Battery Model Research Articles

Mechanism and Data-Driven Fusion SOC Estimation

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy.

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.

Introducing a new model for solid-state batteries: Parameter estimation and sensitivity analysis on diffusion, concentration, and electrochemical kinetics

Despite their potential, solid-state batteries (SSBs) are challenging to optimize due to the complexity of their materials and the early stage of their modeling, especially compared to liquid-electrolyte technologies. Many existing models rely on empirical equations that cannot represent internal mass-transport and kinetic phenomena. Even studies based on mechanistic models often focus on theoretical explanations of experimental phenomena or generating hard-to-obtain experimental data. In this work, a simple yet versatile mechanistic model – able to simulate any battery composed of a metallic anode, solid electrolyte and intercalation cathode – is proposed and used in a parameter estimation routine to identify the material properties of a Li/LiPON/LiCoO2 battery. After validation, a parametric study is made to address how each material property impacts concentration profiles, discharge curves, overpotentials, and core key-performance indexes (KPIs) related to energy efficiency and battery capacity. Findings reveal that cathode diffusion is critical to enhancing Extraction Efficiency, in some cases reaching 98.9% of the theoretical capacity without enhancing any other parameter. In the case of Energy Efficiency being a priority over capacity, electrolyte diffusion and concentration showed a pivotal role, in the range of parameters studied, to increase Voltaic Efficiency up to 97.8%. Despite not directly affecting the Extraction Efficiency, it is discussed how high overpotentials caused by Electrolyte Concentration and Kinetic Constants still can harm battery capacity, for charge–discharge cycles limited by boundaries in electrical potential. Finally, studies simultaneously varying two or more parameters show that, despite extreme property enhancements (up to ca 500x higher than the standard case) indeed lead to exceptional results (up to 96.4% for Voltaic Efficiency and 98.9% for Extraction Efficiency), to use such materials at its full potential will result in other zones of the device usually neglected – such as the overpotentials from the metal anode or even the current collector – may become the new stumbling block, underscoring the significance of considering the battery as a holistic system that will inherently exhibit bottlenecks. This also implies, however, in the perpetual potential for enhancement across its components.

On-Device Personalized Charging Strategy With an Aging Model for Lithium-Ion Batteries Using Deep Reinforcement Learning

On-Device Personalized Charging Strategy With an Aging Model for Lithium-Ion Batteries Using Deep Reinforcement Learning

Physics-Informed Neural Network for Spacecraft Lithium-Ion Battery Modeling and Health Diagnosis

Physics-Informed Neural Network for Spacecraft Lithium-Ion Battery Modeling and Health Diagnosis

Development of a Non-Linear Battery Management System Model for Lithium-Ion Batteries Using MATLAB Simulation

Abstract: This research focuses on developing a comprehensive battery model tailored for Battery Management Systems (BMS) using lithium-ion batteries. Given the increasing reliance on lithium-ion batteries in electric vehicles and portable electronic devices, this study aims to construct a non-linear battery model that accurately reflects the charging and discharging characteristics under various load conditions. The research identifies suitable characteristics of lithium-ion batteries essential for effective battery model construction. A MATLAB-Simulink approach is employed to build the model, which integrates both charging and discharging processes into a single simulation framework. This merged model enhances the existing non-linear models by offering improved accuracy in simulating battery behavior under fixed parameters. The outcome is expected to provide a reference model for future studies and applications in power management systems. Additionally, this research underscores the critical role of BMS in extending battery life and ensuring safety, making it a significant contribution to the field of energy management. The study's findings highlight the potential of the proposed model to serve as a foundation for further research and development in battery technologies.

