Parameter identification and sensitivity analysis of lithium-ion battery via whale optimization algorithm

2021 ◽  
pp. 139574
Author(s):  
Ting-Chen Pan ◽  
En-Jui Liu ◽  
Hung-Chih Ku ◽  
Che-Wun Hong
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Identification ◽  
Lithium Ion Battery ◽  
Optimization Algorithm ◽  
Lithium Ion ◽  
Whale Optimization Algorithm ◽  
Whale Optimization

State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery

2021 ◽  
pp. 014233122110420
Author(s):  
Shuai Wang ◽  
Xiaochen Zhang ◽  
Wengxiang Chen ◽  
Wei Han ◽  
Shoubin Zhou ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Optimization Algorithm ◽  
Prediction Method ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Whale Optimization Algorithm ◽  
Data Sets ◽  
State Of Health ◽  
Whale Optimization ◽  
Maximal Relevance

The state of health (SOH) reflects the health status of the lithium-ion battery and is expected to accurately predicted, so as the corresponding maintenance measures can be taken to ensure the safe operation of the battery. This paper proposed a SOH prediction method based on multi-kernel relevance vector machine (RVM) and whale optimization algorithm (WOA). Firstly, the original features were obtained from the battery voltage and temperature data in charging and discharging phases. Secondly, the minimal-redundancy-maximal-relevance (mRMR) algorithm was introduced to select the optimal feature set. Then, the online model and offline model based on multi-kernel RVM and WOA were constructed. Finally, a hybrid model which combines the online model and offline model was proposed to prediction the SOH of the lithium-ion battery. The performance of the proposed method was evaluated with two kinds of data sets. The experimental results showed that the proposed method obtained higher prediction accuracy in both long-term and short-term periods than other methods.

scholarly journals A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2367 ◽  
Author(s):  
Tan Ding ◽  
Li Chang ◽  
Chaoshun Li ◽  
Chen Feng ◽  
Nan Zhang
Keyword(s):  
Parameter Identification ◽  
Optimization Algorithm ◽  
Mixed Strategy ◽  
Hydraulic Turbine ◽  
Water Hammer ◽  
Whale Optimization Algorithm ◽  
Local Optimum ◽  
Movement Strategies ◽  
Mutation Strategy ◽  
Whale Optimization

For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite’s memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method.

scholarly journals Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Xiaojia Ye ◽  
Wei Liu ◽  
Hong Li ◽  
Mingjing Wang ◽  
Chen Chi ◽  
...  
Keyword(s):  
Solar Cells ◽  
Parameter Identification ◽  
Optimization Algorithm ◽  
Pattern Search ◽  
Whale Optimization Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Mutation Strategy ◽  
Whale Optimization ◽  
Pv Modules

The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

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