Parameter Estimation of Battery Pack in EV using Extended Kalman Filters

Ramachandran, Rakesh; Ganeshaperumal, D.; Subathra, B.

doi:10.1109/incces47820.2019.9167740

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…They made simulations and validations with different conduction cycles to verify the robustness of the method. References [32][33][34][35][36][37][38] operated with the extended Kalman filter to estimate SoC and SOH. In particular, the authors [32,33,39] used the least square method to find the best set of parameters to be applied in the estimation.…”

Section: Methods Based On Filtering Algorithmsmentioning

confidence: 99%

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

et al. 2023

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Recently, electric vehicles have gained enormous popularity due to their performance and efficiency. The investment in developing this new technology is justified by the increased awareness of the environmental impacts caused by combustion vehicles, such as greenhouse gas emissions, which have contributed to global warming and the depletion of oil reserves that are not renewable energy sources. Lithium-ion batteries are the most promising for electric vehicle (EV) applications. They have been widely used for their advantages, such as high energy density, many cycles, and low self-discharge. This work extensively investigates the main methods of estimating the state of charge (SoC) obtained through a literature review. A total of 109 relevant articles were found using the prism method. Some basic concepts of the state of health (SoH); a battery management system (BMS); and some models that can perform SoC estimation are presented. Challenges encountered in this task are discussed, such as the nonlinear characteristics of lithium-ion batteries that must be considered in the algorithms applied to the BMS. Thus, the set of concepts examined in this review supports the need to evolve the devices and develop new methods for estimating the SoC, which is increasingly more accurate and faster. This review shows that these tools tend to be continuously more dependent on artificial intelligence methods, especially hybrid algorithms, which require less training time and low computational cost, delivering real-time information to embedded systems.

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Section: Methods Based On Filtering Algorithmsmentioning

confidence: 99%

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

et al. 2023

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“…An EKF has been suggested for estimating the battery pack's parameters and SOC that has been utilized state-space model of an EV battery. Also, SOC has been compared with the present situation and previous situations, and then it has simulated battery parameters received from a characteristic EV battery pack in [24]. The current conversion of car batteries to second-life storage systems has been characterized by time-and money-consuming activities, including disassembly to the module level, and manual SOH evaluations have been used in expensive diagnostic equipment.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System

Jafari

Byun

2022

IEEE Access

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Electric Vehicles (EVs) reliance on batteries, which currently have lower energy and power densities than liquid fuels and are prone to aging and performance degradation over time, restricts their mainstream adoption. With applications like electric vehicles and grid-scale energy storage, effective management of lithium-ion batteries is a vital enabler for a low-carbon future. Monitoring the battery's condition of health and charge over the lifetime of an EV is, therefore, a highly pertinent issue. Battery Management Systems (BMS) are used during the operation of EVs to monitor, estimate and control battery states to ensure that batteries can function effectively and safely. Additionally, the materials composition, system design, and operating circumstances substantially impact a battery's usable life, making it more challenging to govern and maintain battery systems. This work proposes the structure of a battery digital twin-based battery for the electronic vehicle, which has the potential to enhance BMS situational awareness greatly and enable the optimal functioning of battery storage units. Digitalization and Artificial Intelligence (AI) present an opportunity and offer a Digital Twin (DT) of the EV battery as a solution to the difficulty of onboard computation for the incremental State Of Health (SOH) and State Of Charge (SOC) based on Extreme Gradient Boost (XGBoost) and Extended Kalman Filter (EKF) to predict the state estimate. The battery's condition has been determined by using the EKF, which can provide vital information for maintenance. The battery's usable life can be extended with an accurate estimate of the SOC to continue then a learning-based prediction approach to gauge the battery's health state is suggested in order to increase battery life. A SOC model is frequently retrained to depict the effects of aging, and a SOH model is often performed to foretell the reduction in the highest battery capacity. According to a result, DT models are useful for managing batteries, and full life cycle statistics are important for planning the battery's upgrade path.

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“…The table shows the authors' state space variable selection, the values of the covariance matrices, when available, and the performance of the method for the estimation of the cell voltage. The proposed model differs from those reported in Table 3 for the set of state variables used for the cell representation, which are derived from the SP model [12] shown in Figure 1 and listed hereinafter: the voltage of the RC branch V C , the DC resistance R 0 , the resistance of the RC branch R 1 , and the time constant of the RC branch τ 1 = R 1 C 1 . These parameters have been chosen as state variables because of their high dependency on the cell aging.…”

Section: Extended Kalman Filter Applied To the Cell Parameters Estima...mentioning

confidence: 99%

“…Each EKF elaboration uses the new covariance coefficients given by the children's genes and by using the same reference measured cell quantities. During the evolution of each EKF, the new fitness values for the children are calculated by using the same fitness function introduced in (12).…”

Section: Genetic Algorithm For Ekf Covariance Matricesmentioning

confidence: 99%

“…For this reason, in the literature, there are many examples of the application of KF algorithms for SOC estimation [6][7][8]. SOH cannot be estimated directly using the KF, but it can be obtained through precise estimation of cell parameters, such as internal cell resistance and capacitance [9][10][11][12][13]. In the literature, where more filters are often used simultaneously, the most popular method is that explained by [4], which uses the dual extended Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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Genetically Optimized Extended Kalman Filter for State of Health Estimation Based on Li-Ion Batteries Parameters

et al. 2022

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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.

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Parameter Estimation of Battery Pack in EV using Extended Kalman Filters

Cited by 10 publications

References 9 publications

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System

Genetically Optimized Extended Kalman Filter for State of Health Estimation Based on Li-Ion Batteries Parameters

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