Battery Characteristics Research Articles

Fault detection method for electric vehicle battery pack based on improved kurtosis and isolation forest

ABSTRACT Early detection of abnormal battery characteristics is crucial for ensuring personnel safety and minimizing property damage. Conventional fault diagnosis methods often struggle to detect minor faults in their early stages. To address this challenge, this paper proposes a fault diagnosis method for lithium-ion batteries in electric vehicles that utilizes real-world operational data. Initially, a wavelet threshold denoising algorithm is used to effectively remove voltage data noise while retaining fault characteristics. Subsequently, an early fault warning method based on improved kurtosis index is proposed, which is capable of capturing the weak abnormal features of the battery and issuing warnings. Furthermore, a faulty battery cell localization method based on two-dimension feature extraction and Isolation Forest algorithm is proposed to capture minor fault features and accurately locate faulty cells. Finally, experiments conducted on four operational electric vehicles demonstrated that the early fault warning method could promptly detect faults and issue alarms, while the faulty battery cell positioning method accurately identified the faulty cell, achieving 100 % Accuracy and 0 % False Positive Rate. This confirms the effectiveness of the proposed method. Additionally, Bootstrap was used to evaluate the performance metrics, further verifying the robustness of the method.

Design of a triple port integrated topology for grid-integrated EV charging stations for three-way power flow

Environmental fluctuations, solar irradiance, and ambient temperature significantly affect photovoltaic (PV) system output. PV systems should be efficient at the Maximum Power Point in various weather climates to maximize their potential power output. The Maximum Power Point Tracking (MPPT) technique is employed to plan a specific location that yields the maximum amount of power. Operating dispersed alternative energy sources connected to the grid in this situation makes energy control an unavoidable task. This research article suggests designing a power electronics converter topology that links sustainable resources and electric vehicles to the power grid. There are four modes of operation for this proposed converter topology: grid-to-vehicle, vehicle-to-grid, renewable-to-vehicle, and renewable-to-grid discussed. The three power electronic converters and their uses are discussed, and their controllers are also designed to maintain the energy balance and stability in all cases. The battery characteristics indicate the operating mode. The work primarily focuses on the converter’s Triple Port Integrated Topology (TPIT) power flow and voltage control. Here, three power converters integrate the TPIT with three systems-the electric grid, renewable energy, and electric vehicles-into one system. The source battery and solar photovoltaic (PV) array cells are integrated using unidirectional and bidirectional DC-DC converters. The future scope of the work is to investigate the potential of adding additional ports for integrating other energy resources, such as hydrogen fuel cells or additional renewable sources, to create a more versatile and robust energy management system for EV charging stations.

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

Research on thermal runaway and gas generation characteristics of NCM811 high energy density lithium-ion batteries under different triggering methods

Safety concerns, including thermal runaway and gas generation, present significant challenges for high-energy-density lithium-ion batteries. Thermal abuse, a common trigger for thermal runaway, can be induced by various methods, including heating rods, coils, plates, and lasers. This study compares the impacts of three heating techniques—heating rods, coils, and plates—on thermal runaway and gas generation in a commercially used NCM811 lithium-ion battery, which has a high energy density of 280.24 Wh/kg (the latest cylindrical 46950 model). The study found that the heating coil was the most effective, triggering thermal runaway more quickly and at a higher temperature than the heating plate and rod. Gas production analysis revealed that the heating coil method generated significantly more gas, particularly CO2, than the other methods. The concentrations of gases produced during thermal runaway (CO, CO2, H2, and CH4) varied by heating method, with the heating coil leading to a more complete battery reaction. The safety evaluation highlighted the hazardous nature of the heating rod method, which produced the widest flammable gas concentration range and the highest explosion risk among the tested heating methods. This study provides critical insights into heating techniques in lithium-ion battery thermal runaway scenarios and offers valuable data for improving safety measures in energy storage systems.

