Electric Vehicle Battery Research Articles

Sharing channel choices for electric vehicle battery manufacturers when considering range anxiety among heterogeneous consumers

PurposeThis study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.Design/methodology/approachAmidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.FindingsWe find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.Originality/valueIn the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

Transition from Electric Vehicles to Energy Storage: Review on Targeted Lithium-Ion Battery Diagnostics

This paper examines the transition of lithium-ion batteries from electric vehicles (EVs) to energy storage systems (ESSs), with a focus on diagnosing their state of health (SOH) to ensure efficient and safe repurposing. It compares direct methods, model-based diagnostics, and data-driven techniques, evaluating their strengths and limitations for both EV and ESS applications. This study underscores the necessity of accurate SOH diagnostics to maximize battery reuse, promoting sustainability and circular economy objectives. By providing a comprehensive overview of the battery lifecycle—from manufacturing to recycling—this research offers strategies for effective lifecycle management and cost-effective, environmentally sustainable secondary battery applications. Key findings highlight the potential of second-life EV batteries in ESSs. The integration of the considered diagnostic methods was shown to extend battery lifespan by up to 30%, reduce waste, and optimize resource efficiency, which is crucial for achieving circular economy objectives. This paper’s insights are crucial for advancing sustainable energy systems and informing future research on improving diagnostic methods for evolving battery technologies.

Advancing sustainable mobility: Dynamic predictive modeling of charging cycles in electric vehicles using machine learning techniques and predictive application development

The main goal in this research is to train various machine learning models to predict charging cycles in EV Electric Vehicles) battery systems. The considered models are gradient boosting, random forests, decision trees, and linear regression. Each of these was assessed based on its R-squared score, which is an important statistical measure in indicating the variance proportion yielded by the model. In contrast, the Random Forest model significantly improved, with an R-squared value of 0.83, thereby doing an excellent job in capturing nuances of the data. Only surpassed by the Gradient Boosting model at an astonishing R-squared score of 0.87, it is this excellent score that underlines its capability to predict the outcome quite accurately by modeling complex interrelations. In other words, gradient boosting outran the rest and provided the most robust results concerning drivers of students' performance. It also underlines how important choosing a good model is in educational analytics in order to increase the accuracy of the predictions. The use of these models in the proposed EV Battery Charging Cycle Predictor App results in accurate predictions to aid schedule maintenance and energy-related decisions. This research brings light to the future of advanced machine learning methods in enhancing the battery efficiencies of EVs and the development of electric mobility technologies. It is possible that the future work will imply the additional inclusion of real data and the integration of the application to general energy systems.

Experimental and machine learning study on the influence of nanoparticle size and pulsating flow on heat transfer performance in nanofluid-jet impingement cooling

Maximizing heat transfer efficiency is crucial for enhancing performance and durability in diverse engineering applications, including fuel cells, EV batteries, and solar PV/T systems, thereby advancing sustainable energy innovation. This study investigates thermal dissipation from a simulated heat sink aligned with a PV cell’s back plate via jet impingement cooling. Specifically, it examines the impacts of pulsatile cooling and nanoparticle size in hybrid nanofluids, comprising combinations of Al2O3 and MWCNT in water, with varied nanofluid volume fraction (0.05 vol% ≤ ɸ ≤ 0.3 vol%) and flow Reynolds number (15000 < Re < 40000). Key findings reveal significant influences of nanoparticle size, nanofluid concentration, and pulsating flow on heat transfer performance. Notably, sample D demonstrated the highest heat transfer enhancement, achieving approximately 52.94 % and 79.06 % improvement in continuous and pulsating jet cooling compared to de-ionized water under continuous jet cooling. Machine learning classifiers were employed to identify critical thermal performance parameters, with Reynolds number identified as the most significant factor influencing heat transfer. Random Forest and Gradient Boosting classifiers showed notable accuracy in predicting Nu, emphasizing the role of machine learning techniques in optimizing thermal management strategies for improved heat dissipation from solar PV cell backplates.

