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    MULTI-LAYER CONTROL FRAMEWORK FOR AN ENERGY STORAGE SYSTEM
    62.
    发明申请
    MULTI-LAYER CONTROL FRAMEWORK FOR AN ENERGY STORAGE SYSTEM 有权
    用于能源储存系统的多层控制框架

    公开(公告)号：US20140375125A1

    公开(公告)日：2014-12-25

    申请号：US14292850

    申请日：2014-05-31

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yanzhu Ye ,  Ratnesh Sharma

    IPC: H02J3/00

    CPC classification number: G05F1/66 ,  G05B15/02 ,  G06N5/027 ,  G06N7/02 ,  H02J3/24 ,  H02J3/32 ,  H02J3/383 ,  H02J3/386 ,  H02J7/34 ,  H02J7/35 ,  Y02E10/563 ,  Y02E10/566 ,  Y02E10/763 ,  Y02E10/766 ,  Y02E70/30 ,  Y10T307/367

    Abstract: A multilayer control framework for a power system includes a hybrid storage system (HSS) to store energy using a plurality of energy storage devices; a local controller coupled to the HSS to smooth output power of wind or photovoltaic energy sources while regulating a State of Charge (SoC) of the HSS; and a system-wide controller coupled to the HSS activated upon an occurrence of one or more energy disturbances with a control strategy designed to improve system dynamics to address the one or more energy disturbances.

    Abstract translation: 用于电力系统的多层控制框架包括使用多个能量存储装置来存储能量的混合存储系统（HSS）; 耦合到HSS的本地控制器，以在调节HSS的充电状态（SoC）的同时平滑风能或光伏能源的输出功率; 以及耦合到HSS的系统范围控制器，其在发生一个或多个能量干扰时被激活，具有被设计为改善系统动态以解决一个或多个能量干扰的控制策略。

    Method for Estimating Battery Life in Presence of Partial Charge and Discharge Cycles
    63.
    发明申请
    Method for Estimating Battery Life in Presence of Partial Charge and Discharge Cycles 有权
    在部分充电和放电循环中估计电池寿命的方法

    公开(公告)号：US20140139191A1

    公开(公告)日：2014-05-22

    申请号：US14134197

    申请日：2013-12-19

    Applicant: NEC Laboratories America, Inc.

    Inventor： Babak Asghari ,  Ratnesh Sharma

    IPC: H02J7/00 ,  G01R31/36

    CPC classification number: G01R31/3648 ,  G01R31/3679 ,  H01M10/44 ,  H01M10/48 ,  H01M2010/4278 ,  H02J3/381 ,  H02J3/383 ,  H02J3/386 ,  H02J7/34 ,  H02J9/00 ,  H02J2003/146 ,  Y02E10/563 ,  Y02E10/763 ,  Y02P80/14

    Abstract: A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy. The management framework includes a battery life estimation from a supercycle model, for a time window between two consecutive full charges of a battery, which allows assessing a worst case scenario impact of all partial cycles within a supercycle on the battery life. The battery life estimation then considers each supercycle as a single discharge unit instead of treating each individual discharge period separately.

    Abstract translation: 公开了一种基于能量存储装置寿命估计和能源价格实现最大能量存储装置寿命的管理框架。 管理框架包括来自超级循环模型的电池寿命估计，用于电池的两个连续完全充电之间的时间窗口，其允许评估超级循环内的所有部分循环对电池寿命的最坏情况情景影响。 然后，电池寿命估计将每个超级循环视为单个排出单元，而不是分别处理每个单独的排放周期。

    Use of Second Battery Life to Reduce CO2 Emissions
    64.
    发明申请
    Use of Second Battery Life to Reduce CO2 Emissions 有权
    使用第二个电池寿命来减少二氧化碳排放

    公开(公告)号：US20130211799A1

    公开(公告)日：2013-08-15

    申请号：US13764100

    申请日：2013-02-11

    Applicant: NEC LABORATORIES AMERICA, INC.

    Inventor： Anupama Keeli ,  Ratnesh Sharma

    IPC: G06F17/50

    CPC classification number: G06F17/5009 ,  G06F2217/10 ,  G06F2217/78 ,  H01M10/44 ,  H01M10/48 ,  Y02E40/76 ,  Y02E60/76 ,  Y04S10/545 ,  Y04S40/22

    Abstract: A method for determining use of a second life battery under load conditions to reduce CO2 emissions includes using Monte Carlo simulations to modeling uncertainties of a load profile, a renewable energy profile, and CO2 emissions rate, determining an initial state of charge SOC of the second life battery based on a Gaussian distribution for determining a rate of charging during low emission hours and discharging during high CO2 emission hours of the second life battery and storage size of the second life battery and CO2 emissions reduction.