Boosting predictive accuracy of single particle models for lithium-ion batteries using machine learning

Boosting predictive accuracy of single particle models for lithium-ion batteries using machine learning

Digital technology adoption model for electric vehicle battery recycling supply chain - an influential relationship mapping

Digital technology adoption model for electric vehicle battery recycling supply chain - an influential relationship mapping

Investigating the Level of Metal’s Toxicity in Used Cell Phone Batteries by TCLP and WET Methods

Introduction: Batteries are widely used in all kinds of electrical and electronic equipment. These batteries contain several metals that lead to the leakage of metals into the soil and underground water in the burial places, which pose serious risks to human health and the environment. Materials and Methods: In this study, the concentration of 15 metals (Ag, Al, As, Ba, Cd, Co, Cs, Cu, Fe, Li, Mn, Pb, Sr, Zn, Ni) in different components of 7 used battery models was investigated using Waste Extraction Test (WET) and Toxicity Characteristic Leaching Procedure (TCLP) toxicity. The concentration of metals was measured by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Metal concentrations were compared with the United States Environmental Protection Agency (USEPA) and California Department of Toxic Substances Control (CDTSC) standards. Results: The results showed that the average concentration of metals in both WET and TCLP methods was high, but the concentration of most metals in WET method was relatively higher than in TCLP method. Conclusion: The results showed that the recovery of metals from batteries is necessary, moreover safe burial of batteries is essential to reduce environmental risks.

Enhancing the state-of-charge estimation of lithium-ion batteries using a CNN-BiGRU and AUKF fusion model

Accurate estimation of lithium-ion batteries’ state of charge (SOC) is crucial for the safety of energy storage devices, preventing issues such as overcharging or discharging. This paper introduces a fusion model combining convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and adaptive unscented Kalman filter (AUKF). This method's advantage lies in integrating Kalman filtering with the benefits of neural networks, eliminating the need for complex hyperparameter tuning and battery model construction. Initially, CNN-BiGRU maps variables like voltage, current, and temperature to SOC for initial estimation. The AUKF algorithm then filters these SOC outputs, minimizing fluctuations and enhancing accuracy. This approach ultimately leads to precise and stable SOC estimates. To validate the model's effectiveness, lithium-ion battery datasets across various temperatures were extensively trained and tested. The CNN-BiGRU-AUKF model demonstrated consistent performance under these conditions, with a maximum MSE of 0.00011, MAE under 0.0092, Max AE around 0.02, and a maximum RMSE of 0.017. Compared to similar methods, the proposed approach demonstrates superior SOC estimation accuracy and computational efficiency. Additionally, the model's scalability was validated using training and testing data for SOC estimates from a different type of lithium battery.

A Novel Spotted Hyena Optimizer for the Estimation of Equivalent Circuit Model Parameters in Li-Ion Batteries

Li-ion batteries possess significant advantages like large energy density, fast recharge, and high reliability; hence, they are widely adopted in electric vehicles, portable electronics, and military and aerospace applications. Albeit having their merits, accurate battery modeling is subjected to problems like prior information on internal chemical reactions, complexity in problem formulation, a large number of unknown parameters, and the need for extensive experimentation. Hence, this article presents a reliable Spotted Hyena Optimizer (SHO) to determine the equivalent circuit parameters of lithium-ion (Li-ion) batteries. The methodology of the SHO is derived from the living and hunting tactics of spotted hyenas, and it is efficiently applied to solve the battery parameter estimation problem. Nine unknown battery model parameters of a Samsung INR 18650-25R are determined using this method. The model parameters estimated are endorsed for five different datasets with various discharge current values. Further, the effect of parameter range and its selection is also emphasized. Secondly, for validation, various performance metrics such as Integral Squared Error, mean best, mean worst, and Standard Deviation are evaluated to authenticate the superiority of the proposed parameter extraction. From the computed results, the SHO algorithm is able to explore the search area up to 89% in the case of larger search ranges. The chosen model and range of the SHO precisely predict the behavior of the proposed Li-ion battery, and the results are in accordance with the catalog data.

Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development

Fast growing demands for electric vehicles require better longevity, safety and reliability for next-generation high-energy battery technologies. A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics. Nowadays, Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance, health and safety, but is hurdled by a scarcity of data. To mitigate this issue, this study presents one of the first studies for data development through both experimental studies and three-dimensional (3-D) multi-physics modeling to underpin a deep learning framework with in-depth examination for battery performance and thermal risk prediction. Specifically, Part I focused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model accuracy by two steps: firstly, we validated the multi-physics model against two commercial Lithium-ion batteries, i.e., Panasonic NCR18650B and 18650BD; Then, the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model, such as voltage evolution and maximum local temperature (hot spot). The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.

