Dynamic Characteristics Of Battery Research Articles

Distributed control strategy for transactive energy prosumers in real‐time markets

AbstractThe increasing penetration of distributed energy resources (DERs) has led to increasing research interest in the cooperative control of multi‐prosumers in a transactive energy (TE) paradigm. While the existing literature shows that TE offers significant grid flexibility and economic benefits, few studies have addressed the incorporation of security constraints in TE. Herein, a market‐based control mechanism in real‐time markets is proposed to economically coordinate the TE among prosumers while ensuring secure system operation. Considering the dynamic characteristics of batteries and responsive demands, a model predictive control (MPC) method is used to handle the constraints between different time intervals and incorporate the following generation and consumption predictions. Owing to the computational burden and individual privacy issues, an efficient distributed algorithm is developed to solve the optimal power flow problem. The strong coupling between prosumers through power networks is removed by introducing auxiliary variables to acquire locational marginal prices (LMPs) covering energy, congestion, and loss components. Case studies based on the IEEE 33‐bus system demonstrated the efficiency and effectiveness of the proposed method and model.

Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles

Consistency is an essential factor affecting the operation of lithium-ion battery packs. Pack consistency evaluation is of considerable significance to the usage of batteries. Many existing methods are limited for they are based on a single feature or can only be implemented offline. This paper develops a comprehensive method to evaluate the pack consistency based on multi-feature weighting. Firstly, the features which reflect the static or dynamic characteristics of batteries are excavated. Secondly, a weighted method of multi-feature inconsistency is proposed to evaluate pack consistency. In which case, the entropy weight method is employed to determine the weight. Thirdly, an improved Greenwald-Khanna algorithm based on genetic algorithm and kernel function is developed to cluster batteries. Finally, nine months of electric vehicle data are collated to validate the proposed algorithms. Meanwhile, the main factor affecting consistency change is analyzed. The results show that with the usage of batteries, the difference between the cells becomes more serious, which weakens the pack consistency. Besides, the relationship between the consistency attenuation rate and the driving mileage can be approximated by a first-order function. The higher mileages will aggravate the pack inconsistency. Moreover, it has been proven that the improved clustering algorithm has stronger robustness and classification performance.