Battery Model Research Articles

Design and Control of Radial Electromagnetic Bearing for Flywheel Battery

Abstract High rotational speed is the key to increasing energy storage capacity of flywheel battery. Non-contact bearing ensures reliable operation of high-speed flywheel battery. To design an electromagnetic bearing that meets the requirement of high-speed flywheel battery, firstly, a multi-parameters and multi-objectives optimization model for radial electromagnetic bearing is established. The optimization aims at the smallest spatial volume while ensuring electromagnetic force. A 16.58% reduction of the spatial volume is obtained. Furthermore, the influence of bias current and control current on the electromagnetic force and rotor displacement of radial magnetic bearing are analyzed. The bias current and control current that meet the requirements of current stiffness and displacement stiffness are determined. Based on this, a differential control model for radial magnetic bearing is constructed. Then, a controller based on adaptive discrete sliding mode control is designed. Finally, performance test and data analysis are completed. Results indicate that the designed radial electromagnetic bearing is able to achieve low response error, low system jitter and high robust under different test conditions. Compared with traditional sliding mode control method, under stochastic excitation, the maximum and average tracking errors of bearing displacement are reduced by 62.31% and 42.74%. The energy consumption is decreased by 49.32%.

Research on the impact of lithium battery ageing cycles on a data-driven lithium battery model

Research on the impact of lithium battery ageing cycles on a data-driven lithium battery model

A comprehensive equivalent circuit model of Li-ion batteries for SOC estimation in electric vehicles based on parametric sensitivity analysis

A comprehensive equivalent circuit model of Li-ion batteries for SOC estimation in electric vehicles based on parametric sensitivity analysis

Effect of DM Interaction in the charging process of a Heisenberg spin chain quantum battery

Abstract We investigate the charging performance of an anisotropic XYZ model of Heisenberg Spin Chain Quantum Battery (HS QB) along with different components of Dzyaloshinskii-Moriya Interaction (DMI) for three cases - short range, long range and infinite range interactions. We find that the presence of DMI enhances the charging power and total stored energy of the QB considered here, when compared to HS QB in most of the cases, by considering both local and collective charging protocols. The maximum stored energy increases linearly with the number of spins (N). An extensive scaling of maximum charging power with the number of spins is obtained in which the maximum power approaches a scaling exponent α = 1.846 for collective charging of the model consisting of Heisenberg spin chain with Y component of DMI under infinite range interactions. Finally, we optimize the performance of the battery by measuring the quantum energy fluctuations with in the system.

Detection of internal short circuit in lithium-ion batteries based on electrothermal coupling model

Internal short circuit (ISC) is the main cause of thermal runaway (TR) in lithium-ion batteries, and early detection of ISC is crucial to improve battery safety. This paper introduces a method for detecting ISC and classifying fault severity by analyzing the variations in voltage and surface temperature during battery operation. Firstly, the electrothermal coupling model (ETCM) of the battery was constructed, and the Pearson correlation coefficient (PCC) was used to identify the ISC of the battery through the real-time collected voltage and temperature sensor data When the battery was working. Then the battery model with ISC is updated by using equivalent ISC resistance. The battery status estimation is achieved by integrating the extended Kalman filter (EFK) and sliding mode observer (SMO). Finally, the fault grade is classified according to the state difference between the battery with ISC and normal battery. The batteries with different degrees of ISC were installed in battery packs and verified by the StarSim hardware-in-the-loop (HIL) experiment. The findings suggest that the proposed approach facilitates rapid identification of ISC in the battery pack and enables categorization of fault severity based on the state estimation outcomes derived from analyzing the battery with ISC.

Analysis of the Validity of P2D Models for Solid-State Batteries in a Large Parameter Range

Abstract Simulation models are nowadays indispensable to efficiently assess or optimize novel battery cell concepts during the development process. Electro-chemo-mechano models are widely used to investigate solid-state batteries during cycling and allow the prediction of the dependence of design parameters like material properties, geometric properties, or operating conditions on output quantities like the state of charge. One possibility of classification of these physics-based models is their level of geometric resolution, including three-dimensionally resolved models and geometrically homogenized models, known as Doyle-Fuller-Newman or pseudo two-dimensional models. Within this study, the advantages and drawbacks of these two types of models are identified within a wide range of the design parameter values. Therefore, the sensitivity of an output quantity of the models on one or a combination of parameters is compared. In particular, the global sensitivity, i.e., the sensitivity in a wide range of parameter values, is computed by using the Sobol indices as a measure. Furthermore, the local sensitivity of the difference in the output quantities of both models is evaluated to identify regions of parameter values in which they contain significant deviations. Finally, remarks on the potential interplay between both models to obtain fast and reliable results are given.

MINN: Learning the Dynamics of Differential-Algebraic Equations and Application to Battery Modeling.

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalizability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in modeling real-world dynamic systems for optimization and control purposes. We propose a novel machine learning architecture, termed model-integrated neural networks (MINN), that can learn the physics-based dynamics of general autonomous or non-autonomous systems consisting of partial differential-algebraic equations (PDAEs). The obtained architecture systematically solves an unsettled research problem in control-oriented modeling, i.e., how to obtain optimally simplified models that are physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.

Performance analysis of CKF algorithm for battery SoC estimation with its aging effect

The penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence.

Ultrasonic Nondestructive Testing of Li-ion Batteries for Anomaly Detection and Quality Control: Applications and Signal Processing

Use of ultrasonic nondestructive methods for quality control and testing of different types of Lithium-ion battery is progressing in recent years. This study provides a comprehensive review of the current applications and technical challenges of ultrasonic testing in inspection of lithium-ion batteries. First, basic principles and theoretical modeling of such batteries are shown. Secondly, it summarizes the most common defect types in these batteries. Finally, it talks about signal processing methods used for feature extraction and development.

Low Pass Filter Based Lithium‐ion Battery Equivalent Circuit Model With High Accuracy

AbstractThe equivalent circuit model (ECM) of lithium batteries provides a simplified way to describe their output behaviors. In this paper, a low pass filter‐based ECM of lithium battery is proposed with high accuracy. A voltage source is employed to represent the capability of the lithium battery to store energy chemically, a RC branch paralleled with the voltage source represents the charge transfer process. This RC branch acts as a low pass filter, and the capacitance buffers the charges during charging or discharging process, and this explains the dynamic voltage recovery process. However, with the traditional RC branches‐based ECM, the dynamic voltage is realized by the resistance in parallel with the capacitor, which discharges the capacitor and the RC branch is series‐connected with the voltage source. The proposed ECM is compared with the 1‐RC Thevenin model, which has the same number of model parameters with the proposed model. The parameters are obtained by the hybrid pulse power characterization (HPPC) test. The output voltage accuracy of the 1‐RC Thevenin model and the proposed model with LFP battery and NCM battery is verified with different test conditions. The experimental results show that the proposed ECM has higher accuracy, this validates the correctness of the proposed model.

Analysis of cell balancing of Li-ion batteries with dissipative and non-dissipative systems for electric vehicle applications

This article describes the essential components of contemporary battery management systems (BMS), such as power electronics bidirectional charging and discharging, reverse protection against constant current and voltage, and Li-ion battery cell balancing, which is the process of introducing Li-ion The majority of domestic electrical applications, including e-cars, are powered by batteries. These qualities are crucial for ensuring the safety, longevity, and effectiveness of batteries when it comes to renewable energy sources and electric vehicles (EVs). Two other crucial BMS characteristics that protect against reverse current and overcharging destroying batteries are constant current and voltage reverse protection. Ensuring that every single cell in a battery is in balance is known as evenly charging and discharging every single cell. In-depth discussions of these characteristics' advantages and operation, as well as their uses in EVs, renewable energy systems, and other contexts, are provided in this article. It is seen from the analysis that the non-dissipative lithium-ion battery cell balancing strategy, which significantly enhances safety and efficiency, provides greater benefits than the dissipative balancing approach. The modelling of an SoC charge-controlled Li-Ion battery with an optimum battery voltage of 3.6 V. Its rated capacity of 4 Ah is considered a test cell that has contrasted dissipative and non-dissipative balancing in MATLAB/Simulink with five cells in the battery bulk. It is seen from the analysis that the non-dissipative lithium-ion battery cell balancing strategy provides greater benefits than the dissipative balancing approach.

Enhanced Accuracy in State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles Using Augmented Adaptive Extended Kalman Filter

Accurate assessment of state of charge (SoC) is crucial for the safety and efficiency of lithium-ion batteries used in electric vehicles (EVs). For precise SoC estimation in EV BMSs, an Augmented Adaptive Extended Kalman Filter (AAEKF) has been proposed in this research. To improve the precision of SoC estimation, AAEKF approach combines Kalman filtering methods with adaptive robust control concepts. The effectiveness of the AAEKF algorithm is shown by simulation results and confirmed by comparison with data from LiFePO4 battery research. The proposed AAEKF SoC estimation matches measurement results with errors of less than 3%. Adaptive Extended Kalman Filter (AEKF) is quite sensitive to the accuracy of the battery model, as errors in this region can lead to large estimate errors. Faster convergence to actual SoC, improved real-time responsiveness, accuracy, and resilience to disturbances and parameter fluctuations are all results of AAEKF's efficient handling of nonlinearities in battery systems, adaptation to system changes, errors (Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and overall performance. The maximum estimate error stays under 5% at various external temperatures.

Elucidating effects of form factors on thermal and aging behavior of cylindrical lithium-ion batteries

Cylindrical lithium-ion cells are commercially available in different form factors, e.g., 18650, 21700, 26650, 32700 and 4680. The larger 4680 cells are gaining popularity, especially after the patent filing by Tesla. This paper aims to perform a comparative analysis of the thermal and aging behavior of different form factors using NMC–graphite chemistry during prolonged cycling at moderate operating conditions. A physics-based aging model for lithium-ion batteries is introduced, incorporating side reactions such as solid electrolyte interface (SEI) formation, SEI reformation due to graphite layer cracking, and lithium plating. During cycling, the cell temperature increases with time for all form factors due to internal heat generation. Notably, larger form factor cells experience higher temperatures, attributed to their inferior surface-to-volume ratio for heat dissipation, thereby accelerating both SEI formation and triggering lithium plating in subsequent cycles. This accelerated aging manifests in increased heat generation and prolonged charging for larger cells. The 4680 cell showed the highest rate of capacity loss with a 20% reduction over 480 cycles as compared to the 600 cycles of the 18650 cell. Highlighting accelerated aging in the larger cells due to suboptimal heat dissipation, this study underscores the pressing need for advanced thermal management strategies tailored to the unique challenges posed by larger cells.

Prognostic of lithium-ion batteries using a combination of physical modeling and hybrid multi-layer perceptron particle filter

With the European Union legislative push to phase out internal combustion engines by 2035, the demand for electric vehicles and efficient energy storage solutions, particularly lithium-ion batteries, is set to rise. Addressing this demand necessitates both the optimization of battery lifespan and the development of robust methodologies for real-time assessment of state of health and prediction of remaining useful life. This study introduces a novel hybrid grey-box prognostic and health management framework that combines a physical battery model with a multi-layer perceptron particle filter (MLP-PF) for real-time estimation of degradation parameters. The approach leverages an electrochemical model developed in Modelica to simulate battery voltage and track degradation parameters, thereby capturing the battery dynamic behavior over time. By integrating a data-driven MLP-PF, this method adapts the physical degradation parameters, ensuring ongoing and precise estimation of remaining useful life. Experimental validation, in terms of accuracy and confidence interval coverage, confirms the framework capability in prediction and relative quantification of uncertainties. These results underscore the framework practical utility for battery management systems in electric vehicles, providing an adaptable and accurate tool for decision-makers in battery maintenance and replacement.

A refined model of lithium-ion battery considering agglomerated active material and non-uniform graphite particle

This study presents a refined lithium-ion battery model based on the Newman model, incorporating agglomerate structures in the positive electrode and non-uniform particle size distribution in the negative electrode. The agglomerates in the positive region consist of nano-scale primary particles, pore spaces, and binders, while the negative region features a random distribution of particle sizes. The model employs the volume averaging method and integrates a thermal model to provide a comprehensive simulation framework. The results of the numerical calculations and experimental measurements show that the model is in good agreement with the experimental data. The predicted discharge capacity of the proposed model is significantly higher than that of the traditional P2D model. This improvement is primarily due to the increased diffusion distance in agglomerates and the non-uniform particle sizes, which result in substantial changes in overpotential, electrolyte concentration, and solid-phase lithium-ion distribution. The model effectively captures the complex electrochemical behavior during discharge, underscoring the importance of considering structural heterogeneities in battery modeling.

Fault Diagnosis of Lithium-Ion Batteries Based on the Historical Trajectory of Remaining Discharge Capacity

In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs.

(Keynote) on the Challenges and Opportunities for Parameterizing Multi-Physics Battery Models with Expansion Measurements

Lithium-ion batteries are projected to reach cost targets making them competitive with internal combustion engines for light-duty transportation within the decade [1], which will help drive demand for EVs. Upcoming regulations in Europe, China, and the United States for automotive safety and durability will drive the design of battery systems, and the algorithms for battery management over the next decade. The Global Technical Regulation 20 (GTR20) describes the safety requirements for electric vehicle battery systems. In particular, the proposed regulation addresses the need for early detection and warning of occupants about battery faults and failures. The warning should allow for egress 5 minutes prior to the presence of a hazardous situation inside the passenger compartment caused by a battery cell thermal runaway. However, detecting the failure with enough time can be difficult using the current production battery sensor suites. Durability assessment for Lithium-ion batteries is also tricky because these systems are designed to live for more than 10-15 years, so testing under representative conditions could exceed multiple product development cycles. Accelerated aging tests can shorten the testing time to reach the product's end-of-life conditions, typically defined as 70 or 80% state of health, but a deep physical understanding of the degradation modes that are accelerated by the testing is required to project these results back to the real-world use case and warranty period. Due to the many possible definitions of battery State of Health (SoH) which can be related to both capacity loss and internal resistance growth, the Global Technical Regulation 22 (GTR22) defines their durability requirements in terms of two new related metrics: the State of Certified Energy (SOCE) and the State of Certified Range (SOCR). Both metrics represent a percentage of the certified battery energy or electric range remaining at a given point in time. The SOCE is based on the Society of Automotive Engineers (SAE) metric of Usable Battery Energy (UBE). The GTR22 will also require a way for the consumer to read battery health information and usage data from the vehicle. Physics-based battery models which include degradation mechanisms have great promise in addressing many of these challenges due to the relationship between modeled design parameters and the resulting device performance and durability. The models can enable virtual engineering to assess design tradeoffs before building the first prototype, however, parameterizing these models can be difficult due to the large number of parameters. In this presentation, we will explore the challenges of parameterizing physics-based battery models using conventional current, voltage, and temperature measurements. We will show how the measurement of the cell expansion in the laboratory setting and the fixtures that support the testing [2]. The augmentation of expansion data can inform model development and parameterization of a Single Particle Model with electrolyte dynamics (SPMe) implemented in the PyBaMM (Python Battery Mathematical Modelling) framework [3]. Next, we will demonstrate how the sensor data could be paired with the models to augment diagnostic algorithms to infer electrode-level aging phenomena, improve the state of charge estimation, and provide an early warning of gas generation before cell venting. Finally, we will discuss the practical implementation issues in packaging temperature and strain sensors in automotive battery modules and packs. [1] C. Shen, P. Slowik, A. Beach. “INVESTIGATING THE U.S. BATTERY SUPPLY CHAIN AND ITS IMPACT ON ELECTRIC VEHICLE COSTS.” Feb 2024, ICCT REPORT [2] S. Pannala, A. Weng, I. Fischer, J.B. Siegel, and A.G. Stefanopoulou, “Low-cost inductive sensor and fixture kit for measuring battery cell thickness under constant pressure.” 2022, IFAC-PapersOnLine, 55(37), 712-717. [3] Sravan Pannala et al 2024 J. Electrochem. Soc. 171 010532

Three-Dimensional Microstructure Modeling of Lithium Deposition and Dendrite Formation during Automotive Relevant Cycling

Dendrites generally form during rapid charging of lithium-ion batteries when lithium plating is favored over intercalation. If not removed during discharging cycles, the potential exists for dendrites to grow through the separator and short between the anode and cathode. One metric for diagnosing lithium plating conditions in a computational model is the minimum local potential at the anode, where lithium plating is seen to be thermodynamically favorable below 0.0 V with respect to Li metal. [1-2] Three-dimensional (3D) microstructure model can provide insight to predict the possibility of local lithium plating as well as the possible formation of dendrites. This model can also be used as a tool to detect possible global lithium plating onset. The moving boundary technique with mesh movement can be employed to separate the electrolyte region into a liquid phase and a solid phase.[3] This approach is applied in our 3D microstructure model of a lithium-ion battery[4] to observe the growth of lithium plating as well as dendrites at the anode during rapid charging cycles. Under beginning conditions at the solid/electrolyte interface (SEI), the solid metal phase is deposited at the SEI. Upon growth, the overall electrolyte resistance at the tip of the metal phase is reduced, leading to dendrite growth. The 3D microstructure model provides valuable insight into various conditions that may cause plating, if dendrites are formed due to plating, and whether the dendrites formed may proceed through the separator. References T. R. Garrick, J. Gao, X. Yang and B. J. Koch, J Electrochem Soc, 168, 010530 (2021). U. Janakiraman, T. R. Garrick and M. E. Fortier, J Electrochem Soc, 167, 160552 (2020). T. Jang, L. Mishra, S. A. Roberts, B. Planden, A. Subramaniam, M. Uppaluri, D. Linder, M. P. Gururajan, J.-G. Zhang and V. R. Subramanian, J Electrochem Soc, 169, 080516 (2022). J. Lopata, T. R. Garrick, F. Wang, H. Zhang, Y. Zeng, S. Shimpalee, Journal of The Electrochemical Society, 170, 020530 (2023).

Transient biomass-SOFC-energy storage hybrid system for microgrids peak shaving based on optimized regulation strategy

To address the issues of energy supply instability and peak-shaving in remote microgrids, this paper proposes a biomass-SOFC (Solid Oxide Fuel Cell) -energy storage hybrid system to meet the power demands of the microgrids. Additionally, it integrates the long short-term memory (LSTM) prediction algorithm for peak shaving in the microgrids. Firstly, transient models of biomass gasifier and SOFC and the vanadium redox flow battery (VRFB) model are established, and the experimental results are used to verify the models. Subsequently, the dynamic responses and sensitivity analysis of the biomass gasification process and the internal temperature field of the SOFC are investigated. Next, an analysis is conducted of the differences in VRFB parameters obtained based on the forecasted load and the real load. Finally, the ultimate peak regulation strategy is determined, and the VRFB parameters are reasonably designed. The research results indicate that, following optimization, the designed capacity and maximum power of the VRFB are 6.7 MWh and 1.5 MW, respectively. This result represents a reduction of 23 % and 11.76 % compared to the values before optimization, leading to a total investment decrease of 26.21 % for the energy storage system. In addition, the annual profit from the peak shaving operation of the system is 189,879 dollars.

Fractional-order equivalent circuit model for commercial sodium-ion batteries in a wide temperature range considering aging

With the development of electric vehicles and microgrids, the demand for energy storage is growing rapidly. Sodium-ion batteries, due to their abundant reserves, high energy density, and good safety, are beginning to be used in electric vehicles and energy storage power stations. High-precision modeling is the basis for state estimation and energy management of sodium-ion batteries. Fractional-order equivalent circuit model is favored for its balance between computational complexity and accuracy. In this study, electrochemical impedance spectroscopy tests were conducted on sodium-ion batteries, and a fractional-order equivalent circuit model in a wide temperature range considering aging, was established. The model parameters can be identified online using the global optimization algorithm based on dynamic cycling condition. The model’s accuracy was verified under different temperatures and dynamic working conditions. The results show that, compared to the Thevenin model and second-order equivalent circuit model, the fractional-order equivalent circuit model can better fit the terminal voltage of sodium-ion batteries, with a mean relative error of less than 4%.