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To achieve accurate prediction of the nonlinear behavior of lithium‐ion battery, parameters of the lithium‐ion battery model play a key role for dynamic performance, hence the parameters of the investigated battery model have been estimated. In this work, the electrical equivalent circuit (ECM) models have been used to simulate INR18650‐20R lithium...
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The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the ac...
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... Considering the complexity of the operating circumstances of lithium-ion batteries in practical applications, this paper adopts the dynamic stress test (DST) to accurately confirm the effectiveness of the algorithm for SOC estimation. 31 Discharge experiments were performed on the battery under simulated conditions with a sampling period of 1 s. Figures 10 and 11 display the lithium-ion battery's measured response voltage U and input current I under DST settings. ...
For the use of batteries to be secure and dependable, monitoring the state of charge (SOC) is essential. It is challenging to accurately calculate the SOC of LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 batteries because they have two flat voltage plateaus and their performance is significantly influenced by temperature. To improve SOC estimation accuracy, a battery-based SOC research approach based on dual Kalman filtering is put forth. The improved adaptive unscented Kalman filter (MIASOUKF) is utilized for SOC estimation, while the extended Kalman filter (EKF) is employed for online identification of battery model parameters based on the second-order RC equivalent circuit model. By combining adaptive unscented Kalman filtering (AUKF) and multi-innovation, the estimation accuracy of the algorithm is improved by repeating the use of historical information, and the sampling points are subjected to Schmidt orthogonal transformation during the sampling point selection in AUKF to decrease the computational complexity of the AUKF, making the algorithm easy to implement in real time. Under complex working conditions, the average error of the MIASOUKF-EKF joint estimation algorithm is less than 0.53%. Through the comparison of experiments and simulations, MIASOUKF-EKF algorithm can effectively improve the estimation accuracy of SOC.
... Empirical-based methods evaluate the battery SOH from heuristic aging features. Commonly seen examples include the vertical slope at the corner of the constant current (CC) charging curve [27], the slope of the curve at the end of CC charging mode [28], the maximum slope of the voltage curve in the CC phase [29], and the polarization capacitance of an electric circuit model (ECM) [30]. Although the feature-extraction process is simple and straightforward, this kind of method cannot be guaranteed universal in different battery types due to the lack of mechanism expressions. ...
The state of health (SOH) of batteries is an important but unmeasurable parameter closely related to battery safety and durability. However, most existing SOH estimation strategies rely on a specific load profile (e.g., constant current). To tackle this issue, we here report a method that first converts the dynamic voltage trajectories to the curves corresponding to the constant current profiles using a neural network model. Then, the aging characteristics are selected and the battery SOH is estimated accordingly from the converted voltage data using a Gaussian process regression model. Batteries with different aging degrees are tested with different working conditions to verify the proposed method. Numerically, the errors of the voltage reconstruction are bounded within 2 mV, while the SOH estimation errors under four dynamic working conditions remain within 2%. Our technical approach reduces the dependency of traditional SOH estimation methods on specific working conditions and shows strong potential for practical applications.
... The equivalent circuit models can be categorised into the Rint model, Partnership for a New Generation of Vehicles (PNGV) model, and RC transient models which are branched from the 1 st -order to n th -order transient model [9][10][11][12]. In [13] electrical equivalent circuit models from 1RC to 5RC transient models have been used to describe the LIB. The parameters are evaluated at various temperatures of 0°C, 25°C, and 45°C. ...
Lithium-ion batteries are considered the substantial electrical storage element for electric vehicles (EVs). The battery model is the basis of battery monitoring, efficient charging, and safety management. Non-linear modelling is the key to representing the battery and its dynamic internal parameters and performance. This paper proposes a smart scheme to model the lithium-polymer ion battery while monitoring its present charging current and terminal voltage at various ambient conditions (temperature and relative humidity). Firstly, the suggested framework investigated the impact of temperature and relative humidity on the charging process using the constant current-constant voltage (CC-CV) charging protocol. This will be followed by monitoring the battery at the surrounding operating temperature and relative humidity. Hence, efficient non-linear modelling of the EV battery dynamic behaviour using the Hammerstein-Wiener (H-W) model is implemented. The H-W model is considered a black box model that can represent the battery without any mathematical equivalent circuit model which reduces the computation complexity. Finally, the model beholds the boundaries of the charging process that not affecting on the lifetime of the battery. Several dynamic models are applied and tested experimentally to ensure the effectiveness of the proposed scheme under various ambient conditions where the temperature is fixed at 40°C and the relative humidity (RH) at 35%, 52%, and 70%. The best fit using the H-W model reached 91.83% to describe the dynamic behaviour of the battery with a maximum percentage of error 0.1V which is in good agreement with the literature survey. Besides, the model has been scaled up to represent a real EV and expressed the significance of the proposed H-W model.