The recent developments in EV batteries and REEs recovery processes from spent NiMH batteries

The recent developments in EV batteries and REEs recovery processes from spent NiMH batteries

Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks

Accurate estimation of the state of charge (SoC) in electric vehicle (EV) batteries is essential for effective battery management and optimal performance. This study investigates the application of Kolmogorov-Arnold Networks (KAN) for SoC estimation, comparing its performance against Artificial Neural Networks (ANN) and a hybrid Barnacles Mating Optimizer-deep learning model (BMO-DL). The dataset, derived from simulations involving a lithium polymer cell model (ePLB C020) in an electric car similar to Nissan Leaf EV, encompasses 68,741 instances, divided into training and testing sets. Three KAN models were developed and evaluated based on root mean square error (RMSE), mean absolute error (MAE), maximum error (MAX), and coefficient of determination (R2). Residual analysis indicates that KAN-Model 1 performs the best, with residuals closely clustered around zero and no significant patterns, suggesting reliable and unbiased predictions. KAN-Model 2 also performs well but exhibits some nonlinear trends in the residuals. ANN and BMO-DL models show larger deviations and less consistent performance. These findings highlight the potential of KAN for enhancing SoC estimation accuracy in EV applications.

Design and analysis of a high-efficiency bi-directional DAB converter for EV charging

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.

Reverse logistics for electric vehicles under uncertainty: An intelligent emergency management approach

The frequency and intensity of global disasters, including the COVID-19 pandemic, and natural disasters such as earthquakes, floods, and wildfires, are increasing, necessitating effective emergency logistics management. Climate change significantly contributes to these events, emphasizing the importance of limiting human and environmental impacts. The transportation sector, particularly the automobile industry, ranks second in global carbon emissions, highlighting the need to adopt electric vehicles (EVs) to reduce emissions and minimize the impact of climate change. However, this has led to an increase in demand for lithium-ion batteries. During emergencies, end-of-life (EOL) battery management through reverse logistics is essential because recycling EOL batteries can recover valuable raw materials, decrease landfill waste and costs, and support environmental sustainability. This study proposed a two-phase method for intelligent emergency EV battery reverse logistics management. The first phase employed machine learning to address unpredictable battery demands, whereas the second phase proposed a multi-objective model to minimize carbon emissions through efficient order allocation during uncertain emergencies. The model considers carbon emissions and defect rates as sources of uncertainty, current regulations, and customer environmental awareness. The model is solved using the weighted sum and ε-constraint methods, resulting in non-dominant solutions. The findings indicate that combining the selection of third-party reverse logistics providers (3PRLPs) with optimal order allocation for recycling old batteries during emergencies effectively minimizes environmental impacts and combats climate change.

Takagi-Sugeno fuzzy gain controller for Vehicle-to-Grid (V2G) load frequency control

Load Frequency Control (LFC) has gained more importance with the introduction of deregulated Renewable Energy Sources (RES) connectivity with the grid. Electrical Vehicles (EVs) can feed electricity back into the grid in Vehicle-to-Grid (V2G) mode to maintain stability. However, the increasing number of EVs penetrating the grid causes frequency instability in the power system. If required, EVs may utilize bi-directional chargers to transfer power back to the grid in the V2G mode while they are charging or in a grid-connected state, restoring the frequency instability of the grid. The frequency restoration response time is important to reset the grid frequency fluctuations in the shortest time possible to avoid shutting down the power system. This paper presents a Takagi-Sugeno (T-S) fuzzy linear output controller for LFC in two-area systems with tie-line control. This work models EV batteries as a single lump of large-capacity battery energy storage systems. The EV's battery system provides ancillary power to the two-area power system to reset it to a steady state after a load disturbance. The T-S fuzzy controller's linear output dependency on its inputs enables it to respond efficiently to load variations in the nonlinear two-area power systems. The proposed controller parameters are evaluated from stability analyses and its robustness is tested with sensitivity analysis. It is compared with other fuzzy controllers, and it demonstrates a fast-settling time and reduced frequency deviation response.

Towards a circular business ecosystem of used electric vehicle batteries – A modelling approach

The increase in electric mobility will result in a growing number of used batteries that require an efficient system for handling and recycling valuable materials. The purpose of the research is to create a model of the business ecosystem for post-consumer electric vehicle batteries to identify circular economy business opportunities and limitations. Applying the ecosystem pie modelling methodology, the critical actors involved in electric vehicle battery repurpose and recycling are mapped, including the interdependencies and risks. The modelling is based on interviews with several industrial ecosystem actors and existing research literature. Opportunities and challenges in the circular business ecosystem are identified, relevant to industry strategists and decision-makers. The research findings reveal that downstream circular business ecosystems are more unpredictable and unplanned than upstream linear business ecosystems. The study also highlights the trend towards vertical integration among actors in the battery industry to secure the supply of materials.

