Search Results (28)

High-specific-energy lithium-ion batteries face accelerated degradation and safety risks. To ensure stable and safe operation of such batteries in electric vehicles throughout their service life, this study proposes a quantitative aging mechanism analysis method based on electrode equilibrium potential reconstruction under rest conditions. First, by integrating the single-particle electrochemical model with equilibrium potential reconstruction, a quantitative mapping framework between State of Charge (SOC) and electrode lithiation concentration is established. Subsequently, to address the strong nonlinearity between equilibrium potential and lithiation concentration, the State Transition Algorithm (STA) is introduced to solve the high-dimensional coupled parameter identification problem, enhancing aging parameter estimation accuracy. Finally, the effectiveness of the proposed method was validated using a commercial NCM622/graphite power cell as the research object, and the battery’s aging pathways were analyzed using differential voltage analysis (DVA) and incremental capacity analysis (ICA) methods. Experimental results indicate that the OCV curve fitting achieved a maximum Root Mean Square Error of 0.00932, while quantitatively revealing the degradation patterns of electrode lithiation degrees during aging under both fully charged (SOC = 100%) and fully discharged (SOC = 0%) states. Full article

Fractional-order calculus (FOC) has gained significant attention in electric vehicle (EV) energy storage and management systems, as it provides enhanced modeling and analysis capabilities compared to traditional integer-order approaches. This review presents a comprehensive survey of recent advancements in the application of FOC to EV energy storage systems, including lithium-ion batteries (LIBs), supercapacitors (SCs), and fuel cells (FCs), as well as their integration within energy management systems (EMS). The review focuses on developments in electrochemical, equivalent circuit, and data-driven models formulated in the fractional-order domain, which improve the representation of nonlinear, memory-dependent, and multi-scale dynamics of energy storage devices. It also discusses the benefits and limitations of current FOC-based models, identifies open challenges such as computational feasibility and parameter identification, and outlines future research directions. Overall, the findings indicate that FOC offers a robust framework with significant potential to advance next-generation EV energy storage and management systems. Full article

While Forgetting Factor Recursive Least Square (FFRLS) algorithms with evaluation mechanisms have been developed to address SOC-dependent parameter mapping shifts and their efficacy has been proven in Li-ion batteries, their applicability to lithium–sulfur (Li-S) batteries remains uncertain due to different electrochemical characteristics. This study critically evaluates the applicability of a Fisher information matrix-constrained FFRLS framework for online parameter identification in Li-S battery equivalent circuit network (ECN) models. Experimental validation using distinct drive cycles showed that the identification results of polarization-related parameters are significantly biased between different current excitations, and root mean square error (RMSE) variations diverge by 100%, with terminal voltage estimation errors more than 0.05 V. The parametric uncertainty under variable excitation profiles and voltage plateau estimation deficiencies confirms the inadequacy of such approaches, constraining model-based online identification viability for Li-S automotive applications. Future research should therefore prioritize hybrid estimation architectures integrating electrochemical knowledge with data-driven observers, alongside excitation capturing specifically optimized for Li-S online parameter observability requirements and cell nonuniformity and aging condition consideration. Full article

This study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO₄)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their internal characteristics evolve, influencing performance, efficiency, and longevity. Understanding these changes is crucial for optimizing battery management strategies and ensuring reliable operation across various applications. To analyze these effects, the study utilizes equivalent electrical circuits (EEC) to model the internal behavior of the battery. The individual components of the EEC—such as its resistive, capacitive, and inductive elements—are examined through 3D waveforms, offering a comprehensive visualization of how each parameter responds to cycling. One of the key contributions of this research is the development and implementation of an EEC identification approach that enables a systematic assessment of battery parameter evolution. This technique provides insights into the general trends and variations in electrical behavior based on the state of charge (SoC) of the cell. By analyzing data across a wide range of SoC values—from 0% (fully discharged) to 100% (fully charged)—and tracking changes over 100 charge–discharge cycles, the study highlights the progressive alterations in battery performance. The findings of this investigation offer valuable implications for battery health monitoring, predictive maintenance, and the refinement of state estimation models. Full article

Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias. Full article

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

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

To meet the ever-growing worldwide electric vehicle demand, the development of advanced generations of lithium-ion batteries is required. To this end, modelling is one of the pillars for the innovation process. However, modelling batteries containing a large number of different mechanisms occurring at different scales remains a field of research that does not provide consensus for each particular model or approach. Parametrization as part of the modelling process appears to be one of the issues when it comes to building a high-fidelity model of a target cell. In this paper, a particular parameter identification is therefore discussed. Indeed, even if Butler–Volmer is a well-known equation in the electrochemistry field, identification of its reaction rate constant or exchange current density parameters is lacking in the literature. Thus, we discuss the process described in the literature and propose a new protocol that expects to overcome certain difficulties whereas the hypothesis of calculation and measurement maintains high sensitivity. Full article

The automotive industry is rapidly transitioning to electric vehicles (EVs) in response to the global efforts to reduce greenhouse gas emissions. Lithium-ion battery (LIB) has emerged as the main tool for energy storage in electric vehicles. A widespread adoption of EVs, however, requires a fast-charging technology that can significantly reduce charging time while avoiding any unsafe conditions including short circuits due to failure of the separator in an LIB cell. Therefore, it is necessary to understand the mechanical stresses during fast charging and their long-term effect on the integrity of the separator. This paper presents a novel hybrid model for the prediction of the stress distribution in the separator of a pouch cell under various charging speeds, ambient temperatures, and pack assembly conditions, such as compressive pressures. The proposed hybrid model consists of three sub-models, namely, an electrochemical cell model, a lumped-parameter model, and a solid mechanics model. A robust parameter identification scheme is implemented to determine the model parameters using the experimental data. The separator within the test setup will experience maximum von Mises stress of 74 MPa during 4C charging, i.e., when the charge current in A is four times as high as the capacity of the battery cell in Ah. To assess the evolution of the damage in the separator under the estimated stress during fast charging, creep and fatigue tests are conducted on the separator. Their results indicate a progressive accumulation of damage in the separator, further emphasizing the importance of understanding and mitigating mechanical degradation in separator materials. Full article

Among lithium-ion battery diagnostic tests, electrochemical impedance spectroscopy, being highly informative on the physics of battery operation within limited testing times, deserves a prominent role in the identification of model parameters and the interpretation of battery state. Nevertheless, a reliable physical simulation and interpretation of battery impedance spectra is still to be addressed, due to its intrinsic complexity. An improved methodology for the calibration of a state-of-the-art physical model is hereby presented, focusing on high-energy batteries, which themselves require a careful focus on the high-frequency resistance of the impedance response. In this work, the common assumption of the infinite conductivity of the current collectors is questioned, presenting an improved methodology for simulating the pure resistance of the cell. This enables us to assign the proper contribution value to current collectors’ resistance and, in turn, not to underestimate electrolyte conductivity, thereby preserving the physical relation between electrolyte conductivity and diffusivity and avoiding physical inconsistencies between impedance spectra and charge–discharge curves. The methodology is applied to the calibration of the model on a commercial sample, demonstrating the reliability and physical consistency of the solution with a set of discharge curves, EIS, and a dynamic driving cycle under a wide range of operating conditions. Full article

This study investigates the influence of the considered Electric Equivalent Circuit Model (ECM) parameter dependencies and architectures on the predicted heat generation rate by using the Bernardi equation. For this purpose, the whole workflow, from the cell characterization tests to the cell parameter identification and finally validation studies, is examined on a cylindrical 5 Ah LG217000 Lithium-Ion-Battery (LIB) with a nickel manganese cobalt chemistry. Additionally, different test procedures are compared with respect to their result quality. For the parameter identification, a Matlab tool is developed enabling the user to generate all necessary ECMs in one run. The accuracy of the developed ECMs is evaluated by comparing voltage prediction of the experimental and simulation results for the highly dynamic World harmonized Light vehicle Test Cycle (WLTC) at different states of charges (SOCs) and ambient temperatures. The results show that parameter dependencies such as hysteresis and current are neglectable, if only the voltage results are compared. Considering the heat generation prediction, however, the neglection can result in mispredictions of up to 9% (current) or 22% (hysteresis) and hence should not be neglected. Concluding the voltage and heat generation results, this study recommends using a Dual Polarization (DP) or Thevenin ECM considering all parameter dependencies except for the charge/discharge current dependency for thermal modeling of LIBs. Full article

