Search Results (325)

The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack. Full article

Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB’s health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing. Full article

Lithium-ion batteries are widely applied in the form of new energy electric vehicles and large-scale battery energy storage systems to improve the cleanliness and greenness of energy supply systems. Accurately estimating the state of power (SOP) of lithium-ion batteries ensures long-term, efficient, safe and reliable battery operation. Considering the influence of the parameter identification accuracy on the results of state of power estimation, this paper presents a systematic review of model parameter identification and state of power estimation methods for lithium-ion batteries. The parameter identification methods include the voltage response curve analysis method, the least squares method and so on. On this basis, the methods used for modeling and estimating the SOP of battery cells and battery packs are classified and elaborated, focusing on summarizing the research progress observed regarding the joint estimation method for multiple states of battery cells. In conclusion, future methods for estimating the SOP of lithium-ion batteries and their improvement targets are envisioned based on the application requirements for the safe management of lithium-ion batteries. Full article

The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric vehicles (EVs). To achieve this, the battery pack must operate with constant current charging and discharging modes of operation. Further, in an EV powertrain, maintaining a constant DC link voltage at the input stage of the inverter is crucial for driving the motor load. To satisfy these two conditions simultaneously during the energy transfer, a hybrid energy storage system (HESS) consisting of a lithium–ion battery and a supercapacitor (SC) connected to the semi-active topology of the bidirectional DC–DC converter (SAT-BDC) in this research work. However, generating the duty cycle for the switches to regulate the operation of SAT-BDC is complex due to the simultaneous interaction of the two mentioned constraints: regulating the DC link voltage by tracking the reference and maintaining the battery current at a constant value. Therefore, this research aims to efficiently resolve the issue by incorporating a highly flexible nonlinear model predictive control (NMPC) to control the switches of SAT-BDC. Furthermore, the converter system design is tested for operational performance using MATLAB 2022B with the battery current and the DC link voltage with different priorities. In the NMPC approach, these constraints are carefully evaluated with varying prioritizations, representing a crucial trade-off in optimizing EV powertrain operation. The results demonstrate that battery current prioritization yields better performance than DC link voltage prioritization, extending the lifespan and efficiency of batteries. Thus, this research work further aligns with the conceptual realization of the sustainability goals by minimizing the environmental impact associated with battery production and disposal. Full article

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack’s lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions. Full article

This paper conducts an in-depth study of a wireless, hierarchical structure-based active balancing system for power batteries, aimed at addressing the rapid advancements in battery technology within the electric vehicle industry. The system is designed to enhance energy density and the reliability of the battery system, developing a balancing system capable of managing cells with significant disparities in characteristics, which is crucial for extending the lifespan of lithium-ion battery packs. The proposed system integrates wireless self-networking technology into the battery management system and adopts a more efficient active balancing approach, replacing traditional passive energy-consuming methods. In its design, inter-group balancing at the upper layer is achieved through a soft-switching LLC resonant converter, while intra-group balancing among individual cells at the lower layer is managed by an active balancing control IC and a bidirectional buck–boost converter. This configuration not only ensures precise control but also significantly enhances the speed and efficiency of balancing, effectively addressing the heat issues caused by energy dissipation. Key technologies involved include lithium-ion batteries, battery management systems, battery balancing systems, LLC resonant converters, and wireless self-networking technology. Tests have shown that this system not only reduces energy consumption but also significantly improves energy transfer efficiency and the overall balance of the battery pack, thereby extending battery life and optimizing vehicle performance, ensuring a safer and more reliable operation of electric vehicle battery systems. Full article

