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

电网存储混合电池阵列的模型预测控制

• 批准号: RGPIN-2019-04286
• 负责人: Swan, Lukas
• 金额: $ 1.97万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681903
• 关键词: Model; predictive; control; mixed; battery

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.

我的Discovery Grant计划将制定一种新的模型预测性控制策略，以在第二寿命应用程序中使用重新利用的电动汽车电池来优化电池储能系统的性能。模型可预测的能源存储仍处于起步阶段，并且尚未应用于第二人寿应用。***锂离子电池已成为公用事业级电力储能的选择技术，这是由于寿命长时间和高能量效率。但是，由于高成本（$ 350/kWh），它们通常会得到补贴。大多数锂离子电池目前用于电动汽车（EV）。在车辆寿命结束时，电动电池可以将其重新用于第二寿命以进行电网存储。尽管电池可能已降低到其原始容量的75％，但对于固定应用来说是可以接受的，而且用过的电池的购买价格非常低（<$ 35/kWh）。它可以将各种电动汽车（在不同级别的降解水平）中重新利用，以在集中式设施中带有单个电源转换器的阵列。迄今为止，我们已经测试了Nissan，Tesla，BMW和Chevrolet电池在峰值剃须和频率调节的电网存储服务中的能源性能，从而取得了一系列结果，这是由于多样化的电化学材料，格式（圆柱形，小袋）和热量管理（Quiptive total Managection）（被动，空气，液体）。考虑到它们的特定特征，所有电池都堆叠了服务的电话（功率，持续时间）。例如，使用最节能的电池进行剃须，功能强大的电池进行频率调节，并加速使用生命末端的电池，以将其删除/回收，并用另一个二手的EV电池组替换它们。控制系统是整体性能的关键方面，因为它必须通过排放或充电来预先设置电池最先进的服务，以准备即将到来的服务呼叫预测。第2阶段开发了模型预测控制策略，以最佳操作每个电池以进行技术和经济性能。第3阶段展示/验证了使用多通道电源转换器的二手电动汽车电池组的新控制策略。 ***研究输出是新的储能架构和行业和公用事业的控制策略。这种同时为旧电动汽车的所有者带来了价值（出售其电池组），为公用事业带来了新的低成本存储技术，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。