTHOR]

In the context of an increasingly ambitious European Union (EU) industrial policy and the transition to electromobility, the main objective of the article is to uncover the interaction between the supranational level and domestic policies through examining the buildup of the electric vehicle (EV) battery value chain in illiberal Hungary and liberal Sweden. The study analyses these diverse cases through the lenses of comparative political economy. How do European industrial policy objectives translate into national policies under widely different political conditions? How do the different translations impact on the original objectives of EU policy? Through using a structured-focused comparison, the paper argues that the building of the EV battery industry implies the entrenchment of existing models of capitalism in both cases. Liberal democracy is only compatible with the coordinated market economy model of Sweden, while in Hungary the illiberal regime and the dependent market economy model reinforce one another in face of growing public recognition of the disadvantages of dependency—misallocation of resources, environmental damage, and limits to upgrading. EU strategic objectives are served only by the Swedish model, while the Hungarian model leads to deepening institutional cleavages within the EU and implies growing dependence on Russia and China. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are expected to operate within a narrow temperature window around room temperature for optimal performance and lifetime. Therefore, in cold environments, electric vehicle battery packs must be extensively preheated prior to charge or discharge. However, conventional preheating is accomplished externally, which is slow and thus significantly increases charging times. Recently, internal heating has been demonstrated as a potential solution to quickly and uniformly preheat a lithium-ion pouch cell. However, internal heating has not been evaluated in other battery formats such as cylindrical batteries. In this work, we present a numerical model of a 4680 battery with internal heaters for fast preheating in cold environments. The effects that the number of heater layers, heating duration, resting duration, environmental temperature, and boundary heat transfer coefficient have on the temperature heterogeneity of the battery were investigated. The results show that internal heating alone reduces the temperature variation within the battery by a factor of 5 compared to external heating, and by a factor of 20 when combining internal and external heating. This study further proves that internal preheating of lithium-ion batteries is a promising thermal management strategy, and provides guidance on potential design considerations and heating protocols to implement internal heating. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of aging mechanisms and degradation diagnosis under fast charging in battery electric vehicles is needed. However, there is a lack of tools to capture both macroscopic and microscopic parameters, still focusing on experiment results analysis. To get a comprehensive degradation insight for improved service life, this study explores aging mechanisms in LiNi0.5Co0.2Mn0.3O2 graphite batteries under different fast charging conditions (0.6C to 2C) for up to 1000 equivalent full cycles (EFCs). An improved digital twin is used to quantify aging effects and identify aging modes, combining electrochemical techniques with post‐mortem analysis for assessing chemical and structural degradation. After 1000 EFCs, the dominant aging modes are loss of active material (LAM) in the negative electrode and loss of lithium inventory (LLI), surpassing LAM in the positive electrode. Compared to constant current charging, multistep fast charging effectively mitigates Li plating effects and graphite cracking, resulting in a 13% reduction in LAM. Additionally, optimizing the depth of discharge leads to at least 4% reductions in LLI and 16% in LAMpos. This study proves the electrochemical digital twin's potential for quantifying aging effects and serves as a basis for future physics‐informed machine learning to predict aging behavior and modes. [ABSTRACT FROM AUTHOR]

Electric vehicles are considered as an emerging solution to mitigate the environmental footprint of transportation sector. Therefore, researchers and automotive developers devote significant efforts to enhance the performance of electric vehicles to promote broader adoption of such technology. One of the critical challenges of the electric vehicle is limited battery lifetime and entailed range anxiety. In his context, development of counter-aging control strategies based on precise battery modeling is regarded as an emerging approach that has a significant potential to address battery degradation challenges. This paper presents a combined trade-off strategy to minimize battery degradation while maintaining acceptable driving performance and charge retention in electric vehicles. A battery aging model has been developed and integrated into a full vehicle model. An optimal control problem has been formulated to tackle the afore-mentioned challenges. Non-dominant sorting genetic algorithms have been implemented to yield the optimal solution through the Pareto-front of three contending objectives, based upon which an online simulation has been conducted considering three standard driving cycles. The results reveal the ability of the proposed strategy to prolong the life cycle of the battery and extend the driving range by 25 % and 8 % respectively with minimal influence of 0.6 % on the driveability. [ABSTRACT FROM AUTHOR]

