Lithium-ion batteries are increasingly being deployed in liberalised electricity systems, where their use is driven by economic optimisation in a specific market context. However, battery degradation depends strongly on operational profile, and this is particularly variable in energy trading applications. Here, we present results from a year-long experiment where pairs of batteries were cycled with profiles calculated by solving an economic optimisation problem for wholesale energy trading, including a physically-motivated degradation model as a constraint. The results show that this approach can increase revenue by 20% whilst simultaneously decreasing degradation by 30% compared to existing methods. The physics-based approach increases the lifetime both in terms of years and number of cycles, as well as the revenue per year, increasing the possible lifetime revenue by 70%. This demonstrates the potential to unlock significant extra performance using control engineering incorporating physical models of battery ageing.

中文翻译：

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使用高级模型从电网电池中释放额外价值

锂离子电池越来越多地部署在自由化的电力系统中，它们的使用是由特定市场环境中的经济优化驱动的。然而，电池退化在很大程度上取决于运行情况，这在能源交易应用中尤其多变。在这里，我们展示了一项为期一年的实验的结果，在该实验中，通过解决批发能源交易的经济优化问题计算出的配置文件对电池对进行循环，包括物理驱动的退化模型作为约束。结果表明，与现有方法相比，这种方法可以增加 20% 的收入，同时减少 30% 的退化。基于物理的方法在年数和周期数以及每年的收入方面增加了寿命，将可能的终生收入增加 70%。这证明了使用包含电池老化物理模型的控制工程来释放显着额外性能的潜力。