Comparison of Lithium-Ion Battery Models for Simulating Storage Systems in Distributed Power Generation

Hinz, Hartmut

doi:10.3390/inventions4030041

Cited by 57 publications

(30 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several battery models have been introduced to model the discharge process of the batteries for accurate prediction of EOD [4][5][6]. However, most of these models suffer from an increasing uncertainty in their EOD predictions over time [7]. This is because the batteries degrade with aging and computational models suffer from a reality gap between the physical process and the simulated one.…”

Section: Introductionmentioning

confidence: 99%

Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning

et al. 2021

View full text Add to dashboard Cite

Lithium-ion (Li-I) batteries have recently become pervasive and are used in many physical assets. For the effective management of the batteries, reliable predictions of the end-of-discharge (EOD) and end-of-life (EOL) are essential. Many detailed electrochemical models have been developed for the batteries. Their parameters are calibrated before they are taken into operation and are typically not re-calibrated during operation. However, the degradation of batteries increases the reality gap between the computational models and the physical systems and leads to inaccurate predictions of EOD/EOL. The current calibration approaches are either computationally expensive (model-based calibration) or require large amounts of ground truth data for degradation parameters (supervised data-driven calibration). This is often infeasible for many practical applications. In this paper, we introduce a reinforcement learning-based framework for reliably inferring calibration parameters of battery models in real time. Most importantly, the proposed methodology does not need any labeled data samples of observations and the ground truth parameters. The experimental results demonstrate that our framework is capable of inferring the model parameters in real time with better accuracy compared to approaches based on unscented Kalman filters. Furthermore, our results show better generalizability than supervised learning approaches even though our methodology does not rely on ground truth information during training.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Equivalent circuit battery models are developed by using resistors, capacitors and voltage sources in various combinations [22][23][24][25]. In [26], three equivalent circuital models, the Rint model, the Thevenin model and the Double Polarization (DP) model, are compared.…”

Section: Introductionmentioning

confidence: 99%

Battery Models for Battery Powered Applications: A Comparative Study

et al. 2020

View full text Add to dashboard Cite

Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer’s datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed.

show abstract

“…Electrochemical-based models are suitable for battery design purpose and are very accurate in representing the electrochemical processes occurring in batteries. These models require a variety of cell parameters and complex numerical computational methods that need in-depth knowledge of the battery chemical structure and properties [21]. Although high accuracy can be achieved using these models, they are unsuitable to use in simulations where the electrical terminal behavior of the battery and the state of charge (SOC) need to be determined with reasonable computing times.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, thermal based models have gained researchers' interest due to the increased use of Li-ion batteries in EVs, so understanding the thermal behavior of Li-ion batteries is essential for both the battery operating life requirements and safety considerations [21]. The authors in [22] proposed a novel battery charging control strategy based on a newly developed coupled thermoelectric model that applies the constrained generalized predictive control to maintain the battery health and longer lifetime while fast charging without causing temperature rise during charging, while the authors in [23] attempted to improve battery performance by enabling effective self-heating for Li-ion batteries at low temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…Electric circuit-based models are used to describe the electrical performance of the EV battery by using equivalent circuits composed of passive components such as resistors and capacitors, possibly inductors and a voltage source. Therefore, they are particularly suitable and can be easily incorporated into circuit simulations [21]. The accuracy that is achievable with these models can be maximized by modifying these models to address the dynamic behavior by adding the effects of, for example, state of health (SOH), temperature and capacity fading on battery output characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bidirectional Electrical Vehicle Charger and Grid Interface for Grid Voltage Dip Mitigation

2020

View full text Add to dashboard Cite

Power quality issues have recently become a source of major concern due to the large increase in load demand and the addition of various sources of disturbance at the distribution level. Power quality mainly refers to voltage quality. Sudden load variations can lead to a fall in the line voltage magnitude, creating what is called a voltage sag. Many solutions have been proposed and implemented for voltage sag compensation. Power electronics-based solutions such as grid-connected converters and AC/DC schemes are considered basic units for transient voltage fault ride-through capability. This paper describes a multifunctional intelligent bidirectional electrical vehicle (EV) charger that is able to charge the EV battery at different power ratings in addition to voltage sag compensation. The performance of the proposed system is verified and validated through MATLAB/Simulink simulations (R2020A). The proposed solution can effectively meet three main requirements: charging the EV battery at different power ratings, detecting the voltage sag event, and providing the required active and reactive power compensation for voltage sag compensation.

show abstract

Comparison of Lithium-Ion Battery Models for Simulating Storage Systems in Distributed Power Generation

Cited by 57 publications

References 40 publications

Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning

Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning

Battery Models for Battery Powered Applications: A Comparative Study

A Bidirectional Electrical Vehicle Charger and Grid Interface for Grid Voltage Dip Mitigation

Contact Info

Product

Resources

About