Electric Vehicle Battery Research Articles

Influence of straight and inclined baffles on enhancement of battery thermal management system performance

Battery thermal management systems (BTMSs) are used in electric vehicles (EVs) to regulate the heat generated by batteries while in use. Existing research on the improvement of BTMSs revealed that more research can be done by exploring different strategies to extend the service life and capabilities of EV batteries beyond the current limitations. In this study, effects of orientation of baffles; straight (90°), inclined (60° and 30°), height of baffles, thickness of baffles, positioning of baffles and number of baffles on the performance of conventional Z – Type BTMS were studied using Computational Fluid Dynamics (CFD) approach. The CFD approach was validated with existing experimental result from literature. Findings from the study showed that for straight baffle, the maximum temperature (Tmax) reduces as the height of baffles decreases. Furthermore, increasing the baffle thickness from 1 mm to 2 mm, produced reduction in Tmax by 0.26 K. For inclined baffles, by comparing the BTMS without baffles and BTMS with 2 baffles, Tmax and maximum temperature difference (ΔTmax) reduced by 0.82 K and 0.68 K, respectively at angle 60°, and reduced by 1.65 K and 1.43 K, respectively at angle 30°. The BTMS with 2 baffles, inclined at angle 60°, with height of 6 mm and thickness of 1 mm, yielded the optimum Tmax (Lowest) value of 333.15 K, a reduction by 2.65 K when compared to the conventional Z – Type BTMS. This was also accompanied with a slight increase in ΔP by 1.12 Pa. In conclusion, it can be said that findings from this study will be beneficial in enhancing the design of BTMSs through adequate selection and utilization of baffles orientation.

Collaborative optimization of electric-vehicle battery swapping stations based on energy storage sharing

Due to the extensive integration of distributed renewable energy resources, the Active Distribution Network (ADN) faces numerous challenges, including, for example, renewable energy curtailment and overloading. This paper proposes a collaborative optimization control method for electric-vehicle battery swapping stations that mitigates the mismatching between generation and load demand in the ADN. Energy storage sharing is considered in this study, that allows stations to exchange batteries via the traffic network, and this extends the capacity of Battery-Transferable Swapping Stations (BTSSs). First, the operational principles of the energy storage shared BTSS are carefully analyzed, including external and internal control mechanisms and energy storage sharing. Subsequently, a bi-level optimization model is proposed, whereby the upper level aims to minimize the total operational cost of the ADN and the lower level seeks to maximize the benefit of each BTSS. A two-stage transactive control is introduced to decompose the bi-level situation into two stages: the day-ahead stage and the real-time stage and this facilitates easier problem-solving. Finally, an IEEE 15-node system is utilized to verify the proposed method. The results indicate that the proposed method reduces the renewable energy curtailment, power shortages, and operational costs of the ADN, while at the same time increasing the earnings of BTSSs.

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Artificial Intelligence in Electric Vehicle Battery Disassembly: A Systematic Review

Artificial Intelligence in Electric Vehicle Battery Disassembly: A Systematic Review

Implementation of a multistage predictive energy management strategy considering electric vehicles using a novel hybrid optimization technique

In this paper, a novel artificial intelligence (AI)-based energy management strategy across three levels is designed for isolated microgrids. During the initial phase, AI is not employed. A precise and rapid-response microcontroller, namely the FPGA, is utilized. The performance of the FPGA is experimentally investigated under various operation conditions. In the second phase, AI plays a crucial role in enhancing both the reliability and economic effectiveness of the system. The multi-objective snake optimization (SO) algorithm is employed to attain high technical and economic performance within predefined constraints. Three objective functions are involved in this phase, focusing on the minimization of operational costs, loss of power supply probability (LPSP), and electrical energy losses in the dummy load. In the third level, a novel control strategy-based coordinated model predictive control is presented as part of the proposed AI-embedded energy management strategy. A hybrid optimization algorithm combining the multilayer feed-forward neural networks (MFFNN) and SO algorithms is proposed to predict the output power of backup sources. The microgrid under consideration is supported by a hybrid backup system comprising battery energy storage systems (BESS), electric vehicle (EV) batteries, and fuel cells (FCs). The effectiveness of this hybrid algorithm is evaluated through comparison with many other algorithms, aiming to assess its performance in accurately predicting the output power of backup sources. The results show the feasibility of using AI to achieve efficient operation and energy management in microgrids. The proposed MFFNN-SO algorithm achieves the best performance, as the normalized root mean square error (NRMSE) for FCs, BESS, and EV is about 0.1296 %, 0.0094 %, and 0.1304 %, respectively. The SO algorithm achieves a notable 6.3361% reduction in operating costs, resulting in a final operational cost of 166.4811 $.

