Deep neural network battery life and voltage prediction by using data of one cycle only
Deep neural network battery life and voltage prediction by using data of one cycle only
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DOI：
10.1016/j.apenergy.2021.118134
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发表时间：
2022-01
期刊：
影响因子：
11.2
通讯作者：
Chia-Wei Hsu;R. Xiong;Nan-Yow Chen;Ju Li;N. Tsou
中科院分区：
文献类型：
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作者：
Chia-Wei Hsu;R. Xiong;Nan-Yow Chen;Ju Li;N. Tsou
可充电电池（如LiFePO 4/石墨电池）因制造、充电（能量流入）政策、温度、放电条件等的变化而老化不同。如果我们能够预测电池的老化情况，并根据几个测试周期确定其当前的健康状态和剩余使用寿命，则可以获得巨大的经济和环境价值。在这里，通过开发具有特殊卷积训练策略的新型架构深度神经网络，并利用最近发布的电池循环数据，我们表明，仅使用一个测试周期，就可以预测电池的剩余寿命，平均绝对百分比误差为6.46%。还可以首次准确预测年龄未知的旧电池的逐周期曲线，例如任何给定周期的放电电压，容量和功率曲线。此外，我们的模型可以从数据中提取数据驱动的特征，这些特征对预测属性的影响要比人工选择的特征大得多。这项工作表明，单次循环数据包含足够的信息量，可以高精度地预测电池的基本性能。预计它将提供巨大的经济和环境效益，因为可以更好地规划电池的再利用和回收，减少锂离子电池最终进入垃圾填埋场。
Rechargeable batteries, such as LiFePO4/graphite cells, age differently by variability in manufacturing, charging (energy inflow) policy, temperature, discharging conditions, etc. Great economic and environmental value can be extracted if we can predict how a battery ages and ascertain its current state of health and residual useful life, based on just a few cycles of testing. Here, by developing novel-architecture deep neural networks with a special convolutional training strategy and taking advantage of recently published battery cycling data, we show that one can predict the residual life of a battery to a mean absolute percentage error of 6.46%, using only one cycle of testing. The cycle-by-cycle profiles, such as discharge voltage, capacity, and power curves of any given cycle, of used batteries with unknown age can also be accurately predicted for the first time. Moreover, our models can extract data-driven features from the data which were much more influential on the predicted properties than human-picked features. This work has shown that single cycle data contains a sufficient amount of information to predict essential battery properties with high accuracy. It is expected to provide tremendous economic and environmental benefits since reuse and recycling of batteries can be better planned and less lithium-ion batteries end up in landfills.