State-of-health estimation for lithium battery in electric vehicles based on improved unscented particle filter

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

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An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

IEEE Access ◽

10.1109/access.2018.2854224 ◽

2018 ◽

Vol 6 ◽

pp. 40990-41001 ◽

Cited By ~ 35

Author(s):

Datong Liu ◽

Xuehao Yin ◽

Yuchen Song ◽

Wang Liu ◽

Yu Peng

Keyword(s):

Particle Filter ◽

Lithium Ion Battery ◽

Lithium Ion ◽

State Of Health ◽

Unscented Particle Filter ◽

On Line

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Robust information unscented particle filter based on M‐estimate

IET Signal Processing ◽

10.1049/iet-spr.2018.5151 ◽

2019 ◽

Vol 13 (1) ◽

pp. 14-20 ◽

Cited By ~ 2

Author(s):

Xiao‐Hang Wu ◽

Shen‐Min Song

Keyword(s):

Particle Filter ◽

Unscented Particle Filter ◽

M Estimate

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Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽

10.3390/s21062236 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2236

Author(s):

Sichun Du ◽

Qing Deng

Keyword(s):

Particle Filter ◽

State Space ◽

Adaptive Filtering ◽

Divide And Conquer ◽

Particle Swarm Algorithm ◽

Tracking Accuracy ◽

Filtering Algorithm ◽

Maneuvering Target ◽

Unscented Particle Filter ◽

Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

Robotica ◽

10.1017/s0263574720001046 ◽

2020 ◽

pp. 1-14

Author(s):

Chen Hao ◽

Liu Chengju ◽

Chen Qijun

Keyword(s):

Particle Filter ◽

Humanoid Robots ◽

Dynamic Environments ◽

Measurement Unit ◽

Dual Channel ◽

Nao Robot ◽

Unscented Particle Filter ◽

Monte Carlo Localization ◽

Switch Mechanism ◽

Updating Procedure

SUMMARY Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

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Design and Implementation of Electric Vehicle Power Lithium Battery Protection Board

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.152-153.192 ◽

2010 ◽

Vol 152-153 ◽

pp. 192-196

Author(s):

Ju Hua Huang ◽

Ming Cao ◽

Hang Guo

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Lithium Batteries ◽

Lithium Battery ◽

Short Circuit ◽

Battery Life ◽

Test Results ◽

Charge Balance ◽

Good Test ◽

Li Ion

The performance of power lithium batteries directly affects the performance of electric vehicles. To ensure the safety of power lithium batteries and improve battery life, this paper uses Ricoh R5408 Series Li-ion battery protection IC to design the high-current protection board for electric vehicle, to achieve the power lithium battery group overcharge protection, over-discharge protection, over current, short circuit protection, temperature protection and charge balance protection, and has run on the pure electric vehicles with good test results.

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