• Energy Research

Abstract Due to the wide applications of lithium-ion capacitors (LiCs) in various fields and due to the lack of comprehensive study regarding LiC modeling, there is a need for an accurate electrical model, which can predict the LiC's behavior at different load conditions. Characterization helps better understanding of investigated energy storage system and reveals its functioning range, linear and nonlinear variations and also its limits. As the electrochemical based energy storages are non-linear, the presented model should be linearized in the range of the specific application. Therefore the identification procedures should be performed under various conditions as needed. Results from hybrid pulse power characterization (HPPC) test have been used for the system identification of LiC in the time domain. Since system identification in the time domain cannot provide a complete map of variation of model parameters, a wide range frequency impedance spectroscopy (10 kHz–20 mHz) and at various conditions such as current, state of charge (SoC) levels and temperatures has been performed in order to identify the parameters of assumed model in the frequency domain, which is an unique analysis and never has been performed for lithium ion capacitors. The used model has been chosen based on probable chemical reactions, which occur within a specific frequency range. Frequency domain analysis can identify model variation, which is not possible in time domain identification. In this paper a new identification approach has been introduced based on combination of time and frequency domains. Based on proposed method, identification process in frequency domain helps to identify parameters which are not variable in function of current and SoC variations. Therefore the mentioned parameters can be withdrawn from the cost function in the time domain and it improves the performance of system identification procedure. The proposed method improves the model accuracy around 2% at time domain and around 5% at frequency domain.

The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

The large push for more environmental energy storage solutions for the automotive industry by different actors has led to the usage of lithium-ion capacitors (LICs) combining the features of both lithium-ion batteries (LIBs) and electric-double layer capacitors (EDLCs). In this paper, the thermal behavior of two types of advanced LICs has been thoroughly studied and analyzed by developing a three-dimensional (3D) thermal model in COMSOL Multiphysics®. Such an extensive and accurate thermal 3D has not been fully addressed in literature, which is a key building block for designing battery packs with an adequate thermal management. After an extensive measurement campaign, the high accuracy of the developed model in this paper is proven for two types of LICs, the 3300 F and the 2300 F. An error between the simulation and measurements is maximum 2 °C. This 3D model has been developed to gain insight in the thermal behavior of LICs, which is necessary to develop a thermal management system, which can ensure the safe operation of LICs when used in modules or packs.

Abstract An extensive parametric study of an alternative battery model, the fractional differential model (FDM), is presented for the first time over the entire lifetime of 20Ah NMC cells. The evolution of the FDM's model parameters, identified in the time-domain, is compared to electrochemical impedance spectroscopy results to assess their physico-chemical significance. This comprehensive parametric analysis shows that the FDM is able to capture relevant physico-chemical information with regards to the ohmic resistance over the entire SoC range and battery lifetime. Furthermore, the FDM's electrical performance is compared to the conventional first order RC battery model over the entire battery lifetime. Thanks to the FDM's non-integer derivatives for system states, it is able to capture intrinsic fractional derivative properties such as diffusion dynamics, charge transfer and memory hysteresis, which results in an improved simulation accuracy compared to the RC model. The comparative study showed an improved simulation accuracy of the FDM up to 85% in the lowest SoC range and a minimum improvement of 40% over the entire SoC range, considering the entire battery lifetime. Inherently proving that the FDM's ability to capture highly nonlinear battery behavior in the low SoC region persists over battery lifetime.

This paper represents the optimization of an advanced battery model parameter minimization tool for estimation of lithium-ion battery model parameters. This system is called extended Levenberg–Marquardt. The proposed system is able to predict the nonlinearity of lithium-ion batteries accurately. A fitting percentage of over 99% between the simulation and experimental results can be achieved. Then, this paper contains a new second-order electrical battery model for lithium-ion batteries, extracted on the basis of experimental study and able to predict the battery behavior precisely. Further, in this paper, an extended comparative study of the performances of the various existing electrical battery models in the literature (Rint, RC, Thevenin, and FreedomCar) for lithium-ion batteries against the new developed battery model is presented, on the basis of the optimized battery parameter minimization tool. These battery models have been validated at different working conditions. From the analysis, one can observe that the new proposed battery model is more accurate than the existing ones and that it can predict the battery behavior during transient and steady-state operations. Finally, the new battery model has been validated at different working temperatures. The analysis shows that the error percentage between 0% and 90% depth of discharge at 40 °C is less than 1.5% and at 0 °C less than 5%.

