An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation

Wang, Shunli; Takyi‐Aninakwa, Paul; Jin, Siyu; Yu, Chunmei; Fernandez, Carlos; Stroe, Daniel‐Ioan

doi:10.1016/j.energy.2022.124224

Cited by 227 publications

(56 citation statements)

References 61 publications

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“…Several studies have been conducted on SOC and SOH estimation methods using various methods. For example, a recently proposed SOC estimation method uses a feedforward-long short-term memory model to reflect battery characteristics based on a sliding balance window [ 9 ] and an adaptive square root extended Kalman filter method that minimizes the effect of noise, regardless of the temperature [ 10 ]. Typical methods for estimating SOC and SOH include model-based, data-driven, and Coulomb counting methods [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries

Lee

2022

Sensors

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Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts.

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Section: Introductionmentioning

confidence: 99%

Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries

Lee

2022

Sensors

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“…For the cases of temperature change up to 40 °C, capacity degradation up to 20%. Wang et al [34] , [35] designed an improved feedforward-long short-term memory modeling method to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. The KF algorithm is often only applicable to dynamic models with few model parameters although it can realize accurate online identification of model parameters [36] .…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation of ultracapacitor based on forgetting factor recursive least square and extended Kalman filter algorithm at full temperature range

Ren

Xu²,

Zhang³

et al. 2022

Heliyon

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“…Unlike the total capacity and internal resistance, which are hard to observe online, the cell's SOC can be estimated by the latest filtering and ANN techniques. [14][15][16] Thus, the difference in SOC is always the measure of the cell inconsistency within a battery pack and has become the considering feature for the battery equalization system to decide how urgent the cell balancing should be implemented.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Factor on SOC Inconsistency within a Series‐Connected Lithium‐Ion Battery Pack

Zhang

Yang

Chi

2022

Advcd Theory and Sims

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This paper outlines a new simulation approach to study the effect of factors on the state of charge (SOC) consistency of Li‐ion battery packs. The proposed method is based on a system model and the Monte Carlo method. The system model includes 100 serial connected equivalent circuit models and their respective parameters. This paper divides the internal and external factors into two categories: independent factors such as internal resistance, capacity, Coulombic efficiency, leakage current, and comprehensive factors related to temperature. The Monte Carlo method simulates the differences and distribution of independent factors among cells. Temperature‐dependent factors are modeled with empirical formulas. Simulation results show that the main factors affecting SOC consistency are differences in battery temperature, leakage current, and Coulombic efficiency. Meanwhile, slight differences in capacity and internal resistance have little effect on SOC inconsistency. Furthermore, this paper demonstrates the effectiveness of the system model.

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An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation

Cited by 227 publications

References 61 publications

Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries

Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries

State of charge estimation of ultracapacitor based on forgetting factor recursive least square and extended Kalman filter algorithm at full temperature range

Simulation of Factor on SOC Inconsistency within a Series‐Connected Lithium‐Ion Battery Pack

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