A Computational Review on Localized High‐Concentration Electrolytes in Lithium Batteries

AbstractElectrolyte engineering plays a vital role in improving the battery performance of lithium batteries. The idea of localized high‐concentration electrolytes that are derived by adding “diluent” in high‐concentration electrolytes has been proposed to retain the merits and alleviate the disadvantages of high‐concentration electrolytes, and it has become the focus of attention in high‐voltage lithium batteries, flame‐retardant lithium batteries, and low‐temperature lithium batteries. Extensive efforts have been made to elucidate the fundamentals of localized high‐concentration electrolytes. This review provides an overview of state‐of‐the‐art computational progress in the studies of localized high‐concentration electrolytes, focusing on the application of computational techniques to analyze the redox stability, solvation structures, and interface characteristics of lithium batteries with localized high‐concentration electrolytes. Integrated with experimental approaches, complementing each other, computational methods are believed to be conducive to understanding the working mechanism and designing localized high‐concentration electrolytes for better lithium batteries in the future.

A Novel Method for Obtaining the Electrical Model of Lithium Batteries in a Fully Electric Ultralight Aircraft

This article introduces a novel approach for developing an electrical model of the lithium batteries used in an electric ultralight aircraft. Currently, no method exists in the technical literature for accurately modeling the electrical characteristics of batteries in an electric aircraft, making this study a valuable contribution to the field. The proposed method was validated with an all-electric ultralight aircraft designed and constructed at the Pascual Bravo University Institution. To build the detailed model, a kinematic analysis was first conducted through takeoff tests, where data on the speed, acceleration, time, and distance required for takeoff were collected, along with measurements of the current and power consumed by the batteries. The maximum speed and acceleration of the aircraft were also recorded. These kinematic results were obtained using two batteries made from Samsung INR-18650-35E lithium-ion cells, and different wing configurations of the aircraft were analyzed to assess their impacts on the battery energy consumption. Additionally, the discharge cycles of the batteries were evaluated. In the second phase, laboratory tests were performed on the individual battery cells, and the Peukert coefficient was estimated based on the experimental data. Finally, using the Peukert coefficient and the kinematic results from the takeoff tests, the electrical model of the battery was fine tuned. This model allows for the creation of charging and discharging equations for ultralight lithium batteries. With the final electrical model and energy consumption data during takeoff, it becomes possible to determine the energy usage and flight range of an electric aircraft. The model indicated that the aircraft did not require a long distance to takeoff, as it reached the necessary takeoff speed in a very short time. The equations used to simulate the discharge cycles of the batteries and lithium cells accurately described their energy capacities.

Efficient and Lightweight Model-Predictive IoT Energy Management

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem, and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Equivalent Circuit Model for Electrochemical Impedance Spectroscopy of Commercial 18650 Lithium‐Ion Cell Under Over‐Discharge and Overcharge Conditions

AbstractThis study endeavors to assess the impedance responses exhibited by 18650 Graphite||LiCoO2 (C6||LCO) cells under conditions of over‐discharge and overcharge. The impedance measurements are conducted at a potential of 4.20 V, followed by subjecting the cells to over‐discharge and overcharge scenarios. It is observed that the impedance magnitude experiences augmentation in both instances. Upon reaching a potential of 2.70 V, the electrochemical attributes of the cells revert to their normal state post over‐discharge. However, the impedance characteristics persist even upon exceeding a potential of 4.70 V. These observations imply that overcharge induces enduring alterations in impedance behavior, while over‐discharge is a reversible phenomenon. To delve deeper into the underlying mechanisms, an equivalent circuit process model is constructed. This model elucidates that over‐discharge of Li‐ion batteries is reversible, indicating the potential for restoration of original impedance behavior upon recharge. Conversely, overcharge precipitates permanent alterations in impedance behavior, engendering irreversible changes in battery characteristics. In summation, this study underscores the criticality of meticulously managing the charging and discharging processes of Li‐ion batteries to avert irreversible impairment to impedance behavior, thereby safeguarding battery performance and longevity.

Simulation and Experimental Study on Heat Transfer Performance of Bionic Structure-Based Battery Liquid Cooling Plate

