To solve the problems in estimating the state of health (SOH) of Li-ion batteries due to real-time estimation difficulty and low precision under various operating conditions, the variations of the SOH caused by increa...
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To solve the problems in estimating the state of health (SOH) of Li-ion batteries due to real-time estimation difficulty and low precision under various operating conditions, the variations of the SOH caused by increases of the internal resistance have been analyzed. Based on the second-order RC equivalent circuit model, the short-term effect of the state of charge (SOC) on the internal resistance was considered, which was set under the discharge condition. In addition, the variation of the internal resistance was analyzed in two intervals of 0-1 s and 1-10 s. The extended kalmanfilter (EKF) algorithm was improved to present a novel improvedkalmanfilter (IKF) algorithm to accurately predict the long-term internal resistance under different operating conditions. A computational formula based on the internal-resistance increasing was established and the SOH was estimated. The error of the calculated result when compared with the forgetting factor least square method based on the internal-resistance increasing was controlled to within 4.0% under the HPPC condition, 3.0% under the BBDST condition, and 6.0% under the DST condition. The proposed algorithm has good convergence, helps improve the SOH estimation, and encourages the application of Li-ion batteries.
As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are ch...
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As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified kalmanfilteralgorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard kalmanfilteralgorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.
The environment for indoor positioning becomes increasingly complicated, making it difficult for accurate and fast positioning. To tackle the above problem, an indoor fusion positioning scheme is presented in this pap...
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ISBN:
(纸本)9781538677322
The environment for indoor positioning becomes increasingly complicated, making it difficult for accurate and fast positioning. To tackle the above problem, an indoor fusion positioning scheme is presented in this paper, in which Bluetooth, WWI and RFID data are fused. KILA algorithm and improved kalman filter algorithm are used to provide multiple fusion positioning schemes. The experiment results show that compared with the single positioning method and the traditional filtering algorithms, the proposed fusion method improves indoor positioning significantly and yields to less positioning errors.
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