... Electrical equivalent models are widely exploited to model the complex behavior of battery packs and calculate the estimate of battery states [142,143]. This is because they use certain combinations of basic electrical passive elements: resistors (R) and capacitors (C). ...
The systematic transition of conventional automobiles to their electrified counterparts is an imperative step toward successful decarbonization. Crucial advances in battery storage systems (BSS) and related technologies will enable this transition to proceed smoothly. This requires equivalent developments in several interconnected areas, such as complete battery cycles and battery management systems (BMS). In this context, this article critically examines state-of-the-art battery technologies from the perspective of automakers, provides insightful discussions, and poses open questions with possible answers. The generations of BSS (traditional, current, and futuristic) are first reviewed and analyzed via two distinct qualitative factors (DQFs): key design markers and performance indicators. Based on the introduced DQFs, major development trends and probable evolutions are forecasted. Thereafter, recent modeling and state estimation methods are comprehensively reviewed in relation to high-performance BMS. Accordingly, promising modeling methods are identified as futuristic solutions, leading to an accurate and timely decision for reliable and safer user experience. This article is concluded by presenting a techno-economic assessment of what to expect, as well as highlighting future challenges and opportunities for industry, academia, and policy makers.
... ECM make use of basic electric components such as resistors, capacitors, and voltage sources to describe the battery characteristic [20]. They are simple and easy to implement, which makes them very suited for online applications [21]. However, the ECM parameters are not constant and vary with many internal and external factors such as temperature, SoC level, ageing and C-rate [22]. ...
The precision of equivalent circuit model (ECM)-based state of charge (SoC) estimation methods is vulnerable to the variation of the battery parameters, due to several internal and external factors. In this regard, this study proposes a fuzzy logic method for the approximate estimation of the ECM parameters at different temperatures and SoC levels. The fuzzy inference system is designed to handle the non-linear deviation of the battery parameters from their reference values. On this basis, the extended Kalman filter and smooth variable structure filter are used to estimate the SoC. The two algorithms with fuzzy parameters (FP), namely FP-EKF and FP-SVSF, are tested on a 20 Ah Nickel Manganese Cobalt cell with maximum voltage of 4.2 V. The results show that the maximum root mean square error (RMSE) of the estimated SoC is kept within 1.51% with the FP-EKF and 0.68% with the FP-SVSF. Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP-EKF, and 0.82% with the FP-SVSF, compared to the same algorithms without the proposed FP method. The executable codes are implemented on a low-cost controller, and the average computational time is obtained as 215 μs, which confirms the real-time practicality of the proposed method.
... ECM make use of basic electric components such as resistors, capacitors, and voltage sources to describe the battery characteristic [20]. They are simple and easy to implement, which makes them very suited for online applications [21]. However, the ECM parameters are not constant and vary with many internal and external factors such as temperature, SoC level, ageing and C-rate [22]. ...
The precision of equivalent circuit model (ECM)-based state of charge (SoC) estimation methods is vulnerable to the variation of the battery parameters, due to several internal and external factors. In this regard, this study proposes a fuzzy logic method for the approximate estimation of the ECM parameters at different temperatures and SoC levels. The fuzzy inference system is designed to handle the non-linear deviation of the battery parameters from their reference values. On this basis, the extended Kalman filter and smooth variable structure filter are used to estimate the SoC. The two algorithms with fuzzy parameters (FP), namely FP-EKF and FP-SVSF, are tested on a 20 Ah Nickel Manganese Cobalt cell with maximum voltage of 4.2 V. The results show that the maximum root mean square error (RMSE) of the estimated SoC is kept within 1.51% with the FP-EKF and 0.68% with the FP-SVSF. Moreover, the reduction of the maximum absolute error may reach 0.34% with the FP-EKF, and 0.82% with the FP-SVSF, compared to the same algorithms without the proposed FP method. The executable codes are implemented on a low-cost controller, and the average computational time is obtained as 215 μs, which confirms the real-time practicality of the proposed method.
