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- W4324118478 abstract "Lithium-ion battery has high energy density, strong environmental protection and good performance, and is the most widely used power battery for new energy vehicles. However, the following problem is how can we know the remaining useful life (RUL) of battery, so as to ensure that people can pay attention to the healthy state of batteries and replace them in time, thus ensuring the security of automobile operation. Taking lithium-ion battery as the research object, this paper proposed a backpropagation neural network (BPNN) based on improved particle swarm optimization (IPSO) algorithm to estimate the state of health (SOH) of lithium-ion battery by extracting the health characteristics (HC) of the battery from the charging process. Then, uses the number of charging times when the battery reached the failure threshold as the end of RUL prediction to predict the RUL of the battery. Compared with the genetic algorithm (GA) with BPNN, the IPSO-BP model has better effect and simpler parameter debugging." @default.
- W4324118478 created "2023-03-15" @default.
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- W4324118478 date "2022-11-30" @default.
- W4324118478 modified "2023-09-27" @default.
- W4324118478 title "Research on Data-Driven-Based Remaining Useful Life of Lithium-ion Battery" @default.
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- W4324118478 doi "https://doi.org/10.1109/icsmd57530.2022.10058386" @default.
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