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Comparison of estimated series resistance for a 22.2 V, 6.6 Ah Lithium-ion battery pack, with values available in the literature.
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![FIGURE 14: Li-ion battery bank powered EV traction system [25].](publication/363605749/figure/fig5/AS:11431281084740522@1663360771839/Li-ion-battery-bank-powered-EV-traction-system-25_Q320.jpg)
This paper proposes an accurate and efficient Universal Adaptive Stabilizer (UAS) based online parameters estimation technique for a 400 V Li-ion battery bank. The battery open circuit voltage, parameters modeling the transient response, and series resistance are all estimated in a single real-time test. In contrast to earlier UAS based work on ind...
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Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating a...
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... The Chen and Mora (CM) circuit model is a comprehensive representation that captures the dynamic attributes of the battery's terminal voltage, and variations in battery parameters concerning SOC, and has undergone extensive experimentation over the last decade [13]. Figure 3 depicts the CM equivalent circuit battery model utilized in this study. ...
... In this model, the state variable x 1 represents the battery's SOC, while x 2 corresponds to the voltage across R ts kC ts , and x 3 corresponds to voltage across R tl kC tl . The parallel combination R ts kC ts characterizes the short-term terminal voltage dynamics in response to fluctuations in discharge current, while the parallel combination R tl kC tl characterizes the long-term terminal voltage dynamics in response to variations in discharge current [13]. Eqs. ...
... Eqs. (1)-(4) describe the CM equivalent circuit model [13]. ...
This chapter introduces a battery state of charge (SOC) management technique designed for an electric vehicle traction system that incorporates an indirect field-oriented induction motor drive. The primary goal of this technique is to restrict the change in battery SOC from exceeding a maximum limit, by compensating for the motor speed tracking performance. It employs a fuzzy-tuned model predictive controller (FMPC), where a fuzzy logic controller (FLC) adjusts the input weight in the objective function to ensure that the change in battery SOC does not exceed the maximum permitted value while regulating the motor speed. The various components of the EV traction system are thoroughly modeled, and simulations are conducted using MATLAB/Simulink 2018b. The simulation results, carried out using the New European Drive Cycle (NEDC), verify that the technique limits the change in SOC while controlling the motor speed. This approach offers the advantage of maintaining precise control over the battery bank SOC, which distinguishes it from conventional speed regulators.
... The parameters , , , , and are represented as a function of 1 in (11)-(16) [13]. Details regarding the estimation of the positive constant parameters 1 , 2 , 3 … 21 are given in [14]. ...
... 1 + 6 3 1( 11 )( 1 ) = 7 − 8 1 + 9( 12 )( 1 ) = 10 −11 1 + 12( 13 ) ( 1 ) = − 13 − 14 1 + 15( 14 ) ( 1 ) = − 16 − 17 1 + 18( 15 ) ...
This paper analyzes the battery thermal behaviorand energy consumption of an electric vehicle (EV) tractionsystem using a baseline PI controller and a fuzzy logic controller(FLC). The primary objective of this work is to compare speedregulation performance, battery energy consumption, andbattery temperature effects. The simulation for an indirect field-oriented (IFO) induction motor-driven EV traction system,including a battery thermal model, is developed for the desiredcomparative performance analysis. The simulation results showthat the fuzzy logic controller has lesser battery current ripples,less battery temperature rise and less battery state of charge(SOC) depletion while providing better speed regulation. Thus,the fuzzy logic-based speed controller EV traction system canincrease the vehicle drive range and lifetime of the battery bankwith an improved speed regulation performance than the PI-based speed controller
... In contrast, the battery model parameters were estimated using the Kalman filter and particle swarm optimization (PSO) in [29], [30]. An adaptive parameter estimation (APE) was utilized in [12], [31] to estimate Chen and Mora's battery model parameters, in which the APE technique exhibits a fast computational time and lowerror values. Moreover, hybrid parameter estimation strategies were proposed by [32], [33], in which the APE technique combined with several optimization techniques such as PSO and teaching-learning-based optimization. ...
... To formulate this policy, both the parameter estimation approaches were presented. The APE approach was proposed in [12] and further adopted in [32], [33], [31], [43], [44], [45]. The APE approach inspired this study to develop the OPE approach; therefore, both APE and OPE techniques share a similar parameter structure. ...
... Consequently, a state-of-the-art estimator provides proof of concept and shows how the developed decoupling technique can be integrated with an estimation method. Similarly, other estimation methods with different levels of computation and complexity can also be combined with the proposed decoupling technique [84][85][86][87][88][89]. In addition to reducing the number of sensors and the size of the overall monitoring system, the necessary data communication can be significantly reduced. ...
The capacity and voltage rating of battery packs for electric vehicles or stationary energy storages are increasing, which challenge battery management and monitoring. Breaking the larger pack into smaller modules and using power electronics to achieve dynamic reconfiguration can be a solution. Reconfigurable batteries come with their own set of problems, including many sensors and complex monitoring systems, high-bandwidth communication interfaces, and additional costs. Online parameter estimation methods can simplify or omit many of these problems and reduce the cost and footprint of the system. However, most methods require many sensors or can only estimate a subset of the elements in the module’s equivalent circuit model (ECM). This paper proposes a simple decoupling technique to derive individual modules’ voltage and current profiles from the output measurements without direct measurement at the modules. The determined profiles can achieve a high sampling rate with minimum communication between the battery management system (BMS) and the modules. With accurate profiles, an estimation technique can easily determine the parameters of the modules. Provided simulations and experiments confirm this claim by estimating the parameters of a first-order ECM with a parallel capacitor. The proposed technique reduces the number of sensors from 2N + 2 to only two at the pack’s output terminals.
