A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter Appl Energy, 265 ( 2020 ), Article 114789, 10.1016/j.apenergy.2020.114789
Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the
Particle filter-based state-of-charge estimation and remaining-dischargeable-time prediction method for lithium-ion batteries J. Power Sources, 414 ( 2019 ), pp. 158 - 166 View PDF View article View in Scopus Google Scholar
Improved Particle Swarm Optimization Particle Filtering Method for Lithium-Ion Battery SOC Estimation. CISAI ''24: Proceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence . Lithium-ion batteries, being the main energy source for electric vehicles, need precise State of Charge (SOC) estimation to maintain
The state of charge (SOC) of lithium battery is a key parameter for effective management of battery management systems (BMS). To address the problems of low precision, complex computation and poor robustness of traditional charging state estimation methods, an enhanced algorithm based on Unscented Kalman filter (UKF) is proposed.
This study proposes an adaptive combined method for battery SOC estimation based on a long short-term memory network and unscented Kalman filter algorithm considering battery aging status. Compared to the
To account for the diverse time-varying characteristics of both SOC and model parameters in lithium-ion power batteries, this article introduces a multi-time-scale improved adaptive
Then filtering algorithms are used to update the state equations to obtain the SOH. Guo et al. A quantitative fault diagnosis method for lithium-ion battery based on md-LSTM. IEEE Trans. Transp. Electrif., 1 (1) (July 2024), pp. 1-11. View PDF View article Google Scholar R. Xiong, Y. Sun, C.X. Wang, J.P. Tian, X. Chen, H.L. Li, Q. Zhang. A data-driven
To ensure their safe and efficient use, various areas of batteries get intensively researched, such as thermal runaway [3–5], state estimation [6–9] and battery circuit diagnosis [10–12], etc. Batteries'' state of charge (SOC) is a fundamental and important state for a battery management system (BMS), it equals to the ratio of its current remaining capacity to its
A New Method of Lithium Battery Insulation Fault Diagnosis Based on Double Kalman Filter. Conference paper; First Online: 20 January 2024; pp 379–392; Cite this conference paper ; Download book PDF. Download book EPUB. The Proceedings of 2023 4th International Symposium on Insulation and Discharge Computation for Power Equipment (IDCOMPU2023)
In this paper, an adaptive infinite Kalman filter (AUKF) was used to estimate the state of charge for a 18650 LiNiMnCoO 2 /graphite lithium-ion battery, and its performance was
As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan
Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic,
SOC Estimation Method of Lithium-Ion Battery Based on Multi-innovation Adaptive Robust Untraced Kalman Filter Algorithm
A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data
The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF
An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine. Energy, 214 (2021), Article 118866. View PDF View article View in Scopus Google Scholar Sultana I., Chen Y., Huang S., Rahman M.M. Recycled value-added circular energy materials for new battery application: Recycling strategies, challenges, and
Accurate state of charge estimation is crucial for the safe and efficient operation of lithium-ion batteries. The Kalman filtering algorithm has been widely used in state-of-charge (SOC) estimation. To solve the problem of filter divergence and sensitivity to noise, the joint SOC estimation method is proposed to achieve accurate and robust estimation of SOC, which is
In this research, a novel chaotic firefly - particle filtering (CF - PF) method is proposed for the SOC and SOH co-estimation of lithium-ion batteries, which effectively solves
The accurate estimation of battery state of charge (SOC) is an important function of the battery management system, and the precise state of battery is estimated makes for the stability of the system. Based on the working characteristics of lithium-ion batteries, the article which used intelligent computing method establishes the mathematical model of the lithium-ion
With the continuous deterioration of various environmental pollution problems in recent years [1, 2], electric vehicles (EVs), as a means of transport based on clean energy instead of traditional fuel vehicles [, , ], have gained the attention of the government and other relevant departments.Lithium-ion batteries (LIBs), the main power source of EVs, have the
Accurate estimation of battery state of charge (SOC) is of great significance to improve battery management and service life. An unscented Kalman filter (UKF) method is used to increase the accuracy of SOC
Several methods are commonly used to estimate the State of Charge (SOC) of lithium-ion batteries, including the neural network method, ampere-hour integration method, Kalman filtering method, and open-circuit voltage (OCV) method. 2–6 The ampere-hour integration method suffers from cumulative errors during calculations. 7–9 While the OCV
A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter. Energy, 219 (2020), Article 119603. Google Scholar S. Zhang, X. Guo, X. Zhang. An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery. J. Energy Storage, 32
In recent years, the methods proposed for state of health (SOH) estimation have been mainly divided into experimental methods, model methods, and data-driven methods , .The experimental method is challenging to use in the real-world due to its need for accurate testing of battery capacity and material parameters of battery cells.
