The Experts below are selected from a list of 30645 Experts worldwide ranked by ideXlab platform
Phl Peter Notten - One of the best experts on this subject based on the ideXlab platform.
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Non-Zero Intercept Frequency: An Accurate Method to Determine the Integral Temperature of Li-Ion Batteries
IEEE Transactions on Industrial Electronics, 2016Co-Authors: Luc H. J. Raijmakers, Dmitri L. Danilov, Joop P. M. Van Lammeren, Thieu J. G. Lammers, Henk Jan Bergveld, Phl Peter NottenAbstract:A new impedance-based approach is introduced in which the integral Battery Temperature is related to other frequencies than the recently developed zero-intercept frequency (ZIF). The advantage of the proposed non-ZIF (NZIF) method is that measurement interferences, resulting from the current flowing through the Battery (pack), can be avoided at these frequencies. This gives higher signal-to-noise ratios (SNRs) and, consequently, more accurate Temperature measurements. A theoretical analysis, using an equivalent circuit model of a Li-ion Battery, shows that NZIFs are Temperature dependent in a way similar to the ZIF and can therefore also be used as a Battery Temperature indicator. To validate the proposed method, impedance measurements have been performed with individual LiFePO4 batteries and with large LiFePO4 Battery packs tested in a full electric vehicle under driving conditions. The measurement results show that the NZIF is clearly dependent on the integral Battery Temperature and reveals a similar behavior to that of the ZIF method. This makes it possible to optimally adjust the NZIF method to frequencies with the highest SNR.
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A comparison and accuracy analysis of impedance-based Temperature estimation methods for Li-ion batteries
Applied Energy, 2016Co-Authors: Hpgj Henrik Beelen, Lucas Lucas Raijmakers, Phl Peter Notten, Mcf Tijs Donkers, Henk Jan BergveldAbstract:In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the Battery Temperature is of paramount importance. Electrochemical Impedance Spectroscopy (EIS) can be used to estimate the Battery Temperature and several EIS-based Temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency and the latter step consists of estimating the Battery Temperature based on the measured impedance resulting from the chosen excitation. By distinguishing these steps and by performing Monte-Carlo simulations, all existing methods are compared in terms of accuracy (i.e., mean-square error) of the Temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more-accurate Temperature estimate can be obtained. In case of an unknown State-of-Charge, this novel method estimates the Temperature with an average absolute bias of 0.4°C and an average standard deviation of 0.7°C using a single impedance measurement for the Battery under consideration.
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An Improved Impedance-Based Temperature Estimation Method for Li-ion Batteries
IFAC-PapersOnLine, 2015Co-Authors: Hpgj Henrik Beelen, Lucas Lucas Raijmakers, Phl Peter Notten, Mcf Tijs Donkers, Henk Jan BergveldAbstract:Abstract In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the (internal) Battery Temperature is of paramount importance. Electrochemical impedance spectroscopy (EIS) can be used to estimate the (internal) Battery Temperature and several EIS-based Temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based Temperature estimation methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency (or frequencies) and the latter step consists of estimating the Battery Temperature based on the measured impedance resulting from the chosen excitation(s). By distinguishing these steps and by performing Monte-Carlo simulations, all existing estimation methods are compared in terms of accuracy (mean-square error) of the Temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the Temperature estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more accurate Temperature estimate can be obtained. This novel more-accurate method estimates the Temperature with an rms bias of 0.4°C and an average standard deviation of 0.7° C using a single impedance measurement for the Battery under consideration.
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sensorless Battery Temperature measurements based on electrochemical impedance spectroscopy
Journal of Power Sources, 2014Co-Authors: Lucas Lucas Raijmakers, Van Jpm Lammeren, Mjg Lammers, D Dmitry L Danilov, Phl Peter NottenAbstract:A new method is proposed to measure the internal Temperature of (Li-ion) batteries. Based on electrochemical impedance spectroscopy measurements, an intercept frequency (f0) can be determined which is exclusively related to the internal Battery Temperature. The intercept frequency is defined as the frequency at which the imaginary part of the impedance is zero (Zim = 0), i.e. where the phase shift between the Battery current and voltage is absent. The advantage of the proposed method is twofold: (i) no hardware Temperature sensors are required anymore to monitor the Battery Temperature and (ii) the method does not suffer from heat transfer delays. Mathematical analysis of the equivalent electrical-circuit, representing the Battery performance, confirms that the intercept frequency decreases with rising Temperatures. Impedance measurements on rechargeable Li-ion cells of various chemistries were conducted to verify the proposed method. These experiments reveal that the intercept frequency is clearly dependent on the Temperature and does not depend on State-of-Charge (SoC) and aging. These impedance-based sensorless Temperature measurements are therefore simple and convenient for application in a wide range of stationary, mobile and high-power devices, such as hybrid- and full electric vehicles.
Changsun Ahn - One of the best experts on this subject based on the ideXlab platform.