Lithium ion (Li-ion) battery packs have become the most popular option for powering electric vehicles (EVs). However, they have certain drawbacks, such as high temperatures and potential safety concerns as a result of chemical reactions that occur during their charging and discharging processes. These can cause thermal runaway and sudden deterioration, and therefore, efficient thermal management systems are essential to boost battery life span and overall performance. An electrochemical-thermal (ECT) model for Li-ion batteries and a conjugate heat transfer model for three-dimensional (3D) fluid flow and heat transfer are developed using COMSOL Multiphysics^®. These are used within a novel computational fluid dynamics (CFD)-enabled multi-objective optimization approach, which is used to explore the effect of the mini-channel cold plates’ geometrical parameters on key performance metrics (battery maximum temperature ($T_{max}$), pressure drop ($∆P$), and temperature standard deviation ($T_{\sigma }$)). The performance of two machine learning (ML) surrogate methods, radial basis functions (RBFs) and Gaussian process (GP), is compared. The results indicate that the GP ML approach is the most effective. Global minima for the maximum temperature, temperature standard deviation, and pressure drop ($T_{max}$, $T_{\sigma }$, and $∆P$, respectively) are identified using single objective optimization. The third version of the generalized differential evaluation (GDE3) algorithm is then used along with the GP surrogate models to perform multi-objective design optimization (MODO). Pareto fronts are generated to demonstrate the potential trade-offs between $T_{max}$, $T_{\sigma }$, and $∆P$. The obtained optimization results show that the maximum temperature dropped from 36.38 to 35.98 °C, the pressure drop dramatically decreased from 782.82 to 487.16 Pa, and the temperature standard deviation decreased from 2.14 to 2.12 K; the corresponding optimum design parameters are the channel width of 8 mm and the horizontal spacing near the cold plate margin of 5 mm. Full article

Lithium-ion batteries (LIBs) have the lead as the most used power source for electric vehicles and grid storage systems, and a battery thermal management system (BTMS) can ensure the efficient and safe operation of lithium-ion batteries. Epoxy resin board (ERB) offers a wide range of applications in LIBs due to its significant advantages such as high dielectric strength, electrical insulation, good mechanical strength, and stiffness. This study proposes an air-cooled battery module comprised of sixteen prismatic batteries incorporating an ERB layer between the batteries. To compare the performance of the ERB-based air-cooling system, two other air-cooling structures are also assessed in this study. Three-dimensional numerical models for the three cases are established in this paper, and the heat dissipation processes of the battery module under varying discharge rates (1C, 2C, and 5C) are simulated and analyzed to comprehensively evaluate the performance of the different cooling systems. Comparative simulations reveal that incorporating ERB into the battery assembly significantly reduces battery surface temperatures and promotes temperature uniformity across individual batteries and the entire pack at various discharge rates. Notably, under 5C discharge conditions, the ERB-based thermal management system achieves a maximum battery surface temperature increase of 16 °C and a maximum temperature difference of 8 °C between batteries. Additionally, this paper also analyzes the impact of battery arrangement on air-cooling system performance. Therefore, further optimization of the structural design or the integration of supplementary cooling media might be necessary for such demanding conditions. Full article

Silicon (Si) as the anode material for lithium-ion batteries (LIBs) has attracted much attention due to its high theoretical specific capacity (4200 mAh/g). However, the specific capacity and cycle stability of the LIBs are reduced due to the pulverization caused by the expansion of Si coated on Cu (copper) foil during cycles. In order to solve this problem, researchers have used an ultra-thin Si deposition layer as the electrode, which improves cyclic stability and obtains high initial coulomb efficiency of LIBs. However, suitable substrate selection is crucial to fabricate an ultrathin Si deposition layer electrode with excellent performance, and a substrate with a three-dimensional porous structure is desirable to ensure the deposition of an ultrathin Si layer on the whole surface of the substrate. In this paper, the Si thin layer has been deposited on a binder-free hybrid film of carbon nanotubes (CNTs) and carbon nanocoils (CNCs) by magnetron sputtering. Compared with densely packed CNT film and flat Cu foil, the loose and porous film provides a large surface area and space for Si deposition, and Si can be deposited not only on the surface but also in the interior part of the film. The film provides a large number of channels for the diffusion and transmission of Li⁺, resulting in the rapid diffusion rate of Li⁺, which improves the effective lithium storage utilization of Si. Furthermore, the CNC itself is super elastic, and film provides an elastic skeleton for the Si deposition layer, which eases its volume expansion during charge and discharge processes. Electrochemical tests have showed that the Si/CNT–CNC film electrode has excellent performance as anode for LIBs. After 200 cycles, the Si/CNT–CNC film electrode still had possessed a specific capacity of 2500 mAh/g, a capacity retention of 92.8% and a coulomb efficiency of 99%. This paper provides an effective way to fabricate high performance Si-nanocarbon composite electrodes for LIBs. Full article