    Abstract translation: 用于确定在负载条件下使用第二寿命电池以减少二氧化碳排放的方法包括使用蒙特卡洛模拟来建模负载曲线，可再生能源曲线和二氧化碳排放率的不确定性，确定第二寿命电池的初始充电状态SOC 基于高斯分布的寿命电池，用于在第二寿命电池的高二氧化碳排放时段和第二寿命电池的储存尺寸以及二氧化碳排放减少期间确定低排放时间期间的充电速率和放电。

    Deep learning approach for battery aging model
    65.
    发明授权
    Deep learning approach for battery aging model 有权

    公开(公告)号：US11131713B2

    公开(公告)日：2021-09-28

    申请号：US16273505

    申请日：2019-02-12

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Mehdi Assefi ,  Ratnesh Sharma

    IPC: G01R31/367 ,  G06N3/08 ,  G06N3/04 ,  H01M10/42 ,  G06N20/20 ,  G01R31/382 ,  G06F17/18

    Abstract: A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.

    Demand charge minimization in behind-the-meter energy management systems
    66.
    发明授权
    Demand charge minimization in behind-the-meter energy management systems 有权

    公开(公告)号：US10680455B2

    公开(公告)日：2020-06-09

    申请号：US16180415

    申请日：2018-11-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Ratnesh Sharma ,  Korosh Vatanparver

    IPC: H02J7/00 ,  G06Q50/06

    Abstract: Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) for battery-optimized demand charge minimized operations, including determining an optimal monthly demand charge threshold based on a received customer load profile and a customer load profile and savings. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine a daily optimal demand threshold for each day of a month, selecting a monthly demand threshold by clustering the daily optimal demand thresholds for each day of the month into groups, and determining a dominant group representative of a load pattern for a next month. A mean demand threshold for the dominant group is selected as the monthly demand threshold, and continuous battery-optimized demand charge minimized EMS operations are provided based on the monthly demand threshold using a real-time controller configured for overriding the optimal charging/discharging profiles when a monthly demand threshold violation is detected.

    WEATHER DEPENDENT ENERGY OUTPUT FORECASTING
    67.
    发明申请
    WEATHER DEPENDENT ENERGY OUTPUT FORECASTING 审中-公开

    公开(公告)号：US20200057175A1

    公开(公告)日：2020-02-20

    申请号：US16519509

    申请日：2019-07-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yue Zhang ,  Chenrui Jin ,  Ratnesh Sharma

    IPC: G01W1/10 ,  G01W1/12 ,  G01W1/06 ,  G06N3/04 ,  H02J3/38 ,  G06K9/62

    Abstract: Systems and methods for photovoltaic (PV) output forecasting are provided. The methods include determining whether a weather condition that indicates a first forecasting model to have a greater accuracy than a deep learning-based forecasting model is detected in weather data for a predetermined time span. The method also includes forecasting PV output, by a processing device, using the first forecasting model in response to a determination that the weather condition is detected in the weather data for the predetermined time span. The method further includes predicting PV output using the deep learning-based forecasting model in response to a determination that the weather condition is not detected in the weather data for the predetermined time span.

    BATTERY CAPACITY FADING MODEL USING DEEP LEARNING
    68.
    发明申请
    BATTERY CAPACITY FADING MODEL USING DEEP LEARNING 审中-公开

    公开(公告)号：US20200011932A1

    公开(公告)日：2020-01-09

    申请号：US16458825

    申请日：2019-07-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Hossein Hosseini ,  Ratnesh Sharma

    IPC: G01R31/367 ,  G01R31/36 ,  H01M10/48 ,  G01R31/374

    Abstract: A battery management system is provided. The battery management system includes a memory for storing program code. The battery management system further includes a processor for running the program code to extract features from battery operation data. The processor further runs the program code to train a deep learning model to model a battery degradation process of a battery using the extracted features. The processor also runs the program code to generate, using the deep learning model, a prediction of a battery capacity degradation based on the battery operation data and a current battery capacity of the battery. The processor additionally runs the program code to control an operation of the battery responsive to the prediction of the battery capacity degradation.

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