An estimation method for the state-of-charge of lithium-ion battery based on PSO-LSTM
Source abstract
Abstract
The accuracy of state-of-charge (SOC) estimation will affect the performance of the battery management system. The higher the accuracy the better the performance. To improve the accuracy of SOC estimation, a particle swarm optimization (PSO) based method is proposed to optimize the long short term memory. First, a PSO-Long Short Term Memory (LSTM) estimation model is established by the PSO algorithm, thereby achieving optimal iteration parameters of the model. Then, the PSO-LSTM estimation model is simulated under different working conditions and temperatures. Finally, the voltage, current, and other discharge data of the lithium-ion battery are input into the PSO-LSTM neural network model to compare with the LSTM algorithm. The results show that the estimation accuracy of the optimized PSO-LSTM algorithm model and extended Kalman filter is 2.1% and 1.5%, respectively. The accuracy is improved.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Fault Detection and Control Systems · IoT-based Smart Home Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical Engineering, Xi’an University of Science and Technology , No. 58, Yanta Road, Xi’an, Shaanxi 710054, China
Funding
The 2022 Youth Innovation Team Construction Scientific Research Program of Shaanxi Provincia Education Department · The Shaanxi Innovation Talent Promotion Plan-Science and Technology Innovation Team
References (21)
Show source-provided references
1. A successive flux estimation method for rapid g-function construction of small to large-scale ground heat exchanger10.1016/j.renene.2020.10.074 ↗
2. A robust Kalman filter-based approach for SOC estimation of lithium-ion batteries in smart homes10.3390/en15103768 ↗
3. Collaborative estimation of SOC and SOH of Li-ion battery by MIEKPFEKPF algorithm10.19535/j.1001-1579.2023.02.009 ↗
4. Data-driven identification of lithium-ion batteries: A nonlinear equivalent circuit model with diffusion dynamics10.1016/j.apenergy.2022.119336 ↗
5. Lithium-ion battery state-of-charge estimator based on FBG-based strain sensor and employing machine learning10.1109/jsen.2020.3016080 ↗
6. Super-resolution method for coherent DOA estimation of multiple wideband sources10.1016/j.sigpro.2021.108249 ↗
7. A review of distribution system state estimation methods and their applications in power systems10.3390/electronics12030603 ↗
8. On-road estimation of state of charge of lithium-ion battery by extended and dual extended Kalman filter considering sensor bias10.1002/er.8216 ↗
9. Surface estimation via analysis method: A constrained inverse problem approach10.1109/tuffc.2021.3088013 ↗
10. A high precision method for induction machine parameters estimation from manufacturer data10.1109/tec.2020.3032320 ↗
11. A novel neural network with Gaussian process feedback for modeling the state-of-charge of battery cells10.1109/tia.2022.3170842 ↗
12. An adaptive unscented Kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots10.1109/tcst.2014.2317781 ↗
13. Stable and accurate estimation of SOC using eXogenous Kalman filter for lithium-ion batteries10.3390/s23010467 ↗
14. SOC estimation of a rechargeable Li-ion battery used in fuel-cell hybrid electric vehicles—Comparative study of accuracy and robustness performance based on statistical criteria. Part I: Equivalent models10.3390/batteries6030042 ↗
15. Improved battery SOC estimation accuracy using a modified UKF with an adaptive cell model under real EV operating conditions10.1109/tte.2018.2802043 ↗
16. State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter10.1016/j.apm.2019.09.011 ↗
17. Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li-ion batteries10.1002/er.7643 ↗
18. Data-driven ICA-Bi-LSTM-combined lithium battery SOH estimation10.1155/2022/9645892 ↗
19. SOC prediction optimization algorithm for power battery based on LSTM network with window function
20. State of charge estimation method for lithium-ion batteries in all-electric ships based on LSTM neural network10.2478/pomr-2020-0051 ↗
21. Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms10.1002/er.8541 ↗