Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

Bizeray, Adrien M.; Zhao, Shi; Duncan, Stephen R.; Howey, David A.

doi:10.1016/j.jpowsour.2015.07.019

Cited by 218 publications

(177 citation statements)

References 50 publications

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“…The key step to achieve joint state estimation is to build a simple and accurate battery electrothermal model firstly, then suitable estimation methods such as slide mode observer, Kalman filter observer can be applied accordingly. For example, Bizeray et al [102] proposed a thermal-electrochemical model to achieve a joint estimation of Li-ion battery. After solving the partial differential equations of coupled model by orthogonal collocation approach, battery SOC and internal temperature can be estimated by a modified EKF.…”

Section: Joint State Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Section: Joint State Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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“…A 'zero dimensional' electrochemical model for Li-S cell has recently been published [46] and while there is further work to be done to extend this and parameterize real, physical cells, this is a promising direction for future exploitation. As the state of the art in modelling develops, it is envisaged that it will be possible to explore state-of-the-art techniques for rapid online execution of spatially distributed models [57]. A possible road map is shown in Figure 5, including contributions by control engineers follow developments in fast-executing electrochemical models.…”

Section: State-of-the-art Li-s Cell Modelling and State Estimation Tementioning

confidence: 99%

Lithium-Sulfur Battery Technology Readiness and Applications—A Review

et al. 2017

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Lithium Sulfur (Li-S) battery is generally considered as a promising technology where high energy density is required at different applications. Over the past decade, there has been an ever increasing volume of Li-S academic research spanning materials development, fundamental understanding and modelling, and application-based control algorithm development. In this study, the Li-S battery technology, its advantages and limitations from the fundamental perspective are firstly discussed. In the second part of this study, state-of-the-art Li-S cell modelling and state estimation techniques are reviewed with a focus on practical applications. The existing studies on Li-S cell equivalent-circuit-network modelling and state estimation techniques are then discussed. A number of challenges in control of Li-S battery are also explained such as the flat open-circuit-voltage curve and high sensitivity of Li-S cell's behavior to temperature variation. In the last part of this study, current and future applications of Li-S battery are mentioned.

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“…Different set points for charge stored are tried for this case. In case if there are bounds on the applied current, it is bounded by applying upper and lower limits using relation given in Equation 27. Here, we assume a lower bound of zero, and an upper bound of 120 μA/cm 2 .…”

Section: Generic Model Control For Battery Modelsmentioning

confidence: 99%

Generic Model Control for Lithium-Ion Batteries

Pathak

Kolluri

Subramanian

2017

J. Electrochem. Soc.

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Battery Management Systems (BMS) are critical to safe and efficient operation of lithium-ion batteries and accurate prediction of the internal states. Smarter BMS that can estimate and implement optimal charging profiles in real-time are important for advancement of the Li-ion battery technology. Estimating optimal profiles using physics-based models is computationally expensive because of the non-linear and stiff nature of the model equations, involving the need for constrained nonlinear optimization. In this work, we present an alternative approach to control batteries called as Generic Model Control, or Reference System Synthesis. This work enables robust stabilization and control of battery models to set-point as an alternative approach, eliminating the need to perform optimization of nonlinear models. As compared to the generic model control approaches implemented by previous researchers, we implement the same concept using direct DAE numerical solvers. The results are presented for single input single objective problems, and for constrained problems for various battery models. Lithium-ion batteries are used in a wide range of applications ranging from cell phones to electric vehicles (EVs) to electric grids. With the expected decline in the price of batteries over the next few years, the market for EVs and grids is growing rapidly. Long recharging times, capacity fade and thermal runaway are some of the main issues that need to be addressed in order to use batteries safely and efficiently for a longer time. This necessitates the use of smarter battery management systems that can derive optimal use strategies by exploiting the dynamics of the battery.Battery models based on transport, physical, electrochemical and thermodynamics principles can be used to monitor the internal states of the battery and to obtain optimal control strategies. Such models are computationally expensive which limits their use in control applications in real-time. Various issues, such as the onset of dynamics of some internal states only during use of batteries at high charge/discharge rates, and several other states which are active only while charging or discharging, adds to the challenges of the observability of these physics-based electrochemical models. Hence, various approximated and reduced order models have been proposed in the past, to make the models strongly observable in local intervals. 1,[2][3][4][5] These models are also highly non-linear in nature, which adds significant challenges to implement control strategies along with the abovementioned issues. Past researchers have shown multivariable control techniques like Dynamic Matrix Control (DMC), 6 Internal Model Control (IMC), 7 and Model Predictive Heuristic Control (MPHC) 8 for a variety of systems. However, these models suffer from their reliability on the linear approximations of the experimentally obtained step-response data and do not directly consider the full nonlinear model explicitly. Several researchers have been working to design optimal charging profiles ...

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Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

Cited by 218 publications

References 50 publications

A brief review on key technologies in the battery management system of electric vehicles

A brief review on key technologies in the battery management system of electric vehicles

Lithium-Sulfur Battery Technology Readiness and Applications—A Review

Generic Model Control for Lithium-Ion Batteries

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