Lithium-ion Batteries In Electric Vehicles Research Articles

(Invited) Research and Development Trends in Sodium-Ion Batteries

Lithium-ion batteries (LIBs) are the most used energy storage technology for electronic devices and electric vehicles. However, the increasing demand for lithium may make it difficult to support the widespread use of LIBs in electric vehicles, utility grids, and other applications. Therefore, developing alternative technologies to LIBs is critical for large-scale energy storage. Studies suggest that using sodium-ion batteries with renewable energies can significantly reduce the cost of electricity. Despite the lower energy density, sodium-ion batteries are appealing for several reasons: (1) sodium is abundant, (2) less critical components are used, and (3) the manufacturing knowledge of LIBs can be used. Herein, we report for the first time a hydrothermal synthesis of a new class of sodium-rich cathode material with the formula Na3+xNixV2-x(PO4)2F2O that could enable the compensation of the irreversible sodium loss in the first charge in a full cell with hard carbon. Trends in sodium-ion batteries will be discussed, considering the new developments in layered oxides, oxy-fluoro-phosphates, and Prussian blue cathodes.

A comparative study of overcharge thermal runaway force-electrical-thermal characteristics and safety assessment of lithium batteries with different cathode materials

This work compares the differences in thermal runaway (TR) behavior and force-electrical-thermal characteristics of three predominant types of lithium-ion batteries (LiNixCoyMn1-x-yO2 (NCM), LiFePO4 (LFP), LiCoO2 (LCO)) with the highest market share under various overcharge rates. The voltage and temperature of cells during the experiment are monitored. Early warning based on expansion force is currently a hot research topic. This study analyzes the evolution of expansion force from overcharging to TR, and lays the foundation for expansion force early warning. The results show that LFP batteries exhibit the lowest overcharging tolerance. However, LFP batteries exhibit the lowest TR hazard and only exhibit smoke release, no explosion or fire. NCM and LCO batteries exhibit more serious TR hazards. A novel safety assessment method based on seven experimental characteristics is proposed to evaluate the TR risks and hazards of three types of batteries qualitatively and quantitatively. Overall, this study compares the TR behaviors of three common lithium-ion batteries, introduces the expansion force parameter for comparative analysis, and reveals the mechanisms of multidimensional parameter evolution offering valuable insights for battery selection. The findings guide the safe use and risk management of lithium-ion batteries in electric vehicles.

State of Health Estimation of Lithium-Ion Battery for Electric Vehicle Based on VMD-DBO-SVR Model

State-of-health (SOH) of lithium-ion batteries is an important indicator for measuring performance and remaining life. We propose an innovative prediction model that integrates variational mode decomposition (VMD), Dung Beetle optimizer (DBO), and support vector regression (SVR) algorithms. We extracted relevant features from the discharge characteristic curve and incremental capacity curve. We used Pearson and Spearman correlation coefficient methods for correlation analysis on the extracted health factors (HFs), selecting those that significantly impact SOH as input features. A DBO-SVR model was constructed to establish a nonlinear correlation between HFs and SOH, and the DBO algorithm was used to globally search and optimize the hyperparameters of the SVR model to improve its prediction accuracy. To reduce the impact of noise in battery signals on model performance, VMD technology was introduced to decompose battery signals into multiple intrinsic mode components, to extract useful features and remove noise to further improve prediction accuracy. The proposed method was validated using the NASA battery dataset and compared with other algorithm models. Results showed that the prediction model was significantly better than other models, with a maximum RMSE value of 0.84%, a maximum MAE value of 0.71%, and a stable prediction error value within 1%.

