Li-ion Batteries For Electric Vehicles Research Articles

Recovery of lithium from oxalic acid leachate produced from black mass of spent electric vehicle Li-ion batteries

Lithium (Li) demand is surging due to the adaptation of Lithium-ion batteries (LIBs) in electric vehicles (EV). To ease the supply from virgin sources and meet new legislative recycling targets it's imperative to secure supply from secondary sources. Recently oxalic acid leaching has been reported for early-stage recovery of Li from black mass (BM) of spent EV LIBs. Such processes often face challenges in electrodes separation, thermal treatment of cathode material, and leachate up-concentration. Herein, the synergistic application of benzoyltrifluoroacetone (HBTA) and trioctylphosphine oxide (TOPO) to extract Li from oxalate streams of an industrial BM has been demonstrated for the first time. Under optimum conditions, ∼92 % Li was extracted from the leachate in 6mins, aqueous/organic (A/O) phase ratio 1, and pH 11. McCabe Thiele diagram indicated two counter-current stages for the complete extraction of Li at A/O 1. The slope analysis method indicated equal moles of HBTA required to extract Li and FT-IR spectroscopy confirmed the synergism of the extractants. The organic phase was scrubbed with Li salt and stripped with hydrochloric acid (HCl) resulting in a concentrated (Li ∼4 mol/L) solution precipitated as lithium carbonate (>99 % pure). The transition metals during oxalic acid leaching were reduced and precipitated as respective oxalates due to low solubility. They were successfully recovered using a second leaching step with HCl. The excess acid was recovered as oxalic acid by cooling crystallization, not reported earlier for BM recycling streams, facilitating a closed-loop process. The developed flowsheet significantly advances the early-stage Li recovery processes from BM.

Evaluation of the mechanical shock testing standards for electric vehicle batteries

The safety of Li-ion batteries (LIB) has become an important issue with the continuously increased use of electric vehicles (EV) in the world. In a survivable vehicle crash, when the vehicle needs to maintain structural integrity, crash-induced shock may damage EV's LIB. Therefore, an evaluation of commonly used mechanical shock test standards for EV battery module and pack is performed in this study against the crash-induced shock signals collected from National Highway Traffic Safety Administration (NHTSA) New Car Assessment Program (NCAP) tests. Various shock analysis methods including signal characteristics in time domain, power spectral density (PSD) and shock response spectrum (SRS) in frequency domain, and the acceleration/velocity-change diagram are used for the evaluation. It is found that most peak accelerations of NCAP shock signals significantly exceed the peak accelerations specified in shock testing standards. Crash-induced shocks cannot be fully represented by the half-sine pulse adopted in shock testing standards. In both time and frequency domains, the existing shock testing standards generally underestimate the severities of the crash-induced shocks, and therefore, are non-conservative. It also shows that the correct selection of a filter for the processing of the original crash-induced shock signal is crucial for the specification of EV battery shock environment and shock response analyses. The results obtained in this research can support the development of more reliable shock testing standards for EV batteries.

Development of a Thin-Film NMC System for the Investigation and Suppression of Harmful Reactions Occurring on the Surface of Cathode Materials in Li-Ion Batteries

Materials with the stoichiometry Li(NixMnyCoz)O2 (NMC) have emerged as promising candidates for use as cathodes in Li-ion batteries for electric vehicles. This is primarily due to their high capacity, which increases with the nickel content (Ni-rich NMC). However, the increase in nickel also leads to a decrease in the stability of the material, hindering market introduction [1]. Much of these stability issues emanate from the surface of the material and move subsurface towards the bulk during battery operation [2]. The internal electrode architecture of traditional composite battery cathodes, containing active materials, carbon additives, and binders, complicates the study of the surface reactions. For this reason, we developed a thin-film model system of Ni-rich NMC with a high surface area-to-volume ratio to exacerbate the effect of the surface reactions, enabling easier characterization of the surface without influence of the bulk.The interfacial reactions occurring when NMC is exposed to air was investigated using the thin-film system. These experiments were supported by thermodynamic calculations to propose a mechanism of what reactions occur on the surface. Our study showed that the surface of NMC is stable in inert environments, however, when exposed to air (in particular H2O and CO2) the active material at the surface reacts to form Li2CO3, coinciding with loss of Li2O and protonation of the material, forming transition metal oxyhydroxides. We found that this phenomenon can be reversed by performing a heat treatment, moving Li2O back into the film. However, during the annealing process, an additional reaction between the film and substrate takes place. This, once again, leads to the active material being deficient in Li2O. To compensate for this deficiency, Li2CO3 was deposited onto NMC prior to annealing. The thermodynamical properties of each film were approximated from their electrochemical behavior. It was found that the film treated with Li2CO3 before annealing was thermodynamically similar to what is expected for an NMC cathode. These results contribute to the understanding of the interfacial reactions that take place on the surface of NMC cathodes. Additionally, the study proposes strategies to mitigate and compensate for these reactions.

