Online maximum discharge power prediction for lithium-ion batteries with thermal safety constraints

The current grid code in China in regard to solar forecasting is, in my opinion, underdeveloped, especially in contrast to the rate at which photovoltaics are being installed. As such, explaining the limitations of the grid code and resetting pathways to improve it are thought utilitarian for those scientists and policymakers who are responsible for or aware of the grid code but have not themselves worked on the problem of forecasting. In this perspective article, I should first explain with respect to China's grid code the perceived deficiencies in the current forecasting research and practices, and then outline a five-stage workflow that could completely mitigate the situation. Among other things, the over-reliance on accuracy as the basis for gauging the goodness of forecasts is identified as a root cause for the status quo, and thus, I advocate a holistic forecast verification procedure that encompasses consistency, quality, and value. With that in mind, the proposed workflow for better solar forecasting for grid integration purposes relies on the effective information flow among the weather department, grid operators, and individual plant owners, which is inline with the current grid code. What goes beyond this is that the proposal further introduces a couple of concepts called “hierarchical reconciliation” and “firm forecasting,” which are new but are able to eliminate forecast errors wholly, thus making solar power dispatchable on the system level. With a slight premium incurred, it is now possible to manage solar plants, or variable renewables in general, in the same style as managing conventional fire-powered generators.

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A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries

Ma,

Wu,

Wang

et al. 2024

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Lithium-ion batteries are widely applied in the form of new energy electric vehicles and large-scale battery energy storage systems to improve the cleanliness and greenness of energy supply systems. Accurately estimating the state of power (SOP) of lithium-ion batteries ensures long-term, efficient, safe and reliable battery operation. Considering the influence of the parameter identification accuracy on the results of state of power estimation, this paper presents a systematic review of model parameter identification and state of power estimation methods for lithium-ion batteries. The parameter identification methods include the voltage response curve analysis method, the least squares method and so on. On this basis, the methods used for modeling and estimating the SOP of battery cells and battery packs are classified and elaborated, focusing on summarizing the research progress observed regarding the joint estimation method for multiple states of battery cells. In conclusion, future methods for estimating the SOP of lithium-ion batteries and their improvement targets are envisioned based on the application requirements for the safe management of lithium-ion batteries.

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Online maximum discharge power prediction for lithium-ion batteries with thermal safety constraints

Cited by 3 publications

References 32 publications

Thermal hazard evaluation of 18650 lithium-ion batteries at various discharge rates

Thermal hazard evaluation of 18650 lithium-ion batteries at various discharge rates

The future of solar forecasting in China

A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries

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