The culprit behind the degradation of lithium-ion batteries over time is not lithium, but hydrogen emerging from the electrolyte, a new study finds. This discovery could improve the performance and life expectancy of a range of rechargeable batteries. Contact online >>
The culprit behind the degradation of lithium-ion batteries over time is not lithium, but hydrogen emerging from the electrolyte, a new study finds. This discovery could improve the performance and life expectancy of a range of rechargeable batteries.
Lithium-ion batteries power everything from smart phones and laptops to electric cars and large-scale energy storage facilities. Batteries lose capacity over time even when they are not in use, and older cellphones run out of power more quickly. This common phenomenon, however, is not completely understood.
Now, an international team of researchers, led by the University of Colorado-Boulder, SLAC National Accelerator Laboratory, and Stanford University has revealed an underlying mechanism behind such battery degradation. Their discovery could help scientists develop better batteries, which would allow electric vehicles to run farther and last longer, while also advancing energy storage technologies that would accelerate the transition to clean energy.
The findings were published Sept. 12 in the journal Science.
"We are helping to advance lithium-ion batteries by figuring out the molecular level processes involved in their degradation," said Michael Toney, a senior author of the study and a professor of chemical and biological engineering at the University of Colorado.
"Having a better battery is very important in shifting our energy infrastructure away from fossil fuels to more renewable energy sources," said Toney, who was a senior staff scientist at SLAC when most of this study''s experiments were done.
Transportation is the single largest source of greenhouse gases generated in the U.S, accounting for 28% of the country''s emissions in 2021. In an effort to reduce emissions, many automakers have committed to moving away from developing gasoline cars to produce more EVs instead. But EV manufacturers face a host of challenges, including limited driving range, higher production costs and shorter battery lifespan than conventional vehicles. In the U.S. market, a typical all-electric car can run about 250 miles in a single charge, about 60% that of a gasoline car. The new study has the potential to address all these issues, Toney said.
Rechargeable batteries lose stored energy when they''re not being used because an idle battery undergoes internal chemical reactions that slowly drain its energy. This "self-discharge" process can eventually consume active ingredients in the cathode, where the electron-spent lithium ions collect while the device is in use. This shortens a battery''s life expectancy.
For decades, researchers have assumed that self-discharge inlithium-ion batteriesis caused by the movement of lithium ions, but the new research finds compelling evidence that hydrogen, not lithium, is the true culprit. Using facilities from U.S. Department of Energy''sArgonne National LaboratoryandPacific Northwest National Laboratory, this team discovered that hydrogen atoms from the battery''s electrolyte would move to cathode and the protons will take some of the spots that lithium ions normally bind to.The cathode is also the conduit for electrons while charging the battery, but not so much if many hydrogen atoms – the tiniest of all atoms – are occupying lithium-ion parking spots.
"Numerous electrolyte degradation pathways have been predicted by various theoretical modeling," said Kang Xu, a senior author of this study and the chief scientist at SES AI, a producer of lithium metal batteries. "Our study provides one of the first direct and solid experimental evidence on the hydrogen transfer that led to the electrolyte degradation."
The scientists discovered that the more lithium is pulled out of the cathode during charging, the more hydrogen atoms accumulate on the surface.
"Also, removing lithium from the charged cathode generates a lot of openings on the surface that allow hydrogen atoms to go deeper inside," said Gang Wan, lead author of the study and a research scientist in Stanford''s School of Engineering. "This process induces self-discharge and causes mechanical stress that can cause cracks in the cathode and accelerate degradation."
Environmental Venture and Realizing Environmental Innovation grants include new research projects on methane, self-fertilizing plants, and wastewater.
