Electric Vehicles Research Articles

Research Progress on Solid-State Electrolytes in Solid-State Lithium Batteries: Classification, Ionic Conductive Mechanism, Interfacial Challenges

Solid-state lithium batteries exhibit high-energy density and exceptional safety performance, thereby enabling an extended driving range for electric vehicles in the future. Solid-state electrolytes (SSEs) are the key materials in solid-state batteries that guarantee the safety performance of the battery. This review assesses the research progress on solid-state electrolytes, including polymers, inorganic compounds (oxides, sulfides, halides), and organic–inorganic composites, the challenges related to solid-state batteries in terms of their interfaces, and the status of industrialization research on solid-state electrolytes. For each kind of solid-state electrolytes, details on the preparation, properties, composition, ionic conductivity, ionic migration mechanism, and structure–activity relationship, are collected. For the challenges faced by solid-state batteries, the high interfacial resistance, the side reactions between solid-state electrolytes and electrodes, and interface instability, are mainly discussed. The current industrialization research status of various solid electrolytes is analyzed in regard to relevant enterprises from different countries. Finally, the potential development directions and prospects of high-energy density solid-state batteries are discussed. This review provides a comprehensive reference for SSE researchers and paves the way for innovative advancements in regard to solid-state lithium batteries.

Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data

This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage.

Optimal expansion planning of a self-healing distribution system considering resiliency investment alternatives

This paper proposes a three-stage optimization framework for the expansion planning of a self-healing distribution system that determines the optimal characteristics of distributed generation, energy storage systems, electric vehicle charging stations, and sectionalizing switches for the planning horizon. The main contribution of this model is that the proposed model considers the resilient investment alternatives in the expansion planning exercise to reduce the system's vulnerability against external shocks. The mobile energy storage system commitment in contingent conditions is another contribution of this paper. In the first stage, the optimal location, capacity, and time of installation of the electricity facilities are calculated. Then, the optimal allocation of sectionalizing switches is performed in the second stage. The third stage consists of three levels. In the first and second levels, the optimal normal and contingent operational scheduling are determined, respectively. The system is sectionalized into multi-microgrid systems in contingent conditions. Finally, the resilient investment alternatives for the designed system are evaluated. The proposed model utilizes a self-healing index and resilient expansion planning index to assess the impacts of resilient investment alternatives on the operational scheduling conditions. The proposed model was evaluated using the IEEE 123-bus system. The proposed method reduced the estimated average value of the worst-case energy not supplied by 50.52 % for the 5th year of the planning horizon concerning the no-resiliency investment case. Further, the proposed resilience investment method increased the self-healing index by about 9.32 % concerning the no-resiliency investment case.

Purification of Li2CO3 obtained through pyrometallurgical treatment of NMC black mass from electric vehicle batteries.

The recycling of lithium batteries is essential for a sustainable energy transition. However, impurities in the products obtained from the black mass can lower their market value. In this work, lithium carbonate, which has the highest market share among lithium-based products, is purified using distilled water at controlled temperature, time and stirring speed. The purification process involves dissolving lithium carbonate in distilled water at low temperatures (between 0 and 10°C), followed by crystallization through water evaporation. Optimal conditions yielded lithium carbonate with a purity of 99.66% after two stages of purification. The dependence of the variables on the final purity was deeply analyzed and the thermodynamics of the reaction was studied, confirming the exothermic nature the dissolution reaction.

Mitigating carbon emissions in the taxi service industry: a comprehensive Life Cycle Assessment of batteries for electric taxis and identification of a greener vehicle fleet

Mitigating carbon emissions in the taxi service industry: a comprehensive Life Cycle Assessment of batteries for electric taxis and identification of a greener vehicle fleet

Adoption of electric vehicles: An empirical study of consumers’ intentions

Electric vehicles (EVs) are widely recognized within the transportation industry as highly promising green technology that mitigates carbon dioxide emissions and high energy usage. The public, however, has demonstrated great hesitancy in embracing EVs, and it’s important to learn why this is so. This study employed an integrative approach to personality traits, beliefs, and intentions to understand consumers’ willingness or unwillingness to adopt EVs. The first step in this endeavor was to develop and distribute a survey to gain insight into what potential consumers see as EVs’ major advantages and disadvantages. Structural equation modeling (SEM) was performed on the 743 responses that were collected from the survey, and the results show that personal innovativeness, usefulness, and ease of use positively impact individual’s intentions to acquire an EV; risk was shown to be the most negative influence. A mediation analysis indicated that a person’s level of innovativeness influences their decision about whether or not to adopt an EV. These findings will add to the body of research on sustainable mobility and will equip policymakers and marketers with a deeper understanding of the public’s perceptions of EVs that will enable them to design effective marketing tools that will result in increased sales.