A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier

The escalating applications of Lithium-ion (Li-ion) batteries in renewable energy and electric vehicles underscore the need for enhanced prognostics and health management systems to reduce the risk of sudden failures. Remaining useful life (RUL) determination is one of the most critical tasks in the field of battery prognostics nowadays. Even though statistical and machine learning (ML) methods have proven effective in research setups, many challenges prevent applying these prediction methods to real-life scenarios. These challenges include (1) scarcity of run-to-failure datasets with similar experimental conditions, (2) low data granularity when presented in capacity vs. discharge cycle pairs, and (3) lack of “temporal identifiers” in real-life scenarios. A temporal identifier is any label that provides knowledge about the current degradation state of a working battery. The research question developed for this study was, ‘Can the remaining useful life of a Li-ion battery having limited data without a temporal identifier be predicted?’ The specific aims were to estimate the temporal identifier of limited data and to predict the remaining useful life (RUL). An innovative framework incorporating reliability analysis and deep learning addresses these specific aims. Experimental data is used to test the framework's capabilities, limiting the training dataset to only three batteries and the testing dataset to a small sample (< 10 data points) of another battery. This new approach enabled the RUL prediction to achieve errors as low as ∼5 cycles and root mean square error of 6.24 cycles, outperforming other benchmark studies on Li-ion battery RUL prediction that use more battery degradation data without temporal identifier.

A Study on the Green Laser Weldability of Copper-aluminum Battery Materials

Copper is widely utilized in electric vehicle batteries due to its excellent electrical conductivity, lightweight, and excellent corrosion resistance. However, copper has a high reflectivity of about 97% for infrared lasers, which makes it difficult to achieve stable welding quality. This study investigated using a green laser to weld nickel-coated copper and aluminum materials. After performing welding by changing the laser power and scan speed, the weld cross-section was observed with an optical microscope, and it was confirmed that the welding penetration depth and bead width increased as the heat input increased. The spatter on the bead surface and defects such as pores and cracks in the weld were caused by excessive heat input. Analysis of the weld cross-section using SEM-EDS showed that high heat input increased the formation of intermetallic compounds including CuAl2 and Cu9Al4. The mechanical properties of the weld were examined using a shear tensile test. The analysis results showed that intermetallic compounds caused brittleness in the weld joint, which lowered mechanical properties and caused defects such as pores and cracks. P60 (1.2 kW, scan speed: 250 mm/s), P80 (1.6 kW, scan speed: 375 mm/s), and P100 (1.2 kW, scan speed: 375 mm/s) were examples of excellent mechanical quality. The fastest welding condition, P100 (power: 2 kW, scan speed: 450 mm/s), was suggested as the most efficient welding condition.

Mechanism and Data-Driven Fusion SOC Estimation

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.

Wireless power transfer for deep cycle lithium-ion batteries in electric vehicles using inductive coupling

Wireless power transfer for deep cycle lithium-ion batteries in electric vehicles using inductive coupling

Batteries for electric vehicles: Technical advancements, environmental challenges, and market perspectives

Batteries for electric vehicles: Technical advancements, environmental challenges, and market perspectives

Joint estimation of SOH and RUL for lithium batteries based on variable frequency and model integration

