Lithium battery SOC estimation based on improved sparrow search algorithm and backpropagation neural network
Source abstract
Abstract
Accurate state-of-charge (SOC) estimation is crucial for optimal battery management. This paper proposes a novel method, the Improved Sparrow Search Algorithm-Backpropagation (ISSA-BP) neural network, to address the issue of low estimation accuracy encountered with a single BP neural network. ISSA is used to optimize the initial weights and thresholds of the BP neural network, effectively overcoming its tendency to get stuck in local minima. Compared to the single BP neural network, ISSA-BP demonstrates significantly improved accuracy under two conditions (DST and BJDST), with reductions in root mean square error by 64.0% and 50.9% and mean absolute error by 69.8% and 51.1%, respectively. These results highlight the superior robustness and accuracy of the ISSA-BP algorithm for SOC estimation in lithium batteries.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advanced Algorithms and Applications · Fault Detection and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering and Automation, Hefei University of Technology 1 , Hefei 230009,
The National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology, Hefei University of Technology 2 , Hefei 230009,
Funding
National Natural Science Foundation of China
References (27)
Show source-provided references
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