... Furthermore, the APE was further adopted by [15] to initialize different optimization routines, such as particle swarm optimization (PSO) and the MATLAB f mincon function to enhance the accuracy of the estimated parameters. Moreover, the battery model parameters were estimated for a large battery pack of 400V in [22]. In [14,15,22], a single battery profile was utilized to estimate the model parameters, and they assumed that the parameters are suitable for every cell of the same type. ...
... Moreover, the battery model parameters were estimated for a large battery pack of 400V in [22]. In [14,15,22], a single battery profile was utilized to estimate the model parameters, and they assumed that the parameters are suitable for every cell of the same type. However, this was not fully valid assumption according to [16], in which several battery units of the same types were discharged using the same load to allow a fusion strategy to provide model parameters based on unified quantities of several batteries instead of a single battery. ...
... 17: Run the model based on the estimated parameters (ĉ 7 -ĉ 18 ) and compute R s using Eq. (22). 18: Then, apply curve-fitting to obtain internal resistance parameters (i.e.,ĉ 19 -ĉ 21 ). ...
In this study, the theoretical framework of optimal control was employed to construct an optimal parameter estimation (OPE) methodology that estimates the parameters of an equivalent-circuit battery model backward in time. In contrast, a forward adaptive parameter estimation (APE) method was utilized in the present study for comparison purposes. APE inspired this study to develop the proposed approach, because APE has been examined in previous studies and reported as a fast parameter estimation technique. Both techniques were tested and verified using simulation and experimental results, which showed that OPE exhibited a better performance than APE in terms of computational time and accuracy. Moreover, the proposed strategy was less affected by an increase in the number of samples of identification data. Therefore, the OPE approach is a reliable choice for recomputing the model parameters from time to time, especially when the battery model parameters vary with time owing to temperature and aging effects.
Lithium battery is an important power source of an electrical vehicle (EV). Practically, when a battery is about to fail, it needs to be removed from the load because keeping it connected can lead to permanent damage. Hence, it is important to detect battery failure to sustain the lifespan of the battery and avoid safety issues which will contribute to the sustainability of the EV. However, determining the effective disconnecting or discharging moment of the battery remains a challenging issue in EV applications. In order to solve the abovementioned problem, a control framework and maximum likelihood estimation are proposed to estimate the parameters of a battery model. In addition, both the sparse representation and dynamic mode decomposition (DMD) approaches are applied to protect the physical battery unit from a failure scenario. For comparison purposes, the proposed framework is compared to a Lyapunov-based detection approach along with neural network (NN) and linear discriminant analysis (LDA). Moreover, several state estimation algorithms are applied to estimate battery state of charge (SOC) at each time step: extended Kalman filter, extended Kalman smooth variable structure filter, and cubature Kalman filter. Finally, the experimental results showed that the proposed DMD approach outperforms the sparse representation, NN, LDA, and Lyapunov-based approaches in terms of detection accuracy. Moreover, the proposed DMD strategy is more robust towards the inaccuracy of the estimated SOC. Besides that, the proposed battery model parameter estimation approach exhibited fast processing time; therefore, it is a reliable choice for recomputing the model parameters from time to time to compensate aging effect. An autonomous unmanned aerial vehicle (UAV) was simulated as a proof-of-concept in a MATLAB environment to verify the detection performance of the proposed methods. Both actual and UAV results showed that the DMD demonstrated more robust detection performance than other methods in terms of processing time and detection accuracy.
As the number of behind-the-meter (BTM) photovoltaic (PV) modules installed in residential premises increases, it is important to develop a non-intrusive framework for the real-time assessment of BTM PV generation from the smart meter data of the end users. This framework not only enhances the observability of residential premises but also enables the electric utility to implement various distribution grid operation strategies such as demand response programs, load forecasting, and electric energy procurement, among others. This work proposes a novel non-intrusive approach based on the Universal Adaptive Stabilization (UAS) algorithm to assess the generation of BTM PV modules in real-time using smart meter data obtained from residential customers. The proposed approach is characterized by its simplicity, robustness, and fully unsupervised operation without the need for complicated and detailed system dynamics. The accuracy and convergence of the estimated BTM solar PV generation and residential load consumption to their actual values are proved by a detailed mathematical justification. Further, the effectiveness of the proposed framework is evaluated by comparing it against several advanced algorithms using a publicly available dataset. The results of the evaluation indicate that the proposed framework outperforms existing algorithms by providing more precise and accurate estimates.
Lithium batteries are widely used in power storage and new energy vehicles due to their high energy density and long cycle life. The accurate and real-time estimation for the state-of-charge (SoC) and the state-of-health (SoH) of lithium batteries is of great significance to improve battery life, reliability, and utilization efficiency. In this paper, three cascaded fractional-order sliding-mode observers (FOSMOs) are designed for the estimation of SoC by observing the terminal voltage, the polarization voltage, and the open-circuit voltage of a lithium cell, respectively. Furthermore, to calculate the value of the SoH, two FOSMOs are developed to estimate the capacity and internal resistance of the lithium cell. The control signals of the observers are continuous by utilizing fractional-order sliding manifolds without low-pass filters. Compared with the existing sliding-mode observers for SoC and SoH, weaker chattering, faster response, and higher estimation accuracy are obtained in the proposed method. Finally, the experiment tests demonstrate the validity and feasibility of the proposed observer design method.