Methodology for social life cycle impact assessment enhanced with gender aspects applied to electric vehicle Li-ion batteries

Abstract Purpose The objective of this study is to assess the potential social risks and benefits of EV Li-ion batteries by combining the S-LCA framework with gender aspects throughout all the life cycle phases of the battery. Methods The social life cycle assessment (S-LCA) methodology has been applied to determine social concerns about a lithium-ion (Li-ion) battery pack design for electric vehicles (EVs) from cradle to grave. A questionnaire based on UNEP S-LCA guidelines and literature case studies of S-LCA on batteries and the energy industry has been prepared for each of the stakeholder categories and distributed among experts in the Li-ion battery sector (more than 21 industrial and academic experts representing the whole battery value chain). Furthermore, the social assessment also includes updated gender aspects to provide wider and more comprehensive social impacts to ensure a gender-neutral approach. Results and discussion The Li-ion battery presents a positive social impact in all the stakeholder categories evaluated, where the worker category has the best social performance driven by the highest score (scores range from 0 to 1, where 0 is the worst social performance and 1 is the best) in 13 indicators out of 23. Furthermore, local community, consumers, and society categories have a good social performance attributed to the absence of involuntary resettlement of individuals, the possibility of the product being reused for other purposes and technology accessible and affordable to developing countries, among others. Four out of seven indicators to evaluate the gender aspects and impacts have the highest score, demonstrating a commitment to fostering an inclusive and equitable work environment. The end-of-life phase presents a positive social performance with a score of 0.77 out of 1 attributed to the presence of infrastructure to dispose of product components other than landfill and incineration responsibly, the possibility of the product to be reused for other purposes and clear information provided to consumers on end-of-life options, among others. Conclusions The study presents generally good social impact and gender neutrality on the battery pack design. It gives an insight into the actual status of Li-ion battery social and gender impacts, and the results can be useful to policymakers to design and implement strategies for the welfare of various stakeholders.

A fast charge algorithm for Li-ion battery for electric vehicles

The renewable solar energy industry and electric vehicle industry are today seeking for fast battery pack recharging methods to achieve higher performances, and fast energy recovery for energy storage systems (ESS) and for electric vehicles. The charge rate of batteries impacts directly the temperature which in turn impacts the capacity fade, thus it should be kept low to prevent the cells from warming up. This not only limits the charging rate but also puts us on a trade-off, a long lifetime or a fast recharge. In this study, we tried to achieve fast charging using a new charging method that combine two charging methods, without much deterring the capacity of the battery, in order to be able to maintain a long battery lifetime. Charging time of around 82 min was achieved for a 1.8 Ah battery. We compared our findings with the literature with known charging profiles.

Efficient Leaching of Metal Ions from Spent Li-Ion Battery Combined Electrode Coatings Using Hydroxy Acid Mixtures and Regeneration of Lithium Nickel Manganese Cobalt Oxide

Extensive use of Li-ion batteries in electric vehicles, electronics, and other energy storage applications has resulted in a need to recycle valuable metals Li, Mn, Ni, and Co in these devices. In this work, an aqueous mixture of glycolic and lactic acid is shown as an excellent leaching agent to recover these critical metals from spent Li-ion laptop batteries combined with cathode and anode coatings without adding hydrogen peroxide or other reducing agents. An aqueous acid mixture of 0.15 M in glycolic and 0.35 M in lactic acid showed the highest leaching efficiencies of 100, 100, 100, and 89% for Li, Ni, Mn, and Co, respectively, in an experiment at 120 °C for 6 h. Subsequently, the chelate solution was evaporated to give a mixed metal-hydroxy acid chelate gel. Pyrolysis of the dried chelate gel at 800 °C for 15 h could be used to burn off hydroxy acids, regenerating lithium nickel manganese cobalt oxide, and the novel method presented to avoid the precipitation of metals as hydroxide or carbonates. The Li, Ni, Mn, and Co ratio of regenerated lithium nickel manganese cobalt oxide is comparable to this metal ratio in pyrolyzed electrode coating and showed similar powder X-ray diffractograms, suggesting the suitability of α-hydroxy carboxylic acid mixtures as leaching agents and ligands in regeneration of mixed metal oxide via pyrolysis of the dried chelate gel.