A New Method of Lithium Battery Insulation Fault Diagnosis Based 381. 2 Establishment of Battery Pack Insulation Fault Detection Model . The battery model is used to understand its internal behavior and give the battery prop-erties in the form of equations, this section focuses on the insulation fault diagnosis
Aiming at the shortcomings of existing methods, such as low prediction accuracy and a short prediction period, this paper proposes a real-time update high-order extended
With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC.
As shown in Fig. 3, the battery SOE estimation methods can be classified into three categories such as power integral method, model-based method, and machine learning-based method . Table 3, Table 4 present the commonly used formulas for SOE estimation and a comparison of studies available in the literature on different SOE estimation methods,
The open circuit voltage method is a method to reverse fit SOC based on the open circuit voltage (OCV) of the battery .This method can analyze a large number of experimental data under different settings by controlling a single variable method and can build an open-circuit voltage method under the influence of multiple factors to improve the adaptability
An unscented kalman filtering method for estimation of state-of-charge of lithium-ion battery. Frontiers in Energy Research. January 2023; 10:998002; DOI: 10.3389/fenrg.2022.998002. License; CC BY
A chaotic firefly - particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance Energy (2023)
A novel framework for lithium-ion battery state of charge estimation based on Kalman filter Gaussian process regression Int. J. Energy Res., 45 ( 9 ) ( 2021 ), pp. 13238 - 13249, 10.1002/er.6649
To improve the efficiency of electric vehicles, different methods have been put forward to reasonably estimate the battery SOC. However, the practical application only relying on a single estimation method is vey limited to
3. Approximate SOC Estimation Using the Equivalent Circuit Model of Lithium-Ion Battery by Experimental Tests. In the present study, the experiment test rig and test object are Arbin BT-ML-100V100A (Arbin Company) and NCM532 lithium-ion batteries (4.2 V, 24 Ah), respectively, as shown in Figure 2.The operating temperature range of the battery is −20
Wang et al. introduced a method for predicting lithium battery SOH using differential thermal voltammetry (DTV) combined with Gaussian process regression (GPR). They applied a filtering method to smooth the DTV curve, extracted crucial features indicative of battery degradation, and used the GPR model for accurate SOH estimation . Li et al
To the problem that it is difficult to accurately predict the remaining useful life (RUL) of lithium battery, a prediction model of improved long short term memory network based on particle filter (PF-LSTM) is proposed.
Li X, Ma Y and Zhu JJ proposed a RUL prediction model based on a fusion algorithm. The “virtual observation value” is constructed by using the results of the fusion algorithm. At the same time, Finally, prediction results of the RUL of the lithium-ion battery are achieved by combining unscented particle filtering with the least squares support vector
Currently, global optimization algorithm is a common method for lithium-ion battery parameter identification, however this kind of method may lead to local optimization, which fails to get accurate identification results. In the search range of the global optimization algorithm, there are certain parameter vectors that may cause the battery model to not converge. Such
A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters. Energy 178, 79–88 (2019). This work is supported by the National Key R&D Program of China (2018YFB0104400). S.Y. and X.L. guided the experiments.
This study proposes an adaptive combined method for battery SOC estimation based on a long short-term memory network and unscented Kalman filter algorithm considering battery aging status. Compared to the traditional estimation method, the proposed method demonstrates superior estimation accuracy under various complex operating conditions.
The state of charge (SOC) of lithium battery is a key parameter for effective management of battery management systems (BMS). To address the problems of low precision, complex computation and poor robustness of traditional charging state estimation methods, an enhanced algorithm based on Unscented Kalman filter (UKF) is proposed.
Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.
The accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium-ion batteries. Data-driven SOC estimation is considered an emerging and effective solution. However, existing data-driven SOC estimation methods typically involve direct estimation and lack effective feedback correction.
(a) The SOC estimation and max error is under 3%; (b) error analysis. Considering the various battery types operating on electric vehicles, the lithium iron phosphate (LFP) battery is selected as another typical example to validate the effectiveness of proposed method. Herein, a 1.5 AH LFP battery is operated at 1C discharging test and 25 ℃.
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