Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Li, Jiabo; Min, Yong; Gao, Kangping; Xu, Xinxin; Wei, Meng; Jiao, Shengjie

doi:10.1002/er.6658

Cited by 40 publications

(32 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Third, if a given firefly in space fails to find someone brighter than itself, the firefly will move randomly. The relative fluorescence brightness of fireflies is calculated as shown in Equation (10).…”

Section: Intelligent Weight Decreasing Firefly-particle Filteringmentioning

confidence: 99%

“…In Equation (10), I is the relative fluorescence brightness and I 0 is the maximum fluorescence brightness. The higher the self-fluorescence brightness of the firefly, the better the corresponding objective function value.…”

Section: Intelligent Weight Decreasing Firefly-particle Filteringmentioning

confidence: 99%

“…8 Therefore, a stable and reliable battery management system (BMS) must be adopted in the use of lithium-ion batteries to ensure the safe and stable operation, monitor the service status, and make efficient use of the discharge performance of lithium-ion batteries. 9,10 State-of-charge (SOC) is a key parameter in BMS; it is equivalent to the fuel gauge of vehicles, 11 providing the driver with real-time status of the vehicle and a better driving experience, 12 and it also provides the basis for the calculation of other state parameters of electric vehicles. 13 As the key and difficult point of electric vehicle development, 14,15 SOC estimation has important research significance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

Qiao

Wang

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

Section: Intelligent Weight Decreasing Firefly-particle Filteringmentioning

confidence: 99%

Section: Intelligent Weight Decreasing Firefly-particle Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

Qiao

Wang

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…J 𝜙 represented as identity matrix for𝜙 dimension, applying the Laplace transform to the Equation (12) and by using the Equation (15), the obtained equation is…”

Section: Design Of Adaptive Nonlinear Observermentioning

confidence: 99%

“…In the presence of noise, the EKF method outperforms the KF algorithm in terms of precision. On the other hand, due to the Jacobian matrixes, the EKF technique Coulomb counting method [7] Simple, easy implementation, and fast computation Applicable only for open-loop systems, and this method is very sensitive 2 Open-circuit voltage method [7,8] Simple, easy implementation Applicable only for open-loop systems and unsuitable for flat OCV-SOC curves 3 Kalman-filter-based method [9][10][11] An accurate and commonly used method in literature, closed-loop system Improved methods are only suitable for linear systems, and complexity is more 4 Extended Kalman filter and unscented Kalman filter methods [12][13][14][15][16][17][18] More accurate, robust, and applicable to the nonlinear systems Complex algorithm and use of the mass of matrix 5 Support vector machine method [19][20][21] Accurate and robust Sensitive to the training data and the complex controller is required 6…”

Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer

Sakile

Sinha

2021

Advcd Theory and Sims

View full text Add to dashboard Cite

Estimation of accurate state of charge (SOC) and state of health (SOH) of lithium‐ion batteries has become more difficult in electric vehicles due to various uncertainties in the battery. The main objective of this paper is to estimate the accurate and robust SOC and SOH of the lithium‐ion battery. Here, a first‐order resistor‐capacitor (RC) electrical equivalent circuit model is considered for the analysis and modeling, an adaptive nonlinear observer (ANO) is proposed to convert nonlinear equations into linearized equations. The transfer function is obtained from the linearized equations to estimate the actual parameters and the internal states of the battery. When comparing the proposed ANO to the conventional method extended Kalman filter, the new ANO gives better dynamic results, less SOC error, and high convergence capability. An extra random variable noise is added to the system; the proposed model is suitable for the SOC at current noise factors. The convergence capability of the observer is analyzed with the linear criterion. The simulation results of the proposed ANO are validated through the MATLAB/Simulink platform under the hybrid pulse power characteristics test.

show abstract

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

et al. 2023

View full text Add to dashboard Cite

With the increasing severity of energy crisis and the gradual increase of environmental awareness, countries around the world have devoted themselves to the new energy vehicle industry. [1] As the foundation and core component of electric vehicles, the power battery pack and its management system play a decisive role in the performance and development of electric vehicles. The power battery pack often uses lithium-ion batteries with higher energy density, lower self-discharge rate, and long cycle life. [2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods. [10,11] The modelbased SOC estimation method is widely used because of its high robustness and accuracy under variable battery operating conditions. [12,13] Battery models, as an important part of model-based SOC estimation methods, commonly include electrochemical models, blackbox models and equivalent circuit models (ECMs). The electrochemical model describes the diffusion process and charge transfer process of lithium ions in the cell based on porous electrodes and concentrated solution theory and uses a set of coupled partial differential equations to achieve real-time estimation of SOC. [14][15][16] Ref.[17] designed a nonlinear observer with terminal-voltage feedback injection based on the electrochemical single-particle model, which improved the accuracy of SOC estimation. Although the electrochemical model can reflect the actual electrochemical reaction process inside the battery more accurately, the structure is complicated and not conducive to practical simulation and calculation. The blackbox model establishes an abstract mapping relationship between inputs and outputs, reflecting a generalized direct causal relationship between the factors involved. Real-time estimation of SOC can be achieved by taking real-time state quantities (e.g., voltage, current, etc.) during battery operation as inputs. [18][19][20][21][22] Ref. [23] proposed an improved feedforward-long short-term memory (FF-LSTM) modeling method to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. Although the calculation of the blackbox model is simple, the accuracy of the estimation depends to a large extent on the quality and quantity of the training data, and the training process may take a long time. From the perspective of external electrical characteristics, ECM uses circuit elements such as resistance, capacitance, and voltage source to form a circuit network to express the relationship between voltage and current in the cell. [24] It cleverly avoids the real structure and complex electrochemical reactions inside the battery and can estimate SOC in real-time by combining it with the filtering algorithm. [25][26][27][28][29] Ref. [30] proposed a novel feedback

show abstract

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Cited by 40 publications

References 30 publications

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

Contact Info

Product

Resources

About