Abstract Increasing interest in the successful coordination of electric vehicles and renewable energy sources has recently been shown by researchers and power generation companies, in large part due to its impact on de-carbonization of urban areas and its capability of contributing towards ancillary services. Nevertheless, this coordination requires a bi-directional communication infrastructure, between combined electric vehicles, renewable energy systems, and power plants since one of the main reasons of this combination is to address the temporal fluctuations in renewable power generation. This bi-directional communication enables the power grid to adapt to different power source structures and improves the acceptability of intermittent renewable energy generation. Whereas electric vehicles equipped with lithium-ion batteries appear to be feasible options for stationary energy storage systems, known as a new distributed generation, the flexibility of electric vehicles in vehicle-to-grid connections is completely dependent on the maximum practical capacity and state of charge of each vehicle. Hence this infrastructure for the integration of electric vehicles as new distributed generation and renewable energy systems with electrical grids, emphasizes the need for an off-board state estimation of electric vehicles in aggregators. Moreover, an accurate estimation of the state of health and state of charge when electric vehicles are not charged or discharged by a constant current profile is required to overcome the challenges of existing methods. Each of introduced methods has different limitations, which are presented in this article. This article proposes a novel off-board state estimation technique for such parameters as state of charge and maximum practical capacity by employing a metaheuristic algorithm and an adaptive neuro-fuzzy inference system to overcome the limitations of existing methods. Due to drawbacks of filtering techniques, inverse theory is used in this article to convert the filtering problem to an optimization problem in order to take advantage of its capability. The results exhibit not only a high convergence rate (low settling time) but also a high robustness.

In this paper, advanced equivalent circuit models (ECMs) were developed to model large format and high energy nickel manganese cobalt (NMC) lithium-ion 20 Ah battery cells. Different temperatures conditions, cell characterization test (Normal and Advanced Tests), ECM topologies (1st and 2nd Order Thévenin model), state of charge (SoC) estimation techniques (Coulomb counting and extended Kalman filtering) and validation profiles (dynamic discharge pulse test (DDPT) and world harmonized light vehicle profiles) have been incorporated in the analysis. A concise state-of-the-art of different lithium-ion battery models existing in the academia and industry is presented providing information about model classification and information about electrical models. Moreover, an overview of the different steps and information needed to be able to create an ECM model is provided. A comparison between begin of life (BoL) and aged (95%, 90% state of health) ECM parameters (internal resistance (Ro), polarization resistance (Rp), activation resistance (Rp2) and time constants (τ) is presented. By comparing the BoL to the aged parameters an overview of the behavior of the parameters is introduced and provides the appropriate platform for future research in electrical modeling of battery cells covering the ageing aspect. Based on the BoL parameters 1st and 2nd order models were developed for a range of temperatures (15 °C, 25 °C, 35 °C, 45 °C). The highest impact to the accuracy of the model (validation results) is the temperature condition that the model was developed. The 1st and 2nd order Thévenin models and the change from normal to advanced characterization datasets, while they affect the accuracy of the model they mostly help in dealing with high and low SoC linearity problems. The 2nd order Thévenin model with advanced characterization parameters and extended Kalman filtering SoC estimation technique is the most efficient and dynamically correct ECM model developed.

Abstract The high energy density characteristic of the all-solid-state battery technology makes it one of the promising competitors of current liquid electrolyte-based lithium-ion batteries. However, due to the low ionic conductivity of solid materials and existing issues in active material-solid electrolyte interfaces, all-solid-state batteries show strong nonlinear behavior when the amplitude of input current is relatively high. In this paper, we have developed a methodology based on block-oriented nonlinear systems that can detect, quantize, and model the battery behavior, accurately. For this purpose, two solid-state coin cells, which have different ionic conductivity at the cathode interface are manufactured and excited with multisine input current. The Best Linear Approximation (BLA) method has been used to separate linear frequency response function (FRF) from nonlinear distortion. Then, a Hammerstein-Wiener system has been developed and parametrized to model the nonlinear distortions caused by ionic conductivity variation of the solid electrolyte at the cathode layer. Furthermore, the developed model has been utilized to detect possible faults in the electrode-electrolyte interface based on the nonlinearity level of the measured voltage.