Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Chen, Mengying; Han, Fenghui; Shi, Long; Feng, Yong; Chen, Xue; Gao, Weiqi; Xu, Jinzheng

doi:10.3390/en15072658

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…At any time t, the battery capacity is determined by the previous state of charge (SOC), the available energy in the HRES (Hybrid Renewable Energy System) and the system load requirements. PV modules and wind turbines can be charged when their generating capacity exceeds the load demand 9 .At this time of the battery charging capacity is…”

Section: Battery Modelmentioning

confidence: 99%

Research on collaborative wind and solar energy storage system based on machine learning

Cao,

Sun,

Zhang

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

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On September 22,2020, China has pledged to the UN General Assembly to peak its carbon emissions by 2030 and to become carbon neutral by 2060.Under the expectation of achieving carbon neutrality, the sustainable development of renewable energy is the fundamental way to effectively solve ecological and environmental problems, reduce carbon dioxide emissions, and achieve the gradual replacement of coal and other fossil resources. In this paper in view of the independent scenery power generation system abandon power phenomenon, with a large office in Changchun city, Jilin province as the research object, On the basis of the independent landscape power generation system, the battery storage system is added, using the battery storage charging and discharging characteristics to balance the system power. The first step is to meet the demand of the building's electrical load.

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Section: Battery Modelmentioning

confidence: 99%

Research on collaborative wind and solar energy storage system based on machine learning

Cao,

Sun,

Zhang

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

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“…In [74], battery state-of-health monitoring and remaining usable life (RUL) prediction were investigated using an enhanced particle filter (PF) technology. In [75], to achieve a precise SoC estimate, a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model was implemented. The federal urban driving schedule (FUDS) test and the dynamic street test (DST) are two dynamic battery tests used to assess the proposed approach.…”

Section: Reqmentioning

confidence: 99%

“…Changes in temperature and loading current have a direct impact on how well SOC is estimated. The adaptive H-infinity filter (AHIF) can fully accommodate the fractional-order model and operation condition differences created by various temperatures and loading currents based on reliable parameter identification [75]. The Kalman filter and its variants have been used frequently and successfully for the estimation of various states due to their superiority compared to other algorithms.…”

Section: Reqmentioning

confidence: 99%

Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

et al. 2022

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The online battery management system (BMS) is very critical for the safe and reliable operation of electric vehicles (EVs) and renewable energy storage applications. The primary responsibility of BMS is data assembly, state monitoring, state management, state safety, charging control, thermal management, and information management. The algorithm and control development for smooth and cost-effective functioning of online BMS is challenging research. The complexity, stability, cost, robustness, computational cost, and accuracy of BMS for Li-ion batteries (LiBs) can be enhanced through the development of algorithms. The model-based and non-model-based data-driven methods are the most suitable for developing algorithms and control for online BMS than other methods present in the literatures. The performance analysis of algorithms under different current, thermal, and load conditions have been investigated. The objective of this review is to advance the experimental design and control for online BMS. The comprehensive overview of present techniques, core issues, technical challenges, emerging trends, and future research opportunities for next-generation BMS is covered in this paper with experimental and simulation analysis.

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“…Ref. [40] used a hysteresis resistor–capacitor ECM to reduce the estimation error due to hysteresis to improve the accuracy of SOC estimation. Ref.…”

Section: Introductionmentioning

confidence: 99%

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

et al. 2023

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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

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Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Cited by 10 publications

References 28 publications

Research on collaborative wind and solar energy storage system based on machine learning

Research on collaborative wind and solar energy storage system based on machine learning

Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

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