Accurate State of Charge(SOC) estimation is critical for improving the battery *** order to realize the accurate estimation of SOC for electric vehicle(EV) batteries,considering the complex operating mode and nonlinea...
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Accurate State of Charge(SOC) estimation is critical for improving the battery *** order to realize the accurate estimation of SOC for electric vehicle(EV) batteries,considering the complex operating mode and nonlinear characteristics of EV batteries,this paper proposes a particle filter algorithm for estimating SOC based on the battery data from EVs operating in *** determine the state-space model for EV batteries,the data are used to estimate the parameters of the ***,based on the actual collected data,the experiments are designed to demonstrate the particlefilter *** results indicate that the estimation values of particle filter algorithm are close to the true values and the error is comparatively ***,the particle filter algorithm has high accuracy in the SOC estimation for EV batteries.
A comparison between the hybrid method (PHANN - Physical Hybrid Artificial Neural Network) and the 5 parameter Physical model, which have been determined by the particle filter algorithm, is presented here. These meth...
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ISBN:
(纸本)9783319447810;9783319447803
A comparison between the hybrid method (PHANN - Physical Hybrid Artificial Neural Network) and the 5 parameter Physical model, which have been determined by the particle filter algorithm, is presented here. These methods have been employed to perform the day-ahead forecast of the output power of a photovoltaic plant. The aim of this work is to assess the forecast accuracy of the two methods.
Considering the problem of declining in accuracy caused by particles impoverishment in the Iterated Unscented Kalman particle filter algorithm, self-adaptive artificial fish swarm algorithm with changing step optimize...
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Considering the problem of declining in accuracy caused by particles impoverishment in the Iterated Unscented Kalman particle filter algorithm,self-adaptive artificial fish swarm algorithm with changing step optimized...
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ISBN:
(纸本)9781509009107
Considering the problem of declining in accuracy caused by particles impoverishment in the Iterated Unscented Kalman particle filter algorithm,self-adaptive artificial fish swarm algorithm with changing step optimized Iterated Unscented Kalman particle filter algorithm was presented in this *** uses the alternation of behaviors of preying and swarming in the self-adaptive artificial fish swarm algorithm with changing step to optimize the re-sampling process firstly,which makes prior particles move towards the high likelihood region,the problem of sample impoverishment was *** a result,the estimation accuracy of the system state was *** results proves the effectiveness of the proposed algorithm.
Aiming at the problems of time-varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particlefilter algor...
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Aiming at the problems of time-varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second-order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particlefilter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage.
In the battery management system (BMS), the state of charge (SOC) of lithium-ion batteries is an indispensable part, and the accuracy of SOC estimation has attracted wide attention. Accurate SOC estimation can improve...
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In the battery management system (BMS), the state of charge (SOC) of lithium-ion batteries is an indispensable part, and the accuracy of SOC estimation has attracted wide attention. Accurate SOC estimation can improve the efficiency of battery use while ensuring battery safety and improving battery life. Taking ternary lithium battery as the research object, this paper proposes a parameter identification method using adaptive forgetting factor recursive least squares and an improved joint unscented particle filter algorithm to estimate SOC. Firstly, an adaptive method is used to select the appropriate forgetting factor value to improve the accuracy of the forgetting factor recursive least squares (FFRLS) method. Meanwhile, the improved particle swarm (IPSO) optimization algorithm that incorporates variable weights and shrinkage factors is utilized to make the best choice of the noise for the unscented Kalman filter (UKF) algorithm in order to improve the estimation accuracy of the UKF algorithm. At the same time, the UKF algorithm is used as the suggestion density function of the particlefilter (PF) algorithm to form the unscented particlefilter (UPF) algorithm. In this paper, the AFFRLS algorithm and IPSO-SDUPF algorithm are combined to estimate the SOC of Li-ion batteries in real time. Experimental results under different working conditions show that the proposed algorithm has good convergence and high stability for SOC estimation of lithium-ion batteries. The maximum estimation errors of this algorithm are 1.137% and 0.797% for BBDST and DST conditions at 25 degrees C, and 1.015% and 1.029% for BBDST and DST conditions at 35 degrees C, which are lower than those of the commonly used algorithms of EKF, SDUKF, IPSO-SDUKF, and SDUPF, and provide a reference for future. The maximum estimation errors are lower than those of the commonly used EKF, SDUKF, IPSO-SDUKF, and SDUPF algorithms, which provide a reference for the future high-precision SOC estimation of Li-ion
State of energy (SOE) estimation of lithium-ion batteries is the basis for electric vehicle range prediction. To improve the estimation accuracy of SOE under complex dynamic operating conditions. In this paper, ternar...
