Department of Mathematics and Physics, Catholic University of the Sacred Heart, 25133 Brescia, Italy.
Sensors (Basel). 2023 Apr 30;23(9):4404. doi: 10.3390/s23094404.
This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control methodology that not only ensures safety and optimizes the charging current, but also substantially reduces the computational complexity with respect to traditional model-based approaches. In addition to their high computational costs, model-based approaches are also hindered by their need to accurately know the model parameters and the internal states of the battery, which are typically unmeasurable in a realistic scenario. In this regard, the deep learning-based methodology described in this work was been applied for the first time to the best of the authors' knowledge, to scenarios where the battery's internal states cannot be measured and an estimate of the battery's parameters is unavailable. The reported results from the statistical validation of such a methodology underline the efficacy of this approach in approximating the optimal charging policy.
这项工作研究了深度学习网络在电池充电领域的有效性。通过引入一种创新的控制方法，可以确保安全性并优化充电电流，同时与传统基于模型的方法相比，大大降低了计算复杂度。除了计算成本高之外，基于模型的方法还受到需要准确知道电池模型参数和内部状态的限制，而在实际情况下，这些参数和状态通常是无法测量的。在这方面，本文所描述的基于深度学习的方法首次被应用于作者所知的最佳情况，即电池内部状态无法测量且无法获得电池参数估计的情况。这种方法的统计验证结果表明了该方法在逼近最佳充电策略方面的有效性。
