Electric vehicles (EVs) are spreading rapidly in the market due to their better responsiveness and environmental friendliness. An accurate diagnosis of EV battery status from operational data is necessary to ensure reliability, minimize maintenance costs, and improve sustainability. This paper presents a deep learning approach based on the long short-term memory network (LSTM) to estimate the state of health (SOH) and degradation of lithium-ion batteries for electric vehicles without prior knowledge of the complex degradation mechanisms. Our results are demonstrated on the open-source NASA Randomized Battery Usage Dataset with batteries aging under changing operating conditions. The randomized discharge data can better represent practical battery usage. The study provides additional end-of-use suggestions, including continued use, remanufacturing/repurposing, recycling, and disposal; for battery management dependent on the predicted battery status. The suggested replacement point is proposed to avoid a sharp degradation phase of the battery to prevent a significant loss of active material on the electrodes. This facilitates the remanufacturing/repurposing process for the replaced battery, thereby extending the battery's life for secondary use at a lower cost. The prediction model provides a tool for customers and the battery second use industry to handle their EV battery properly to get the best economy and system reliability compromise.
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Estimating energy left in discarded alkaline batteries: Evaluating consumption and recovery opportunities
Each year, a significant number of single-use alkaline batteries with untapped energy are discarded. This study aims to analyze the usage patterns of alkaline batteries based on a dataset of 1021 used batteries, ranging from Size AA to 9V, collected from households in the State of New York. We measure the energy loss resulting from underutilized batteries and examine the corresponding environmental and economic impacts on a national scale. Discarded AA alkaline batteries maintain about 13 % of their initial energy, that results in an estimated annual energy loss of 660 MWh for all AA alkaline batteries in the U.S., and about 40 MWh in New York State. Annually in the U.S., consumers discard AA alkaline batteries with approximately $80 million worth of unused energy, including $4.8 million in New York State alone. We also show that the lifecycle impact of batteries should be multiplied by 1.25 to account for their underutilization. To address these issues, we propose actionable recommendations for improving battery consumption practices and facilitating End-of-Life/Use (EoL/U) recovery processes. The findings show the need for policy interventions to better manage battery usage and disposal toward reducing energy waste and mitigating environmental impacts.
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- Award ID(s):
- 2324950
- PAR ID:
- 10543954
- Publisher / Repository:
- Elsevier Ltd.
- Date Published:
- Journal Name:
- Waste Management
- Volume:
- 189
- Issue:
- C
- ISSN:
- 0956-053X
- Page Range / eLocation ID:
- 58 to 67
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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