Electric Vehicle Battery Research Articles

Application of Hypothesis Test in Studying Chinese Electric Vehicle Battery Stock Market

Hypothesis test is a basic method in statistics and is the foundation for many superior methods. It has a wide range of applications in many subjects. Specifically, hypothesis test can be used to find connections and make predictions basing on known information when studying stock market. This article will mainly focus on the application of t-test and bootstrap hypothesis test in studying Chinese electric vehicle battery stock market, a stock market that is complicated and hard to study when using traditional methods. The effectiveness of the two methods will be tested by being used to study the distribution of P/E ratio of stocks in the market. It shows that bootstrap hypothesis test is a more reliable and effective method while t-test relies severely on some unrealistic assumptions and cannot reach at a conclusion at times. Finally, some conclusions will be made based on bootstrap hypothesis test and will also be examined to further confirm its accuracy.

Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles

Battery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation.

A new configuration for enhanced integration of a battery–ultracapacitor system

The principal shortcoming of the batteries in electric vehicles is their limited specific power. The potential solution is having a hybrid energy storage system (HESS) that integrates the ultracapacitor (high specific energy) with the battery (high specific energy), thus ensuring high specific energy and power. Classically, a bidirectional DC–DC converter integrates the two sources, but the converter introduces a delay during the charging–discharging transition of the ultracapacitor. This work proposes a novel converter topology that interfaces the two sources, eliminating an existing transition delay, providing a fault-tolerant operation in case of a switch failure, and efficiently charging the ultracapacitor. Additionally, an algorithm is presented that facilitates energy sharing between the battery and the ultracapacitor, preventing overloading of the battery and ensuring that the ultracapacitor contributes to the peak load demand. The proposed configuration and energy management algorithm are validated experimentally using a 24V laboratory prototype.

Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework

As the market share of electric vehicles (EVs) continues to grow, driving range capability has emerged as a primary concern for drivers, car manufacturers, and policymakers. Accurate real-time energy consumption prediction is essential in mitigating range anxiety and fostering the adoption of EVs. Consequently, an EV energy consumption prediction framework that comprehensively considers vehicle and environmental factors, with special attention to individual driving styles and driving conditions, constructed based on long short-term memory (LSTM) and Transformer models is proposed. This framework addresses the issue of long-term dependencies and captures characteristics of time-series data with increased efficiency. Upon rigorous evaluation, the model achieved the mean absolute percentage error (MAPE) of 4.63 % for state of charge (SOC) of EV's battery in experimental dataset, outperforming LSTM and multivariate regression (MLR). The ablation experiment shows that the MAPE of the model is reduced by 18.47 % and 15.27 % respectively after considering the individual driving style of drivers and the driving conditions of vehicles for both two types of vehicles. Based on this framework, a long-distance EV energy consumption prediction strategy based on short-distance is proposed, with a MAPE of 6.7 % when SOC value is reduced from 90 to 40 in the selected dataset.

State of health estimation method based on real data of electric vehicles using federated learning

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is of paramount importance for the development and operation of electric vehicles, as it directly impacts driving performance. Currently, there are numerous methods for estimating SOH, but only some are suitable for real-world data, most existing methods for estimating SOH use data of lithium-ion battery cells from laboratory operations to train and test. Firstly, obtaining data usage of battery from the real world takes work. Secondly, the model for estimating SOH must be representative and generalized because they are trained on separate data for each electric vehicle. Lastly, collecting data for centralized training may lead to huge data uploads This study goes beyond that by using battery data from real-world vehicle operations. This study is based on 10 in-service electric trucks over 3 years, adopting methods such as utilizing data acquisition technology from battery management systems (BMS) and other controllers, as well as vehicle networking data reporting technology, to propose a SOH estimation method utilizing a new machine learning approach based on artificial neural network (ANN) and federated learning (FL) to predict the SOH of lithium batteries in real-world scenarios. This study developed an ANN local model and compared it with the popular long short-term memory (LSTM) model, finding that the ANN model is relatively more suitable as a local model for electric vehicles. In addition, we apply the FL framework to train the model to reduce the amount of uploaded electric vehicles’ battery data. We verified the results of the model on experimental data. Meanwhile, we analyzed the model on actual data by comparing its mean absolute and relative errors. The results show that federated training method achieves better generalization in predicting SOH compared to centralized training method and another traditional machine learning models. This study provides an effective solution of electric vehicle batteries SOH prediction and offers new ideas and methods for the generalization of traditional machine learning models.