Optimization and Structural Analysis of Automotive Battery Packs Using ANSYS

The development of new energy vehicles, particularly electric vehicles, is robust, with the power battery pack being a core component of the battery system, playing a vital role in the vehicle’s range and safety. This study takes the battery pack of an electric vehicle as a subject, employing advanced three-dimensional modeling technology to conduct static and dynamic analyses. Through weight reduction and structural optimization, an innovative power battery pack design scheme is proposed, aiming to achieve a more efficient and lighter electric vehicle power system. The main research tasks are as follows: Firstly, we designed the main load-bearing components of a certain electric vehicle’s power battery pack and established a three-dimensional (3D) model. Then, the model was simplified according to the actual stress conditions of the power battery pack of the electric vehicle and imported into finite element analysis (FEA) software. Next, based on the fundamental principles of the finite element method (FEM), we conducted static analyses under three conditions: bumpy road sharp left turn, bumpy road sharp right turn, and bumpy road emergency braking. The analysis results indicate that the strength of the battery pack meets the allowable requirements, suggesting that the lower housing design has significant redundancy, providing guidance for subsequent optimization. Finally, through modal analysis, we extracted the first six modes of the power battery box, with the first mode frequency being 33.69 Hz. This suggests that the battery pack may experience resonance during actual operation. Based on the static and modal analysis results, we proposed a structural optimization and lightweight design solution for a certain electric vehicle battery pack and compared it with the pre-optimization data.

Solar Powered Electric Vehicle Charging Station With Integrated Battery Storage System

ABSTRACTThe shift towards electrical vehicles (EVs) can be an important alternative to internal combustion engines for sustainable energy solutions. However, increased EV adoption will increase the charging demand, and there will be a load on the grid electricity. Integrating solar photovoltaic systems with EV charging infrastructure will not only support environmental goals, but also ensure a more resilient and self‐sufficient energy system. A standalone PV system is a good option to reduce the stress on the grid for charging EVs. This present work pivots on the design and performance assessment of a solar photovoltaic system customized for an electric vehicle charging station in Bangalore, India. For this purpose, we have used the PVsyst software to design and optimize a standalone PV system with battery energy storage for EV charging stations. The result shows that 51.1 kWp PV system will be sufficient to meet the energy demand of the charging station by producing 98 313 kWh array energy. The proposed system showed a good average performance ratio of 68.90%. This study shows that the integration of standalone solar photovoltaic systems with EV charging stations is crucial in India and other countries to alleviate grid stress and promote sustainable energy use. This approach not only supports the transition to cleaner transportation but also enhances energy security and reduces dependency on fossil fuels.

System analysis with life cycle assessment for NiMH battery recycling.

The nickel metal hydride (NiMH) battery technology has been designed for use in electric vehicles, solar-powered applications and power tools. These batteries contain the critical and strategic raw materials cobalt, nickel and several rare earth elements (REE). When designing a battery recycling process, there are several choices to be made regarding end-products and process chemicals. The aim of this study is to investigate and compare the environmental and economic sustainability of different recycling options for NiMH batteries by taking projected market developments into consideration and by applying life cycle assessment and life cycle costing methods. The comparative study is limited to recovery of the REEs. Two hydrometallurgical processes for recovery of the REEs from the anode material are compared with extraction of REEs from primary sources in China. The processes compared are a high-temperature sulfation roasting process and a process based on hydrochloric acid leaching followed by precipitation of REE oxalates. By comparing the different recycling approaches, the hydrochloric acid process performs best. However, the use of oxalic acid has a large impact on the overall sustainability footprint. For the sulfation roasting process, the energy, sodium hydroxide and sulphuric acid consumption contribute most to the total environmental footprint. This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

The Recycling of Lithium from LiFePO4 Batteries into Li2CO3 and Its Use as a CO2 Absorber in Hydrogen Purification

