Model predictive control of a mixed battery array for electricity grid storage

我的发现资助计划将开发一种新的模型预测控制策略，以优化电池储能系统的性能，该系统使用重新利用的电动汽车电池，用于电网储能的第二次生命应用。能量存储的模型预测控制还处于起步阶段，尚未应用于第二生命应用。锂离子电池由于寿命长、能效高，已成为公用级电网储能的首选技术。然而，由于成本高（350美元/千瓦时），它们往往得到补贴。目前大多数锂离子电池用于电动车辆（EV）。在车辆寿命结束时，EV电池可以重新用于电网存储的第二寿命。虽然电池可能已经退化到其原始容量的75%，但这对于固定应用来说是可以接受的，并且旧电池的极低购买价格是有吸引力的（<35美元/千瓦时）。我的计划提出了一个“混合电池阵列”，该阵列认识到电动汽车电池的持续发展及其作为人口函数的广泛地理分布。它将各种各样的电动汽车电池（处于不同的退化水平）重新利用到集中设施中的单个电源转换器阵列中。迄今为止，我们已经测试了日产，特斯拉，宝马和雪佛兰电池在调峰和频率调节的电网存储服务中的能量性能，由于电化学材料，形式（圆柱形，袋）和热管理（被动，空气，液体）的多样性，实现了一系列结果。下一步是创建一个模型预测控制系统，该系统可以在所有电池中最佳分配堆叠服务呼叫（功率，持续时间），同时考虑到它们的特定特性。例如，使用最节能的电池进行调峰，使用功能强大的电池进行频率调节，以及加速使用接近寿命结束的电池，以便将其移除/回收并用另一个使用过的EV电池组替换它们。控制系统是整体性能的关键方面，因为它必须通过放电或充电来预先定位电池的充电状态，为即将到来的服务呼叫预测做准备。我的发现资助计划有三个阶段：第一阶段测试电池，以绘制能量容量、效率、功率和退化的趋势，以创建离散的电池模型。第2阶段开发模型预测控制策略，以优化每个电池的技术和经济性能。第3阶段演示/验证了带多通道功率转换器的旧EV电池组的新控制策略。 研究成果是工业和公用事业的新能源存储架构和控制策略。这同时为旧电动汽车的所有者带来了价值（出售他们的电池组），为公用事业带来了新的低成本储能技术，并且可以在加拿大及其他地区的所有司法管辖区进行商业化。

My Discovery Grant program will develop a new model-predictive-control strategy to optimize performance of battery energy storage systems using repurposed electric vehicle batteries in a second-life application for electricity grid storage. Model-predictive-control of energy storage is in its infancy, and has not yet been applied to second-life applications. Lithium ion batteries have become the choice technology for utility grade electricity grid energy storage due to long life and high energy efficiency. However, they are often subsidized due to high cost ($350/kWh). Most lithium ion batteries are presently used for electric vehicles (EV). At end of vehicle life the EV batteries may be repurposed into a second-life for grid storage. While the batteries may have degraded to 75% of their original capacity, this is acceptable for stationary applications, and the very low purchase price of used batteries is compelling (<$35/kWh). My program proposes a "mixed battery array" that recognizes the continued evolution of EV batteries and their wide geographic dispersion as a function of population. It repurposes batteries from a wide variety of EVs, at different levels of degradation, into an array with individual power converters at a centralized facility. To date, we have tested energy performance of Nissan, Tesla, BMW, and Chevrolet batteries in electricity grid storage services of peak shaving and frequency regulation, achieving a range of results due to the diverse electrochemical materials, format (cylindrical, pouch), and thermal management (passive, air, liquid). The next step is to create a model predictive control system that optimally apportions stacked-service calls (power, duration) across all the batteries, taking into account their specific characteristics. For example, using the most energy efficient batteries for peak shaving, the powerful batteries for frequency regulation, and accelerating usage of batteries that are near end of life so as to remove/recycle them and replace them with another used EV battery pack. The control system is a critical aspect to overall performance as it must pre-position the battery state-of-charge by discharging or charging in preparation for upcoming service call forecasts. My Discovery Grant program has three phases: Phase 1 tests batteries to map trends in energy capacity, efficiency, power, and degradation to create discrete battery models. Phase 2 develops the model predictive control strategy to optimally operate each battery for technical and economic performance. Phase 3 demonstrates/validates the new control strategy on used EV battery packs with multi-channel power converters. Research outputs are new energy storage architectures and control strategies for industry and utilities. This simultaneously brings value to owners of old EVs (to sell their battery packs), brings a new low-cost energy storage technology to utilities, and can be commercialized in all jurisdictions of Canada and beyond.