Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of On-Site Reference Tests

Dubarry, Matthieu; Tun, Moe; Baure, George; Matsuura, Marc; Rocheleau, Richard

doi:10.3390/electronics10131593

Cited by 13 publications

(2 citation statements)

References 52 publications

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“…Because the current was not constant, capacity and time were uncorrelated, which offered an opportunity to study two different datasets, V vs. Q and V vs. t . While using voltage versus capacity is standard, it might not be the best solution for deployed systems because the V vs. t dataset should be less error-prone than the V vs. Q one, as capacity is not directly measurable but derived from time and current 46 . The V vs. Q dataset is, however, expected to be easier to diagnose because the area under a d Q /d V peak corresponds to capacity, and, at low rates, it is independent of the applied current because it has a finite value.…”

Section: Discussionmentioning

confidence: 99%

Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics

2023

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Photovoltaics supply a growing share of power to the electric grid worldwide. To mitigate resource intermittency issues, these systems are increasingly being paired with electrochemical energy storage devices, e.g., Li-ion batteries, for which ensuring long and safe operation is critical. However, in this operation framework, secondary Li-ion batteries undergo sporadic usage, which prevents the application of standard diagnostic methods. Here, we propose a diagnostic methodology that uses machine learning algorithms trained directly on data obtained from photovoltaic charging of Li-ion batteries. The training is carried out on synthetic voltage data at various degradation conditions calculated from clear sky model irradiance data. The method is validated using synthetic voltage responses calculated from plane of array irradiance observations for a photovoltaic system located in Maui, HI, USA. We report an average root mean square error of 2.75% obtained for more than 10,000 different degradation paths with 25% or less degradation on the Li-ion cells.

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Section: Discussionmentioning

confidence: 99%

Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics

2023

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“…Because current was not constant, capacity and time were uncorrelated, which offered an opportunity to study two different datasets, V vs. Q and V vs. t.. While using voltage versus capacity is more traditional, it might not be the best solution for deployed systems because the V vs. t dataset should be less error-prone than the V vs. Q one as capacity is not directly measurable, but derived from time and current [39]. The V vs. Q dataset is however expected to be easier to diagnose because the area under a dQ/dV peak corresponds to capacity and, at low rates, it is independent of the applied current because it has a nite value.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Direct Diagnosis of PV Connected Batteries

Dubarry¹,

Costa²,

Matthews³

2022

Meet. Abstr.

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Photovoltaic systems are providing a growing share of power to the electric grid worldwide. As more and more of these systems are tied with battery energy storage to balance their intermittency, being able to ensure long and safe operation of these batteries will become critical. Because of their sporadic usage, far from the typical constant current or constant power, diagnosability without having to perform lengthy maintenance cycles will be a key factor to accelerate deployment. This work proposes a new methodology to enable the training of machine learning algorithms so they could be applied directly to photovoltaic (PV) battery charging data for opportunistic diagnosis. This is done by training the algorithms on synthetic voltage data under different degradations calculated from clear-sky model irradiance data. Validation was performed on synthetic voltage responses calculated from plane of array irradiance observations for a PV system located in Maui, HI, USA. Herein, we will present the result of our proof-of-concept study where we investigated the diagnosability of the voltage response of PV charged batteries under 18 different cloud coverages scenarios and for more than 10,000 different degradations. We also evaluated the effect of training conditions on clear-sky irradiance data by analyzing the resulting differences from training on 700,000 different voltage curves for a single day vs. 45,000 different voltage curves for of each of the 18 days used for cloud coverage estimation. In addition, we considered and quantified the effect of cell-to-cell variations. Training was performed on five state-of-the-art machine learning algorithms for battery diagnosis which included decision tree ensemble methods and neural networks. Finally, because the diagnosis was done outside of constant current, the capacity- and time-based information were decorrelated and compared. While time-base diagnosis is the most interesting for deployed systems, it was shown to be less accurate than its capacity counterpart for the tested algorithms. Yet, the accuracy of the diagnosis is still satisfactory for days where cloud effect is limited (average RMSE of 2.5% compared to 1.3% for capacity based if 50% or more clear skies). Summarizing, we will show that the degradation of PV-connected batteries could be diagnosed opportunistically without the need for maintenance cycles using well-trained state-of-art machine learning algorithms. Calculated RMSEs were below 2% for days with 50% or more clear skies considering more than 10,000 different degradation paths with 25% or less of the three thermodynamic degradation modes. In addition to the results themselves, and before real PV battery degradation data becomes available, this work is showing the significant benefits of using synthetic data to understand the expected variations and to start preparing adapted tools for when data become available.

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Operational Validation of a Power Distribution Algorithm for a Modular Megawatt Battery Storage System

Koltermann

Jacqué

Figgener

et al. 2023

Batteries & Supercaps

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Large-scale battery storage systems have become popular for various grid services in recent years. A worldwide market growth for battery storage has led to increased competition in several grid service markets. Modular large-scale battery storage systems require a safe, highly available, and intelligent energy management system (EMS) in order to be economically competitive. One component of this EMS is the control for distributing the power requests between individual battery units of the large-scale battery storage system. As the EMS is usually undisclosed intellectual property of the system manufacturers, there is only little information on real-world operation available. To contribute, we present a rule-based power distribution algorithm (SPDA) in this paper and validate it through field tests on a 6 MW/7.5 MWh system that is providing frequency containment reserve to the German power grid. The results show that especially when combining different battery technologies, the SPDA can exploit individual technological strengths. In this way, the state of charge of the batteries, energy throughput and power load of the batteries can be controlled to extend the lifetime. Moreover, the SPDA managed to shift nearly 80 % of the energy throughput to one battery unit to protect less cyclic stable batteries and make use of the advantage of cyclic stable battery technologies, while fulfilling all grid service requirements. By shifting those large quantities of the energy throughput to more cyclic stable battery units, the large-scale battery storage system experienced in sum up to 45 % less cyclic aging with the SPDA than with a symmetrical power distribution algorithm. Furthermore, the operational efficiency of a large-scale battery storage system can be significantly improved via additional software adaptations of the power distribution, depending on the system layout.

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Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of On-Site Reference Tests

Cited by 13 publications

References 52 publications

Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics

Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics

Data-Driven Direct Diagnosis of PV Connected Batteries

Operational Validation of a Power Distribution Algorithm for a Modular Megawatt Battery Storage System

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