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- W3174556710 abstract "With the large-scale promotion of new energy vehicles, the demand for power batteries such as the lithium-ion type has increased. Its service lifespan is closely related to the service condition. The important embodiment of the service condition is the battery state of charge, which can reflect its residual capacity. Through accurate residual capacity, the battery application strategy can be planned to realize the battery operation of the best condition. The long service lifespan can be realized by adjusting the voltage and current. Therefore, real-time accurate state estimation has a significant effect on battery management. The extended Kalman filtering algorithm is introduced to estimate the state value under complex working conditions. The overview of the state-of-charge estimation is first conducted. After that, the equivalent modeling construction method is explored. The coordinate transformation treatment is implemented in the binary iterative calculation algorithm, according to which the algorithm is implemented. In the extended calculation process, the iterative prediction and correction strategies are introduced into the test. Consequently, the pulse-power characteristic test is conducted to obtain the estimation features, considering a thermal influencing effect, a time-varying current condition, and a complex current rate verification." @default.
- W3174556710 created "2021-07-05" @default.
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- W3174556710 date "2021-01-01" @default.
- W3174556710 modified "2023-09-23" @default.
- W3174556710 title "Battery state-of-charge estimation methods" @default.
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- W3174556710 doi "https://doi.org/10.1016/b978-0-323-90472-8.00009-3" @default.
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