GA-ELM-based adaptive Kalman estimator for SOC of lithium-ion batteries

Ren, Yitong; Ding, Jie; Hao, Tianyun

doi:10.1007/s11581-022-04751-9

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…Fuzzy logic has problems like large amount of calculation and large requirement for storage space. As a mainstream method, neural network is widely utilized, such as convolutional neural network [11], back propagation neural network (BPNN) [12], extreme learning machine (ELM) [13] and recurrent neural network [14].…”

Section: Introductionmentioning

confidence: 99%

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

Shen

Ding

Hao

2023

Preprint

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This paper provides an insight into the estimation of state of charge (SOC) of lithium-ion batteries, and multi-measurement Kalman filters are constructed to achieve better estimation accuracy. The filter based estimation scheme consists of two sub Kalman filters, one based on the Thevenin equivalent circuit model and the other based on the Elman neural network model. They share the same state equation but have different measurements, namely from the terminal voltage and the Elman neural network, respectively. The optimal weighted error covariance matrix in Kalman filter is computed to obtain the final estimated SOC with the results from two sub filters. The training set of the experiment is from five mixed charging and discharging cycles and the test set is the LA92 driving cycle. The experimental results show that multi-measurement Kalman filtering has higher accuracy in SOC estimation, both the mean absolute error and the root mean square error are less than 0.3\%.

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Section: Introductionmentioning

confidence: 99%

Elman neural network and equivalent circuit model based multi-measurement Kalman filters for SOC estimation

Shen

Ding

Hao

2023

Preprint

Self Cite

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“…Lipu et al 19 presents a SOC estimation model for lithium-ion batteries, employing an ELM enhanced by a gravitational search algorithm that of approach, achieving higher accuracy than that of conventional neural network methods. Ren et al 20 present an adaptive Kalman estimator that combines a genetic algorithm-optimized ELM (GA-ELM) with the Ah integration method for lithium-ion batteries' SOC estimation, achieving an accuracy error of less than 1.2%.…”

Section: Introductionmentioning

confidence: 99%