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An accurate battery model is one of the most important factors to improve the capability of battery state of charge (SoC) estimation. In this paper, battery hysteresis behaviors under different SoC are considered to decrease battery model error, and the hysteresis voltage based battery model (HVBBM) is presented. The experiment result shows that th...
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Lithium-ion batteries have the advantages of high energy density, long life, and environmental friendliness, and are widely used as sources of energy in new energy vehicles. The charge state (SOC) of lithium-ion battery greatly represents the remaining service time of the battery, and in electric vehicles, it greatly determines the range of the ele...
Citations
... The models used in describing LiBs are the electrochemical models (EMs), data-driven models (DDMs) and equivalent circuit models (ECMs). The EM model uses complex partial derivative equations and boundary conditions in describing the electrochemical process that takes place within the cell by giving a detailed insight on the chemical reaction responsible for the internal parameter generation and dynamic behavior during different operating conditions [9]. These models are known to be highly accurate [10], but for them to be used efficiently, thorough knowledge of the battery chemicals structure as well as characteristics must be precisely determined [11]. ...
... Equivalent circuit models consist largely of voltage sources, resistors and capacitors that are used for describing the behavior of the LiB [13]. Mohammed [9] argued, though ECMs do not provide information into the electrochemical reactions that takes place within the battery, they should be adopted due to easy implementation, low number of parameters to tune, and low complexity in setting up their state equations [14], [15]. ...
... A global pattern search algorithm (GPSA) is used to estimate values of the LiB model internal parameters, { , 0 , 1 , 2 , 1 , 2 }. To generate the parameters, sequence of points is computed that best minimizes the objective function ∑ (̂− ) [9]. The algorithm works by generating vectors, vi used to find which points to search at an iteration count. ...
p> The global transition from fossil-based automobile systems to their
electric-driven counterparts has made the use of a storage device inevitable. Owing to its high energy density, lower self-discharge, and higher cycle lifetime the lithium-ion battery is of significant consideration and usage in electric vehicles. Nevertheless, the state of charge (SOC) of the battery, which cannot be measured directly, must be calculated using an estimator. This paper proposes, by means of a modified priori estimate and a compensating proportional gain, an improved extended Kalman filter (IEKF) for the estimation task due to its nonlinear application and adaptiveness to noise. The improvement was achieved by incorporating the residuals of the previous state matrices to the current state predictor and introducing an attenuating factor in the Kalman gain, which was chosen to counteract the effect of the measurement and process noise resulting in better accuracy performance than the conventional SOC curve fitting-based estimation and ampere hour methods. Simulation results show that the standard EKF estimator results in performance with an error bound of 12.9% due to an unstable start, while the modified EKF reduces the maximum error to within 2.05% demonstrating the quality of the estimator. </p
... There are also external factors related to operating temperature, improper charging and discharging cycles, and overcharging and discharging [40,41]. [24]. ...
... Thanks to several improvements in Li-ion battery technologies recently, they have become safer, eliminating explosion hazards as much as possible and their chemistry is less toxic, both to nature and to humans. Battery state of charge (SOC) is an essential internal parameter that plays a vital role in utilizing battery energy efficiency, operating safely under various realistic conditions and environments, and extending battery life [3,4]. The SOC is a piece of valuable information on the remaining capacity available during the operation of EV car. ...
... The SOC is a piece of valuable information on the remaining capacity available during the operation of EV car. As the central internal state of the battery, the SOC is continuously supervised by a battery management system (BMS), which is integrated into the EV energy storage system (ESS) structure to power the traction powertrain [1][2][3][4][5][6]. The SOC can be calculated directly by a simple open-loop integration operation, known as the coulomb counting method or the ampere method since it accumulates the charge transferred between the battery and the environment over time. ...