The thermal performance of a lithium-ion battery pack for an electric vehicle by adding straight rib turbulators in battery cooling plate channels has been numerically investigated in this paper and the numerical model of the battery pack has been validated by experimental data, which exhibits a satisfactory prediction accuracy. The effects of rib shapes, rib angles, rib spacings, and irregular gradient rib arrangement configurations on the flow and heat transfer behaviors of battery pack cooling plates have been thoroughly explored and analyzed in this paper. In addition, the thermal performance of the ribbed battery cooling plates was examined at actual high-speed climbing and low-temperature heating operating conditions. The results indicate that compared to the original smooth cooling plate, the square-ribbed battery cooling plate with a 60° angle and 5 mm spacing reduced the maximum battery temperature by 0.3 °C, but increased the cross-sectional temperature difference by 0.357 °C. To address this issue, a gradient rib arrangement was proposed, which slightly reduced the maximum battery temperature and lowered the cross-sectional temperature difference by 0.445 °C, significantly improving temperature uniformity. The thermal performance of the battery thermal management system with this gradient rib configuration meets the requirements for typical electric vehicle operating conditions, such as high-speed climbing and low-temperature heating conditions. Full article

In recent years, with the rapid development of new energy vehicles, the safety issues of lithium-ion batteries have attracted attentions from all sectors of society. Research has found that during the thermal runaway process of lithium-ion batteries, aerosol emissions usually occur earlier than other gases. Accurate and timely measurement of these aerosol concentrations can help to warn the power battery pack fires. However, existing aerosol sensors are unable to meet the requirements of real-time monitoring and high precision. This article proposes an ionization mechanism based aerosol sensor that works at principles of field emission, field charging and gas discharge, and investigates its static and dynamic response characteristics. The sensor is manufactured and assembled using Microelectro Mechanical Systems processing technology. The sensor exhibits superior performances in terms of range, sensitivity, nonlinearity, repeatability, response time, and other aspects. The study provides a new solution for current aerosol detection with great potential for application. Full article

A Y-type air-cooled structure has been proposed to improve the heat dissipation efficiency and temperature uniformity of battery thermal management systems (BTMSs) by reducing the flow path of air. By combining computational fluid dynamics (CFD) methods, the influence of the depths of the distribution and convergence plenums on the airflow velocity through battery cells was analyzed to improve heat dissipation efficiency. Adjusting the width of the first and ninth cooling channels can change the air velocity of these two channels, thereby improving the temperature uniformity of the BTMS. Further discussion was conducted regarding the influences of inlet and outlet depths. When the inlet width and outlet width were 20 mm, the maximum temperature and maximum temperature difference of the Y-type BTMS were 39.84 °C and 0.066 °C at a discharge rate of 2.5 °C, respectively; these temperatures were 1.537 °C (3.68%) and 0.059 °C (47.2%) lower than those of the T-type model. Meanwhile, the energy consumption of the sample also decreased by 13.1%. The results indicate that the heat dissipation performance of the proposed Y-type BTMS was improved, achieving excellent temperature uniformity, and the energy consumption was also reduced. Full article

