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

My Discovery Grant计划将开发一种新的模型预测控制策略，在电网存储的第二生命应用中使用重新定位的电动汽车电池来优化电池能量存储系统的性能。储能的模型预测控制还处于起步阶段，尚未应用于二次生命的应用。锂离子电池具有寿命长、能量效率高等优点，已成为公用事业级电网储能的首选技术。然而，由于成本高(350美元/千瓦时)，它们经常得到补贴。目前，大多数锂离子电池用于电动汽车(EV)。在车辆寿命结束时，电动汽车电池可能会被重新用于电网存储的第二次生命。虽然电池可能已经退化到其原始容量的75%，但对于固定应用来说，这是可以接受的，而且旧电池非常低的购买价格很有吸引力(&lt；35美元/千瓦时)。我的计划提出了一个“混合电池阵列”，它承认电动汽车电池的持续发展及其广泛的地理分散性是人口的函数。它将各种不同退化程度的电动汽车的电池重新调整用途，变成一个中央设施中带有单独电源转换器的阵列。到目前为止，我们已经测试了日产、特斯拉、宝马和雪佛兰电池在电网调峰和频率调节存储服务中的能量性能，由于电化学材料、格式(圆柱形、气囊)和热管理(被动、空气、液体)的不同，取得了一系列结果。下一步是创建一个模型预测控制系统，考虑到它们的特定特性，在所有电池之间优化分配堆叠服务呼叫(功率、持续时间)。例如，使用最节能的电池进行调峰，使用功率最大的电池进行频率调节，以及加速使用即将用完的电池，以便将其拆卸/回收并更换为另一个旧电动汽车电池组。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Discovery Grant计划分为三个阶段：第一阶段测试电池，以绘制能量容量、效率、功率和退化的趋势图，以创建离散的电池模型。第二阶段开发了模型预测控制策略，以优化每个电池的操作，以获得技术和经济性能。阶段3演示/验证了新的控制策略，适用于带有多通道电源转换器的二手电动汽车电池组。研究成果是用于工业和公用事业的新的能量存储架构和控制策略。这同时为旧电动汽车的车主带来了价值(出售他们的电池组)，为公用事业公司带来了一种新的低成本能量存储技术，并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.