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA–CNN–Bi-LSTM deep learning model

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

High-precision state of charge estimation of electric vehicle lithium-ion battery energy storage system based on multi-scale optimized time-varying bounded smoothing variable structure filtering algorithm

High-precision state of charge estimation of electric vehicle lithium-ion battery energy storage system based on multi-scale optimized time-varying bounded smoothing variable structure filtering algorithm

Design and research of heat dissipation system of electric vehicle lithium-ion battery pack based on artificial intelligence optimization algorithm

This research focuses on the design of heat dissipation system for lithium-ion battery packs of electric vehicles, and adopts artificial intelligence optimization algorithm to improve the heat dissipation efficiency of the system. By integrating genetic algorithms and particle swarm optimization, the research goal is to optimize key design parameters of the cooling system to improve temperature control and extend battery life. In the process of algorithm implementation, genetic algorithm improves the diversity of population through crossover and mutation operations, thus enhancing the global search ability. Particle swarm optimization (PSO) improves local search accuracy and convergence speed by dynamically adjusting inertia weight and learning factor. The effects of different design schemes on heat dissipation performance were systematically evaluated by using computational fluid dynamics (CFD) software. The experimental results show that the efficiency of the cooling system is significantly improved after the application of the optimization algorithm, especially in the aspects of temperature distribution uniformity and maximum temperature reduction. The optimization algorithm also successfully shortens the thermal response time of the system and improves the adaptability and stability of the system under different working conditions. The computational complexity and execution time of these algorithms are also analyzed, which proves the efficiency and feasibility of these algorithms in practical applications. This study demonstrates the practicability and effectiveness of artificial intelligence optimization algorithm in the design of heat dissipation system of lithium-ion battery pack for electric vehicles, and provides valuable reference and practical guidance for the progress of heat dissipation technology of electric vehicles in the future.

Heat generation rate estimation of lithium-ion batteries for electric vehicles by BP-based optimized neural network

Accurate estimation of heat generation rate (HGR) of lithium-ion batteries (LIBs) is a critical and essential task for their decent thermal management, thereby facilitating the safe driving of electric vehicles (EVs). In order to improve the accuracy of HGR estimation and reduce the structural complexity of network, a data-driven strategy is developed through integrating Bayesian optimization (BO), Adam optimization, and Principal Component Analysis (PCA) with Back Propagation (BP) neural network. The BO algorithm is utilized to optimize the hyperparameters of the BP neural network for prediction accuracy enhancement. The PCA is employed to extract the feature matrix thereby reducing the complexity of inputs. The Adam optimization algorithm is used to improve computational efficiency. The performance of the proposed strategy was validated based on a dataset derived from lab-scale experiments, as well as a publicly available dataset regarding practical driving conditions. The test results show that the proposed strategy can achieve accurate HGR estimation and result in a mean absolute error (MAE) of 0.0504 W, and a root mean square error (RMSE) of 0.0628 W, and a R2 of 0.9998. Compared to some other HGR estimation methods, the proposed strategy achieved a significant enhancement in accuracy indexes, indicating its superior accuracy and robustness.

Dynamic internal resistance modeling and thermal characteristics of lithium-ion batteries for electric vehicles by considering state of health

Attempting to solve the problem of inconsistent dynamic thermal characteristics caused by transient changes in internal resistance of lithium-ion batteries (LIBs) for electric vehicles (EVs) under service conditions, taking a 9.4Ah prismatic LIB as the study object, the internal resistance data under different state of health (SOH), state of charge (SOC), temperature (T), charge/discharge rates of LIB are obtained through the mixed-rate hybrid pulse power characterization (MR-HPPC) test. Based on this, a dynamic internal resistance model (DIRM) considering the SOH is proposed by the prediction of the least squares support vector machines (LS-SVM) model. In this study, a thermoelectric coupling model and a resistance-thermal co-simulation of the LIB are established based on DIRM, and the temperature rise characteristics under constant rates and dynamic operating conditions are investigated at different SOH states. The maximum DIRM error (DIRME) between DIRM-based simulated maximum transient temperature (Tmax-tra) and test Tmax-tra is 0.88 °C at constant charge/discharge rate operating conditions, while the maximum DIRME between DIRM-based simulated Tmax-tra and test Tmax-tra is 0.21 °C under dynamic operating conditions. The results can provide a theoretical support for improving the modeling precision of the heat generation of LIBs with different aging levels for accurate thermal management design.

