Model Predictive Control-Based Fast Charging for Vehicular Batteries

Yan, Jingyu; Xu, Guoqing; Qian, Huihuan; Xu, Yangsheng; Song, Zhibin

doi:10.3390/en4081178

Cited by 48 publications

(20 citation statements)

References 32 publications

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“…The solutions, developed on the basis of linear quadratic optimal control, will be presented as controlled charging laws expressed in explicit equations. The proposed methods differ from existing ones in [8,11,12] in two aspects. From the viewpoint of application, they keep into account both user specifications and battery health.…”

Section: Introductionmentioning

confidence: 91%

“…This calls for the deployment of closedloop model-based control. Constrained optimal control has thus been used in [8,11,12], in conjunction with electrochemical or equivalent circuit models, to address fast charging subject to input, state and temperature constraints for health. To mitigate the computational cost, a rule-based, easier-to-implement method is proposed in [13,14] to handle charging under constraints using an on/off strategy.…”

Section: Introductionmentioning

confidence: 99%

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Health-Aware and User-Involved Battery Charging Management for Electric Vehicles Using Linear Quadratic Control

Fang

Wang

2015

Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Cont

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This paper studies control-theory-enabled charging management for battery systems in electric vehicles (EVs). Charging is a crucial factor for the battery performance and life as well as EV users anxiety. Existing methods run with two shortcomings: insufficiency of battery health awareness during charging,and failure to include the user into the charging loop. To address such issues, we propose to perform charging that deals with both health protection and user-specified charging needs or objectives. Capitalizing on the linear quadratic control theory, a set of charging strategies are developed. A simulation-based study demonstrates their effectiveness and potential. We expect that charging with health awareness and user involvement will improve not only the battery longevity but also user satisfaction. American Society of Mechanical Engineers (ASME)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTThis paper studies control-theory-enabled charging management for battery systems in electric vehicles (EVs). Charging is a crucial factor for the battery performance and life as well as EV users' anxiety. Existing methods run with two shortcomings: insufficiency of battery health awareness during charging, and failure to include the user into the charging loop. To address such issues, we propose to perform charging that deals with both health protection and user-specified charging needs or objectives. Capitalizing on the linear quadratic control theory, a set of charging strategies are developed. A simulation-based study demonstrates their effectiveness and potential. We expect that charging with health awareness and user involvement will improve not only the battery longevity but also user satisfaction.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Health-Aware and User-Involved Battery Charging Management for Electric Vehicles Using Linear Quadratic Control

Fang

Wang

2015

Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Cont

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“…To this end, we need a temperature model for the battery. References [19] and [23] describe the temperature model of the battery as a linear system with two states, namely, T core and T air , and reference [18] uses the nonlinear heat transfer equation with a single state. Simulations show that the dynamics of T air have negligible fluctuations around the ambient temperature.…”

Section: Optimal Charging Problem Considering Temperaturementioning

confidence: 99%

Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model

Abdollahi

Han

Balasingam

et al. 2016

Journal of Power Sources

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In this paper, the first of a series of papers on optimal battery charging, we present a closed-form solution to the problem of optimally charging a Li-ion battery. A combination of three cost functions is considered as the objective function: time-to-charge (TTC), energy losses (EL), and a temperature rise index (TRI). First, we consider the cost function of the optimization problem as a weighted sum of TTC and EL. We show that the optimal charging strategy in this case is the well-known Constant Current-Constant Voltage (CC-CV) policy with the value of the current in the CC stage being a function of the ratio of weighting on TTC and EL and of the resistance of the battery. Then, we extend the cost function to a weighted sum of TTC, EL and TRI and derive an analytical solution for the problem. It is shown that the analytical solution can be approximated by a CC-CV with the value of current in the CC stage being a function of ratio of weighting on TTC and EL, resistance of the battery and the effective thermal resistance. Index Terms battery charging, optimal charging, time to charge, open circuit voltage, state of charge.

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“…Therefore, designing a high-quality battery charger is essential. The objectives of a high-quality charger include high charging efficiency, short charging time and prolonged cycle life [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method

et al. 2013

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Lithium ion (Li-ion) batteries have been widely used in various kinds of applications, including consumer electronics, green energy systems and electrical vehicles. Since the charging method has a significant influence on the performance and lifetime of Li-ion batteries, an intelligent charging algorithm which can properly determine the charging current is essential. In this study, a fuzzy-logic-control-based (FLC-based) five-stage Li-ion battery charger is proposed. The proposed charger takes the temperature rise and the gradient of temperature rise of battery into account, and adjusts the charging current accordingly. To further improve the performance of the proposed FLC, the fuzzy-based Taguchi method is utilized to determine the optimal output membership functions (MFs). Comparing with the conventional constant current-constant voltage (CC-CV) method, the charging time, charging efficiency, average temperature rise and the obtained cycle life of the Li-ion battery are improved by about 58.3%, 1.65%, 26.7% and 59.3%, respectively.

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Model Predictive Control-Based Fast Charging for Vehicular Batteries

Cited by 48 publications

References 32 publications

Health-Aware and User-Involved Battery Charging Management for Electric Vehicles Using Linear Quadratic Control

Health-Aware and User-Involved Battery Charging Management for Electric Vehicles Using Linear Quadratic Control

Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model

Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method

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