Report Reveals the Big Potential of Used EV Batteries
A recent report from Bloomberg New Energy Finance reveals that as many as one-third of electric car batteries are expected to be reused by 2025. The estimates are that there will be around 29-gigawatt hours of used batteries from EV’s by 2025, and of this 10…
A recent report from Bloomberg New Energy Finance reveals that as many as one-third of electric car batteries are expected to be reused by 2025. The estimates are that there will be around 29-gigawatt hours of used batteries from EV’s by 2025, and of this 10 gigawatts will be recycled to be used in storing energy for buildings, utilities, and homeowners. Just 10 gigawatts alone is enough to power over 1.5 million American households or around ten natural gas or coal power plants.
Electric car batteries are not indestructible. They only have a shelf life of around 8 to 10 years before they are no longer efficient enough to be used in automobiles. It is the same thing that happens with any electrical battery, like a laptop or phone. Nothing lasts forever. However, EV’s are still relatively new, so there may not be too many used batteries around for another few years, but when they do start becoming inefficient for cars there will be lots of them, and so companies are preparing now to find ways to make the most out of them.
Even when the batteries are no longer efficient enough to power a vehicle, they will still hold around 70% of useful energy which can be used in other ways. By packaging the batteries together in the basement of a building, or connecting them to the power grid could enable them to use the energy in times of need. Currently, it costs between $100-200 per kilowatt hour for used electric car batteries, but experts estimate this cost to fall to around $49 per kilowatt hour by 2018. This will be a great way to keep used batteries from piling up in landfill sites and will save us money in the long run too.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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