The safe and stable operation of lithium-ion batteries in electric vehicles is crucial. Aiming at the problems of a large workload of online estimation and prediction and inability to weigh the whole life cycle of the battery in the joint estimation of SOH (State of Health) and RUL (Remaining Useful Life) for traditional lithium-ion batteries, this paper proposes an optimization scheme for the joint analysis of SOH-RUL. First, the article extracts suitable health features. It utilizes an integrated Extreme Learning Machine (ELM) to estimate the SOH of the battery and inputs the estimates into a Regression Vector Machine (RVM) model for RUL prediction. To better cope with the phenomenon of "sudden capacity change" in batteries, this paper divides the entire life cycle into the early and late stages. A lower estimation frequency and model integration are employed in the early stage.In contrast, the estimation frequency and model integration are increased later to balance speed and safety in total life cycle estimation. Finally, validation was performed on the NASA and CALCE datasets, comparing the effect of constant estimated frequency and model integration with that of varying estimated frequency and model integration at different aging stages. The RMSE of SOH estimation error for both datasets is within 2% during the early and late stages, and the SOH estimation error for the entire lifecycle is within 2%. The absolute RUL prediction error for the NASA dataset is below five times; for the CALCE dataset, it is below 20 times in most cases.

Analysis of cooperative cooling performance between the battery and cabin of pure electric vehicles

Abstract The cooperative cooling of the cabin and battery in pure electric vehicles (PEVs) is crucial for the comfort, thermal safety of the occupants, and the lifespan of the batteries. Therefore, a well-designed thermal management system (TMS) is particularly important. Thus, this paper constructs an cooperative cooling for the battery pack circuit and the cabin circuit in parallel, aiming to enhance the vehicle system’s integration and reduce energy consumption. Firstly, a one-dimensional simulation model of the cooperative TMS is established, and the model is calibrated through experiments. Subsequently, the system’s performance is analyzed under different ambient temperatures. The study finds that as the ambient temperature increases, the system’s energy consumption rises, and the Coefficient of Performance (COP) decreases. Moreover, when the system switches from the simultaneous cooling mode of the cabin and battery to the sole cooling mode of the cabin, the temperature of the cabin will fluctuate to a certain extent, within a range of 2°C. At same time, the COP value also increases with the switch of the mode. Finally, the analysis of the simultaneous cooling mode of the cabin and battery side under different ambient temperatures reveals that as the ambient temperature increases from 34°C to 42°C, the exhaust temperature, pressure ratio, and compressor speed increase accordingly, while the compressor efficiency decreases. The condenser and chiller experience a reduction in their heat transfer efficiency by 59.4 watts per degree Celsius and 218.5 watts per degree Celsius, respectively. Conversely, the evaporator’s heat transfer efficiency undergoes an enhancement of 6.77 watts per degree Celsius.

Progress in direct recycling of spent lithium nickel manganese cobalt oxide (NMC) cathodes

With the widespread use of lithium-ion batteries (LIBs) in portable electronics and electric vehicles (EVs), the end-of-life (EOL) LIBs are projected to reach 1336 GWh by 2040 under the sustainable development scenario. Proper recycling is urgently needed to minimize the release of hazardous waste and reduce mining activities by reintroducing critical minerals into the supply chain. Lithium nickel manganese cobalt oxide (LiNixMnyCozO2, NMCs) cathodes have become dominant in the LIB market, especially with the increasing production of EVs, which are also the most valuable components in EOL LIBs. Unlike pyrometallurgical and/or hydrometallurgical methods, which convert spent NMCs into metals or metal compounds, direct recycling technologies aim to maximize the value of spent cathodes by restoring their degraded structure and composition. This review summarizes direct recycling methods for NMC cathodes published in the last decade and provides insights into the challenges and future development of direct recycling techniques.

Fixed Frequency Highly Efficient Resonant Converter With Low Component Count for CC and CV Charges of Electric Vehicles Batteries

Fixed Frequency Highly Efficient Resonant Converter With Low Component Count for CC and CV Charges of Electric Vehicles Batteries

Enhanced high-rate performance and cyclic stability of LiNi0.8Co0.1Mn0.1O2 through LiAlO2 and polyaniline double-layer coating

LiNi0.8Co0.1Mn0.1O2 (NCM811) is widely utilized in electric vehicle batteries due to its high capacity and low cost, but its poor high-rate performance and cyclic stability restrict its applications. In this study, the NCM811 was surface coated both with ionic conductor LiAlO2 and electrical conductor polyaniline to simultaneously improve its high-rate performance and cyclic stability. Results show that the coated NCM811 has excellent capacity retention rate, namely 93.6% (25 ℃) and 83.9% (50 ℃) at 10C after 100 cycles. These results demonstrate that prepared double-layer coating is effective way to improve high-rate performance of Ni-rich cathode materials.