Developmental Trajectories of Electric Vehicle Research in a Circular Economy: Main Path Analysis

This study explored the development history and future trends of academic research on electric vehicles (EVs) in a circular economy. We collected 4127 articles on circular economy and EVs from the Web of Science database, and main path analysis indicated that academic research in the field of EVs in a circular economy has covered the following topics in chronological order: EVs as a power resource; vehicle-to-grid (V2G) technology; renewable energy and energy storage grids; smart grid and charging station optimization; and sustainable development of energy, water, and environmental systems. Through cluster analysis and data mining, we identified the following main research topics in the aforementioned field: recycling and reuse of EV batteries, charging stations and energy management, V2G systems and renewable energy, power frequency control systems, dynamic economic emissions, and energy management. Finally, data mining and statistical analysis revealed the following emerging research topics in this field from 2020 to 2023: microgrids, deep learning, loop supply chain, blockchain, and automatic generation control. Various achievements have been attained in research on EVs in a circular economy; however, challenges related to aspects such as sustainable battery recycling charging infrastructure and renewable energy integration remain.

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.

Enhanced electric vehicle battery management system employing bat algorithm with chaotic diversification strategies

AbstractAs the demand for electric vehicles (EV) continues to increase, the need for effective charging and switching of battery systems becomes more important. This article presents a method using the Bat Algorithm (BA) improved by chaotic diversification as well as social education to optimize the power source replacement and the electric vehicle charging procedure. The plan is intended to solve the issues of payment delay and battery management failure. The algorithm searches for better positions by combining chaotic diversity, while social learning supports the coordination of battery stations. Thanks to extensive simulation and real‐world testing, our approach shows significant improvements in optimization and a reduced payback period. The results show that the suggested approach outperforms the current algorithms in terms of rotation speed and good solution. This research supports the development of efficient transportation by providing practical solutions to increase the efficiency of electric vehicle transfer and payment and ultimately encourage greater effort.

Optimizing Energy and Delay in Task Offloading for Connected Vehicles with Proximal Policy Optimization

Electric-powered intelligent connected vehicles are becoming the pivotal point of the automotive industry. As vehicles integrate more applications, the computation tasks they generate increase substantially. Cloud servers cannot handle these tasks promptly, and current electric vehicles (EVs) have limited energy and computing resources. Multi-Access edge computing (MEC) performs various activities in proximity to the vehicles, resulting in decreased latency and the preservation of EV battery power. However, MEC servers have finite processing resources and may be unable to satisfy the required latency restrictions. We propose a task offloading scheme to optimize the allocation of computational resources from roadside servers across several EVs. We develop a mathematical model to optimize both computation latency and EV energy, represented as a Markov decision process (MDP). To address this, we employ the deep reinforcement learning-proximal policy optimization (DRL-PPO) algorithm. The implementation of our mathematical model, which is based on an MDP, together with the use of the DRL-PPO algorithm, showcases notable decreases in both energy consumption and latency when compared to alternative benchmark deep reinforcement learning (DRL) approaches.