The growing adoption of lithium iron phosphate (LiFePO4) batteries in electric vehicles (EVs) and renewable energy systems has intensified the need for sustainable management at the end of their life cycle. This study introduces an innovative method for recycling lithium from spent LiFePO4 batteries and repurposing the recovered lithium carbonate (Li2CO3) as a carbon dioxide (CO2) absorber. The recycling process involves dismantling battery packs, separating active materials, and chemically treating the cathode to extract lithium ions, which produces Li2CO3. The efficiency of lithium recovery is influenced by factors such as leaching temperature, acid concentration, and reaction time. Once recovered, Li2CO3 can be utilized for CO2 capture in hydrogen purification processes, reacting with CO2 to form lithium bicarbonate (LiHCO3). This reaction, which is highly effective in aqueous solutions, can be applied in industrial settings to mitigate greenhouse gas emissions. The LiHCO3 can then be thermally decomposed to regenerate Li2CO3, creating a cyclic and sustainable use of the material. This dual-purpose process not only addresses the environmental impact of LiFePO4 battery disposal but also contributes to CO2 reduction, aligning with global climate goals. Utilizing recycled Li2CO3 decreases the demand for virgin lithium extraction, supporting a circular economy. Furthermore, integrating Li2CO3-based CO2 capture systems into existing industrial infrastructure provides a scalable and cost-effective solution for lowering carbon footprints while securing a continuous supply of lithium for future battery production. Future research should focus on optimizing lithium recovery methods, improving the efficiency of CO2 capture, and exploring synergies with other waste management and carbon capture technologies. This comprehensive strategy underscores the potential of lithium recycling to address both resource conservation and environmental protection challenges.

Statistical evaluation of mechanical load on technicians during electric vehicle battery replacement using Python simulation

Electric vehicle (EV) battery replacement poses significant mechanical strain on technicians, increasing the risk of musculoskeletal disorders (MSDs). This study uses Python-based simulations and biomechanical modeling to evaluate the mechanical load distribution during the battery replacement process, focusing on joint stress and technician posture. The analysis covers different task phases, including battery removal, transport, and installation, with statistical methods used to assess forces and torques applied to critical joints. The results indicate that the removal and installation phases exert the highest mechanical loads on the lower back and shoulders. The study suggests ergonomic interventions such as workstation redesign, lifting tools, and improved posture techniques to reduce the risk of injury and enhance the safety and efficiency of EV maintenance tasks.

Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

ABSTRACTLithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real‐time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends.

A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy consumption.

The current research effort is focused on improving the effective use of the multiple disparate sources of data available by proposing a novel maximum likelihood based probabilistic data fusion approach for modeling residential energy consumption. To demonstrate our data fusion algorithm, we consider energy usage by fuel type variables (for electricity and natural gas) in residential dwellings as our dependent variable of interest, drawn from residential energy consumption survey (RECS) data. The national household travel survey (NHTS) dataset was considered to incorporate additional variables that are not available in the RECS data. With a focus on improving the model for the residential energy use by fuel type, our proposed research provides a probabilistic mechanism for appropriately fusing records from the NHTS data with the RECS data. Specifically, instead of strictly matching records with only common attributes, we propose a flexible differential weighting method (probabilistic) based on attribute similarity (or dissimilarity) across the common attributes for the two datasets. The fused dataset is employed to develop an updated model of residential energy use with additional independent variables contributed from the NHTS dataset. The newly estimated energy use model is compared with models estimated RECS data exclusively to see if there is any improvement offered by the newly fused variables. In our analysis, the model fit measures provide strong evidence for model improvement via fusion as well as weighted contribution estimation, thus highlighting the applicability of our proposed fusion algorithm. The analysis is further augmented through a validation exercise that provides evidence that the proposed algorithm offers enhanced explanatory power and predictive capability for the modeling energy use. Our proposed data fusion approach can be widely applied in various sectors including the use of location-based smartphone data to analyze mobility and ridehailing patterns that are likely to influence energy consumption with increasing electric vehicle (EV) adoption.