The popularity of LiFePO4 (LFP) batteries in electric vehicles and energy storage has raised concerns about their disposal and recycling after application. Traditional recycling methods have economic and environmental limitations. Direct recycling is the most promising method. However, irreversible structural degradation and unavoidable impurities hinder the practical application of direct recycling. Here, a sustainable strategy, the methanol-citric acid separation of spent electrode scraps followed by the repair of the separated LFP through the residual polyvinylidene fluoride (PVDF), is proposed for direct recycling. The methanol-citric acid solvent can completely separate the electrode scraps into damage-free spent LFP and non-corrosive Al foil at room temperature. Through the solid-phase sintering method, as the PVDF content is 5 wt% in the spent LFP materials, the crystallinity and microstructure regenerate well, and a fluorine-doped carbon three-dimensional conductive network structure is coated on regenerated LFP particles. The conductive carbon black, which still remains stable in the regenerated LFP, is used again in the battery. The regenerated LFP cathode materials exhibit a good discharge capacity of 141.5 mA h g-1 and a retention rate of 99.6% at 1C after 100 cycles. Our work provides an environmentally friendly and cost-efficient strategy for the recovery of spent LFP. [ABSTRACT FROM AUTHOR]

The molten Li2CO3 transformation of CO2 to oxygen and graphene nanocarbons (GNCs), such as carbon nanotubes, is a large scale process of CO2 removal to mitigate climate change. Sustainability benefits include the stability and storage of the products, and the GNC product value is an incentive for carbon removal. However, high Li2CO3 cost and its competitive use as the primary raw material for EV batteries are obstacles. Common alternative alkali or alkali earth carbonates are ineffective substitutes due to impure GNC products or high energy limitations. A new decarbonization chemistry utilizing a majority of SrCO3 is investigated. SrCO3 is much more abundant, and an order of magnitude less expensive, than Li2CO3. The equivalent affinities of SrCO3 and Li2CO3 for absorbing and releasing CO2 are demonstrated to be comparable, and are unlike all the other alkali and alkali earth carbonates. The temperature domain in which the CO2 transformation to GNCs can be effective is <800 °C. Although the solidus temperature of SrCO3 is 1494 °C, it is remarkably soluble in Li2CO3 at temperatures less than 800 °C, and the electrolysis energy is low. High purity CNTs are synthesized from CO2 respectively in SrCO3 based electrolytes containing 30% or less Li2CO3. The transformation of CO2 to oxygen and graphene nanocarbons using lithium carbonate as an electrolyte is a promising, large-scale process for CO2 removal and valorization, but lithium carbonate is already in high demand as an important battery material. Here, the authors report the use of strontium carbonate as an alternative electrolyte in the electrochemical reduction of CO2 to carbon nanotubes. [ABSTRACT FROM AUTHOR]

The increasing number of electric Vehicles (EVs) and their influence on the power grid present difficulties that this article addresses by suggesting optimal planning methods for EV charging and discharging. EV charging and discharging operations are effectively managed by creating both locally and globally optimal planning schemes. Future transportation could be changed by the widespread adoption of dynamic wireless power transfer systems in conjunction with EVs, as they would enable speedier travel and continuous EV battery recharging. Dynamic wireless power transfer is a practical answer to problems with electric vehicles. The electrification of automobiles will have a significant influence on the power infrastructure due to the increase in demand for electricity. In this study, we provide an optimal planning method worldwide and a locally optimal strategy for EV charging and discharging. To minimize the total cost of all EVs that charge and discharge during the day, we propose an optimization problem for global planning in which the charging powers are optimized. The simulation results demonstrate that the proposed planning schemes can effectively reduce the total electricity cost for EV owners while also minimizing the impact on the power grid. The globally optimal planning scheme achieves the lowest electricity cost, while the locally optimal scheme provides a good balance between cost reduction and computational complexity. [ABSTRACT FROM AUTHOR]

This study proposes a novel mathematical model for the Multi-Day Tourist Trip Design Problem with Stochastic Travel Time and Partial Charging for Battery Electric Vehicle (MD-TTDP-STT-PCBEV). To the best of our knowledge, no prior study has fully incorporated the use of BEVs into TTDP models. Given the limited driving range of BEVs, the model requires decisions regarding the locations and policy for recharging the vehicle's battery. The problem also incorporates real-world uncertainty by considering travel time as a random variable subjected to normal distribution. The model is formulated using chance-constraint programming, aiming to find optimal tourist routes for BEVs that maximize tourist satisfaction. Numerical experiments were conducted to compare solutions between stochastic and deterministic environments. Computational experiments using the LINGO optimization solver demonstrated that the total rating scores obtained from the stochastic model with chance-constraint programming were generally lower than those from the deterministic model due to travel time uncertainties. These results highlight the importance of incorporating real-world uncertainty and variability to achieve more accurate and reliable planning. [ABSTRACT FROM AUTHOR]

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack's lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions. [ABSTRACT FROM AUTHOR]