Comparative study of thermodynamic & kinetic parameters measuring techniques in lithium-ion batteries

Quantitative analysis of the aging process of lithium-ion batteries by using electrochemical thermodynamic and kinetic parameters such as electrochemical potential, Li stoichiometry, and Li inventory loss is a key research topic in the development of Li-ion battery for electric vehicles and smart grids. It is generally known the above parameters can be acquired through the analysis of Electromotive Force (EMF) or Open-Circuit Voltage (OCV) curves. In this work, we proposed and applied five EMF measurement techniques to obtain the EMF-SoC relationships in LFP/Gr single-layer laminated pouch cells at different temperatures and States of Health (SoH), and comprehensively examined them from the viewpoints of eight evaluation dimensions. A Python program, named Degradation Modes Analysis (DMA), is used to diagnose the thermodynamic degradation modes of the battery automatically. Furthermore, electrochemical kinetic parameters were also extracted along with the depolarization process. For faster and more accurate aging diagnosis, we recommend an optimal blend of short relaxation time GITT (Short-Rest-GITT) and extrapolated EMF (Extrap-EMF) to reach the most precise EMF measurements and gain the most comprehensive information about battery thermodynamics and kinetics at the same time.

Effective incorporation of cobalt near surface region for cobalt-free cathodes in lithium-ion batteries

Cobalt-free cathode materials, particularly Ni-rich layered oxide cathode materials, are ideal for electric vehicle Li-ion batteries, offering high energy density and cost-effectiveness. However, high Ni content in high-temperature synthesis leads to issues like increased Li/Ni cation mixing and reduced rate capability, mainly due to the absence of Co. Therefore, this study investigated the utilization of a small amount of Co instead of completely removing it to enhance electrochemical performance. A simple pre-calcination process was used to induce Co substitution beneath the Ni-rich layered oxide cathode materials surface. A coating process was also performed for comparison. The Li/Ni cation mixing in the Co-substituted sample during high-temperature calcination was 2.43%, demonstrating a superior value compared to the Co-coating process. Rate capability tests showed superior performance for Co-substituted sample (164.2 mAh g−1) at 2 C compared to coated sample (151.0 mAh g−1). Moreover, Co substitution improved not only the cycle stability but also the high-temperature stability at an elevated state of charge.This study focuses on the surface modification of cobalt-free cathode, providing direct insight into the effects of Co substitution and typical Co coating and suggests an optimal process that uses small amounts of Co.

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.

A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature

This paper presents a novel architecture, termed Fusion-Fission Optimisation (FuFi) based Convolutional Neural Network with Bi-Long Short Term Memory Network (FuFi-CNN-Bi-LSTM), to enhance state of charge (SoC) estimation performance. The proposed FuFi-CNN-Bi-LSTM model leverages the power of both Convolutional Neural Networks (CNN) and Bi-Long Short Term Memory Networks (Bi-LSTM) while utilizing FuFi optimization to effectively tune the hyperparameters of the network. This optimization technique facilitates efficient SoC estimation by finding the optimal configuration of the model. A comparative analysis is conducted against FuFi Algorithm-based models, including FuFi-CNN-LSTM, FuFi-Bi-LSTM, FuFi-LSTM, and FuFi-CNN. The comparison involves assessing performance on SoC estimation tasks and identifying the strengths and limitations of models. Furthermore, the proposed FuFi-CNN-Bi-LSTM model undergoes rigorous testing on various drive cycle tests, including HPPC, HWFET, UDDS, and US06, at different temperatures ranging from -20 to 25 degrees Celsius. The model’s robustness and reliability are assessed under different real-world operating conditions using well-established evaluation indexes, including Relative Error (RE),Mean Absolute Error (MAE), R Square (R2), and Granger Causality Test. The results demonstrate that the proposed FuFi-CNN-Bi-LSTM model achieves efficient SoC estimation performance across a wide range of temperatures at higher and lower ranges. This finding signifies the model’s efficacy in accurately estimating SoC in various operating conditions.

