Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data - Part B : cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates

Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A : storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing

An application of nature inspried algorithm based dual-stage frequency control strategy for multi micro-grid system

In this article, a dual-stage frequency control strategy is proposed to maintain the reliable operation of an islanded interconnected micro-grid system. The controller helps micro-grid in frequency stabilization and maintains the load demand balance during abnormal operating conditions. The multi-microgrid system comprises a biodiesel generation unit wind turbine, redox flow battery (RFB) in area-1 and BDG, organic Rankine cycle-based solar thermal power unit, and a capacitive energy storage system (CES) unit in area-2. The CES maintains the transient frequency deviation and RFB fulfills the long-term power demand during abnormal conditions. In this proposed work, a novel and efficient optimization technique entitled modified African Buffalo Optimization (MABO) has been implemented for optimal tuning of PIDF, PID-PDF, and PDF controllers. Further, the performance of MABO is compared with recently deployed optimization techniques i.e. Corrected Moth Search Optimization (CMSO) and African Buffalo Optimization (ABO). Eigen-values stability analysis and bode plot analysis are considered to validate the efficacy of the proposed controller design. In addition, a sensitivity analysis has been executed to manifest the potential of the proposed strategy for a wide variation in micro-grid specifications, magnitude, and fluctuation of step/random power load disturbance. The various performance analyses validate the dominance of the proposed approach in damping frequency oscillations during severe disturbances and uncertain conditions

Cascade‐IλDμN controller design for AGC of thermal and hydro‐thermal power systems integrated with renewable energy sources

Abstract Expert, intelligent and robust automatic generation control (AGC) scheme is requisite for stable operation and control of power system (PS) integrated with renewable energy sources (RES) under sudden/random small load disturbances. Large frequency deviations appear if AGC capacity is inept to compensate for the imbalances of generation and demand. In this paper, a cascade‐fractional order ID with filter (C‐IλDμN) controller is proposed as an expert supplementary controller to promote AGC recital adequately in electric power systems incorporated with RES like solar, wind and fuel cells. The imperialist competitive algorithm is fruitfully exploited for optimizing the controller parameters. First, a 2‐area reheat thermal system is examined critically and then to authorize the worth of the proposed controller, the study is protracted to a 2‐area multi‐source hydro‐thermal system. The prominent benefits of C‐IλDμN controller with/without renewable energy sources consist of its great indolence to large load disturbances and superiority over various optimized classical/fuzzy controllers published recently. The sensitivity study validates the robustness of the recommended controller against ±20% deviations in the system parameters and random step load perturbations