... Also, it is noteworthy to know that the battery model accuracy significantly impacts SOC estimation. The well-known equivalent circuit model (ECM) is suitable for online estimation due to its simplicity and mastering well the relationship between parameters [1], [3][4][5][6]. The traditional methods include the most popular Kalman filter (KF) algorithms, among them linear KF and linearized extended KF (EKF) [5,6,15,16], and nonlinear unscented KF (UKF) [7], ensemble KF (EnKF) [17], particles filter (PF) [18], which are commonly used as a nonlinear filter estimation methods. ...
This research investigated different nonlinear models, state estimation techniques and control strategies applied to rechargeable Li-ion batteries and electric motors powered and adapted to these batteries. The finality of these investigations was achieved by finding the most suitable design approach for the real-time implementation of the most advanced state estimators based on intelligent neural networks and neural control strategies. For performance comparison purposes, was chosen as case study an accurate and robust EKF state of charge (SOC) estimator built on a simple second-order RC equivalent circuit model (2RC ECM) accurate enough to accomplish the main goal. An intelligent nonlinear autoregressive with exogenous input (NARX) Shallow Neural Network (SSN) estimator was developed to estimate the battery SOC, predict the terminal voltage, and map the nonlinear open circuit voltage (OCV) battery characteristic curve as a function of SOC. Focusing on nonlinear modeling and linearization techniques, such as partial state feedback linearization, for “proof concept” and simulations purposes in the case study, a third order nonlinear model for a DC motor (DCM) drive was selected. It is a valuable research support suitable to analyze the performance of state feedback linearization, system singularities, internal and zero dynamics, and solving reference tracking problems.
... This parameter is defined as remaining battery capacity during the time period when the battery discharges. The main drawback is that LIB SOC cannot be measured directly due to the lack of an accurate measurement sensor; thus, a proper estimation technique is required to prevent dangerous situations and battery performance degradation [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The majority of Li-ion battery SOC estimation algorithms are modelbased, among them the most popular extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and adaptive nonlinear observers (ANOE) are intensively used and well documented in the literature of the field [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. ...
... The main drawback is that LIB SOC cannot be measured directly due to the lack of an accurate measurement sensor; thus, a proper estimation technique is required to prevent dangerous situations and battery performance degradation [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The majority of Li-ion battery SOC estimation algorithms are modelbased, among them the most popular extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and adaptive nonlinear observers (ANOE) are intensively used and well documented in the literature of the field [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The performance of these SOC estimators and terminal voltage predictors, such as estimation accuracy, convergence, and robustness to changes in the battery model parameters and initial "guess value" for battery SOC, as well as to the real-world driving conditions, is limited by many factors, such as the type of the application, the aggressivity of the battery model nonlinearity, uncertainties and unmodeled battery dynamics, battery model accuracy, and the difficulties experienced to find the best values for tuning parameters, among others. ...
... For a better understanding of the battery model concept and further developments for the design and implementation of state estimation (SOC) algorithms, a first-order RC equivalent circuit model (ECM) was chosen [12]. This straightforward model has been proven to be efficient and well suited in real-time implementations for many HEV applications. ...
The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive neural fuzzy inference system (ANFIS) battery model, which is simple, accurate, practical, and well suited for real-time implementations in HEV/EV applications, with this being one of the main contributions of this research. On the basis of this model, we built four state of charge (SOC) estimators of high accuracy, assessed by a percentage error of less than 0.5% in a steady state compared to the 2% reported in the literature in the field. Moreover, these estimators excelled by their robustness to changes in the model parameters values and the initial “guess value” of SOC from 80–90% to 30–40%, performing in the harsh and aggressive realistic conditions of the real world, simulated by three famous driving cycle procedure tests, namely, two European standards, WLTP and NEDC, and an EPA American standard, FTP-75. Furthermore, a mean square error (MSE) of 7.97 × 10−11 for the SOC estimation of the NARX SNN SOC estimator and 5.43 × 10−6 for voltage prediction outperformed the traditional SOC estimators. Their effectiveness was proven by the performance comparison with a traditional extended Kalman filter (EKF) and adaptive nonlinear observer (ANOE) state estimators through extensive MATLAB simulations that reveal a slight superiority of the supervised learning algorithms by accuracy, online real-time implementation capability, in order to solve an extensive palette of HEV/EV applications.
