Managing the surge:A comprehensive review of the entire disposal framework for retired lithium-ion batteries from electric vehicles

Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has escalated.This paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and recycling.Firstly,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening efficiency.Secondly,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic issues.In the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery technologies.Particularly,we introduce several global leading recyclers to illustrate their industrial processes and technical intricacies.Furthermore,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal framework.We hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.

Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold regions.Alternating current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating advantages.This paper introduces the recent advances in AC heating from the perspective of practical EV applications.First,the performance degradation of EVs in low-temperature environments is introduced briefly.The concept of AC heating and its research methods are provided.Then,the effects of various AC heating methods on battery heating performance are reviewed.Based on existing studies,the main factors that affect AC heating performance are analyzed.Moreover,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and compared.The evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted out.Finally,the perspectives and challenges of AC heating are discussed.This paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

The Future Trend of E-Mobility in Terms of Battery Electric Vehicles and Their Impact on Climate Change: A Case Study Applied in Hungary

The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

With the dramatic increase in electric vehicles(EVs)globally,the demand for lithium-ion batteries has grown dramatically,resulting in many batteries being retired in the future.Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery aging level on service and provide regroup strategy of the retied batteries in secondary use.There are still limitations on the current rapid battery capacity estimation methods,such as direct current internal resistance(DCIR)and electrochemical impedance spectroscopy(EIS),in terms of efficiency and robustness.To address the challenges,this paper proposes an improved version of DCIR,named pulse impedance technique(PIT),for rapid battery capacity estimation with more robustness.First,PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency,in which the coherence analysis is used to guide the selection of a reliable frequency band.The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method,which obtains more information on the battery capacity evaluation.Second,various statistical variables are used to extract aging features,and Pearson correlation analysis is applied to determine the highly correlated features.Then a linear regression model is developed to map the relationship between extracted features and battery capacity.To validate the performance of the proposed method,the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series.The results reveal that the proposed PIT can provide comparative indicators to EIS,which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

A model for end-of-life vehicles management based on green manufacturing

Like any industrial product, the vehicles also do have end of their service lives and ultimately result in end-of-life vehicle (ELV) at a certain stage of the vehicle life. The concern of this paper is to make a general review on ELV management process and present a model for ELV management on the basis of green manufacturing so as to reduce the environmental impact and minimize the resources loss due to the ELVs. The various phases of vehicle life cycle starting from design, manufacturing and utilization, occurrence of ELV and then the storage, depollution, dismantling and finally the shredding and post shredder material processing phases of ELV for the resources recovery, all of these phases need to be considered to give totality to the model, which makes end-of-life vehicles management more benign for environment, and more efficient for the usage of resources.

End-of-life vehicles processing,towards a thermal treatment of auto shredder residue

All over the world,the management of End-of-life Vehicles(ELV) and Automobile Shredder Residue(ASR) is an increasing issue for the car industry.The setting up of several environmental directives,among others the notion of extended producer responsibility,encourage car manufacturers to find alternatives solutions to waste disposal.For 2017,China aims for the recyclability and energy recovery of 95% of total weight of used cars,and in order to reach this rate,the development of some ASR thermal processes could be envisaged.With this research,an overview of ELV management was given and the different solutions about ASR thermal treatment were presented.It is showed that in spite of its big heterogeneity,the high heating value of ASR makes pyrolysis and gasification very interesting,compared to incineration or disposal of in landfills.

Towards Realistic Vibration Testing of Large Floor Batteries for Battery Electric Vehicles (BEV)

This contribution shows an analysis of vibration measurement on large floor-mounted traction batteries of Battery Electric Vehicles(BEV).The focus lies on the requirements for a realistic replication of the mechanical environments in a testing laboratory.Especially the analysis on global bending transfer functions and local corner bending coherence indicate that neither a fully stiff fixation of the battery nor a completely independent movement on the four corners yields a realistic and conservative test scenario.The contribution will further show what implication these findings have on future vibration&shock testing equipment for large traction batteries.Additionally,it will cover an outlook on how vibration behavior of highly integrated approaches(cell2car)changes the mechanical loads on the cells.

Prediction and recovery of end-of-life vehicles of China

With the rapid development of automobile industry,car ownership of China continuously increases end-of-life vehicles(ELVs)as well.This paper analyzes the current situation of car production,car ownership and ELVs in China based on the data in the past ten years,also forecasts the number in a decade with the help of the grey system model GM(1,1)and the Matlab software.Then it reviews the recovery methods and modes of ELVs recycling.Then based on the problems existed in the ELVs recycling industry,it gives out some suggestions which are conductive to the development of ELVs recycling industry in China.

Battery Management System with State ofCharge Indicator for Electric Vehicles

Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.

A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles,as it provides the main power source for the transmission system.However,manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration.This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution,which further lead to reduction of cycle life and battery capacity over time.To solve this problem,this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance.Experiments were first performed by dismantling battery modules for the measurement of performance parameters.The kmeans clustering and support vector clustering(SVC)algorithms were then employed to produce battery modules composed of 12 cells each.Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period,while air cooling was provided.It was found that the SVC-clustered battery module in Category 3 exhibited the best performance,with a maximum observed temperature of 32℃.By contrast,the maximum observed temperatures of the other battery modules were higher,at 40℃for Category 1(manufacturer),36℃for Category 2(manufacturer),and 35℃for Category 4(k-means-clustered battery module).

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.

Fuzzy Model for Estimation of the State-of-Charge of Lithium-Ion Batteries for Electric Vehicles

A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.

SOC distribution-based modeling for lithium-ion battery for electric vehicles using numerical optimization

In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.

Online model identification of lithium-ion battery for electric vehicles

In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.

Online SOC estimation based on modified covariance extended Kalman filter for lithium batteries of electric vehicles

To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.

A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersphere boundary only describes the distribution of unlabeled samples,while the distribution of faulty samples cannot be effectively described and easilymisses detecting faulty data due to the imbalance of sample distribution.Meanwhile,selecting parameters is critical to the detection performance,and empirical parameterization is generally timeconsuming and laborious and may not result in finding the optimal parameters.Therefore,this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance.By incorporating faulty samples into the underlying SVDD model,training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples,and the hypersphere boundary ismodified to classify the samplesmore accurately.The Bayesian Optimization NSVDD(BO-NSVDD)model was constructed to quickly and accurately optimize hyperparameter combinations.In the experiments,electric vehicle operation data with four common fault types are used to evaluate the performance with other five models,and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data,especially in the imperceptible early and minor faults,which has seen very obvious advantages.Finally,the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.

Identification of dynamic model parameters for lithium-ion batteries used in hybrid electric vehicles

An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.

State of charge and health estimation of batteries for electric vehicles applications:key issues and challenges

Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.

Economy Analysis of a Recycle Model of End-of-Life Vehicle

In order to evaluate recycle economy of end-of-life vehicles quantitatively,an economy model based on a recycle model of end-of-life vehicles and recycle cost analysis. With a practical example of recycling engines of end-of-life vehicles ,the validity of the recycle economy model and good recycle economy of the end-of-life vehicle engine were justified. It is concluded that ① remanufacture-ability of the part or component of the vehicles; ② the organization and management level of a recycle corporation; ③ policies and regulations of the government are crucial factors to raise the recycle economy of the end-of-life vehicle.

Development and Verification of the Equilibrium Strategy for Batteries in Electric Vehicles

The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.