Computational Modelling of Heat Transfer through Aluminium Metal Foams for LiFePO4 Battery Cooling

Abstract: Temperature is crucial for battery pack durability and power. Folded fin and serpentine channel cooling methods are mostly used to cool the pack. However, fluid absorption during cooling can reduce capacity and cause downstream temperatures to be higher than upstream. Consistent cooling is vital to prevent temperature variation and increase battery pack lifespan. This work is concerned with the computational study of heat dissipation from open-cell aluminium metal foam for cooling LiFePO4 battery packs. The battery module consists of six pieces of pouch cell and three pieces of the aluminium foam heat sink. In the present study, aluminium foams are positioned between the LiFePO4 battery modules that are arranged in a vertical manner. Thermal interaction between the battery module and aluminum foam was studied. The effect of pore density on heat dissipation performance at different mass flow rates was explored. It has been discovered that aluminium foam with suitable porosity and pore density can efficiently cool the LiFePO4 battery pack. This paper provides a theoretical framework for designing a thermal management system for lithium- ion batteries using aluminium foam. Background: Metal foam cooling is an established technique for thermal management of Lithiumion batteries in electric vehicles. Objective:: The present study aims to analyze heat transfer through aluminium metal foams for vertically aligned LiFePO4 battery pack cooling. Methods: The Darcy extended Forchheimer (DEF) model examines fluid flow through metallic foams, using the local thermal non-equilibrium model to determine heat transfer. Results: The impact of the density of pores in the aluminium foam on the average wall temperature and temperature difference along the battery surface is determined. The variation of heat transfer of lithium-ion battery modules for different mass flow rates is also studied. Conclusion: The results indicate that utilizing aluminium foam as a heat transfer medium for battery modules significantly enhances their thermal management performance.

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.

Review on preheating systems for Lithium-ion batteries of electric vehicles under low temperature circumstance

Nowadays, energy and environmental protection issues have become the focus of most attention. The development of electric vehicles which depends on lithium ion battery as power source is one of the most applicable way in dealing with these issues. Lithium-ion battery (LIB) is highly favoured for its outstanding features in energy density, cycle life and energy retention. However, its severe sensitivity to working temperatures leads to problems when driving electric vehicles. Therefore, researchers and engineers have explored approaches to guaranteeing a suitable working temperature for LIB, one of which is the battery preheating system. To clarify the advancement of this system, both internal and external preheating methods studied in recent years are summarized, and the discussion for future research is included.

Optimal charging for lithium-ion batteries to avoid lithium plating based on ultrasound-assisted diagnosis and model predictive control

Lithium plating in lithium-ion batteries for electric vehicles, occurring due to low-temperature or high-rate charging, is a significant factor impacting safety and service life. To address this issue, a novel adaptive charging approach is proposed, combining ultrasound-assisted diagnosis and model predictive control (MPC). In the method, a discrete state-space electrochemical model is used to describe the dynamic characteristics of the battery, and a model predictive controller (MPC) is utilized to optimize the charging current to avoid lithium plating. Considering that factors such as battery performance degradation and variable working temperature affect the battery model's judgment of lithium plating, an ultrasound-assisted diagnosis method is used to determine the critical point of lithium plating. The effectiveness of the method is validated through low-temperature charging and cycle aging experiments. The results indicate that without complex model parameter calibration in different temperatures, the new charging method not only has a higher charging speed than constant current charging, but also can effectively suppress the occurrence of lithium plating on the negative electrode of the battery. The method is expected to be applied in electrochemical energy storage systems to enhance safety and service life.