Experimental investigation into thermal characteristics of subcooled flow boiling in horizontal concentric annuli for cooling ultra-fast electric vehicle charging cables

In an effort to reduce the charging time of electric vehicles (EVs), the thermal management of the charging system has emerged as one of the most urgent issues. The charging rate has been restricted to avoid thermal failure especially in charging cable. Recently, a direct contact cooling technique that utilizes subcooled flow boiling for charging cables has been proposed and has shown promising thermal management performance. However, there has been a lack of clear explanation regarding its thermal characteristics due to its intrinsically complicated flow features. This study investigates the thermal characteristics of subcooled flow boiling in a horizontally aligned concentric annular tube intended to simulate the direct contact cooling technique employed for charging cables. For the experimental investigation, the wall temperature measurements and high-speed flow visualization images acquired from the transparent test module annulus, with inner and outer diameters of 6.35 mm and 22.0 mm, were utilized. Three key axial thermal characteristics of subcooled flow boiling in annulus have been analyzed, and they are flow regime transition, variation of heat transfer mechanisms, and occurrence of critical heat flux (CHF). The transition of flow regimes and the variation of dominant heat transfer mechanisms in the axial direction are mutually influencing due to the coupled effects between the hydrodynamic and thermal characteristics of simultaneously developing flows. The flow regime changes from partially developed boiling (PDB) to fully developed boiling (FDB) as the intensities of nucleate boiling and bubble recondensation vary in the axial direction, while the dominant heat transfer mechanism shifts from single-phase convection to nucleate boiling, corresponding to the flow regime transition from PDB to FDB. The departure from nucleate boiling (DNB) type CHF is observed, manifested by the wavy interface propagating from the far downstream to the upstream region, and the intensities of nucleate boiling and bubble recondensation are also identified as the dominant parameters in determining the occurrence of CHF. Charging time estimation reveals that the proposed method can achieve an 80 % charge for 100-kWh EV batteries in 98 s, while maintaining the cable wire temperature below the safety limit of 80 °C. This strongly supports its potential as a promising thermal management technique for future ultra-fast charging systems.

A new electrolyte for molten carbonate decarbonization

The molten Li2CO3 transformation of CO2 to oxygen and graphene nanocarbons (GNCs), such as carbon nanotubes, is a large scale process of CO2 removal to mitigate climate change. Sustainability benefits include the stability and storage of the products, and the GNC product value is an incentive for carbon removal. However, high Li2CO3 cost and its competitive use as the primary raw material for EV batteries are obstacles. Common alternative alkali or alkali earth carbonates are ineffective substitutes due to impure GNC products or high energy limitations. A new decarbonization chemistry utilizing a majority of SrCO3 is investigated. SrCO3 is much more abundant, and an order of magnitude less expensive, than Li2CO3. The equivalent affinities of SrCO3 and Li2CO3 for absorbing and releasing CO2 are demonstrated to be comparable, and are unlike all the other alkali and alkali earth carbonates. The temperature domain in which the CO2 transformation to GNCs can be effective is <800 °C. Although the solidus temperature of SrCO3 is 1494 °C, it is remarkably soluble in Li2CO3 at temperatures less than 800 °C, and the electrolysis energy is low. High purity CNTs are synthesized from CO2 respectively in SrCO3 based electrolytes containing 30% or less Li2CO3.

Life-cycle environmental impacts of reused batteries of electric vehicles in buildings considering battery uncertainty

Advancements in various technologies have made it possible to recycle end-of-life batteries from electric vehicles (EV) into a stationary energy storage system (ESS) within residential buildings. As a result, promoting a circular economy between buildings and means of transportation has emerged as a major concern. Therefore, this study aimed to quantitatively assess the environmental impacts (life -cycle carbon Carbon dioxide (CO2) emissions) of ESS utilizing used batteries instead of new batteries from the life cycle perspective of lithium-ion batteries (LIBs) considering the uncertainty in energy communities. To this end, a probabilistic life cycle assessment (LCA) was performed using a Monte Carlo simulation of the energy community of South Korea. The results of this study demonstrated that reusing batteries as ESS in buildings could further improve the overall environmental sustainability of the ESS compared to using new batteries. As a result, when reused batteries were utilized, annual carbon emissions decreased by 2.8 %–18.5 % according to battery usage purposes. More specifically, it was shown that battery reuse can reduce greenhouse gas emissions and environmental impacts. To more rationally evaluate life-cycle CO2 emissions from a resource circulation perspective, the usage purpose and efficiency of the battery should be considered during the entire battery life cycle (transportation and building sectors). These findings are expected to provide valuable insights to policymakers, industrial sectors, and research institutes related to battery reuse and to help guide the future of transportation and building sectors in a sustainable direction from a resource circulation perspective.