... Many protection ICs (Mutyala et al., 2014) switch the battery input and output of the circuit based on the above battery safety considerations. These battery protection ICs and MOSFETs are manufactured by Texas Instruments (BQ29700) (Farag, 2013), Seiko's S-8200A Series ICs, and DW01-P. The schematics for all Battery protection ICs are the same for BQ29700, on will be connected to the two MOSFETs denoted as charging (CHG) and discharging (DSG), and terminals. ...
... Because the rate drops rapidly during the constant voltage cycle, it cannot be used to estimate when the battery will be fully recharged. Calculating the time needed to charge a lithium battery from empty by using the manufacturer's C rate would be impractical because the C rate changes as the battery's capacity changes during the charging process (Farag, 2013). ...
... Step 5: Calculating the a posteriori (Mohammed, 2013) estimateŝ ...
The use of batteries for diverse energy storage applications is increasing, primarily because of their high energy density, and lithium-ion batteries (LiBs) are of particular significance in this regard. However, designing estimators that are robust to compute the state of charge (SOC) of these batteries in the presence of disturbance signals arising from different battery types remains a challenge. Hence, this paper presents a hybrid estimator that combines the extended Kalman filter (EKF) and sliding mode observer (SMO) via a switching function and tracking closed loop to achieve the qualities of noise cancellation and disturbance rejection. Hybridization was carried out in such a way that the inactive observer tracks the output of the used observer, simultaneously feeding back a zero-sum signal to the input gain of the used observer. The results obtained show that noise filtering is preserved at a convergence time of .01 s. Also, the state of charge estimation interval improves greatly from a range of [1, .93] and [.94, .84] obtained from the extended Kalman filter and sliding mode observer, respectively, to a range of [1, 0], in spite of the added disturbance signals from a lithium–nickel (INR 18650) battery type.
... Batteries are used to enhance the dynamic performance and shave the peak loads of the power system. The open circuit voltage (OCV) is defined distinctively for discharging and charging at a specified state-of-charge (SoC) due to the hysteresis effect [29]. The total voltage drop from OCV is then modeled here based on the first-order equivalent circuit to account for the internal resistance and polarization (OCV relaxation). ...
The electrified hybrid shipboard power system with high-level integration of renewable energy resources and energy storage system has become the new trend for the all-electric ship (AES) configuration. However, the traditional energy management system (EMS) is not able to fulfill the increasingly complex control requirements, and a more advanced EMS control algorithm is required to handle the multiple power sources and even achieve optimal energy management control. This paper proposes supervisory energy management with an improved adaptive model predictive control (AMPC) strategy to optimize the power split of the hybrid power sources and to reduce the total cost of ownership (TCO) of vessel operation, which considers not only the fuel and emission cost but also the power source degradation. In order to achieve real-time implementation, the AMPC-based EMS software has been developed and deployed to a programmable logic controller (PLC) hardware.Ahybrid fuel cell-fed shipboard power system with a DC-grid configuration is modeled and operated on a hardware-in-the-loop (HIL) setup. The prototyping controller verification tests have been performed with this HIL plant in the lab environment. Three typical tugboat load profiles with power fluctuations are implemented as case studies. Lastly, a cost study was carried for a ten-year long-term vessel operational cycle. The proposed AMPC-based EMS can achieve up to 12.19% TCO savings compared to those of a traditional rule-based control strategy.
... Among all metals the choice of Magnesium is not evident either, especially from Fig.2. First, we have discarded Beryllium due to its price and toxicity, and Boron due to the [2] absence of a reliable zero-carbon production method. Among others (Li, Mg, Al, Si, Zn, Ti, Fe) we considered those for which stable non-powdered metal combustion with air have been demonstrated (Li, Mg) [3] [4]. ...