Power Battery Recycling Model of Closed-Loop Supply Chains Considering Different Power Structures Under Government Subsidies

With the rapid growth of the electric vehicle industry, the recycling of power batteries has attracted significant attention. In light of current circumstances, the question of how the government can incentivize relevant stakeholders to actively engage in recycling and improve its efficiency has become increasingly pressing. In this context, this study analyses and develops four closed-loop supply chain recycling models to investigate how different government subsidy recipients under varying power structures influence recycling efficiency, profitability, and the overall supply chain structures. The following conclusions are derived from numerical simulations: (1) Government subsidies serve to elevate recycling prices, expand profit margins, and consequently boost the volume of recycled batteries, thus incentivizing corporate engagement in recycling initiatives. (2) When the processor assumes the role of the leader in the Stackelberg game framework, it can maximize the overall efficiency and profitability of the supply chain. (3) The sensitivity coefficient and the competition coefficient are closely interrelated, exerting opposing impacts on the recycling decision made by enterprises. (4) The supply chain leader plays a crucial role in ensuring orderly supply chain development, with government subsidies of the supply chain being transmitted to its members through the leader. Consequently, this study offers a theoretical foundation for the government to enhance policy-making and for enterprises to make informed decisions. It also holds significant practical relevance in addressing the challenges associated with power battery recycling.

Single- and Three-Phase Dual-Active-Bridge DC–DC Converter Comparison for Battery Electric Vehicle Powertrain Application

Dual-active-bridge (DAB) DC–DC converters are of great interest for DC–DC conversion in battery electric vehicle (BEV) powertrain applications. There are two versions of DAB DC–DC converters: single-phase (1p) and three-phase (3p) architectures. Many studies have compared these architectures, selecting the 3p topology as the most efficient. However, there is a gap in the literature when comparing both architectures when single-phase-shift (SPS) modulation is not used to drive the converter. The aim of this study was to compare 1p and 3p DAB DC–DC converters driven by optimal modulation techniques appropriate for BEV powertrain applications. Mathematical loss models were derived for both architectures, and their performances were compared. A case study of a 100 kW converter was considered as an example to visualize the overall efficiency of the converter for each layout. The 1p DAB DC–DC converter architecture outperformed the 3p layout in both its Y–Y and D–D transformer configurations. The higher performance efficiency, lower number of components, and reduced design complexity make the 1p DAB DC–DC converter topology a favorable choice for BEV powertrain applications.

Offline and Online Exergy-Based Strategies for Hybrid Electric Vehicles

Abstract Exergy-based control strategies for ground hybrid electric vehicles (HEVs) enable to pursue unconventional optimization goals that are inaccessible when standard energy-based modeling frameworks based on fuel consumption minimization are used. In this work, we formulate and solve offline and online exergy-based optimization strategies for military HEVs aimed at the minimization of genset exergy destruction and thermal emissions to increase vehicle efficiency and minimize the risk of thermal imaging detection, respectively. We refer to the offline version of these strategies as exergy minimization strategies (ExMSs). Adaptive ExMSs (A-ExMSs) are then formulated for online implementation. Moreover, charge increasing (CI) ExMSs and A-ExMSs are developed to charge the battery as much as possible during a driving mission that is followed by a silent watch phase. To assess the performance of the proposed strategies, the results obtained by the ExMSs and A-ExMSs are compared to the benchmark solutions obtained by Dynamic Programming.

Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

Numerical analysis of flow-induced noise during the charging of a fuel cell electric vehicle

Numerical analysis of flow-induced noise during the charging of a fuel cell electric vehicle

Recent Advancements and Future Prospects in Lithium‐Ion Battery Thermal Management Techniques

ABSTRACTLithium‐ion batteries (LiBs) are the leading choice for powering electric vehicles due to their advantageous characteristics, including low self‐discharge rates and high energy and power density. However, the degradation in the performance and sustainability of lithium‐ion battery packs over the long term in electric vehicles is affected due to the elevated temperatures induced by charge and discharge cycles. Moreover, the thermal runaway (TR) issues due to the heat generated during the electrochemical reactions are the most significant safety concern for LiBs, as inadequate heat dissipation can be potentially hazardous, leading to explosions and fires. Considering the safety of EVs and for better performance, understanding the mechanism of TR is of paramount importance. This review provides a comprehensive analysis of the TR phenomenon and underlying electrochemical principles governing heat accumulation during charge and discharge cycles. Furthermore, the article explores the cell modeling and thermal management techniques intended for both individual lithium‐ion battery cells and larger battery packs, with a particular emphasis on enhancing fire prevention and safety measures. The main goal of this review paper is to offer new insights to the developing battery community, assisting in the development of efficient battery thermal management systems (BTMS) using enhanced cooling methodologies. This article could also support the advancement of next‐generation electric vehicle battery packs equipped with built‐in safety features to improve the cycle life of LiBs and prevent thermal runaway accidents.

A New Artificial Intelligence-Based System for Optimal Electricity Arbitrage of a Second-Life Battery Station in Day-Ahead Markets

Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets.