Battery Model Research Articles

A mechanism-data driven resistance transfer algorithm for lithium-ion batteries and its application to thermal modeling

In this paper, a resistance transfer algorithm (RTA) is proposed to transfer a known battery's resistance data to a new battery, it saves the cost of testing required for thermal modeling. Firstly, the resistance linearization process is derived based on the Arrhenius equation, and a calibration algorithm for the resistance transfer from the reference battery to the target battery is presented. Then, taking the NCM622 battery (50 Ah) as the reference battery, the RTA is performed on an NCM622 battery (51 Ah) to verify the feasibility. Three schemes are compared to explore the appropriate test number of the target battery required for RTA. Subsequently, the RTA is applied to construct the thermal model of the target battery, and the simulation of the NCM622 (51 Ah) battery temperature rise is completed. Finally, RTA is utilized for resistance transfer and temperature rise simulation at different temperatures for four different batteries to further explore the universality. The verification results demonstrate that the proposed RTA can save a significant amount of resistance testing time and the thermal model based on RTA can supply precise temperature rise prediction. In the cost-effective 4-SOC scheme, the number of resistance test points can be reduced by 55 %, the maximum error of the transferred resistance remains within 0.1495 mΩ and the maximum error of the simulated temperature rise at three ambient temperatures stays below 1.727 °C.

Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

Early Internal Short Circuit Diagnosis for Lithium-Ion Battery Packs Based on Dynamic Time Warping of Incremental Capacity

Timely identification of early internal short circuit faults, commonly referred to as micro short circuits (MSCs), is essential yet poses significant challenges for the safe and reliable operation of lithium-ion battery (LIB) energy storage systems. This paper introduces an innovative diagnostic method for early internal short circuits in LIB packs, utilizing dynamic time warping (DTW) applied to incremental capacity (IC). Initially, the terminal voltages of all cells within the LIB pack are ordered at any moment to determine the median terminal voltage, which is then used to generate the median IC curve. This curve acts as a reference benchmark that represents the condition of healthy cells in the pack. Subsequently, the DTW algorithm is utilized to measure the similarity between each cell’s IC curve and the median IC curve. Cells exhibiting similarity scores that exceed a specified threshold are identified as having MSC faults. Lastly, for the cells diagnosed with MSC conditions, a method for estimating short-circuit resistance (SR) based on variations in maximum charging voltage is devised to quantitatively evaluate the severity and evolution of the MSC. Experimental findings reveal that the proposed method effectively identifies MSC cells in the LIB pack and estimates their SRs without the necessity of a battery model, thereby affirming the method’s validity.

System-Level Dynamic Model of Redox Flow Batteries (RFBs) for Energy Losses Analysis

This paper presents a zero-dimensional dynamic model of redox flow batteries (RFBs) for the system-level analysis of energy loss. The model is used to simulate multi-cell systems considering the effect of design and operational parameters on energy loss and overall performance. The effect and contribution of stack losses (e.g., overpotential and crossover losses) and system losses (e.g., shunt currents and pumps) to total energy loss are examined. The model is tested by using literature data from a vanadium RFB energy storage. The results show that four parameters mainly affect RFB system performance: manifold diameter, stack current, cell standard potential, and internal resistance. A reduction in manifold diameter from 60 mm to 20 mm reduced shunt current loss by a factor of four without significantly increasing pumping loss, thus boosting round-trip efficiency (RTE) by 10%. The increase in stack current at a low flow rate increases power, while the cell standard potential and internal resistance play a crucial role in influencing both power and energy output. In summary, the modeling activities enabled the understanding of critical aspects of RFB systems, thereby serving as tools for system design and operation awareness.

A Piezocatalysis Strategy to Enable Efficient Redox in Solid‐State Battery

Piezocatalysis is considered to be a favorable catalytic technology by utilizing external mechanical stimulation to promote the specific redox reactions. The application of piezocatalysis in batteries is promising but faces formidable challenges. Herein, the operation principles of piezocatalysis were successfully demonstrated by constructing a solid‐state Li−Se (S) battery model with interfacial stress accumulation. Lead zirconate titanate with an ultra‐high piezoelectric coefficient was applied as the piezoelectric catalyst. The uniformity of the dipole orientation in materials was essential for achieving piezocatalysis in batteries. The accumulated high‐stress converts to piezopotential for catalyzing most reaction processes in batteries, while the rapid stress change prevents the internal charge reorganization, ensuring continuous efficient catalysis. The internal stress change alters the internal polarisation strength, driving excess screening charge to participate in the electrochemical reaction. Under piezocatalysis, solid‐state Li−Se batteries exhibited a initial discharge capacity of 670.9 mAh g−1 (99.4% of the theoretical value) at 0.1C, and Li−S batteries showed a capacity of 1463 mAh g−1 at 0.2C, which was still maintained up to 1084 mAh g−1 at an elevated rate of 0.5C. This piezocatalysis strategy provides a strong theoretical basis and design specification for enhancing Li−Se (S) cell reaction kinetics.

A Piezocatalysis Strategy to Enable Efficient Redox in Solid-State Battery.

Piezocatalysis is considered to be a favorable catalytic technology by utilizing external mechanical stimulation to promote the specific redox reactions. The application of piezocatalysis in batteries is promising but faces formidable challenges. Herein, the operation principles of piezocatalysis were successfully demonstrated by constructing a solid-state Li-Se (S) battery model with interfacial stress accumulation. Lead zirconate titanate with an ultra-high piezoelectric coefficient was applied as the piezoelectric catalyst. The uniformity of the dipole orientation in materials was essential for achieving piezocatalysis in batteries. The accumulated high-stress converts to piezopotential for catalyzing most reaction processes in batteries, while the rapid stress change prevents the internal charge reorganization, ensuring continuous efficient catalysis. The internal stress change alters the internal polarisation strength, driving excess screening charge to participate in the electrochemical reaction. Under piezocatalysis, solid-state Li-Se batteries exhibited a initial discharge capacity of 670.9 mAh g-1 (99.4% of the theoretical value) at 0.1C, and Li-S batteries showed a capacity of 1463 mAh g-1 at 0.2C, which was still maintained up to 1084 mAh g-1 at an elevated rate of 0.5C. This piezocatalysis strategy provides a strong theoretical basis and design specification for enhancing Li-Se (S) cell reaction kinetics.

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.

The Influence of Thermophysical Parameters on Thermal Runaway Characteristics for Pouch-Type Lithium-Ion Batteries

Abstract The thermal variation during the temperature rise process of batteries is closely related to multiple physical parameters. Establishing a direct relationship between these parameters and thermal runaway (TR) features under abusive conditions is challenging using theoretical equations due to complex electrochemical and thermal coupling. In this paper, a high-temperature thermal runaway model of pouch-type lithium-ion battery is established through electrical-thermal coupled approach, demonstrating a good agreement between the simulation and experimental results. The results reveal distinct trends in thermal parameters of the early temperature rise, trigger time for TR and peak temperature during TR process, for varying convective heat transfer coefficient, cell specific heat capacity, cell density and cell thermal conductivity. Across various convective heat transfer coefficients, the rates of temperature increase, moments of TR, and peak temperatures within a battery emerge as the cumulative outcomes of competing processes of the intricate exothermic secondary reactions within the battery, and the heat transfer with the surroundings. Batteries with lower heat capacity exhibit reduced thermal inertia and heightened sensitivity to temperature changes. Alterations in the thermal capacity of a battery wield a profoundly significant impact upon the moment of thermal runaway within the battery. Enhancing the thermal conductivity yields limited improvements in heat dissipation during thermal runaway primarily due to the relatively small geometrical scale of the battery. Results of this paper can provide valuable insights for size optimization design, thermal management system optimization design, thermal runaway safety warning and prevention of Lithium-ion batteries.

Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm

Accurate battery modeling is crucial for optimizing the performance and safety of Lithium-ion batteries (LiBs), particularly in applications such as electric vehicles and smart grids. This paper introduces the Information Sharing Group Teaching Optimization Algorithm (ISGTOA), a novel human-based metaheuristic algorithm designed to estimate the 21 parameters of third-order Equivalent Circuit Model (ECM) for LiBs. Unlike existing methods, ISGTOA effectively manages the increased complexity of a model through advanced group teaching strategies and sophisticated information-sharing mechanisms inspired by classroom dynamics. We validated the effectiveness of ISGTOA using two distinct datasets—High Dynamic Profile (HDP) and Urban Dynamometer Driving Schedule (UDDS)—across various LiB chemistries (NCA and NMC) and temperature conditions (−5 °C, 25 °C and 45 °C). The results demonstrate that ISGTOA achieves superior parameter estimation accuracy and convergence speed compared to established and recent optimization methods such as TLBO, TLSBO, AISA, MTBO, and DTBO. Specifically, ISGTOA attained a minimum RMSE of 8.357 mV with rapid convergence and high stability in the HDP dataset and minimized RMSE to 10 mV within ten iterations at both 25 °C and 45 °C in the UDDS dataset. Additionally, ISGTOA exhibited high efficiency (up to 97.75 %) under varying thermal environments. This work not only introduces a novel algorithm for complex battery modeling but also sets the stage for future advancements in real-time battery management systems, emphasizing the importance of robust, versatile, and efficient parameter estimation techniques.

High-fidelity reconstruction of porous cathode microstructures from FIB-SEM data with deep learning

Accurate modeling of lithium-ion battery (LIB) electrode microstructures provides essential references for understanding degradation mechanisms and optimizing materials. Traditional segmentation methods often struggle to accurately capture the complex microstructures of porous LIB electrodes in focused ion beam scanning electron microscopy (FIB-SEM) data. In this work, we develop a deep learning model based on the Swin Transformer to segment FIB-SEM data of a lithium cobalt oxide electrode, utilizing fused secondary and backscattered electron images. The proposed approach outperforms other deep learning methods, enabling the acquirement of 3D microstructure with reduced particle elongated artifacts. Analyses of the segmented microstructures reveal improved electrode tortuosity and pore connectivity crucial for ion and electron transport, emphasizing the necessity of accurate 3D modeling for reliable battery performance predictions. These results suggest a path toward voxel-level degradation analysis through more sensible battery simulation on high-fidelity microstructure models directly twinned from real porous electrodes.

State of Charge Estimation for Lithium-Ion Battery Based on Fractional-Order Kreisselmeier-Type Adaptive Observer

For the safe and efficient use of lithium-ion batteries, the state of charge (SOC) is a particularly important state variable. In this paper, we propose a method for the online estimation of SOC and model parameters based on a fractional-order equivalent circuit model. Firstly, we constructed a fractional-order battery model that includes pseudo-capacitance and determined the values of the circuit elements offline using the least squares method from actual input–output data based on the driving profile of an automobile. Compared to the integer-order battery model, we confirmed that the proposed fractional-order battery model has higher accuracy. Secondly, we constructed a fractional-order Kreisselmeier-type adaptive observer as an observer that performs state estimation and parameter adjustment simultaneously. Applying the general adaptive law to the battery model results in a redundant design with many adjustable parameters, so we proposed an adaptive law that reduces the number of adjustable parameters without compromising the stability of the observer. The effectiveness of the proposed method was verified through numerical simulations. As a result, the high estimation accuracy and convergence of the proposed adaptive law were confirmed.

Co-Estimating State of Charge and Capacity of Automotive Lithium-Ion Batteries Under Deep Degradation Using Multiple Estimators

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery’s lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery’s lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources.

Inverse Open Circuit Voltage Curve Model for LiCoO2 Battery at Different Temperatures

Lithium-ion batteries are widely used in a variety of applications. For effective battery management, accurate estimation of the state of charge (SOC) is essential. One of the most commonly employed methods for SOC estimation relies on the open circuit voltage (OCV) curve with respect to SOC. However, inverting the OCV-SOC function is not always straightforward. This paper proposes a novel analytical function that directly models the inverse OCV-SOC function, providing a more efficient and reliable method for SOC estimation. Moreover, the dependency of the proposed function on battery temperature is also being investigated, allowing for a wider application of the method under different OCV measuring conditions.

Enhanced Wavelet Transform Dynamic Attention Transformer Model for Recycled Lithium-Ion Battery Anomaly Detection

Rapid advancements in electric vehicle (EV) technology have highlighted the importance of lithium-ion (Li) batteries. These batteries are essential for safety and reliability. Battery data show non-stationarity and complex dynamics, presenting challenges for current monitoring and prediction methods. These methods often fail to manage the variability seen in real-world environments. To address these challenges, we propose a Transformer model with a wavelet transform dynamic attention mechanism (WADT). The dynamic attention mechanism uses wavelet transform. It focuses adaptively on the most informative parts of the battery data to enhance the anomaly detection accuracy. We also developed a deep learning model with an improved Transformer architecture. This architecture is tailored for the complex dynamics of battery data time series. The model accounts for temporal dependencies and adapts to non-stationary behavior. Experiments on public battery datasets show our approach’s effectiveness. Our model significantly outperforms existing technologies with an accuracy of 0.89 and an AUC score of 0.88. These results validate our method’s innovation and effectiveness.

Optimizing Energy Management and Sizing of Photovoltaic Batteries for a Household in Granada, Spain: A Novel Approach Considering Time Resolution

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles.

Modelling of a Cylindrical Battery Mechanical Behavior under Compression Load

The extensive utilization of lithium-ion (Li-ion) batteries within the automotive industry necessitates rigorous measures to ensure their mechanical robustness, crucial for averting thermal runaway incidents and ensuring vehicle safety. This paper introduces an innovative methodology aimed at homogenizing the mechanical response of Li-ion batteries under compression load, using Finite Element Method (FEM) techniques to improve computational efficiency. A novel approach is proposed, involving the selective application of compression loads solely to the Jelly Roll and its casing, achieved by cutting the battery heads. Through this method, distinct mechanical behaviors are identified within the battery force displacement curve: an elastic region, a zone characterized by plastic deformation, and a segment exhibiting densification. By delineating these regions, our study facilitates a comprehensive understanding of the battery’s mechanical response under compression. Two battery models were employed in this study: one representing the battery as a solid volume, and another featuring the jelly roll as a solid volume enclosed by a shell representing the casing. The material utilized was LS Dyna MAT24, chosen for its piecewise characteristics’ definition, and its validation was primarily conducted through the curve fitting method applied to the force–displacement curve, taking in account the three regions of the compression force behavior. This approach not only optimizes computational resources but also offers insights crucial for enhancing the mechanical stability of Li-ion batteries in automotive applications.

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

Digital Twin Battery Modeling and Simulations: A New Analysis and Design Tool for Rechargeable Batteries

Digital Twin Battery Modeling and Simulations: A New Analysis and Design Tool for Rechargeable Batteries

Microgrid control under uncertainty

Microgrids – decentralized electrical grids that can function both in conjunction with wide area macrogrids and without – are a powerful tool to address energy resiliency and climate change mitigation. Microgrid control, however, remains a challenge; their bespoke nature and the existence of multiple sources of uncertainty lead to a control problem that traditional grid modeling and control techniques are ill-suited to handle. We build a microgrid interface to simulate microgrids under uncertainty and devise off-policy reinforcement learning algorithms to control microgrids. Our algorithms, which incorporate domain randomization and random network distillation for exploration and computational efficiency, achieve performance better than model predictive control and rule based control benchmarks under battery model uncertainty on seven of ten tested scenarios. Our model code is available at https://github.com/ahalev/Microgrid-Control-Under-Uncertainty and our microgrid simulator is available at https://github.com/ahalev/python-microgrid.