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Computationally Efficient Stochastic Model Predictive Controller for Battery Thermal Management of Electric Vehicle
IEEE Transactions on Vehicular Technology, 2020Co-Authors: Seho Park, Changsun AhnAbstract:The performance and safety of batteries of electric vehicles deteriorate when the Battery Temperature is too low or too high. The thermal management system regulating the Battery Temperature consumes considerable electric energy, particularly, for cooling the Battery. To maximize the vehicle driving range, the means of controlling the Battery Temperature should minimize the energy consumption. In this paper, stochastic model predictive control is applied to the Battery-cooling controller. Effective model predictive control requires a good but simple system model with proper estimation of near-future disturbances. The components of the Battery cooling system are modeled to represent the energy-consumption and heat exchange mechanism with some assumptions for simplicity. The future information is estimated using historical driving data in a stochastic sense. For real-time implementation, an unequally spaced probability distribution is introduced when designing a stochastic model of future heat generation. The proposed control method shows significantly lower energy consumption while maintaining an acceptable Temperature regulation performance compared to other Temperature controllers, such as a thermostat-type controller and a model predictive controller with simply assumed future information. The proposed predictive controller shows robust performance compared to typical controllers by utilizing the stochastic estimation of future information.
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Stochastic Model Predictive Controller for Battery Thermal Management of Electric Vehicles
2019 IEEE Vehicle Power and Propulsion Conference (VPPC), 2019Co-Authors: Seho Park, Changsun AhnAbstract:The performance and safety of a Battery for electric vehicles get worse when the Battery Temperature is very low or very high. The thermal management system regulating Battery Temperature consumes considerable electric energy, in particular, for cooling the Battery. To maximize the vehicle driving range, control of Battery Temperature should consider the consumed energy while regulating the Battery Temperature. In this paper, model predictive control is applied to the Battery cooling system. An effective model predictive control (MPC) requires a good but simple system model with proper estimation of near-future disturbances. The components of the Battery cooling system are modeled to represent the energy consuming and heat transferring mechanism with some assumptions for the simplicity. The future information is estimated using historical driving data in the stochastic sense. A stochastic model predictive control (SMPC) is designed to minimize the energy consumption with acceptable Temperature regulation. The proposed control method shows significantly reduced energy consumption while keeping acceptable Temperature regulation performance compared to a typical Temperature controller, such as a thermostat-type controller. The MPC with forecasted future information enables preemptive control actions and thus prevents unnecessarily frequent cooling system operations due to instantaneous Temperature changes, which reduces energy consumption.
Lucas Lucas Raijmakers - One of the best experts on this subject based on the ideXlab platform.
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A comparison and accuracy analysis of impedance-based Temperature estimation methods for Li-ion batteries
Applied Energy, 2016Co-Authors: Hpgj Henrik Beelen, Lucas Lucas Raijmakers, Phl Peter Notten, Mcf Tijs Donkers, Henk Jan BergveldAbstract:In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the Battery Temperature is of paramount importance. Electrochemical Impedance Spectroscopy (EIS) can be used to estimate the Battery Temperature and several EIS-based Temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency and the latter step consists of estimating the Battery Temperature based on the measured impedance resulting from the chosen excitation. By distinguishing these steps and by performing Monte-Carlo simulations, all existing methods are compared in terms of accuracy (i.e., mean-square error) of the Temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more-accurate Temperature estimate can be obtained. In case of an unknown State-of-Charge, this novel method estimates the Temperature with an average absolute bias of 0.4°C and an average standard deviation of 0.7°C using a single impedance measurement for the Battery under consideration.
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An Improved Impedance-Based Temperature Estimation Method for Li-ion Batteries
IFAC-PapersOnLine, 2015Co-Authors: Hpgj Henrik Beelen, Lucas Lucas Raijmakers, Phl Peter Notten, Mcf Tijs Donkers, Henk Jan BergveldAbstract:Abstract In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the (internal) Battery Temperature is of paramount importance. Electrochemical impedance spectroscopy (EIS) can be used to estimate the (internal) Battery Temperature and several EIS-based Temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based Temperature estimation methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency (or frequencies) and the latter step consists of estimating the Battery Temperature based on the measured impedance resulting from the chosen excitation(s). By distinguishing these steps and by performing Monte-Carlo simulations, all existing estimation methods are compared in terms of accuracy (mean-square error) of the Temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the Temperature estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more accurate Temperature estimate can be obtained. This novel more-accurate method estimates the Temperature with an rms bias of 0.4°C and an average standard deviation of 0.7° C using a single impedance measurement for the Battery under consideration.