This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm × 20 mm × 3 mm, and the negative pole size is 22 mm × 20 mm × 3 mm. Experimental testing of the Li-ion battery’s heat generation model parameters, in conjunction with bionic structure and micro-channel features, has led to the development of this innovative cooling system. The traditional bionic liquid cooling plate’s structure is often singular; however, the flow path of the liquid cooling plate designed in this paper is based on the combination of the distribution of human blood vessel branches and the structure of insect wing veins. The external dimension of the liquid cooling plate is 152 mm × 100 mm × 6 mm (length × width × height). Utilizing numerical simulation and thermodynamic principles, we analyzed the heat transfer efficacy of the bionic liquid cooling module for power batteries. Specifically, we investigated the impact of varying coolant flow rates and the contact radius between flow channels on the thermal performance of the bionic battery modules. Our findings indicate that a liquid flow rate of 0.6 m/s achieves a stable maximum surface temperature and temperature differential across the bionic battery liquid cooling module, with a relatively low overall system power consumption, suggesting room for further enhancement of heat transfer performance. By augmenting the contact radius between flow channels, we observed an initial increase in the maximum surface temperature, temperature differential, and inlet–outlet pressure differential at a flow rate of 0.2 m/s. However, at flow rates equal to or exceeding 0.4 m/s, these parameters stabilized across different design Scenarios. Notably, the pump power consumption remained consistent across various scenarios and flow rates. This study’s outcomes offer valuable insights for the development of liquid-cooled battery thermal management systems that are energy-efficient and offer superior heat transfer capabilities.

A real-world battery state of charge prediction method based on a lightweight mixer architecture

This paper proposes a lightweight model based on the improved Mixer architecture—TrendFlow-Mixer, designed for efficient prediction of the state of charge (SOC) of lithium-ion batteries. SOC accuracy is crucial for the safe operation, usage, and maintenance of power batteries. When predicting the SOC of real-world batteries, the algorithm's generalization, robustness, and computational efficiency across different battery types must be considered. To this end, TrendFlow-Mixer introduces targeted improvements to battery characteristics, significantly alleviating the Mixer architecture's lag in SOC prediction. The model's core structure integrates the Fast Fourier Transform and arrangement transformation convolutional structures. This allows for efficient extraction of domain-specific spatial features and enables cross-channel correlation interactions. This reduces the model's parameter size while enhancing its adaptability to new domains. The model quickly captures data trend changes and identifies data flow patterns, improving computational efficiency by 70 % compared to Transformer-based architectures. TrendFlow-Mixer employs a dual-prediction head, combining rapid feature change analysis with a nonlinear prediction output to generate SOC predictions. This design facilitates fine-tuning of the model's structure and parameters while enhancing overall flexibility. Experimental results show that in a laboratory dynamic condition dataset, the model achieves a minimum mean absolute error (MAE) of 0.11 %, with the maximum prediction error under extreme temperatures controlled within 4.66 %. In validation using real-world data from the BMW i3, the mean absolute percentage error remains around 1 %, with a minimum MAE of 0.465 %. The results demonstrate that TrendFlow-Mixer possesses high computational efficiency, excellent generalization, and adaptability, effectively predicting SOC trends.

Self-attention network-based state of charge estimation for lithium-ion batteries with gapped temperature data

The accurate estimation of the state of charge (SOC), a critical indicator of the energy stored in lithium-ion batteries, is essential for ensuring reliable and safe battery management. The influence of temperature on the battery characteristics substantially affects the SOC estimation accuracy. Owing to the broad operational temperature range of batteries, it is vital to address the various temperature conditions. This study proposes a model structure for data-driven SOC estimation to enhance accuracy under diverse temperature conditions. The model leverages the analysis of the SOC characteristics derived from the measured data. The proposed structure incorporates parallel-connected self-attention and long-short-term memory modules, thus providing an innovative approach for effectively capturing intricate features in SOC estimation. This study primarily focused on evaluating the capability of the proposed model to achieve satisfactory SOC estimation for untrained temperature conditions when trained with gapped temperature data, thus emphasizing its practicality. To assess the feasibility of the proposed method, experiments were performed under a broad range of fixed and varying temperature conditions, including seasonal and daily changes. The experimental results demonstrated that the root-mean-square errors of the estimated SOC were 0.4101% and 1.5611% at fixed and time-varying temperatures, respectively, including the subzero ranges. These results highlight the robustness of the proposed model under various temperature conditions and its applicability to real-world battery operational temperatures.