Technology foresight in Indonesia: developing scenarios to determine electrical vehicle research priority for future innovation

Purpose The purpose of this study is to determine research areas that are most favorable in supporting the development and manufacturing of electric vehicle (EV) components locally in Indonesia for 2025–2035. Therefore, will provide direction for the formulation of the related government policies and programs. Consequently, an EV technology research priority must be identified. Design/methodology/approach A technology foresight (TF) procedure which consists of a STEEPV analysis, followed by scenarios development and expert elicitation techniques, was conducted to determine an EV technology research priority that may direct future specific local component innovations, and therefore businesses. Findings The results of this study indicate that research in a range of EV battery technologies, technologies relating to a variety of key components (to increase local content) and autonomous systems were important to support the local development and manufacturing of EV components in Indonesia. Research limitations/implications In this study, the scenarios development process was conducted based on selected available experts, mostly internally from BRIN. Some biased opinions may be present. Originality/value There have not been any TF studies regarding the development of EV technology research priority in Indonesia.

Effect of fast charging on degradation and safety characteristics of lithium-ion batteries with LiFePO4 cathodes

Fast charging compatibility is a desirable feature for batteries to shape a promising future in our rechargeable world. Enabling fast charging of advanced energy-dense Li-ion batteries for growing electric vehicle (EV) markets depends on the breakthrough of material chemistry and optimization of charging strategy. Lithium iron phosphate (LiFePO4, or LFP) is a pivotal cathode material in state-of-the-art EV batteries due to the merits of high thermal stability, long cycle lifetime, and high-temperature performance. However, degradation-safety interactions of LFP-based Li-ion batteries under fast charging conditions and low temperatures remain elusive. In this study, we cycle LFP cells under different fast charging strategies and thermal environments to understand their effects on degradation pathways, where the capacity, voltage, temperature, coulombic inefficiency, internal resistance, and impedance spectroscopy are evaluated. Half-cell studies are implemented to assess electrode-level capacity retentions. Post-mortem analyses are conducted to reveal physicochemical changes in electrodes. Accelerating rate calorimeter tests are carried out to measure evolutions of cell-level thermal instabilities after fast charging tests. This research article highlights the significant roles of graphite-centric lithium plating and LFP-centric transition metal dissolution in driving substantial electrochemical degradations, suggesting that a mild low temperature does not necessarily reduce the LFP cell lifetime.

Comparative analysis of data-driven electric vehicle battery health models across different operating conditions

The work covers the development of a data-driven algorithm and computes the performance of learning models for lithium-ion battery state of health (SOH) estimation. A wide range of environmental and temperature conditions (15 °C, 25 °C, and 35 °C) at different charging and discharging rates of 1C and 2C are used for electric vehicle battery health estimation. The result of the tested data of cell ‘a’ is validated with a different set of cell ‘b’ on identical test parameters, and the results are tabulated and compared. At 25 °C, the mean absolute errors for the regression algorithms decision tree (DT), k-nearest neighbor (KNN), and random forest (RF) are 3.78641E-03, 3.62524E-03, and 6.16931E-03. The mean absolute percent error for regression algorithms DT, KNN, and RF is 1.48921E-03, 1.40631E-03, and 2.40260E-03. The root mean square error for regression algorithms DT, KNN, and RF is 1.26813E-02, 9.73320E-03, and 1.17238E-02, and the mean squared error for regression algorithms DT, KNN, and RF is 1.60816E-04, 9.47351E-05, and 1.37448E-04. The results show that the KNN and DT methods accurately estimate the SOH under diversified operating conditions in comparison with RF methods and can foster advanced battery health monitoring systems.