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sensorless Battery Temperature measurements based on electrochemical impedance spectroscopy
Journal of Power Sources, 2014Co-Authors: Lucas Lucas Raijmakers, Van Jpm Lammeren, Mjg Lammers, D Dmitry L Danilov, Phl Peter NottenAbstract:A new method is proposed to measure the internal Temperature of (Li-ion) batteries. Based on electrochemical impedance spectroscopy measurements, an intercept frequency (f0) can be determined which is exclusively related to the internal Battery Temperature. The intercept frequency is defined as the frequency at which the imaginary part of the impedance is zero (Zim = 0), i.e. where the phase shift between the Battery current and voltage is absent. The advantage of the proposed method is twofold: (i) no hardware Temperature sensors are required anymore to monitor the Battery Temperature and (ii) the method does not suffer from heat transfer delays. Mathematical analysis of the equivalent electrical-circuit, representing the Battery performance, confirms that the intercept frequency decreases with rising Temperatures. Impedance measurements on rechargeable Li-ion cells of various chemistries were conducted to verify the proposed method. These experiments reveal that the intercept frequency is clearly dependent on the Temperature and does not depend on State-of-Charge (SoC) and aging. These impedance-based sensorless Temperature measurements are therefore simple and convenient for application in a wide range of stationary, mobile and high-power devices, such as hybrid- and full electric vehicles.
Seho Park - One of the best experts on this subject based on the ideXlab platform.
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Computationally Efficient Stochastic Model Predictive Controller for Battery Thermal Management of Electric Vehicle
IEEE Transactions on Vehicular Technology, 2020Co-Authors: Seho Park, Changsun AhnAbstract:The performance and safety of batteries of electric vehicles deteriorate when the Battery Temperature is too low or too high. The thermal management system regulating the Battery Temperature consumes considerable electric energy, particularly, for cooling the Battery. To maximize the vehicle driving range, the means of controlling the Battery Temperature should minimize the energy consumption. In this paper, stochastic model predictive control is applied to the Battery-cooling controller. Effective model predictive control requires a good but simple system model with proper estimation of near-future disturbances. The components of the Battery cooling system are modeled to represent the energy-consumption and heat exchange mechanism with some assumptions for simplicity. The future information is estimated using historical driving data in a stochastic sense. For real-time implementation, an unequally spaced probability distribution is introduced when designing a stochastic model of future heat generation. The proposed control method shows significantly lower energy consumption while maintaining an acceptable Temperature regulation performance compared to other Temperature controllers, such as a thermostat-type controller and a model predictive controller with simply assumed future information. The proposed predictive controller shows robust performance compared to typical controllers by utilizing the stochastic estimation of future information.
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Stochastic Model Predictive Controller for Battery Thermal Management of Electric Vehicles
2019 IEEE Vehicle Power and Propulsion Conference (VPPC), 2019Co-Authors: Seho Park, Changsun AhnAbstract:The performance and safety of a Battery for electric vehicles get worse when the Battery Temperature is very low or very high. The thermal management system regulating Battery Temperature consumes considerable electric energy, in particular, for cooling the Battery. To maximize the vehicle driving range, control of Battery Temperature should consider the consumed energy while regulating the Battery Temperature. In this paper, model predictive control is applied to the Battery cooling system. An effective model predictive control (MPC) requires a good but simple system model with proper estimation of near-future disturbances. The components of the Battery cooling system are modeled to represent the energy consuming and heat transferring mechanism with some assumptions for the simplicity. The future information is estimated using historical driving data in the stochastic sense. A stochastic model predictive control (SMPC) is designed to minimize the energy consumption with acceptable Temperature regulation. The proposed control method shows significantly reduced energy consumption while keeping acceptable Temperature regulation performance compared to a typical Temperature controller, such as a thermostat-type controller. The MPC with forecasted future information enables preemptive control actions and thus prevents unnecessarily frequent cooling system operations due to instantaneous Temperature changes, which reduces energy consumption.
Ala A. Hussein - One of the best experts on this subject based on the ideXlab platform.
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Experimental modeling and analysis of lithium-ion Battery Temperature dependence
2015 IEEE Applied Power Electronics Conference and Exposition (APEC), 2015Co-Authors: Ala A. HusseinAbstract:Battery performance is strongly dependent on the ambient Temperature. For example, at moderate Temperatures, the Battery performance is optimal, whereas at extreme Temperatures, the Battery performance is not optimized and sometimes unexpected. In order to predict the Battery behavior, a model that involves the Battery's underlying dynamics is usually used. The majority of dynamic Battery models are derived at only one single Temperature (room Temperature), which can easily lead to failure in predicting the Battery performance when the Temperature varies. Therefore, adding some Temperature dependence to those models can make the Battery management system more reliable, safer, and moreover, prolong the Battery lifetime. In this paper, a 3.6V/1100mAh lithium-ion (Li-ion) Battery cell is tested at Temperature between -30°C and +50°C and its main parameters are measured. The measured parameters include the discharge capacity, the charge and discharge resistance, and the open-circuit voltage, which comprise the main parameters of equivalent electric-circuit based models. Experimental testing results and observations are presented in this paper.
