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Using Machine Learning to Optimize Energy Consumption of HVAC Systems in Vehicles

机译：采用机器学习优化车辆HVAC系统的能耗

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摘要

The detachment and calculation of functionalities from a vehicle into a cloud creates new chances. By linking different data sources with the in-vehicle data in the cloud, an optimization of these functionalities in terms of energy efficiency can be applied. For example, the Heating, Ventilation and Air Conditioning (HVAC) consumes up to 30% of total energy in a vehicle. Electric vehicles in particular lead to these high values because they are not able to recover the waste heat from combustion engines for interior heating. Therefore, the optimization of energy efficient strategies with respect to the vehicle energy management system becomes more relevant. Forecasts of the interior vehicle temperature are directly related to the HVAC energy consumption. This work focuses on the implementation and accuracy evaluation of Recurrent Neural Networks (RNN) for interior vehicle temperature forecasting.
机译：从车辆到云中的功能的分离和计算会产生新的机会。通过将不同的数据源与云中的车载数据链接，可以应用这些功能的优化。例如，加热，通风和空调（HVAC）在车辆中消耗高达30％的总能量。电动车辆特别导致这些高值，因为它们不能从用于内部加热的燃烧发动机中恢复废热。因此，关于车辆能量管理系统的节能策略的优化变得更加相关。内部车辆温度的预测与HVAC能量消耗直接相关。这项工作侧重于对内部车辆温度预测的经常性神经网络（RNN）的实施和准确性评估。

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