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Estimating the state of charge of lithium-ion batteries using improved marginal particle filter with Genetic Operators and M-H Algorithm
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Ramazan Havangi *   |
| University of Birjand , rhavangi@gmail.com |
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Abstract: (516 Views) |
| Estimating the state of charge of lithium- ion batteries is of great importance not only for optimal energy management, but also for ensuring safe operation, preventing charging and discharging, and as a result reducing the life of the battery. However, this parameter cannot be measured directly from the battery terminals. Therefore, there is a need to estimate it. In this paper an improved auxiliary marginal particle filter is presented to estimate the state of charge of lithium-ion batteries. In the proposed method, unlike the particle filter, sampling is done on the marginal distribution and the sampling dimensions do not increase with the passage of time. In addition, genetic operators and M-H algorithm have been used in the proposed method to increase diversity among particles. The use of genetic operators and the M-H algorithm causes the resampled particles to asymptotically approximate the samples from the posterior probability density function of the true state and increases the compatibility. The performance of the proposed method for estimating the state of charge of the battery has been compared with the estimation of the state of charge based on the developed particle filter and traceless particle filter. The results show the effective performance of the proposed method in comparison with other methods. The proposed method to obtain the same estimation accuracy as the particle filter requires far fewer particles and the amount of calculations is low. The root mean square error in the proposed method with different particles is close to 0.007, while in other methods, the root mean square error increases with the decrease of particles. |
Article number: 2 |
| Keywords: Lithium ion battery, state of charge estimation, Marginal particle filter, genetic algorithmو M-H algorithm |
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Full-Text [PDF 10317 kb]
(132 Downloads)
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Type of Study: Research |
Subject:
Modeling and Simulation
Received: 2023/10/29 | Accepted: 2023/12/6 | Published: 2024/09/17
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