Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion

Qiao, Xin; Wang, Zhixue; Hou, Enguang; Liu, Guangmin; Cai, Yaqi

doi:10.3390/en15124373

Cited by 15 publications

(8 citation statements)

References 46 publications

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“…The SOC-OCV curve is generally defined as the voltage difference between the positive electrode (PE) and the negative electrode (NE) of a battery [32] . This measurement is made at different SOC points when the battery system is in a state of equilibrium without any current flow [33] .…”

Section: Related Workmentioning

confidence: 99%

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Su,

Ge,

Qiao

2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Su,

Ge,

Qiao

2024

Heliyon

Self Cite

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“…The open-circuit voltage method (OCV) [5], the internal resistance method [6], the ampere-hour integral method [7], and the Kalman filtering and its extension algorithm [8] are the most common SOC estimation methods. However, the open-circuit voltage method (OCV) is usually not used for real-time SOC estimation, because when using OCV for SOC estimation, the battery needs to be statically processed until in a stable state, so real-time estimation of SOC cannot be performed [9,10]. When using the internal resistance method to measure the SOC value of the battery, it is necessary to measure the internal resistance of the battery first and then infer the SOC value of the battery according to the known relationship between the internal resistance of the battery and the SOC.…”

Section: Related Workmentioning

confidence: 99%

Study of SOC Estimation by the Ampere-Hour Integral Method with Capacity Correction Based on LSTM

et al. 2022

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The estimation of the state of charge (SOC) of a battery’s power is one of the key technologies in a battery management system (BMS). As a common SOC estimation method, the traditional ampere-hour integral method regards the actual capacity of the battery, which is constantly changed by the usage conditions and environment, as a constant for calculation, which may cause errors in the results of SOC estimation. Considering the above problems, this paper proposes an improved ampere-hour integral method based on the Long Short-Term Memory (LSTM) network model. The LSTM network model is used to obtain the actual battery capacity variation, replacing the fixed value of battery capacity in the traditional ampere-hour integral method and optimizing the traditional ampere-hour integral method to improve the accuracy of the SOC estimation method. The experimental results show that the errors of the results obtained by the improved ampere-hour integral method for the SOC estimation are all less than 10%, which proves that the proposed design method is feasible and effective.

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“…Owing to the energy crisis and environmental pollution, new energy has attracted an increasing amount of attention. Lithium-ion batteries (LIBs) are widely used in electric vehicles and energy storage systems due to the advantages of having high energies, high power densities and environmental protection properties [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Combined State of Charge and State of Energy Estimation for Echelon-Use Lithium-Ion Battery Based on Adaptive Extended Kalman Filter

et al. 2023

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To ensure the safety and reliability of an echelon-use lithium-ion battery (EULIB), the performance of a EULIB is accurately reflected. This paper presents a method of estimating the combined state of energy (SOE) and state of charge (SOC). First, aiming to improve the accuracy of the SOE and SOC estimation, a third-order resistor-capacitance equivalent model (TRCEM) of a EULIB is established. Second, long short-term memory (LSTM) is introduced to optimize the Ohmic internal resistance (OIR), actual energy (AE), and actual capacity (AC) parameters in real time to improve the accuracy of the model. Third, in the process of the SOE and SOC estimation, the observation noise equation and process noise equation are updated iteratively to make adaptive corrections and enhance the adaptive ability. Finally, an SOE and SOC estimation method based on LSTM optimization and an adaptive extended Kalman filter (AEKF) is established. In simulation experiments, when the capacity decays to 90%, 60% and 30% of the rated capacity, regardless of whether the initial value is consistent with the actual value, the values of the SOE and SOC estimation can track the actual value with strong adaptive ability, and the estimated error is less than 1.19%, indicating that the algorithm has a high level of accuracy. The method presented in this paper provides a new perspective for estimating the SOE and SOC of a EULIB.

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Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion

Cited by 15 publications

References 46 publications

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Study of SOC Estimation by the Ampere-Hour Integral Method with Capacity Correction Based on LSTM

Combined State of Charge and State of Energy Estimation for Echelon-Use Lithium-Ion Battery Based on Adaptive Extended Kalman Filter

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