The safety of lithium-ion batteries is critical to the safety of battery electric vehicles (BEVs). The purpose of this work is to develop a method to predict battery thermal runaway in full electric vehicle crash simulation. The thermal–electrical–mechanical-coupled finite element analysis is used to model an individual lithium-ion battery cell, a battery module, a battery pack, and a battery electric vehicle with 24 battery modules in a live circuit connection. The lithium-ion battery is modeled using a representative approach, with each internal battery component individually modeled to represent its geometric shape and realistic thermal, mechanical, and electrical properties. A resistance heating solver and Randles circuit model built with a generalized voltage source are used to simulate the electrical behavior of the battery. The thermal simulation of the battery considers the heat capacity and thermal conductivity of different cell components, as well as heat conduction, radiation, and convection at their interfaces. The mechanical property of battery cell and battery module models is validated using spherical punch tests. The electrical property of the battery cell and battery module models is verified against CircuitLab simulation in an external short-circuit test. The simulation results for the battery module’s internal resistance are consistent with both experimental data and literature values. The multi-physics coupling phenomenon is demonstrated with a cylindrical compression simulation on the battery module. The multi-physics BEV model with 24 live battery modules is used to simulate the external short-circuit test and the side pole impact test. The simulation run time is less than 24 h. The results demonstrated the feasibility of using a representative battery model and multi-physics analysis to predict battery thermal runaway in full electric vehicle crash analysis. Full article

Lithium-ion batteries, with their high energy density, long cycle life, and non-polluting advantages, are widely used in energy storage stations. Connecting lithium batteries in series to form a battery pack can achieve the required capacity and voltage. However, as the batteries are used for extended periods, some individual cells in the battery pack may experience abnormal failures, affecting the performance and safety of the battery pack. At the same time, as batteries operate in complex environments, the data collected by sensors are susceptible to random noise and drift interference, which can affect the accuracy of anomaly detection in individual battery cells. In order to solve this problem, this article proposes an anomaly detection method for battery cells based on Robust Principal Component Analysis (RPCA), taking the historical operation and maintenance data of a large-scale battery pack from an energy storage station as the research subject. Firstly, theRPCA is used to denoise the observed voltage data of the battery cells to an extreme degree, obtaining a baseline charging state curve for a cell consistency assessment. This also solves the problem of sensor outputs being affected by random noise. To further detect and identify abnormal battery cells, the RPCA is used to extract outlier components. Based on the Average Deviation-3σ principle and by utilizing Gaussian distribution probability characteristics, battery cells are conducted to screen, and the serial numbers of the anomaly cells are obtained. Finally, the effectiveness and accuracy of this anomaly detection method for battery cells are compared and verified through different statistical distributions. Full article

Thermal runaway characteristics and hazards of lithium-ion batteries under low ambient pressure in-flight conditions are studied in a dynamic pressure chamber. The influence of ambient pressures (95 kPa and 20 kPa) and packaging forms (cylindrical and pouch commercial batteries) were especially investigated. The results show that the values of heat release, temperature, and $\mathrm{C}{\mathrm{O}}_2$ concentration decrease with the reduction in pressure from 95 kPa to 20 kPa, while the total hydrocarbon and $\mathrm{C}\mathrm{O}$ increase. Without violent fire, explosion, and huge jet flames, the thermal hazards of TR fire under 20 kPa are lower, but the amount of toxic/flammable gas emissions increases greatly. The amount of $\mathrm{C}\mathrm{O}$ and hydrocarbons varies inversely with the thermal hazards of fire. Under low-pressure environments of cruise altitude, the thermal hazards of TR fire for pouch cells and the toxic/potentially explosive hazards of gas emissions of cylindrical cells need more attention. The performance of TR hazards for two packaging types of battery is also different. Pouch cells have higher thermal hazards of fire and lower combustible/toxic emitted gases than cylindrical cells. The thermal runaway intensity of individual cells decreases under lower ambient pressure, but the burning intensity increases dramatically when thermal runaway occurs in a battery pack. The open time of a safety valve (rupture of the bag) is shortened, but the trigger time for a thermal runaway varies for different formats of batteries under 20 kPa. Those results may be helpful for the safety warning and hazard protection design of Li batteries under low-pressure conditions. Full article