Experimental and Numerical Investigation of the Use of Ultrasonic Waves to Assist Laser Welding.

This study evaluates the enhancement of laser welding using ultrasonic waves aimed at reorganising the intermetallic position in such a fashion that leads to increased mechanical properties of welds in battery pack assemblies for electric vehicles. The experiment employed 20 kHz and 40 kHz High-Power Ultrasound Transducers (HPUTs) in both contact and contactless modes. A simplified experimental configuration is suggested to represent conditions similar to those found in electric vehicle battery pack assemblies. Measurements of vibration transmission to aluminium alloy 1050 plates revealed more than a 1000-fold increase in acceleration amplitude in contact mode compared to contactless mode. The 20 kHz transducer in contactless mode demonstrated superior performance, showing a 10% increase in load and 27% increase in extension compared to welding without ultrasonic assistance. On the other hand, the 40 kHz transducer, while still improved over non-ultrasonic methods, showed less pronounced benefits. This suggests that lower-frequency ultrasonic assistance (20 kHz) is more effective in this specific context. The study explores ultrasonic assistance in laser welding copper (Cu101) to aluminium alloy 1050 using 20 kHz and 40 kHz HPUTs, showing that both transducers enhance microstructural integrity by reducing copper homogenisation into aluminium, with the 20 kHz frequency proving more effective in this context. A numerical simulation was conducted to evaluate the transmission of pressure into the molten pool of the weld, correlated with the vibration results obtained from the 20 kHz transducer. The numerical simulation confirms that no cavitation is initiated in the molten pool area, and all improvements are solely due to the ultrasonic waves.

Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations.

Integrated Extended Kalman Filter and Deep Learning Platform for Electric Vehicle Battery Health Prediction

As the demand for electric vehicles (EVs) rises globally, ensuring the safety and reliability of EV battery systems becomes paramount. Accurately predicting the state of health (SoH) and state of charge (SoC) of EV batteries is crucial for maintaining their safe and consistent operation. This paper introduces a novel approach leveraging deep learning methodologies to predict battery SoH, focusing on implementing a system prototype for real-world applications. The proposed system integrates an extended Kalman filter (EKF) with a deep learning framework, forming a system prototype known as FELL, aimed at EV battery diagnosis and prediction. We devise an algorithm utilizing the EKF to estimate the SoH of the battery. We present a detailed overview of the system architecture and implementation, showcasing its predictive capabilities. Experimental results demonstrate the effectiveness of the system in accurately estimating battery SoH with notable improvements in prediction accuracy. Additionally, the FELL system provides users with real-time predictions and comparative analysis across multiple prediction models, offering valuable insights for EV battery management.

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.

Electric vehicle batteries waste management and recycling challenges: a comprehensive review of green technologies and future prospects

Electric vehicle batteries waste management and recycling challenges: a comprehensive review of green technologies and future prospects

Sustainable Management of Rechargeable Batteries Used in Electric Vehicles

A Life Cycle Assessment (LCA) quantifies the environmental impacts during the life of a product from cradle to grave. It evaluates energy use, material flow, and emissions at each stage of life. This report addresses the challenges and potential solutions related to the surge in electric vehicle (EV) batteries in the United States amidst the EV market’s exponential growth. It focuses on the environmental and economic implications of disposal as well as the recycling of lithium-ion batteries (LIBs). With millions of EVs sold in the past decade, this research highlights the necessity of efficient recycling methods to mitigate environmental damage from battery production and disposal. Utilizing a Life Cycle Assessment (LCA) and Life Cycle Cost Assessment (LCCA), this research compares emissions and costs between new and recycled batteries by employing software tools such as SimaPro V7 and GREET V2. The findings indicate that recycling batteries produces a significantly lower environmental impact than manufacturing new units from new materials and is economically viable as well. This research also emphasizes the importance of preparing for the upcoming influx of used EV batteries and provides suggestions for future research to optimize the disposal and recycling of EV batteries.