Jingmei Yu, Yaoyang Cai, Yingxin Huang, Xinle Yang; Remaining useful life prediction of lithium-ion batteries based on FEEMD-LSTM-TAM-OKELM. AIP Advances 1 November 2024; 14 (11): 115229. https://doi /10.1063/5.0236673
In response to the needs of today''s new energy era, lithium-ion batteries are based on advanced manufacturing technology and have unique advantages such as high energy density, low self-discharge rate, and long life.1–3 People''s demand for convenient battery storage, green environmental protection, long cycle life, etc., widely used in urban construction of energy storage equipment, electric power transport, renewable energy systems, and other areas play an indispensable role.4
However, due to the chemical reaction inside the lithium-ion battery and the influence of external environmental factors, the degradation of the battery is inevitable. If there is no human treatment, lithium-ion battery life will be significantly reduced, its power system will have functional failure problems, which may lead to significant safety accidents.5 In battery health detection and management, it is necessary to predict the future battery capacity and Remaining Useful Life (RUL). Timely and accurate RUL prediction can effectively avoid accident risks.6
It takes a lot of time for some signal processing methods to decompose the battery capacity sequence, so the time for decomposing the capacity sequence must be reduced.
To solve the above problems, it is necessary to achieve effective decomposition of the battery capacity sequence and reduce the impact of the capacity regeneration phenomenon as soon as possible.24 This paper proposes a RUL prediction method based on Fast Ensemble Empirical Mode Decomposition (FEEMD)-LSTM-Temporal Attention Mechanism (TAM)-Online Kernel Extreme Learning Machine (OKELM). The main contributions of this article are as follows:
In this paper, the FEEMD decomposition method was adopted to decompose the capacity degradation data sequence of lithium-ion batteries into a series of relatively stable Intrinsic Mode Functions (IMFs) sequences, which enhanced the stability and accuracy of data decomposition and reduced the time of capacity sequence decomposition.
The LSTM-TAM is used to predict the overall trend of the battery capacity data. The LSTM layer is good at processing time series information, and it can process the input vector through a recursive execution method, which relies on the past hidden state and the current input. TAM algorithm can automatically weight the critical features of time series data to effectively obtain the importance of different time points. Therefore, the network can better characterize the attenuation trend of battery capacity.
For the vibration signal data of the attenuation sequence, the OKELM algorithm is used to predict, which adopts the "accelerated version" of extreme learning machine algorithm. For large-scale lithium-ion battery capacity degradation data, real-time high-speed calculation, efficient modeling, automatic learning of optimal parameters, and adaptive adjustment model can be achieved, which can well adapt to the characteristics of the vibration signal data. The battery capacity prediction is achieved while maintaining high prediction accuracy.
The feasibility and effectiveness of the proposed method are verified on the NASA battery and CALCE dataset. The prediction error in the NASA dataset is no more than one cycle. The prediction error in the CALCE dataset should not exceed two cycles.
The structure of this paper is as follows: Sec. II describes the theory and structure of the FEEMD-LSTM-TAM-OKELM prediction method. Section III introduces the experimental dataset and the hardware and software setup for the experiments. Section IV presents the experimental process and the results of data analysis in detail, and compares them with other models. Section V provides the conclusion.
FEEMD is a signal-processing method based on empirical mode decomposition, which can decompose time series into a set of different IMFs, considering the fractal characteristics of the signal to enhance the decomposition effect of the signal.25
FEEMD will first calculate the corresponding noise standard deviation of a set of signals using the fractal dimension of the signals, then add the noise based on the noise standard deviation to generate a set of signals containing noise, and finally decompose and average this set of signals using the EMD method to obtain the final decomposition of the signals. The two important parameters used in FEEMD are the amplitude of the additional white noise k, and the EMD''s maximum number of operations M, which are set to 0.1 and 100, respectively. The detailed steps of the FEEMD algorithm are as follows:
Initialize the amplitude of white noise k and the maximum number of operations N of EMD.
FEEMD algorithm introduces the fractal characteristics of the signal so that the addition of noise is more consistent with the actual change of the signal so that it can reflect the change characteristics of the signal itself more accurately. Due to the complexity and nonlinearity of battery capacity degradation data, the FEEMD algorithm can more accurately extract the degradation trend and periodic change characteristics of lithium-ion batteries, to better mine the signs of battery capacity decay and achieve RUL prediction.
LSTM is an optimized version of Recurrent Neural Network (RNN), which is mainly used to process time series data and make predictions. The LSTM model manages the information retained in the data through the input gate, the forget gate, and the output gate to improve the gradient disappearance problem that occurs in the training process of the RNN model. The LSTM model structure is shown in Fig. 1.
Architecture of the LSTM model.
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