Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization

Yang, Xiaoling; Chen, Long; Xu, Xing; Wang, Wei; Qiling, XU; Lin, Yuqing; Zhou, Zhiyong

doi:10.3390/en10111811

Cited by 33 publications

(19 citation statements)

References 17 publications

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“…The vice versa is true for low temperatures. If the subject temperature is very high it affects the battery life, consequently, if the temperature is too low they accelerate battery aging [2], [3], [120]. Therefore, these two parameters must be observed during battery balancing to increase battery life.…”

Section: Experimental Results For Battery Pack Health Analysismentioning

confidence: 99%

Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles

Omariba

Zhang

Sun³

2019

IEEE Access

217

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Batteries are gaining entry into every home and office for they are widely used because of their variant benefits. However, these batteries are prone to failure caused by charge imbalance in the batteries connected in either series or parallel, which can sometimes be catastrophic and hence they require to be properly monitored in a real-time manner. There exist many battery balancing schemes which are broadly grouped into either passive or active schemes. All these schemes have their own advantages and disadvantages, and hence it is upon the user to decide on which scheme will best work for them. However, research has proven that the hybrid scheme will be the best as it couples the benefits of all schemes. This study will review the various battery cell balancing methodologies and evaluate their relationship with battery performance. At present there are a few studies tackling the mechanical vibration of battery balancing performance. This study shows that battery balancing performance during long-term should be evaluated from various temperature and vibration frequencies.INDEX TERMS Battery cell balancing, electric vehicles, hybrid schemes, and performance optimization.

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Section: Experimental Results For Battery Pack Health Analysismentioning

confidence: 99%

Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles

Omariba

Zhang

Sun³

2019

IEEE Access

217

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“…However, it is normally quite difficult to obtain the real parameters in electrochemical models. Therefore, heuristic methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for the parameter identification of the electrochemical model [14,58,59]. Despite the requirement of knowledge of the electrochemical process, the computational burden should also be considered if an electrochemical model is used in BMS.…”

Section: Electrochemical Modelmentioning

confidence: 99%

Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

et al. 2018

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As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

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“…In ECM implementation, pulse charging and discharging experiments can be applied to perform offline parameter identification [24]. When conducting SOC estimation, battery cell parameter identification conditions are originally designed based on excitation response analysis [25], making it possible to conduct essential verifications in terms of model accuracy.…”

Section: Parameter Identificationmentioning

confidence: 99%

Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics

Omariba

Zhang

Kang

et al. 2020

WEVJ

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There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation.

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Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization

Cited by 33 publications

References 17 publications

Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles

Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles

Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics

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