Model-free detection and quantitative assessment of micro short circuits in lithium-ion battery packs based on incremental capacity and unsupervised clustering

Timely diagnosis of micro short circuit (MSC) faults is crucial for ensuring the safe operation of lithium-ion battery energy storage systems. Existing diagnostic methods face limitations such as high dependency on battery models, difficulty in determining accurate diagnostic thresholds, or low computational efficiency. This work presents a model-free approach for the detection and quantitative assessment of MSCs in lithium-ion battery packs, with incremental capacity (IC) and unsupervised clustering. First, the IC is extracted from charging voltage data to effectively characterize MSC faults in lithium-ion batteries. Next, principal component analysis is used to map the high-dimensional feature space onto a two-dimensional plane to facilitate fault detection and result visualization. Then, an unsupervised clustering algorithm is employed for anomaly detection to identify MSC cells within the battery pack. For the detected MSC cells, a method based on the maximum charging voltage difference between adjacent cycles is designed to estimate the MSC resistance, quantitatively assessing the severity and evolution stage of the MSC. Experimental results show that the accuracy of MSC detection is 99.17 % and the minimum relative error of short-circuit resistance estimation is 1.20 %, which demonstrates the effectiveness and feasibility of the proposed method.

Plasticity analysis and a homogenized constitutive model of compressible multi-layer structure of battery

A homogenized constitutive model for the compressible multi-layer structure of battery (CMLSB) under external loading is essential for optimizing the structural design of electric assemblies. Currently, there is no specific constitutive model that is both mechanically explanatory and operationally applicable to CMLSB under varied loading conditions. In this study, due to limited understanding of the in-plane behavior of CMLSB, an analytical model was developed to investigate plasticity in this specific direction using the strain probing method. The observed plastic characteristics inspired the formulation of a novel two-dimensional constitutive framework for CMLSB in the in-plane direction.By integrating this new constitutive framework with one-dimensional plastic descriptions, a hybrid constitutive model was introduced and implemented in finite element software. Calibration and validation of the model were performed using a commercial pouch cell battery and its segments under various loading conditions. Finite element simulations with the hybrid model demonstrated remarkable accuracy in predicting the mechanical behavior of the cell under various in-plane and out-of-plane compression scenarios. Additionally, simulations were carried out to analyze the impact of cell packaging and air pressure. The new hybrid battery model is considered a user-friendly, physically interpretable, and high-fidelity tool, poised to significantly facilitate the comprehensive design of electric devices.

Immersion cooling of a cylindrical battery module: An optimum configuration using CFD, NSGA-II and desirability function approach

Immersion cooling offers promising advantages for cylindrical battery modules, particularly in applications where compact design, efficient thermal management, and enhanced safety are critical factors. In this study, the geometric, thermal, and dynamic parameters of an immersion-cooled battery module with 14 NCM cylindrical cells are analyzed using CFD methods. The developed battery model is experimentally validated, establishing the constraints through numerical analysis of the impact of design parameters on response values. The design parameters are optimized using a multi-objective optimization technique. The study models the battery module with mineral oil cooling and examines the impact of each parameter on variables such as maximum battery module temperature (Tmax), pressure drop (ΔP), maximum temperature variance (ΔTmax), and battery module mass index (Im). A Design of Experiment set, created with Central Composite Design (CCD), is solved using CFD methods, and quadratic correlation equations are derived using backward regression. The regression models for Tmax, ΔTmax, ΔP, and Im show high accuracy with R2 values close to 1.0. Optimization is performed using the entropy weight-based composite desirability function approach, yielding optimum values for cell center spacing (t), coolant mass flow rate (ṁc), and coolant inlet temperature (Ti) at 20.606 mm, 1.5 kg/s, and 23 °C, respectively. The optimized design sets are validated through CFD analysis, showing a maximum deviation of 4.27 %. Reducing t boosts thermal performance but lowers hydraulic performance, while the reverse occurs for ṁc. As Ti decreases, effective cooling improves, but temperature uniformity suffers.