This paper conducts an in-depth study of a wireless, hierarchical structure-based active balancing system for power batteries, aimed at addressing the rapid advancements in battery technology within the electric vehicle industry. The system is designed to enhance energy density and the reliability of the battery system, developing a balancing system capable of managing cells with significant disparities in characteristics, which is crucial for extending the lifespan of lithium-ion battery packs. The proposed system integrates wireless self-networking technology into the battery management system and adopts a more efficient active balancing approach, replacing traditional passive energy-consuming methods. In its design, inter-group balancing at the upper layer is achieved through a soft-switching LLC resonant converter, while intra-group balancing among individual cells at the lower layer is managed by an active balancing control IC and a bidirectional buck–boost converter. This configuration not only ensures precise control but also significantly enhances the speed and efficiency of balancing, effectively addressing the heat issues caused by energy dissipation. Key technologies involved include lithium-ion batteries, battery management systems, battery balancing systems, LLC resonant converters, and wireless self-networking technology. Tests have shown that this system not only reduces energy consumption but also significantly improves energy transfer efficiency and the overall balance of the battery pack, thereby extending battery life and optimizing vehicle performance, ensuring a safer and more reliable operation of electric vehicle battery systems. [ABSTRACT FROM AUTHOR]

This study focuses on the transformation of Jaguar XJ40 vehicles to electric power, with the main equipment being a permanent-magnet synchronous motor (PMSM), lithium iron phosphate (LFP) batteries, an on-board charger (OBC) system, and a battery management system (BMS). The process involves integrating the PMSM with the vehicle's existing transmission system. This research compares the driving range of battery electric vehicles (BEVs) using different testing methods under the same conditions: simulation, dynamometer (dino), and actual on-road testing. Based on Raminthra's public roads (RITA drive cycle), one drive cycle covers 7.64 km in 11.25 min. The simulation test by MATLAB/SIMULINK R2016a predicts a driving distance of up to 282.14 km. The dino test, using a chassis dynamometer to simulate driving conditions while the vehicle remains stationary, indicates a driving distance of 264.68 km. In contrast, actual on-road tests show a driving distance of 259.09 km, accounting for real-world driving conditions, including variations in speed, road types, weather, and traffic. The motor achieves 95% efficiency at 2400 rpm and 420 Nm torque. The simulated distance differs from the actual road distance by approximately 8.17%, suggesting reasonable accuracy of the model. [ABSTRACT FROM AUTHOR]

Lithium ion (Li-ion) battery packs have become the most popular option for powering electric vehicles (EVs). However, they have certain drawbacks, such as high temperatures and potential safety concerns as a result of chemical reactions that occur during their charging and discharging processes. These can cause thermal runaway and sudden deterioration, and therefore, efficient thermal management systems are essential to boost battery life span and overall performance. An electrochemical-thermal (ECT) model for Li-ion batteries and a conjugate heat transfer model for three-dimensional (3D) fluid flow and heat transfer are developed using COMSOL Multiphysics®. These are used within a novel computational fluid dynamics (CFD)-enabled multi-objective optimization approach, which is used to explore the effect of the mini-channel cold plates' geometrical parameters on key performance metrics (battery maximum temperature ( T m a x ), pressure drop ( ∆ P ), and temperature standard deviation ( T σ )). The performance of two machine learning (ML) surrogate methods, radial basis functions (RBFs) and Gaussian process (GP), is compared. The results indicate that the GP ML approach is the most effective. Global minima for the maximum temperature, temperature standard deviation, and pressure drop ( T m a x , T σ , and ∆ P , respectively) are identified using single objective optimization. The third version of the generalized differential evaluation (GDE3) algorithm is then used along with the GP surrogate models to perform multi-objective design optimization (MODO). Pareto fronts are generated to demonstrate the potential trade-offs between T m a x , T σ , and ∆ P . The obtained optimization results show that the maximum temperature dropped from 36.38 to 35.98 °C, the pressure drop dramatically decreased from 782.82 to 487.16 Pa, and the temperature standard deviation decreased from 2.14 to 2.12 K; the corresponding optimum design parameters are the channel width of 8 mm and the horizontal spacing near the cold plate margin of 5 mm. [ABSTRACT FROM AUTHOR]