Transductive Transfer Learning-Assisted Hybrid Deep Learning Model for Accurate State of Charge Estimation of Li-Ion Batteries in Electric Vehicles

Transductive Transfer Learning-Assisted Hybrid Deep Learning Model for Accurate State of Charge Estimation of Li-Ion Batteries in Electric Vehicles

Thermal management of Li-ion batteries in electric vehicles by nanofluid-filled loop heat pipes

An analytical model is developed to determine the thermal performance of a Loop Heat Pipe filled (LHP) with copper oxide–water and alumina–water nanofluids for battery thermal management in electric vehicles. The thermal performances of the LHP are predicted for different heat loads and nanoparticle concentrations. It is demonstrated that for fast charging operation corresponding to a heat load of 150 W, the LHP ensures evaporator temperatures of less than 60 °C for a heat sink temperature of 40 °C. The heat transport capacity of the LHP is enhanced and the evaporator temperature is deceased by augmenting the nanoparticle concentration. The water–CuO nanofluid-filled LHP performs better than the water–Al2O3 nanofluid-filled one. The addition of the nanoparticles increases the LHP total pressure drop and the driving capillary pressure. The capillary limit of the water–CuO nanofluid-filled LHP is hardly affected by CuO nanoparticle concentration until 6% beyond which the capillary limit starts decreasing. For the water–Al2O3 nanofluid-filled LHP, the capillary limit decreases when Al2O3 nanoparticle concentration increases. Beyond 6% Al2O3 nanoparticle concentration, the capillary limit of the Al2O3-filled LHP becomes lower than the water-filled one.

Numerical investigation on the thermal management of Li-ion batteries for electric vehicles considering the cooling media with phase change for the auxiliary use

This study investigates the thermal management of a battery module of an electric vehicle for the recovery of waste heat. In the evaluation of this waste heat, a two-phase flow system is used to provide a more effective heat transfer. The battery system was formed of 6 serial connected prismatic Lithium-Ion cells. It was aimed to design a 134.55 kW electric vehicle battery system. For this aim, 360 battery modules were used. The maximum amount of waste heat obtained from these modules was recorded as 6.192 kW. Since it is aimed to use this waste heat in the auxiliary cycles such as air conditioning and extra electricity production, R600a was used as the cooling media. A parametric study was conducted for different discharging rates (C) and depths of discharging (DoD) through computational fluid dynamics (CFD) for performing the thermal analysis of the battery module. The heat transfer coefficient was determined ranging between 1.10 and 5.49 kWm−2 K−1. The cooled module temperature was recorded between 290.93 K and 317.31 K, whereas it was recorded as 332.57 K for the non-cooled system. For secondary purposes, the refrigerant mass rate ranging between 0.2760 kgs−1 and 0.6559 kgs−1 was obtained in a temperature range of 283 K and 313 K.

Direct Lithium Extraction Using Intercalation Materials.

Worldwide lithium demand has surged in recent years due to increased production of Li-ion batteries for electric vehicles and stationary storage. Li supply and production will need to increase such that the transition towards increased electrification in the energy sector does not become cost prohibitive. Many countries have taken policy steps such as listing Li as a critical mineral. Current commercial Li mining is mostly from dedicated mine sources, including ores, clays, and brines. The conventional ways to extract Li+ from those resources are through chemical processing and includes steps of calcination, leaching, precipitation, and purification. The environmental and economic sustainability of conventional Li processing has recently received increased scrutiny. Routes such as direct Li+ extraction may provide advantages relative to conventional Li+ extraction technologies, and one possible route to direct Li+ extraction includes leveraging intercalation materials. Intercalation material processing has recently demonstrated high selectivity towards Li+ as opposed to other cations. Reviews and reports of direct Li+ extraction with intercalation materials are limited, even as this technology has started to show promise in smaller-scale demonstrations. This paper will review selective Li+ extraction via intercalation materials, including both electrochemical and chemical methods to drive Li+ in and out, and efforts to characterize the Li+ insertion/deinsertion processes.

A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.