Lithium-Ion Battery Health Assessment Method Based on Double Optimization Belief Rule Base with Interpretability

Health assessment is necessary to ensure that lithium-ion batteries operate safely and dependably. Nonetheless, there are the following two common problems with the health assessment models for lithium-ion batteries that are currently in use: inability to comprehend the assessment results and the uncertainty around the chemical reactions occurring inside the battery. A rule-based modeling strategy that can handle ambiguous data in health state evaluation is the belief rule base (BRB). In existing BRB studies, experts often provide parameters such as the initial belief degree, but the parameters may not match the current data. In addition, random global optimization methods may undermine the interpretability of expert knowledge. Therefore, this paper proposes a lithium-ion battery health assessment method based on the double optimization belief rule base with interpretability (DO-BRB-I). First, the belief degree is optimized according to the data distribution. Then, to increase accuracy, belief degrees and other parameters are further optimized using the projection covariance matrix adaptive evolution strategy (P-CMA-ES). At the same time, four interpretability constraint strategies are suggested based on the features of lithium-ion batteries to preserve interpretability throughout the optimization process. Finally, to confirm the efficacy of the suggested approach, a sample of the health status assessment of the B0006 lithium-ion battery is provided.

Estimating the Values of the PDE Model Parameters of Rechargeable Lithium-Metal Battery Cells using Linear EIS

Abstract We introduce a partial-differential-equation model for rechargeable lithium-metal battery (LMB) cells whose parameter values are fully identifiable from cell-level experiments. From this model, we formulate a computationally tractable transfer-function (TF) model for use within optimization loops. A strategy is proposed for regressing the TF model to cell electrochemical impedance spectroscopy (EIS) measurements to estimate parameter values. We validate the regression using a synthetic dataset before application to a single-layer LMB pouch cell. The voltage RMSE between the fully identified model's predictions and laboratory measurements is about 4 mV for a GITT profile. We provide MATLAB code to simulate the model in COMSOL, compute cell impedance from the TF model, and perform model regression.

Performance Evaluation of Model-Based Online Condition Monitoring Algorithms for Li-Ion Battery State Estimation

This discovery examines and tests four model-based charge-state (SOC) estimation methods for lithium-ion (Li-ion) batteries. This work evaluates some parts of the SOC estimation, such as error distribution evaluation, rise time estimation, consumption time estimation, etc., instead of the former probing. The battery comparison model is introduced and the state function of the model is inferred. The first step is to study four model-based SOC estimation strategies. The four systems are then tested using simulation and analysis. To mimic the driving conditions of an electric vehicle, the Urban Dynamometer Driving Schedule (UDDS) flow profiles are used. A genetic calculation is then used to find the limits of the model to determine the optimal limits of the Li-ion battery model. The simulations are run continuously and without interruption, and the results are analyzed. To test the device in circle discovery, a battery test bench is developed and uses a Li-ion battery.

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.

Efficiency of Battery Systems from the Point of View of Economic Return

Abstract Battery energy storage systems (BESS) are becoming increasingly important and their number of applications in energy systems is constantly increasing. Although batteries cannot solve the problem of electricity storage in the long term, BESS systems have proven to be suitable for short-term provision of flexibility within the day. Their real usability is shaped by the provision of balancing services to ensure a stable supply of electricity in distribution networks. BESS systems were able to respond to the dynamic development of the electricity market in recent years and achieve excellent results from the point of view of economic return. The aim of the contribution is to present the results achieved in selected cases of BESS use and to determine their economic return. The size of the battery (installed power and capacity) is designed to be able to work in individual modes and operating cases. A critical element for the design of the battery size is the technical conditions for the provision of balancing services. On the basis of market research, we determine the costs of acquiring and operating the battery, including the need for a software superstructure for controlling the battery itself and ensuring the necessary business functions. According to the defined criteria, we evaluate the technical operation of the battery located at the point of consumption (behind the meter) using the battery model in several operating cases. Subsequently, we analyse its sales based on the development of market prices and the conditions in which the battery is placed.