Evolving cobalt cycle and urban mines in the EU, 1988–2020

Urban mines are vital for resource security and resilience, but the intricacies of their recycling processes are often overlooked. This study uses dynamic Material Flow Analysis to delve into the EU's cobalt cycle from 1988 to 2020, focusing on urban cobalt mining potential. We observed a significant shift in EU cobalt consumption patterns, with the demand for electric vehicle batteries surpassing that for superalloys, thereby positioning batteries as the primary cobalt consumers. Moreover, batteries increasingly dominate the cobalt in-use stock. The recycling process experiences substantial losses, especially in collection and pretreatment stages, reaching up to 87 %. Our findings indicates that urban cobalt mines could account for 57–66 % of the EU's total cobalt consumption, amounting to 166–193 kt in a high scenario. These insights emphasize the changing dynamics of the EU cobalt cycle and the importance of efficient urban cobalt mine management.

Electric Vehicle Battery Management System with Charge Monitor and Fire Protection

This paper outlines the development and execution of an Electric Vehicle Battery Management System (BMS) outfitted with charge surveillance and fire prevention functionalities. As the utilization of electric vehicles (EVs) continues to grow, ensuring the safety and optimal functioning of battery packs becomes increasingly crucial. The suggested BMS merges sophisticated monitoring algorithms with fire suppression systems to alleviate risks linked with overcharging and thermal runaway incidents. Through thorough examination and validation procedures, our findings underscore the efficacy of the implemented BMS in ameliorating battery performance and guarding against potential dangers, thereby fostering the widespread embrace of electric mobility

An enhanced fuzzy IDOCRIW-COCOSO multi-attribute decision making algorithm for decisive electric vehicle battery recycling method

An adaptation to electric mobility quickens waste management tasks for recyclers to end-to-end processing of marketed electric vehicle batteries. Especially lithium-ion batteries play a prominent role in electrifying the world for e-transport technology innovation. This research offers a multi-attribute decision-making (MADM) structure for finding the best performance e-vehicle recycling techniques. The structured algorithm combines an advanced stratified MADM strategy with e-transportation recycling techniques. The optimal algorithm evaluates the results of qualitative attributes and alternatives using a weighted-ranking MADM approach. The importance of attributes is calculated using a blending of dual objective-weighted approaches: entropy and CILOS methods, viz., the aggregated IDOCRIW approach. The ranking of alternatives is determined through the COCOSO method in a hesitation environment. The q-rung orthopair picture fuzzy set (q-ROPFS) is used to cope with uncertainty and vagueness in decision analysis. The feasibility and robustness of the suggested algorithm were validated through different MADM methods and by altering crucial ranking-dependent parameters in the problem.

A review of direct recycling methods for spent lithium-ion batteries

The increasing demand for lithium-ion batteries (LIBs) in new energy storage systems and electric vehicles implies a surge in both the shipment and scrapping of LIBs. LIBs contain a lot of harmful substances, and improper disposal can cause severe environment damage. Developing efficient recycling technology has become the key to the sustainable growth of the LIBs industry. At present, the extraction of high-value materials from spent LIBs using pyrometallurgical and hydrometallurgical processes is most usual. However, they consume a lot of energy and lead to secondary environmental pollution, which is not consistent with the idea of creating a green circular economy. Direct recycling has been suggested as a possible alternative method of dealing with the spent LIBs under non-destructive conditions in the further. Compared with traditional metallurgical technologies, direct regeneration significantly reduces the consumption of energy and chemical reagents, and has a high selectivity for certain metal ions, which is environmentally friendly. However, direct cycling is still in its infancy with many scientific and technological barriers. In this review, we first consider the necessity of recycling spent LIBs, and then summarize the failure mechanisms of degraded cathode materials in order to choose a corresponding regeneration method. The direct cycling technologies, including hydrothermal, solid-state, eutectic medium and electrochemical regeneration are introduced separately from the perspective of the experimental process, operating parameters, regeneration principles, advantages, and effects. Furthermore, the current problems and potential future research on the direct cycling of spent LIBs are also discussed.

Resistance analysis of laser-welded aluminum lead and tab for electric vehicle battery: Experiment and simulation

In pouch-type Li-ion batteries used in electric vehicles, battery cell tabs are electrically connected to an external lead of the same substance. Laser welding is a promising technique for joining these materials. This study investigates the fundamental question of whether electrical resistance measurements can be used to reliably estimate the welding state. An Al lead (400 μm thick) was welded spot-by-spot to a stack of 30 Al tabs (each tab 13 μm thick) using a laser beam incident on the lead surface. By adjusting the laser irradiation conditions, three different welding states (shallow, intermediate, and deep states) were intentionally produced. However, these structures exhibited no appreciable difference in the resistance measured across the welded region. The resistance was on the order of 10−4 Ω regardless of the welding state. Simulations were conducted for various joint structures, including those with voids, to derive the current flow through the structures and joint resistances. The simulation results corresponded well with the experimental results. Our study indicates that the electrical estimation of the welding state is challenging, because extremely low resistances are obtained even if the lead and tab are locally connected with narrow current paths.

