Optimal Charging of Li-Ion Batteries With Coupled Electro-Thermal-Aging Dynamics

Pérez, Hector E.; Hu, Xiaosong; Dey, Satadru; Moura, Scott J.

doi:10.1109/tvt.2017.2676044

Cited by 246 publications

(112 citation statements)

References 28 publications

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“…They are expected to be close if M is large enough. Figure 3 shows the NRMSE obtained by solving the original NLS problem (8) and applying the C-NLS and R-NLS methods, respectively. It is first seen that the NLS leads to extremely poor estimation accuracy, due to the intrinsic non-convexity as aforementioned.…”

Section: Simulation Resultsmentioning

confidence: 99%

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One-shot parameter identification of the Thevenin’s model for batteries: Methods and validation

Tian

Wang

Chen

et al. 2020

Journal of Energy Storage

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Parameter estimation is of foundational importance for various model-based battery management tasks, including charging control, state-of-charge estimation and aging assessment. However, it remains a challenging issue as the existing methods generally depend on cumbersome and time-consuming procedures to extract battery parameters from data. Departing from the literature, this paper sets the unique aim of identifying all the parameters offline in a one-shot procedure, including the resistance and capacitance parameters and the parameters in the parameterized function mapping from the state-of-charge to the open-circuit voltage. Considering the well-known Thevenin's battery model, the study begins with the parameter identifiability analysis, showing that all the parameters are locally identifiable. Then, it formulates the parameter identification problem in a prediction-error-minimization framework.As the non-convexity intrinsic to the problem may lead to physically meaningless estimates, two methods are developed to overcome this issue. The first one is to constrain the parameter search within a reasonable space by setting parameter bounds, and the other adopts regularization of the cost function using prior parameter guess. The proposed identifiability analysis and identification methods are extensively validated through simulations and experiments.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Provided the I-V data in the constant-current discharging stage (encompassed by the black dotted-line box in Figure 7), the C-NLS and R-NLS methods were applied to extract parameter estimates, using the information setting in Table 1. Meanwhile, the original NLS problem (8) was also solved as a benchmark.…”

Section: Parameter Identification With Training Datamentioning

confidence: 99%

One-shot parameter identification of the Thevenin’s model for batteries: Methods and validation

Tian

Wang

Chen

et al. 2020

Journal of Energy Storage

View full text Add to dashboard Cite

show abstract

“…In order to capture battery electric behaviours (e.g., current, voltage, SOC) and thermal behaviours (e.g., surface and internal temperature) simultaneously, several coupled electro-thermal models have been developed in the literature, including both lumpparameter and distributed-parameter models [37][38][39]. Further, Goutam et al [40] proposed a three-dimensional electro-thermal model to estimate battery SOC and calculate heat generation.…”

Section: Battery Coupled Electro-thermal Modelmentioning

confidence: 99%

“…Researchers need to select the suitable evolutionary algorithm and the parameters empirically. Charging battery based on the pseudo-spectral optimization is also a popular and effective method in the real battery charging applications [139,140]. Many complicated charging conditions can be considered by pseudo-spectral optimization.…”

Section: Optimization Of Battery Charging Approachmentioning

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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“…Combined electro-thermal models have also been developed in [21][22][23], usable for thermal property determination resulting from specific input current profiles. An electro-thermal model with aging dynamics was presented in [24], with a focus on finding a compromise between charge time, aging, and balanced charge scenarios using static charge currents.…”

Section: Introductionmentioning

confidence: 99%

Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging

et al. 2018

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Fast charging is an exciting topic in the field of electric and hybrid electric vehicles (EVs/HEVs). In order to achieve faster charging times, fast-charging applications involve high-current profiles which can lead to high cell temperature increase, and in some cases thermal runaways. There has been some research on the impact caused by fast-charging profiles. This research is mostly focused on the electrical, thermal and aging aspects of the cell individually, but these factors are never treated together. In this paper, the thermal progression of the lithium-ion battery under specific fast-charging profiles is investigated and modeled. The cell is a Lithium Nickel Manganese Cobalt Oxide/graphite-based cell (NMC) rated at 20 Ah, and thermal images during fast-charging have been taken at four degradation states: 100%, 90%, 85%, and 80% State-of-Health (SoH). A semi-empirical resistance aging model is developed using gathered data from extensive cycling and calendar aging tests, which is coupled to an electrothermal model. This novel combined model achieves good agreement with the measurements, with simulation results always within 2°C of the measured values. This study presents a modeling methodology that is usable to predict the potential temperature distribution for lithium-ion batteries (LiBs) during fast-charging profiles at different aging states, which would be of benefit for Battery Management Systems (BMS) in future thermal strategies.

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Optimal Charging of Li-Ion Batteries With Coupled Electro-Thermal-Aging Dynamics

Cited by 246 publications

References 28 publications

One-shot parameter identification of the Thevenin’s model for batteries: Methods and validation

One-shot parameter identification of the Thevenin’s model for batteries: Methods and validation

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

Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging

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