scholarly journals A Novel Joint Support Vector Machine - Cubature Kalman Filtering Method for Adaptive State of Charge Prediction of Lithium-Ion Batteries

2021 ◽  
pp. ArticleID:210823
Author(s):  
Qianqian Song ◽  
Keyword(s):  
Support Vector Machine ◽  
Lithium Ion Batteries ◽  
Kalman Filtering ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Filtering Method

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

scholarly journals A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 195398-195410
Author(s):  
Jiabo Li ◽  
Min Ye ◽  
Wei Meng ◽  
Xinxin Xu ◽  
Shengjie Jiao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector

scholarly journals On-Line Estimation Method of Lithium-Ion Battery Health Status Based on PSO-SVM

2021 ◽  
Vol 9 ◽  
Author(s):  
Ran Li ◽  
Wenrui Li ◽  
Haonian Zhang ◽  
Yongqin Zhou ◽  
Weilong Tian
Keyword(s):  
Support Vector Machine ◽  
Estimation Method ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Joint Estimation ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Battery Management System ◽  
Safe Driving

Battery management system (BMS) refers to a critical electronic control unit in the power battery system of electric vehicles. It is capable of detecting and estimating battery status online, especially estimating state of charge (SOC) and state of health (SOH) accurately. Safe driving and battery life optimization are of high significance. As indicated from recent literature reports, most relevant studies on battery health estimation are offline estimation, and several problems emerged (e.g., long time-consuming, considerable calculation and unable to estimate online). Given this, the present study proposes an online estimation method of lithium-ion health based on particle swarm support vector machine algorithm. By exploiting the data of National Aeronautics and Space Administration (NASA) battery samples, this study explores the changing law of battery state of charge under different battery health. In addition, particle swarm algorithm is adopted to optimize the kernel function of the support vector machine for the joint estimation of battery SOC and SOH. As indicated from the tests (e.g., Dynamic Stress Test), it exhibits good adaptability and feasibility. This study also provides a certain reference for the application of BMS system in electric vehicle battery online detection and state estimation.

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