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State of energy (SOE) estimation of lithium-ion batteries is the basis for electric vehicle range prediction. To improve the estimation accuracy of SOE under complex dynamic operating conditions. In this paper, ternary lithium-ion batteries are used as the object of study and propose a hybrid approach that combines a particle swarm optimization-based forgetting factor recursive least squares method with an improved curve-increasing particle swarm optimization-extended particle filter algorithm for accurate estimation of the state of energy of lithium-ion batteries. Firstly, for the accuracy defects of the FFRLS method, the particle swarm optimization algorithm is used to optimize the initial value of the optimal parameters and the value of the forgetting factor. Secondly, the curve-increasing strategy is introduced into particle swarm optimization to solve the sub-poor problem of extended particlefiltering. Experimental validation through different working conditions at multiple temperatures. The results show that the maximum error of parameter identification using the PSO-FFRLS algorithm is stabilized within 1.5%, and the SOE estimation error is within 1.5% for both BBDST and DST conditions at both temperatures. Therefore, the algorithm has high accuracy and robustness under different complex working conditions. The estimation results prove the effectiveness of the energy state estimation.
This paper proposes an advanced particlefilter (PF) algorithm based on the quantum particle swarm optimization method (QPSO) and adaptive genetic algorithm (QAPF). After resampling of the PF, the position updating eq...
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This paper proposes an advanced particlefilter (PF) algorithm based on the quantum particle swarm optimization method (QPSO) and adaptive genetic algorithm (QAPF). After resampling of the PF, the position updating equation of the QPSO is applied to improve the particle distribution. Then replace the individuals with lower fitness with those with higher fitness. The genetic operation from the adaptive genetic algorithm (AGA) is then applied to increase the accuracy and sample diversity. An frame size adaptive adjustment model is proposed to reduce the number of useless features and improve the accuracy of target positioning. Multiple simulations of the nonlinear target tracking model are carried out, and the results demonstrate that the numerical stability, efficiency and accuracy of our QAPF algorithm are significantly better than those of other similar algorithms. QAPF is also compared with similar tracking algorithms via a set of tracking experiments. Our experiments on the OTB-100 dataset prove that the QAPF algorithm is much better than the PF, PF improved by particle swarm optimization (PSO-PF) and PF advanced by genetic algorithm (GAPF) tracking algorithms and other typical generative trackers in terms of the tracking precision, success rate, efficiency and robustness.
Aiming at the problems that the lack of theoretical basis for the selection of particle set sampling variance and the resampling methods in traditional particle filter algorithms, and sampling process is easily distur...
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Aiming at the problems that the lack of theoretical basis for the selection of particle set sampling variance and the resampling methods in traditional particle filter algorithms, and sampling process is easily disturbed by noise, an uncertainty structural response reconstruction method based on the information fusion of multi-source particlefilters is proposed. Firstly, the sampling variance of particle set is analogous to the accuracy index of sensors, and a number of independent particlefiltering samples from different sources are performed to ensure the independence of particles. Then, abnormal filters are screened and eliminated according to relative percentage error (RPE) threshold of preliminary reconstruction, and the state estimation results of remained particlefilters are fused by the multi-source sensors information fusion technique to approximate to the real state values with high accuracy. Finally, the fused state values and the state space models are employed to reconstruct the responses of key positions, and the effectiveness of the proposed method is verified by numerical example of the space truss structure and the cantilever beam test. The results show that the proposed method can reduce the influence of the above uncertainties on reconstruction results, effectively improve the particle impoverishment problem, the filtering stability is good and the reconstruction accuracy is high.
In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of disc...
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In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of discontinuous gas concentration gradient and sparse gas environment in three-dimensional space, particlefilter, and whale swarm optimization algorithm are integrated to locate gas source. Firstly, the Z-shape search and comb search are used to locate the plume, and then, the particle filter algorithm is combined with the whale optimization method to guide the particle movement, and the random inertial disturbance term is designed to improve the convergence speed and search accuracy of the algorithm. Experimental results in three-dimensional environment show that the proposed information-driven particlefilter whale optimization hybrid algorithm effectively guides the robot in localizing gas source within a certain range, significantly enhancing both the efficiency and accuracy of localization compared to other algorithms.
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