To accurately estimate the state of charge (SOC) of the lithium-ion battery (LiB), a fractional-order multi-dimensional Taylor network (FMTN) model was proposed in the
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AI Customer ServiceAccurate and rapid prediction of thermal runaway propagation in a battery module and pack is essential for the thermal safety design and thermal runaway warning of
AI Customer ServiceThe experiment results show that the fractional-order BPNN can learn the battery degradation trend and maintain estimation accuracy within 4.5% for the whole capacity
AI Customer ServiceThe fractional-order equivalent circuit model can reflect the internal reaction mechanism of a lithium-ion battery well. This article aims to design an effective model and
AI Customer ServiceThe fractional-order equivalent circuit model can reflect the internal reaction
AI Customer ServiceFirst, the predicted SOC of the battery is obtained by using the method of
AI Customer ServiceMoreover, the fractional-order neural network is the combination of battery fractional-order modeling with machine learning; thus, more fractional-order information may be added into the network design to develop a physics
AI Customer ServiceIn this work, we implant the Butler–Volmer (BV) equation and the fractional-order model representation into a model-based physics-informed neural network (M-PINN) to simulate
AI Customer ServiceStadler is expanding its market lead in Germany in alternative propulsion with a third contract to supply battery-electric trains. Over the next five years, Stadler will deliver at least 113 battery
AI Customer ServiceThis paper introduces a deep generative adversarial network-based approach (TS-DCGAN) for battery data augmentation to address the challenge of limited data
AI Customer ServiceWith encoded battery knowledge, the proposed fractional-order PIRNN would accelerate the convergence speed in training process and achieve improved prediction
AI Customer ServiceThis study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The
AI Customer ServiceThis study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and
AI Customer ServiceFirst, the predicted SOC of the battery is obtained by using the method of unscented Kalman filter based on the first-order RC equivalent circuit model of the battery.
AI Customer ServiceAccurate and rapid prediction of thermal runaway propagation in a battery
AI Customer ServiceBy comparison, the accuracy of second-order RC network is higher than that of first-order network, close to third-order network and less complex than third-order network.
AI Customer ServiceThe experiment results show that the fractional-order BPNN can learn the battery degradation trend and maintain estimation accuracy within 4.5% for the whole capacity curve during battery
AI Customer Servicebattery charging and discharging experiments is required to train the network, thereby self-learning the network parameters and extracting the fitting relationship.
AI Customer ServiceThe experiment results show that the fractional-order BPNN can learn the battery degradation
AI Customer ServiceIn this work, we also investigate SOC estimation by employing a model-based approach. Model-based procedures typically utilize three influential models: the
AI Customer ServiceIn this work, we also investigate SOC estimation by employing a model
AI Customer ServiceThe application of fractional-order models to create lower-order models to represent physical systems (e.g., the battery characteristics for the state of charge estimation)
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AI Customer ServiceTo accurately estimate the state of charge (SOC) of the lithium-ion battery
AI Customer ServiceA recurrent neural network with fractional order dynamics is used for assessing the health of LFP rechargeable automotive batteries through incremental capacity analysis.
AI Customer ServiceWith encoded battery knowledge, the proposed fractional-order PIRNN would accelerate the convergence speed in training process and achieve improved prediction accuracies.
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AI Customer ServiceThe estimation of battery SOC usually involves nonlinear properties, such as charge/discharge efficiency, self-discharge, capacity decay, etc. BPNN is capable of building complex nonlinear relationship models that can effectively capture these nonlinear relationships through the combination of multiple layers of neurons.
The model-based method, taking the equivalent circuit model as an example, can reflect the internal state and behavior of the battery to a certain extent, and can provide relatively accurate state estimation and prediction under certain working conditions, combined with related algorithms.
An improved gated recurrent unit neural network for state-of-charge estimation of lithium-ion battery. Appl. Sci. 2022, 12, 2305.
model that describes the battery dynamics is indispens-able for state estimation, essential validation for the established indirect model accuracy should be exercised on the RBFNN. The sampling data obtained from the bat-tery charging and discharging experiment is then adopted in the test for validation.
The state of battery charge is a kind of state of nonlinear system, so it can estimate its state effectively and provide accurate estimation results.
Consequently, KF based on a discrete linear state-space model has been spotlighted for use in battery SOC estimation. To accommodate the nonlinearity, UKF based on RBFNN model is proposed (ie, original RBFNN-UKF model), where SOC is defined as the internal state and can be estimated indirectly based on the voltage (ie, output var-iable) error.
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