Lithium-ion batteries are commonly used in electric vehicles, embedded systems, and portable devices, including laptops and mobile phones. Electrochemical models are widely used in battery diagnostics and charging/discharging control, considering their high extractability and physical interpretability. Many artificial intelligence charging algorithms also use electrochemical models for to enhance operation efficiency and maintain a higher state of health. However, the parameter identification of electrochemical models is challenging due to the complicated model structure and the high count of physical parameters to be considered. In this manuscript, a comprehensive electrochemical lithium-ion battery model is proposed for the charging and discharging processes. The proposed model accounts for all dynamic characteristics of the battery, including the cell open-circuit voltage, cell voltage, internal battery impedance, charging/discharging current, and temperature. The key novelty of the proposed model is the use of simulated open-circuit voltage and simulated changes in entropy data instead of experimental data to provide battery voltage and temperature profiles during charging and discharging cycles in the development of the final model. An available experimental dataset at NASA for an LCO 18,650 battery was utilized to test the proposed model. The mean absolute error for the simulated charging cell voltage and temperature values were 0.05 V and 0.3 °C, compared with 0.14 V and 0.65 °C for the discharging profile. The simulation results proved the effectiveness and accuracy of the proposed model, while simplicity was the key factor in developing the final model, as shown in the subsequent sections of the manuscript. Full article

This paper proposes an approach for the accurate and efficient parameter identification of lithium-ion battery packs using only drive cycle data obtained from hybrid or electric vehicles. The approach was experimentally validated using data collected from a BMW i8 hybrid vehicle. The dual polarization model was used, and a new open circuit voltage equation was proposed based on a simplification of the combined model, with the aim of reducing the number of parameters to be identified. The parameter identification was performed using NEDC data collected on a rolling road dynamometer; the results showed that the proposed model improved the accuracy of terminal voltage estimation, reducing the peak voltage error from 2.16% using the Nernst model to 1.28%. Furthermore, the robustness of these models in maintaining accuracy when new drive cycles were used was evaluated by comparing WLTC simulations with experimental measurements. The proposed model showed improved robustness, with a reduction in RMS error of more than 50% compared to the Nernst model. These findings are significant because they will improve the accuracy of model-based battery management systems used in electric vehicles, allowing for improved performance prediction without the requirement of recharacterization for different drive cycles or individual cell characterization. Full article

The state of health (SOH) is among the most important parameters to be monitored in lithium-ion batteries (LIB) because it is used to know the residual functionality in any condition of aging. The paper focuses on the application of the extended Kalman filter (EKF) for the identification of the parameters of a cell model, which are required for the correct estimation of the SOH of the cell. This article proposes a methodology for tuning the covariance matrices of the EKF by using an optimization process based on genetic algorithms (GA). GAs are able to solve the minimization problems for the non-linear functions, and they are better than other optimization algorithms such as gradient descent to avoid the local minimum. To validate the proposed method, the cell parameters obtained from the EKF are compared with a reference model, in which the parameters have been determined with proven procedures. This comparison is carried out with different cells and in the whole range of the cell’s SOH, with the aim of demonstrating that a single tuning procedure, based on the proposed GA process, is able to guarantee good accuracy in the estimation of the cell parameters at all stages of the cell’s life. Full article

An effective model of battery performance is important for battery management systems to control the state of battery and cell balancing. The second-order equivalent circuit model of a lithium-ion battery is studied in the present paper. The identification methods that include the multiple linear regression (MLR), exponential curve fitting (ECF) and Simulink design optimization tool (SDOT), were used to determine the model parameters. The aim of this paper is to compare the validity of the three proposed algorithms, which vary in complexity. The open circuit voltage was measured based on the pulse discharge test. The voltage response was collected for every 10% SOC in the interval between 0–100% SOC. The battery voltages calculated from the estimated parameters under the constant current discharge test and dynamic discharge tests for electric vehicles (ISO and WLTP) were compared to the experimental data. The mean absolute error and root mean square error were calculated to analyze the accuracy of the three proposed estimators. Overall, SDOT provides the best fit with high accuracy, but requires a heavy computation burden. The accuracy of the three methods under the constant current discharge test is high compared to other experiments, due to the nonlinear behavior at a low SOC. For the ISO and WLTP dynamic tests, the errors of MLR are close to that of SDOT, but have less computing time. Therefore, MLR is probably more suitable for EV use than SDOT. Full article