Correction: Yalçın, S.; Herdem, M.S. Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging. Energies 2024, 17, 2883

Correction: Yalçın, S.; Herdem, M.S. Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging. Energies 2024, 17, 2883

Analytical Estimation of the C-Rate and Numerical and Experimental Investigation of the EV Battery Pack

Analytical Estimation of the C-Rate and Numerical and Experimental Investigation of the EV Battery Pack

Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems

Heat Transfer Modeling and Optimal Thermal Management of Electric Vehicle Battery Systems

Optimizing peak-shaving cooperation among electric vehicle charging stations: A two-tier optimal dispatch strategy considering load demand response potential

The increase in the grid connection of electric vehicles (EVs) provides great potential for peak load regulation and valley filling of the grid. In order to solve the challenges brought by the integration of new energy vehicles into the power grid and give full play to the potential of EV demand response, this paper proposes a two-layer optimal dispatch strategy for the “distribution network-charging station” system. First, assess EV load demand response potential. The research area is divided into different functional areas by using the data of urban interest points, and the travel habits of users are analyzed by using EV driving data. On this basis, considering the influencing factors such as EV battery capacity (SOC), charging electricity price and spatial characteristics, the EV load response potential evaluation model is constructed by integrating user electricity price response and charging power response. Secondly, taking the evaluation value of EV response potential as the range of load adjustment, in order to optimizing peak-shaving cooperation among EV charging stations and obtaining the peak-shaving measures of each charging station, a “distribution network-charging station” double-layer optimal dispatch is carried out. The upper-level distribution network scheduling aims at minimizing the unfinished peak shaving rate and distribution network loss, and optimizes the scheduling power allocation of the peak tasks of each charging station. The lower-level charging station scheduling is based on the principle of maximizing user charging satisfaction and charging station economic benefits, and completes the peak-shaving scheduling determined by the superior. Finally, the strategy is applied in a specific area of Shaanxi Province, and the scheduling results show that the strategy is more economically feasible.

Investigating the potential of a battery swapping method at refuel stations for electric vehicle: A case study of INDIA

Electric vehicles (EVs) are introduced to mitigate environmental problems and develop sustainable modes of transport across the globe. Researchers often report that the non-availability of charging infrastructure is the primary concern for EV adoption. The traditional plug-in EV charging method has a higher waiting time, discouraging users from adopting EVs. Many countries, including India, are promoting an alternative battery swapping method (BSM) for EV charging to reduce the waiting time. But the user preference and willingness to adopt BSM remain unexplored, especially in developing countries. The existing refuel stations are a potential location for developing charging infrastructure that provides the EV charging service and helps create awareness among users because of their wide visibility. Thus, this study aims to bridge the research gap by exploring the influential factors, user preferences, and willingness to use the BSM at refuel stations. For this purpose, 1013 samples were collected from road users who visited 51 refueling stations using the random sampling method in the twin cities of Ahmedabad and Gandhinagar. An integrated partial least squares-structural equation modelling with the artificial neural network method was adopted in this study. The study’s findings reveal that the development of public EV charging facilities at refuel stations significantly impacts the user’s willingness to shift to EVs. The convenience and cost-related motivating factors like a lesser waiting time compared to the plug-in charging method, reduced range anxiety, no concern about battery usage, reduced initial purchase cost of an EV, and comparatively lower maintenance costs significantly motivates user’s willingness to adopt BSM. Similarly, battery-related demotivating factors like the non-reliable range of the swapped battery, non-standard battery design in terms of type, size, capacity, and brand across the country, additional cost for leasing or renting the battery, and chances that EV batteries might be replaced with fake batteries concern BSM adoption.