The growing awareness of environmental issues, climate policies, and rapidly developing technologies is contributing to the increasing number of battery electric vehicles (BEVs) around the world. A key requirement for their widespread implementation is providing a charging infrastructure that allows users to operate these vehicles comfortably. Lack of access to charging stations can be a major barrier to the development of electromobility in a given area. Therefore, each additional charging infrastructure can support a change in the structure of the vehicle fleet. One of the key challenges facing this transformation is the selection of suitable locations for charging stations. It is necessary to ensure that they are uniformly distributed so that range anxiety for EV users is reduced and equal access to charging infrastructure is provided to all residents. One of the most important stakeholders in this market is local authorities. Therefore, the objective of this research was to develop a method of determining optimal locations for electric vehicle charging stations (EVCSs) from the perspective of local authorities that also takes into account equal access to the charging infrastructure for all residents, which seems to be a unique approach to this problem. We used commonly available spatial data as input to enable the method to be applied on a larger scale and over an urban area. We carried out our research using a case study: the city of Gliwice in Poland. The city area was divided into hexagonal basic fields, for which potentials for locations of new charging stations were calculated. The analysis was carried out using the geographic information system (GIS) QGIS (ver. 3.34). [ABSTRACT FROM AUTHOR]

Addressing the need for multiple health indicators is critical to improving prediction accuracy and reducing the limitation of reliance on a single health indicator. This paper presents an AutoML model for accurately forecasting the life span of lithium-ion batteries (LIBs) in electric vehicles. Unlike previous studies focusing solely on constant current (CC) and constant voltage (CV) charging, our AutoML approach includes ICA and direct analysis to generate eight novel multi-dimensional health indicators. These novel comprehensive indicators characterizing battery aging, including the position values of CC charging (IC_CC_P), the peak of CV charging (IC_CV_ H), and the peak/position of CC discharging (IC_D_H, IC_D_ P), are chosen based on correlation analysis and combined with four health indicators from direct analysis, i.e., capacity, cycle, cluster, and checkpoint, to improve prediction accuracy. K-means clustering is employed to group similar data points. At the same time, the ruptures library facilitates the implementation of a novel algorithm for change point detection, allowing for the identification of checkpoints within each battery dataset. Experimental validation using NASA's AMES LIB cycle life datasets demonstrates a significant performance improvement from existing recent methods, with 58.93 % lower mean absolute error (MAE) and 52.66 % lower root-mean-square error (RMSE) compared to recent AutoML-based methods. This shows a significant improvement in forecasting the RUL of electric vehicle LIBs using the proposed AutoML-based approach with novel multi-dimensional health indicators. [ABSTRACT FROM AUTHOR]

• A tripartite evolutionary game model of participation in recycling is constructed. • The government does not provide enough incentives for consumers. • In this recycling system, the government will gradually reduce its regulation. • The regulation of power battery recycling by the Chinese government is imperfect. • Improving compensation can improve consumers' recycling enthusiasm. While electric vehicles (EVs) are developing at a high speed in China, the power battery market is facing a decommissioning peak. The problem is that the recycling situation of domestic power batteries is not ideal, partly due to neglect by consumers. By considering the recycling system, mode, and policy of China's EV power batteries, we construct a tripartite evolutionary game model of the government, consumers and EV manufacturers; analyse the stable strategy adjustment mechanisms of tripartite participation in this recycling cooperation game; and simulate the tripartite evolutionary game. The results show that when the initial willingness of the government, consumers and EV manufacturers to recycle power batteries is not strong, the government takes the lead, driving EV manufacturers and consumers to participate in power battery recycling. When the government, consumers and EV manufacturers have medium or high levels of initial willingness, the government evolves and chooses a nonregulation strategy. In addition, by simulating the impact of changes in consumer-related influencing factors on this tripartite evolutionary game, we find that subsidies for recycling power batteries are a key factor affecting consumers' strategy choices and that boosting recycling compensation for consumers can improve their enthusiasm to participate in such recycling. Therefore, to improve the recycling of power batteries for EVs, in terms of both efficiency and percentage of deployment, the Chinese government should strengthen public education on power battery recycling, further integrate informal recycling channels, and balance the distribution of profits among consumers for recycling compensation. [ABSTRACT FROM AUTHOR]

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles is expected to substantially raise electricity demand. This could require significant grid investment to maintain secure electricity supply, which has traditionally been provided through infrastructure upgrades. The potential of smart technologies like Vehicle-to-Grid (V2G) to contribute to security of supply has prompted the need to quantify their impact. We hypothesize that the F-Factor methodology can effectively quantify V2G's security of supply contribution. Applying F-Factor analysis to V2G through optimization modeling and sensitivity studies, we find that key parameters like V2G charger ratings, EV battery capacities, and load profile peakiness significantly influence the results. We conclude that the F-Factor provides a valuable tool for assessing V2G's potential to enhance security of supply, with implications for more efficient grid planning in the context of transport electrification. [ABSTRACT FROM AUTHOR]