Life after use: circular supply chains for second-life of electric vehicle batteries

Despite the increasing interest in second-life of electric vehicle batteries, the characteristics of supply chains to support such products are yet to be developed. The purpose of this paper is to investigate the key supply chain characteristics required to effectively accommodate circular models for second-life electric vehicle batteries. Through a longitudinal case study, we map possible application areas and customer segments, necessary key activities, and potential actors that might emerge in future supply chains for these products. The findings are discussed in relation to product-oriented, use-oriented, and result-oriented circular models. The suggested circular models and their comparisons provide insights into how different supply chain dimensions are configured for different circular business models, and the advantages, disadvantages, and prerequisites associated by each model.

An enhanced current chopping control strategy for SRM drives using Harris Hawks optimization algorithm

This research proposes an Optimized Current chopping Control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements, comprising minimized torque ripple to reduce vibrations and acoustic noises, maximized output torque to enhance vehicle acceleration, and improved efficiency, which contributes to extending the EV's battery life. Therefore, an optimization problem is formulated and solved offline, incorporating a CCC-based SRM drive model. The control variables for this optimization problem are the switching angles of the SRM. A multi-objective function is chosen to combine three performance indices: torque ripple, average torque, and efficiency. The Harris Hawks Optimization (HHO) method is utilized in this paper to solve the optimization problem and find the optimal switching angles based on the selected objective function. HHO demonstrates a strong search capability that can effectively handle the nonlinear magnetization characteristics of SRMs. Constraints on the switching angles are also included in the optimization problem to control the phase current's RMS value and power consumption. The optimized switching angles are applied to a current chopping control (CCC) strategy and an asymmetric half-bridge converter to implement the proposed HHO-based CCC drive. Moreover, to demonstrate the effectiveness of the proposed HHO-based CCC drive, a comparative analysis based on simulations and experimental measurements is presented against other CCC approaches for SRM drives, including modified particle swarm algorithm (MPSO)-based CCC drives and analytical-based CCC drives.

Development of a cooling system for marine power batteries

For green shipping, lithium-ion battery-powered ships are rapidly developing. Compared with power batteries for electric vehicles, the large volume and capacity of cells in electric ships make it difficult to dissipate heat, thus requiring efficient cooling. Herein, a novel shape-stabilized phase change material (SSPCM) channel/thermoelectric cooler (TEC) cooling system for marine power batteries was proposed and experimentally studied. It uses TEC for localized cooling and seawater flowing inside the SSPCM as coolant. First, the arrangement of the TECs was experimentally determined. Then, the cooling performance of the system was verified by comparative experiments. The results show that, compared to the cooling system without TECs, the maximum temperature difference was reduced by 28.8 %, with the maximum temperature and temperature difference of 49.8 °C and 3.7 °C, respectively. Compared with the single cooling method, the maximum temperature and temperature difference were reduced by 15.5 % and 53.3 %, respectively. Finally, the experiments were conducted to explore the impact of various parameters on the cooling performance of the system. The unique combination of SSPCM channel, seawater, and TEC provides a new strategy for efficiently cooling marine power batteries at high charge/discharge rates.

On the potential of vehicle-to-grid and second-life batteries to provide energy and material security

The global energy transition relies increasingly on lithium-ion batteries for electric transportation and renewable energy integration. Given the highly concentrated supply chain of battery materials, importing regions have a strategic imperative to reduce their reliance on battery material imports through, e.g., battery recycling or reuse. We investigate the potential of vehicle-to-grid and second-life batteries to reduce resource use by displacing new stationary batteries dedicated to grid storage. Based on dynamic material flow analysis, we show that equipping around 50% of electric vehicles with vehicle-to-grid or reusing 40% of electric vehicle batteries for second life each have the potential to fully cover the European Union’s need for stationary storage by 2040. This could reduce total primary material demand from 2020–2050 by up to 7.5% and 1.5%, respectively, which could ease geopolitical risks and increase the European Union’s energy and material security. Any surplus capacity could be used as a strategic reserve to increase resilience in the face of emergencies such as blackouts or adverse geo-political events.