... So, there is high requirement to step up these voltages. DC-DC converters are basic building blocks of modern high frequency switching power supplies [4,5,6,7]. Since the output from these photo voltaic cells are DC, we require a converter that could take the low DC voltage as input and gives output as high DC voltage. We use boost converters to step up these voltages. ...
The project aims to develop a non-isolated DC-DC Converter which is distinguished by its higher gain when compared with the conventional boost converters. These converters find its applications in fields where there is high demand for stepping up of voltages to a greater extent which includes photo-voltaic cells and fuel cells applications. Here, the converter is developed for continuous conduction mode of operation with the help of voltage and current wave forms, conversion ratio and simulations. In addition to this, the paper also discusses the comparison between the theoretically obtained results with the simulated and experimental results.
... With the help of mobile app and service management, the services would be much smoother. Depending on the location accessibility and roads the efficiency of services varies [1]- [6]. Objectives of the proposed work framed as: ...
... In any model based on SOC estimation, we can view the battery as composed of a linear dynamic state equation and highly nonlinear OCV-SOC function. It is also known that the SOC cannot be measured directly and that is why we estimate or use an indirect means from the measured signals to be treated as a nonlinear function [48]. Using the discharge pulses and OCV, the fitting function was as the following: ...
The design of mathematical models is based on conservation laws and also on the fundamental principles of modeling: structure, parameters, and physical meaning. Those kinds of modeling should have specific capabilities to deal with different working conditions and environments coping with challenges that include but are not limited to battery capacity, life-cycle, or the attempts to manipulate the current profiles during operation. Introducing memristive elements in batteries will be ideal to satisfy these fundamentals and goals of modeling, whereas addressing the recycling and sustainability concerns on the environmental impact by the placement of TiO2 memristor into this model can promote a recovery hierarchy via recycling and dispatching a slight amount to disposal as the previous focus was mainly concentrated on availability. As for battery materials, modeling, performing, and manufacturing all have proliferated to grasp the possible sustainability challenges inherited in these systems. This paper investigated electrochemical impedance spectroscopy to study this model and the dynamic behavior inside the battery. We found a solution to address the existing battery limitations that elucidate the battery degradation without affecting the performance, correspondingly by employing the dampest least-squares combination with nonlinear autoregressive exogenous for identifying such model and its associated parameters because of its embedded memory and fast convergence to diminish the influence of the vanishing gradient. Lastly, we found that this model is proven to be efficient and accurate compared to actual experimented data to validate our theory and show the value of the proposed model in real life while assuming Normal Gaussian distribution of data error with outstanding results; the auto-correlations were within the 95% confidence limit, the best validation was 2.7877, and an overall regression of 0.99993 was achieved.
... 3 Lithiumion batteries also have a number of advantages, including their small size, light weight, extended cycle life, high efficiency, low self-discharge rate, and reliability. 4 They are also found in a variety of devices, including laptops, cell phones, and communication systems. However, many challenges are encountered in the operation of lithium-ion batteries in EVs. ...
The State of Charge (SOC) estimation of lithium-ion batteries is complex due to the various nonlinear uncertainties present in the battery. However, in this paper, a new nonlinear state observer (NSO) is proposed to be designed for the estimation of accurate and robust SOC. This proposed observer is suitable for both continuous and discrete-time nonlinear systems. To design the nonlinear observer, two-RC equivalent circuit model state equations are simulated for the dynamic behavior of the lithium-ion battery. The seventh-order polynomial fitting approach is assumed for the nonlinear relationship between open-circuit voltage (OCV) and SOC, and the exponential fitting method is used to estimate the battery's offline parameters. Lyapunov's stability criterion achieves the stability and convergence capability of the proposed method. An urban dynamometer driving schedule (UDDS) cycle was adopted to estimate the performance of the proposed observer by comparing it with the well-established methods like unscented Kalman filter (UKF) and sliding mode observer (SMO) algorithms, and it was found that the proposed observer achieved better performance like accurate SOC, high convergence capability, and less SOC error.
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