Study on the thermal runaway behavior and mechanism of 18650 lithium-ion battery induced by external short circuit

This study investigated the external short circuit (ESC) characteristics of 18650-type NCM lithium-ion batteries under different states of charge (SOC) and short-circuit currents. The research includes the macroscopic electro-thermal characteristics, microscopic morphology, structural damage, and internal damage evolution mechanism of short-circuited batteries. The tests provided an in-depth study of external short circuit (ESC) failure and thermal runaway (TR) behavioral characteristics. The results indicate that all the temperature changes of the short-circuit batteries were found to be in an upward and then downward trend. The surface temperature rise rate of batteries with low SOC were faster, and the maximum surface temperature rise of batteries with high SOC were higher. And with the increase of short-circuit current rate, the temperature rise gradually increased. In particular, thermal runaway occurred at 25C and the maximum temperature exceeds 500 °C. Different SOC batteries showed different degree of voltage fluctuation. The voltage curve of low SOC batteries showed “falling-rising-falling” trend with a brief platform. High SOC batteries formed a distinct voltage platform near 2.75 V. As the short-circuit current increased, the voltage platform shortened, followed by the rapid drop to zero. X-ray CT and SEM test results showed that the deformation of the battery jelly roll was mainly due to the separator shrinkage and tearing. Major changes occurred in the cathode and separator, including secondary particle rupture and irreversible pore blockage in the separator. The damage mechanism of ESC and its evolution are revealed. The results of this study can provide support for the research work on the thermal safety design of lithium batteries.

Investigating the Thermal Runaway Behavior and Early Warning Characteristics of Lithium-Ion Batteries by Simulation

Investigating the Thermal Runaway Behavior and Early Warning Characteristics of Lithium-Ion Batteries by Simulation

Rapid acquisition of battery impedance across multiple scenarios using DRT analysis

Quick and accurate electrochemical impedance spectroscopy (EIS) measurement has always been challenging. This paper proposes a reliable and fast measurement method, which, based on battery characteristics, utilizes a distribution of relaxation times (DRT) extraction algorithm to construct a characteristic spectrum and map it to a binary sequence. The proposed method for constructing characteristic sequences is applicable to battery measurements in multiple scenarioes. The paper additionally introduces a hybrid reconstruction filtering (HRF) method that amalgamates the DRT reconstruction technique and non-parametric estimation techniques. The filtering method does not require preset parameters, resulting in high accuracy and applicability. Simultaneously, combining the characteristic sequence with HRF ultimately obtains a smooth and complete EIS curve under varying states of charges (SOCs), temperatures, and capacities. The proposed testing framework obtains impedance from 0.1 Hz to 2000 Hz <13.2 s. The overall measurement error for batteries with different internal resistances does not exceed 2 %. This work provides a reliable characteristic optimization sequence for large-scale battery impedance measurements, offering a method that is both simple and highly versatile.

Construction and Analysis of Battery Equivalent Circuit Model Considering Liquid Phase Lithium Ion Diffusion Under High-Rate Conditions

In order to obtain the internal state of lithium-ion battery quickly and accurately, the problem that the internal state of the battery is difficult to estimate quickly and accurately is solved. The discharge performance of lithium ion is affected by the electrode current during the charge and discharge process. This paper analyzes the key factors of the electrochemical model at high rate, considers the change of lithium ion concentration in the liquid phase of the battery, integrates the electrochemical mechanism and the equivalent circuit, and establishes a mechanism equivalent circuit model with more computational advantages through circuit elements. The equivalent circuit model can describe the physical characteristics of the lithium battery. Under high rate constant current and dynamic conditions, the model shows good electrical characteristics. Compared with the traditional empirical equivalent circuit model, the accuracy of the model is improved, and the state quantity is reduced by 15 compared with the electrochemical model. The model is of great significance in practical application.

Study on the parameter identification of lithium-ion battery model under high-rate pulse discharge conditions

With the rapid development of the new energy sector and lithium-ion (Li-ion) battery technologies, using Li-ion batteries with high energy density and high discharge rate to form the primary energy subsystem can further improve the compactness and miniaturization level of pulsed power systems. In traditional electric power systems, Li-ion batteries normally operate under the discharge condition of low current and long time. However, as the main power source of pulsed power systems, it is required that Li-ion batteries should have the capability for high-rate pulse discharge. To ensure the safe and reliable operation of the battery energy storage system, it is necessary to conduct the parameter identification of the Li-ion battery model to establish an accurate pulse discharge model under such working conditions. This paper focuses on the discharge characteristics of a cylindrical high-rate single Li-ion battery with a capacity of 3 Ah. At a 20 C rate, the discharge data were obtained when the pulse repetition frequency was 10 Hz and the duty cycle was 90%. By using the mathematical expressions of the Thevenin equivalent circuit model, the recursive relationship of the circuit parameters was obtained, and the parameter identification of the battery model was conducted based on the experimental data by using the genetic algorithm. The results show that the fitting accuracy of the parameter identification reaches 0.9995, indicating the high precision of the model for the Li-ion battery. This work provides a significant reference for the modeling and parameter identification of Li-ion batteries under high-rate pulse discharge conditions.

