Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation

Duong, Van-Huan; Bastawrous, Hany Ayad; See, Khay Wai

doi:10.1016/j.apenergy.2017.07.056

Cited by 47 publications

(15 citation statements)

References 33 publications

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“…The RLS and its variants have been used widely to determine the parameters of Li-ion battery model [124][125][126][127][128][129][130][131][132][133]. The RLS has been applied to identify the characteristic of the battery for a 1 st order ECM [124,129,132]. The OCV estimator was designed to determine the SOC.…”

Section: Recursive Least Square (Rls)mentioning

confidence: 99%

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Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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Section: Recursive Least Square (Rls)mentioning

confidence: 99%

“…Table 11 lists the performance of the RLS SOC estimation method. [128] ≤± 3% Duong et al 2017 [129] ≤± 1.8% Xia et al 2017 [130] ≤± 1.41% Ali et al 2018 [132] ≤± 1.93% Zhang et al 2018 [133] ≤± 1%…”

Section: Recursive Least Square (Rls)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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“…The first dataset of the [31]. Therefore, maintaining the accuracy of the monitoring system has become a major challenge.…”

Section: Datasetsmentioning

confidence: 99%

“…Therefore, maintaining the accuracy of the monitoring system has become a major challenge. SOC estimation under dynamic load curves can be found in some literature [31]- [36]. The battery cell was tested with four drive cycles, such as DST, US06, FUDS, and BJDST at different discrete temperatures.…”

Section: Datasetsmentioning

confidence: 99%

Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries

Mamo

Wang

2020

IEEE Access

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Evaluating the state-of-charge of the battery's current cycle is one of the major tasks in the charge management of rechargeable batteries. We propose a long short-term memory model with an attention mechanism to estimate the charging status of two lithium-ion batteries. Data from three dynamic tests such as dynamic stress test, supplemental federal test procedure-driving schedule, and federal urban driving schedule are used to evaluate our model at different temperatures. One dataset or two datasets are used as the training data, and the other datasets are used as the test data. The model achieves the predictive root mean square errors of 0.9593, 0.8714, and 0.9216 at three different temperatures for the FUDS dataset. Moreover, the predictive RMSE of the proposed model is lower than 1.41 for all our experiments. We use the Monte Carlo dropout technique to verify the robust of the proposed model. INDEX TERMS Attention mechanism, lithium-ion battery, long short-term memory, state-of-charge. NOMENCLATURE

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“…This phenomenon obviously reflects the limitation of Thevenin model. This disadvantage can be mitigated by constructing the relation between resistor, capacitor and SOC, state of health (SOH) and temperature [6], [7], yet the model built by this manner is still labor-intensive and time-consuming and usually has low generalization ability to new batteries [8]. So, the electrical model cannot completely satisfy the satellite BMS.…”

Section: Introductionmentioning

confidence: 99%

A Simplified Extension of Physics-Based Single Particle Model for Dynamic Discharge Current

Chen

Wang

et al. 2019

IEEE Access

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The prediction of the battery temperature and terminal voltage under dynamic load condition is crucial for a satellite battery management system. Restricted by parameter measurability and computing resources, equivalent circuit model has been commonly used in battery management system. But this model cannot satisfy the necessary performance under dynamic load for usual work of satellites. On account of this problem, a combined temperature single particle model is developed for 18650 cells in this paper. The proposed model consists of two sub-models, an electrochemical model and a thermal model, which are coupled together in an iterative manner through physicochemical temperature dependent parameters. The electrochemical sub-model mainly simplifies the calculation of lithium-ion concentration in electrode, while an expression for battery temperature distribution is employed in the thermal sub-model. In addition, genetic algorithm is adopted to estimate model parameters by exciting the battery under different operation conditions. This proposed model can provide accurate predictions of terminal voltage and surface temperature at various operating conditions and the proper simplification of mathematical structure making it ideal for real-time battery management system application. Finally, the model is validated against both constant and dynamic load conditions.INDEX TERMS Battery management systems, mathematical model, satellites, thermal analysis.

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Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation

Cited by 47 publications

References 33 publications

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries

A Simplified Extension of Physics-Based Single Particle Model for Dynamic Discharge Current

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