Extending Remaining Useful Life of Second-life Battery Energy Systems

该项目旨在开发一个框架，以重复使用具有不同退化水平的二次电池组。这包括一种新型的机器学习和在线电池状态估计算法，该算法不需要SLB过去的用例历史数据，一种用于平衡每个电池组中能量的高级控制算法，以及一种优化的模块化逆变器架构，具有集成的电压提升能力，可管理电池并满足控制目标。这不仅通过延迟电子废物或回收周期而使环境受益，而且还通过能源产品和服务的循环经济帮助澳大利亚制造业。

The project aims to develop a framework to reuse second-life battery packs with different degradation levels. This includes a novel machine learning and online battery state estimation algorithm that does not require past use case historical data of the SLBs, an advanced control algorithm to balance the energy in each battery pack and an optimized modular inverter architecture with integrated voltage boosting capability to manage the batteries and meet the control objectives. This benefits not only the environment through delayed e-waste or recycling cycles but also helps the Australian manufacturing sector through a circular economy of energy products and services.