Reuse and recycling of retired electric vehicle (EV) batteries offer a sustainable waste management approach but face decision-making challenges. Based on the process-based life cycle assessment method, we present a strategy to optimize pathways of retired battery treatments economically and environmentally. The strategy is applied to various reuse scenarios with capacity configurations, including energy storage systems, communication base stations, and low-speed vehicles. Hydrometallurgical, pyrometallurgical, and direct recycling considering battery residual values are evaluated at the end-of-life stage. For the optimized pathway, lithium iron phosphate (LFP) batteries improve profits by 58% and reduce emissions by 18% compared to hydrometallurgical recycling without reuse. Lithium nickel manganese cobalt oxide (NMC) batteries boost profit by 19% and reduce emissions by 18%. Despite NMC batteries exhibiting higher immediate recycling returns, LFP batteries provide superior long-term benefits through reuse before recycling. Our strategy features an accessible evaluation framework for pinpointing optimal pathways of retired EV batteries. Reuse and recycling of retired electric vehicle batteries offer sustainable waste management but face decision challenges. Ma et al. present a strategy with an accessible economic and environmental evaluation framework for treating these batteries. [ABSTRACT FROM AUTHOR]

As the EV and battery market continues to grow, understanding and mitigating battery thermal runaway remains a critically important area of research. Under controlled experimental conditions, researchers have employed a range of different techniques to initiate battery fires and understand both how and why they occur. These tests give important information about how a battery cell catastrophically fails under abuse conditions. However, recognizing the changes in the internal structure of the cell is vital to understanding the mechanisms of thermal runaway which enables the development of effective mitigation strategies. During the last decade, X-ray imaging has emerged as an important diagnostic tool for battery degradation, electrochemical performance, and safety diagnostics. Researchers use X-ray imaging to directly observe the internal changes in the cell during all stages of thermal runaway. Depending on the initiation method, it can sometimes take minutes, hours, or days for a cell to undergo failure. However, the actual final reactions that cause thermal runaway occur over fractions of a second. High-speed cameras, operating at up to 40,000 frames per second, can capture the interactions between electrodes at the final moments of thermal runaway. In this article, the basics of high-speed X-ray imaging and how this technique has been applied to investigating battery thermal runaway are discussed. [ABSTRACT FROM AUTHOR]

Thermal Energy Storage (TES) plays a pivotal role in the fire protection of Li-ion batteries, especially for the high-voltage (HV) battery systems in Electrical Vehicles (EVs). This study covers the application of TES in mitigating thermal runaway risks during different battery charging/discharging conditions known as Vehicle-to-grid (V2G) and Grid-to-vehicle (G2V). Through controlled simulations in Simulink, this research models real-world scenarios to analyze the effectiveness of TES in controlling battery conditions under various environmental conditions. This study also integrates Machine Learning (ML) techniques to utilize the produced data by the simulation model and to predict any probable thermal spikes and enhance the system reliability, focusing on crucial factors like battery temperature, current, or State of charge (SoC). Feature engineering is also employed to identify the key parameters among all features that are considered for this study. For a broad comparison among different models, three different ML techniques, logistic regression, support vector machine (SVM), and Naïve Bayes, have been used alongside their hybrid combination to determine the most accurate one for the related topic. This study concludes that SoC is the most significant factor affecting thermal management while grid power consumption has the least impact. Additionally, the findings demonstrate that logistic regression outperforms other methods, with the improving feature to be used in the hybrid models as it can increase their efficiency due to its linearity capture capability. [ABSTRACT FROM AUTHOR]

Recycling processes of lithium-ion batteries used in electric and hybrid vehicles are widely studied today. To perform such recycling routes, it is necessary to know the composition of these batteries and their components. In this work, three pouch and three cylindrical LIBs were discharged, dismantled, and characterized, having their compositions known and quantified. The dismantling was performed using scissors, pliers, and a precision cutter equipment. The organic liquid electrolyte was quantified via mass loss after it evaporated at 60 °C for 24 h. The separators were analyzed using Fourier-transform infrared spectroscopy (FTIR), and the cathode and anode active materials were analyzed using a scanning electronic microscope coupled to an energy-dispersive spectroscope (SEM-EDS), X-ray diffraction (XDR), and energy-dispersive X-ray fluorescence spectrometry (EDXRF). All LIBs were identified by type (NCA, NMC 442, NMC 811, LCO, and two LFP batteries), and a preliminary economic evaluation was conducted to understand their potential economic value (in USD/t). Both results (characterization and preliminary economic evaluation) were considered to discuss the perspective of recycling towards a circular economy for end-of-life LIBs. [ABSTRACT FROM AUTHOR]