A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles

World Electric Vehicle Journal, Journal Year: 2025, Volume and Issue: 16(2), P. 57 - 57

Published: Jan. 21, 2025

The effective administration of lithium-ion batteries is key to the performance and durability electric vehicles (EVs). This systematic mapping study (SMS) thoroughly examines optimization methodologies for battery management, concentrating on estimation state health (SoH), remaining useful life (RUL), charge (SoC). findings disclose various methods that boost accuracy reliability SoC, including enhanced variants Kalman filter, machine learning models like long short-term memory (LSTM) convolutional neural networks (CNNs), as well hybrid frameworks combine Grey Wolf Optimization (GWO) Particle Swarm (PSO). For estimating SoH, prevalent data-driven techniques include support vector regression (SVR) Gaussian process (GPR), alongside merging with conventional heighten predictive accuracy. RUL prediction sees advancements through deep techniques, especially LSTM gated recurrent units (GRUs), improved using algorithms such Harris Hawks (HHO) Adaptive Levy Flight (ALF). underscores critical role integrating advanced filtering learning, in developing management systems (BMSs) enhance reliability, extend lifespan, optimize energy EVs. Moreover, innovations synthetic data generation generative adversarial (GANs) further augment robustness precision strategies. review lays out a thorough framework future exploration development EV batteries.

Language: Английский

Research on the remaining useful life prediction method for lithium-ion batteries based on feature engineering and CNN-BiGRU-AM model

Ionics, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Language: Английский