Journal
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
Volume 9, Issue 4, Pages 4050-4061Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JESTPE.2020.3004972
Keywords
State of charge; Estimation; Lithium-ion batteries; Aging; Heuristic algorithms; Temperature; Dynamic recurrent neural network (DRNN); Li-ion battery; self-adaptive weight particle swarm optimization (SWPSO); state of charge (SOC); state of health (SOH)
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Funding
- National Natural Science Foundation of China [61873180, 61374122]
- Open Fund of Operation and Control of Renewable Energy and Storage Systems (China Electric Power Research Institute) [EPRI 4124-190885]
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An improved dynamic recurrent neural network method combined with a self-adaptive weight particle swarm algorithm was proposed to accurately estimate the state of charge and state of health of lithium-ion batteries, improving estimation accuracy and generalization ability.
To realize accurate estimation of state of charge (SOC) and state of health (SOH), Li-ion battery's operating characteristic is analyzed in this article while fully considering temperature, degree of aging, and other practical factors that could impact their operating status. On the basis of the nonlinear autoregressive with exogenous input (NARX) architecture, an improved dynamic recurrent neural network (DRNN) with the ability of dynamic mapping is established, which is more suitable than the static network for estimating the batteries' state with strongly nonlinear and dynamic behaviors. Meanwhile, a self-adaptive weight particle swarm optimization (SWPSO) algorithm is introduced for training the network. Compared with the gradient descent algorithm, the SWPSO algorithm could improve the error convergence speed and avoid falling into local optimum. The validation results highlight that the presented method is able to improve the estimation accuracy of the SOC and SOH under different conditions including temperature, current, and degree of aging and has strong robustness and ability of generalization.
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