Lithium-ion battery thermal management for electric vehicles using phase change material: A review

Lithium-ion (Li-ion) batteries in electric vehicles (EVs) present a promising solution to energy and environmental challenges. These batteries offer numerous advantages, including high energy density, endurance, minimum self-discharge, and long life, accelerating their adoption in EVs. High temperatures can lead to thermal runaways, causing safety hazards such as short circuits and explosions. Conversely, low temperatures can trigger the formation of lithium dendrites, resulting in failures and operational issues. To address these concerns, phase change materials (PCM) are being explored to store and release thermal energy without significant temperature changes. This review paper presents an overview of PCM for battery thermal management systems. It examines and compares thermal management strategies employed for Li-ion batteries, highlighting their merits, drawbacks, and cost-effectiveness. Different types of heating and cooling mechanism are summarized. Furthermore, the study discusses potential future developments in the field to enhance the thermal management of Li-ion batteries in EVs.

Lithium-ion battery state-of-charge estimation based on long-short-term memory neural network and square-root cubature Kalman filter

Due to the widespread use of Li-ion batteries in electric vehicles, battery management systems for monitoring the status and ensuring the safe operation of Li-ion batteries have been extensively studied. Online monitoring of the state of charge (SOC) is crucial for lithium-ion batteries, but achieving precise SOC estimation is a difficult task due to battery dynamics and the influence of factors such as current, temperature, and operating conditions on SOC variability. This paper introduces a novel approach that combines a Long Short-Term Memory (LSTM) network with a square-root cubature Kalman filter (SRCKF) to address this challenge. To tackle the issue at hand, the proposed methodology employs a two-step approach. Initially, the LSTM network is utilized to capture the complex relationship between the state of charge (SOC) and the measured variables, including current, voltage, and temperature. Subsequently, the output of the LSTM network is subjected to smoothing using the SRCKF technique, leading to precise and consistent SOC estimation. A notable advantage of this method is its ability to simplify the arduous task of parameter tuning for the LSTM network, eliminating the need for constructing a battery model. The experimental results demonstrate that the maximum error in estimating the state of charge (SOC) using this particular method is constrained within the 5% threshold. Compared with using a separate LSTM method at different temperatures, by using the combination method, the root mean square error and maximum error of SOC estimation are greatly reduced.

Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles

State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN)-based approach for SoC estimation in EVs. This HMDNN uses Mountain Gazelle Optimizer (MGO) as a training algorithm for the deep neural network. Our method leverages the intrinsic relationship between the SoC and the voltage/current measurements of the EV battery to accurately estimate the SoC in real time. We evaluate our approach on a large dataset of real-world EV charging data and demonstrate its effectiveness in comparison to traditional SoC estimation methods. Four diverse Li-ion battery datasets of electric vehicles are employed which are the dynamic stress test (DST), Beijing dynamic stress test (BJDST), federal urban driving schedule (FUDS), and highway driving schedule (US06) with different temperatures of 0oC,25oC,45oC. The comparison is made with Mayfly Optimization Algorithm based DNN, Particle Swarm Optimization based DNN and Back-Propagation based DNN. The evaluation indices used are normalized mean square error (NMSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE). The proposed algorithm achieves 0.1% NMSE and 0.3% RMSE on average on all datasets, which validates the effective performance of the proposed model. The results show that the proposed neural network-based approach can achieve higher accuracy and faster convergence than existing methods. This can enable more efficient EV operation and improved battery life.

Online Soft Short-Circuit Diagnosis of Electric Vehicle Li-Ion Batteries Based on Constant Voltage Charging Current

Online Soft Short-Circuit Diagnosis of Electric Vehicle Li-Ion Batteries Based on Constant Voltage Charging Current

Electrification of Transport: Advantages and Prospects

Electrification has become the main mode of new energy transportation. This paper compared electric vehicles with internal combustion and hydrogen fuel cell vehicles. Less environmental pollution, higher efficiency of power system, and lower cost were the most favourable attributes of electric vehicles. In addition, comparing Li-ion and other batteries, the greatest energy density, the lowest self-discharge rate, and the longest cycle life made Li-ion the most suitable choice for electric vehicles. However, the Li-ion battery in electric vehicles still has some limitations. The 400 km driving range limits electric vehicles in urban and developed areas. It also made traditional fossil fuel vehicles the best choice for people with long-range travel needs. Under low temperatures, the capacity decline and polarization effects of Li-ion batteries made electric vehicles unable to meet the needs of use in extreme environments. A higher energy density and a wider range of working temperatures are needed to advance further in transportation.