Advancements in Battery Thermal Management for High-Energy-Density Lithium-Ion Batteries in Electric Vehicles: A Comprehensive Review

Lithium-ion batteries are frequently utilized in electric vehicles because of their high energy density and prolonged cycle life. Maintaining the right temperature range is crucial since lithium-ion batteries' performance and lifespan are highly sensitive to temperature. This study discusses a practical battery heat control system in this setting. The phenomenon of heat generation and significant thermal problems with lithium-ion batteries are reviewed in this work. The studies on various battery thermal management systems (BTMS) are then thoroughly analysed and arranged into groups based on thermal cycle possibilities. Direct refrigerant two-phase cooling, second-loop liquid cooling, and cabin air cooling are all components of the BTMS. Phase change material cooling, heat pipe cooling, and thermoelectric element cooling are all future parts of the BTMS. The maximum temperature and maximum temperature differential of the batteries are examined for each BTMS, and a suitable BTMS that addresses the drawbacks of each system is discussed. Finally, a novel BTMS is suggested as a practical thermal management solution for lithium-ion batteries with high energy density.

World experience of legislative regulation for Lithium-ion electric vehicle batteries considering their second-life application in power sector

Understanding and incorporating global regulatory experiences and standards related to battery management is of greatest importance, particularly when considering the rapid evolution of the electric vehicle (EV) market and its implications for energy storage and sustainability. This is especially relevant for Ukraine, where the burgeoning secondary market for EVs and a keen interest in renewable energy sources underscore the need for proactive policy-making and standardization to address the challenges of battery second life and recycling. This article delves into the role of Electric Vehicle Lithium-Ion batteries within the ambit of the circular economy, underscoring the significance of legislative frameworks across the globe with a particular focus on European initiatives in light of Ukraine's EU integration ambitions. This encompasses extending battery life through recycling and repurposing, thereby ensuring both economic viability and minimal environmental footprint. The narrative outlines the varied legislative landscapes internationally, noting the differences in strategies from Asia's technological and safety emphasis to Europe's robust regulatory directives aimed at battery lifecycle management. In Europe, the drive towards sustainable battery utilization is marked by comprehensive policies like the EU Battery Directive and the emerging Regulation on Batteries and Waste Batteries, which set forth ambitious recycling targets and introduce innovative concepts like the battery passport. Drawing from this global overview, the article posits a set of recommendations for Ukraine, suggesting the development of extensive battery management legislation, adoption of European standards to smooth the path towards EU membership, investment in recycling infrastructures, fostering of public-private partnerships, and public awareness initiatives. These recommendations are designed to elevate Ukraine's position in the sustainability, promoting environmental stewardship and economic competitiveness. The growing importance of secondary lithium-ion batteries for electric vehicles in supporting and harmonizing renewable energy sources is emphasized, and accordingly, the need for adequate legislation and standardization to support a closed-loop economy. Keywords: Lithium-Ion Batteries, Second-Life Application, EV Battery Life Cycle, Circular Economy, Repurpose, Reuse, Recycling, Standards, Regulation, Legislation.

Analysis of State-of-Health Estimation Approaches and Constraints for Lithium-Ion Batteries in Electric Vehicles

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system’s modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery’s efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation.

A simulation-driven prediction model for state of charge estimation of electric vehicle lithium battery

Accurately predicting the state of charge (SOC) of lithium-ion batteries in electric vehicles is crucial for ensuring their stable operation. However, the component values related to SOC in the circuit typically require estimation through parameter identification. This paper proposes a three-stage method for estimating the SOC of lithium batteries in electric vehicles. Firstly, the parameters of the constructed second-order RC circuit are identified using the Forgetting Factor Recursive Least Squares (FFRLS) method. Secondly, an innovative approach is employed to construct a battery simulation model using modal-data fusion method. Finally, the predicted values of the simulation model are corrected using the unscented Kalman filter (UKF). Validation through datasets demonstrates the high precision of this method in parameter identification. Moreover, in the comparison of SOC prediction corrections with Particle Filter (PF), Extended Kalman Filter (EKF), and the proposed UKF on simulated prediction data and experimental test data. The proposed method achieves the lowest root mean square error (RMSE) of 0.0025 for simulation prediction data and 0.0186 for experimental test data. It also maintained its error within 5 % on actual data.