An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries

Abstract Capacity sorting is the primary prerequisite for the stepwise utilization of decommissioned power batteries. This study proposes an improved relative integral capacity-K-means clustering (RIC-KMC) method for preliminary sorting of decommissioned power battery capacity. Firstly, according to the electrochemical aging characteristics of decommissioned power batteries, combined with the ampere-time integration algorithm, a new lossless extraction method of the capacity characteristics of Lithium-ion batteries based on the segment voltage is proposed. Second, to minimize the interference of environmental factors and sampling errors on the charging capacity, the relative amount of charging capacity is introduced. Finally, to complete the capacity sorting before the ladder utilization, an improved RIC-KMC method is proposed, which combines the electrochemical aging feature values of decommissioned power batteries extracted from the segment voltage charging capacity with the K-means clustering algorithm. Using three sets of battery charging and discharging data to validate the sorting algorithm, the capacity sorting errors are reduced by 0.926%, 12.381%, and 10.185%, respectively, which verification the validity of the proposed RIC-KMC method. This study provides a new solution for capacity pre-sorting of decommissioned power batteries for stepwise utilization.

Fly-ash derived crystalline Si (cSi) Improves the capacity and energy density of LiNi0.8Co0.1Mn0.1O2 battery: Synthesis and performance

For decades, graphite has been the key ingredient in the anode of Li-ion batteries (LIBs). The search for high-capacity anode materials for Li-ion batteries (LIBs) has led to increasing interest in silicon (Si) as a potential replacement for graphite. This study presents an innovative approach to synthesizing crystalline Si (cSi) from type C fly ash, addressing both the need for improved LIB anodes and the challenge of industrial waste management. Our novel process involves a stepwise method of extraction, gelation using various mineral acids (HCl, H2SO4, and HNO3), magnesiothermic reduction, and purification. X-ray diffraction and energy-dispersive X-ray spectroscopy confirmed the production of high-purity SiO2 and cSi. Notably, a cSi-doped graphite anode (5 % Si) paired with LiNi0.8Mn0.1Co0.1O2 (NMC811) in a cylindrical cell achieved a minimum specific discharge capacity of 388 mAh/ganode after formation, surpassing the theoretical capacity of graphite (372 mAh/g). This demonstrates that even a small amount of cSi can significantly enhance anode performance. Our scalable, simple, and economical process offers a dual benefit: improving battery characteristics while providing an efficient method for fly ash waste utilization. This approach paves the way for sustainable production of high-performance LIB anodes while addressing critical waste management challenges.

Aging Characteristics of Lithium-ion Battery under Fast Charging Based on Electrochemical-thermal-mechanical Coupling Model

Aging Characteristics of Lithium-ion Battery under Fast Charging Based on Electrochemical-thermal-mechanical Coupling Model

Estimation of state of charge for polymer solid-state batteries: Ensemble learning models and temperature impact study

In addition to the research and development of solid electrolytes to improve battery performance, an efficient battery management system (BMS) is a must to ensure safe use and extend battery life, and state of charge (SOC) estimation is critical in the BMS. This paper presents a novel temperature-influenced method for estimating the SOC of polymer-based solid-state batteries in real-time, using machine learning to capture the relationship between SOC and battery characteristics at different temperatures. The results show that accurate SOC estimation results can be achieved at different temperatures, with an average root mean square error of 1.42 %. Furthermore, the method uses Shapley Additive explanation (SHAP) to reduce the degree of black box nature of the model and analyzes the electrochemical processes inside the battery at different temperatures through relaxation time distribution. Accurate estimates of SOC and guidelines for appropriate operating temperatures can serve as a basis for BMS decision-making and improve the lifetime of polymer-based solid-state batteries.