Fossil fuel scarcity, global warming and non-constant energy production through renewable energies, lead to investigate innovative ways for energy storage. Reactive metal-based storage systems are a new alternative to support the clean energy transition according to the COP26 climate pledges. Herein, the case of Magnesium is argued to be the optimal energy storage media due to a good balance between energy density, combustion kinetics and storage properties. The roadmap for the technical feasibility study is proposed regarding both production and combustion technology. Potential technical problems and limitations are discussed for a number of outlined scenarios of Magnesium utilization as energy carrier.
... The battery-accurate SOC estimation problem has not been efficiently solved [27,28]. References [29][30][31][32] provided a detailed SOC estimation in terms of overall research progress, future development trends, and the source of SOC estimation. However, there is no systematic explanation of the SOC calculation process and algorithm selection and how to deal with uncertain environmental conditions and battery pack grouping in EVs. ...
Electric vehicles (EVs) have acquired significant popularity in recent decades due to their performance and efficiency. EVs are already largely acknowledged as the most promising solutions to global environmental challenges and CO2 emissions. Li-ion batteries are most frequently employed in EVs due to their various benefits. An effective Battery Management System (BMS) is essential to improve the battery performance, including charging-discharging control, precise monitoring, heat management, battery safety, protection, and also an accurate estimation of the State of Charge (SOC). The SOC is required to provide the driver with a precise indication of the remaining range. At present, different types of estimation algorithms are available, but they still have several challenges due to their performance degradation, complex electrochemical reactions, and inaccuracy. The estimating techniques, average error, advantages, and disadvantages are examined methodically and independently in this paper. The article presents advanced SOC estimating techniques such as LSTM, GRU, CNN-LSMT, and hybrid techniques to estimate the average error of the SOC. A detailed comparison is presented with merits and demerits and it helps researchers in the implementation of EV applications. This research also identifies several factors, challenges, and potential recommendations for enhanced BMS and efficient estimating approaches for future sustainable EV applications.
... At this point, the pressure difference is no longer a change in momentum but represents a change in kinetic energy. Substituting in Eq. (35), an expression is obtained for the thrust in a form where the area of the disk swept by the propeller and the density of the air appear explicitly: ...
... Schematic representation of a Li-ion battery during discharging.[35] Figure 25 shows the long way lithium-ion batteries have come since the year 1991: in 2005 the battery energy density was 200 Wh/kg and this has risen to 304 Wh/kg in 2020 thanks University of Campania "Luigi Vanvitelli": Department of Engineering | 44 to profound developments in the automotive field. ...
This thesis work was carried out in collaboration with the Fluid Dynamics Laboratory of CIRA (Italian Aerospace Research Centre) and is part of the VENUS (inVestigation of distributEd propulsion Noise and its mitigation through wind tUnnel experiments and numerical Simulations) project, funded by the European Community. The main aim of the VENUS project is the development of an aerodynamic and aeroacoustics configuration for a regional aircraft that can drastically reduce consumption of fuels and environmental impact. To this end, CIRA is studying the possibilities offered by DEP (distributed electric propulsion) system. The aim of this thesis work is the aerodynamic analysis, through the use of the commercial Ansys Fluent software and open-source solver SU2, of the effects of installing a rotating thrust propeller on a finite span wing section at 0, 4, and 8 deg of angle of attack. The actuator disk theory was used to model the propeller as a surface of discontinuity with a pressure jump profile and a tangential velocity profile. For Fluent simulations a proper user defined function (UDF), developed by means of an in-house written Matlab script, was considered to provide the pressure jump and the tangential velocity, as requested by the FAN boundary condition of the Fluent sofware. The script allows to automate the writing of the UDF once propeller performances are available. The use of periodic boundary conditions, applied to the domain side, allowed to simulate a distributed electrical configuration for an infinite wing. The hub was modelled with a source at zero velocity and pressure. Preliminary analyses were performed on a test case, consisting of an actuator disk immersed in an isolated domain, to compare the results with the ones available from the UZEN solver, developed by CIRA. The actual configuration was then analysed, and the results showed an increase in lift and drag with respect to the power off condition.