Hydrogen Reduction Behavior of NCM-based Lithium-ion Battery Cathode Materials

As the demand for lithium-ion batteries for electric vehicles is increasing, it is important to recover valuable metals from waste lithium-ion batteries. In this study, the effects of gas flow rate and hydrogen partial pressure on hydrogen reduction of NCM-based lithium-ion battery cathode materials were investigated. As the gas flow rate and hydrogen partial pressure increased, the weight loss rate increased significantly from the beginning of the reaction due to the reduction of NiO and CoO by hydrogen. At 700 °C and hydrogen partial pressure above 0.5 atm, Ni and Li2O were produced by hydrogen reduction. From the reduction product and Li recovery rate, the hydrogen reduction of NCM-based cathode materials was significantly affected by hydrogen partial pressure. The Li compounds recovered from the solution after water leaching of the reduction products were LiOH, LiOH·H2O, and Li2CO3, with about 0.02 wt% Al as an impurity.

Dual-gate design enables intrinsic safety of high-energy batteries

The safety issue hampers the application of high-energy lithium-ion batteries in electric vehicles, grid energy storage, electric ships and aircrafts. The chemical cross-talk, which refers to the migration of energetic intermediates between cathode and anode, initiates battery self-heating and accelerates the intensive heat release during battery thermal failure. However, blocking the chemical cross-talk is extremely hard. Previously, we may expect a thermally stable separator can guard the gate between battery cathode and anode, however, large amount of hot gas breaks the defense mechanically not thermally. Draining the cross-talk gas is of equal importance with blocking it. Herein, a dual-gate design notion is proposed, using separator as “block gate” and vent valve as “removal gate” to regulate the spatial distribution of energetic species to reduce the major heat release during battery thermal runaway. The design is validated by more than 50 battery chemistries. Typically, the maximum temperature of NMC811||Gr pouch cell decreases from >800 °C to <300 °C, the highest temperature rising rate decreases from 761.0 °C/s to 0.013 °C/s. The morphology and crystalline change of cathode verify that the chemical cross-talk is successfully suppressed. Moreover, such design has little side effect on the electrochemical performance of batteries. The dual-gate design breaks the bottleneck for the safety design of high energy batteries, providing insight into the safe utilization of electrochemical energy storage materials.

The suppression of thermal propagation using spray cooling with R410A in overheated lithium battery pack

Currently, the thermal safety control of lithium-ion batteries for electric vehicles has emerged as a prominent research focus in both academia and industry. During the transition from overheating to the initial stage of thermal runaway, lithium batteries undergo an overheating decomposition reaction, which presents an opportunity for proactive prevention of thermal runaway. In this context, this study proposes a spray cooling method and performs numerical simulations to investigate the impact of spray cooling on suppressing overheating propagation in battery packs. Specifically, we examine the suppressor effect of spray cooling on propagating overheating within the battery pack. The cooling effect of R410A spray was investigated at three typical positions prone to overheating. We analyzed changes in thermal behavior characteristics of individual batteries within the pack and evaluated its inhibitory effect on propagating of overheating. The spray strategy is also improved and optimized by increasing the number of spray nozzles and spraying in advance. The results demonstrate that the implementation of the spray cooling method in this study effectively mitigates the average temperature of the overheated battery, thereby preventing the overheating decomposition reaction. Simultaneously, the spray cooling effectively suppresses the heat propagation of excessively heated batteries within the battery pack, thereby preventing and delaying the onset of thermal runaway in the battery pack.

Enhanced thermal management of electric vehicle lithium-ion batteries with Al2O3–MWCNT–ethylene glycol hybrid nanofluid-based helical coiled pulsating heat pipe (HC-PHP)

Enhanced thermal management of electric vehicle lithium-ion batteries with Al2O3–MWCNT–ethylene glycol hybrid nanofluid-based helical coiled pulsating heat pipe (HC-PHP)