Predictive Battery Thermal Management of Electric Vehicles using Deep Learning
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Improving the energy efficiency of battery electric vehicles increases their range and reduces well-to-wheel emissions. An efficient battery thermal management reduces the energy consumption while taking temperature- dependent battery ageing and power availability into account. This work presents a method for a predictive cooling strategy to reduce the energy consumption, using information about the route ahead and Quantile Neural Networks (Q*NN) for accurate predictions.
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Improving the energy efficiency of battery electric vehicles increases their range and reduces well-to-wheel emissions. An efficient battery thermal management reduces the energy consumption while taking temperature- dependent battery ageing and power availability into account. This work presents a method for a predictive cooling strategy to reduce the energy consumption, using information about the route ahead and Quantile Neural Networks (Q*NN) for accurate predictions.
Beschrijving
(2)
Bol
Improving the energy efficiency of battery electric vehicles increases their range and reduces well-to-wheel emissions. An efficient battery thermal management reduces the energy consumption while taking temperature- dependent battery ageing and power availability into account. This work presents a method for a predictive cooling strategy to reduce the energy consumption, using information about the route ahead and Quantile Neural Networks (Q*NN) for accurate predictions.
AmazonPagina's: 224, Editie: Eerste editie, Paperback, Universität Karlsruhe TH
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