<scp>Lithium‐ion</scp>battery<scp>SOC</scp>/<scp>SOH</scp>adaptive estimation via simplified single particle model

Cen, Zhaohui; Kubiak, Pierre

doi:10.1002/er.5374

Cited by 62 publications

(29 citation statements)

References 49 publications

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“…The model proposed as mentioned in Reference 17 considers the influence of impedance on the cathode, anode, and the electrolyte characteristics. A novel electrochemistry‐based model for estimating both SOC and SOH is proposed and as mentioned in Reference 18 The authors simplify the partial differential equations of the single particle model into ordinary differential equations, thereby reducing the computational complexity and making the model feasible for online BMS implementation. In practice, electrochemistry‐based models generally require complex calculations for identifying the multitude of parameters that they often have.…”

Section: Related Workmentioning

confidence: 99%

Novel battery wear leveling method for large‐scale reconfigurable battery packs

et al. 2020

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Summary As the market and the application areas of high capacity battery energy storage systems are rapidly increasing, there is a correspondingly high interest in the topic of minimizing battery state of health degradation in battery packs. In this article, a novel method for battery management in large‐scale battery packs is introduced, aiming to minimize battery degradation by enforcing a special wear leveling (WL) policy, adapted from the flash memory arrays. Using this method in conjunction with a hybrid mathematical‐electrochemical battery model, a reconfigurable battery management system (BMS) is proposed and evaluated. The results of the performance analysis and in‐depth comparisons with other state‐of‐the‐art solution shows that the proposed method achieves significantly longer operating times for the battery packs—for example, 415% improvement over the classical BMS in the load current variation scenario. As the computing and memory requirements are relatively low, the new battery WL method can also be implemented on embedded systems with limited resources.

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Section: Related Workmentioning

confidence: 99%

Novel battery wear leveling method for large‐scale reconfigurable battery packs

et al. 2020

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“…The electrochemical model is a principle model that describes the electrochemical reaction in a battery. [19][20][21][22][23][24][25][26][27] Although this model can accurately describe a battery's internal characteristics, its application scenario is limited due to its setup complexity. In an ECM, a battery is composed of a series of electronic components.…”

Section: Introductionmentioning

confidence: 99%

“…Since empirical models only reflect the estimated relationship between voltage, SOC, and current, and do not accurately describe the transient features of the battery, its application scope is limited. The electrochemical model is a principle model that describes the electrochemical reaction in a battery 19‐27 . Although this model can accurately describe a battery's internal characteristics, its application scenario is limited due to its setup complexity.…”

Section: Introductionmentioning

confidence: 99%

Real‐time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system

et al. 2021

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Partnership for a new generation of vehicles (PNGV) model is a conventional battery equivalent circuit model (ECM). However, identifying the best parameters for this model is a challenge. In this study, the PNGV model is transformed into a directly identifiable difference equation to identify its parameters. Subsequently, the model reference adaptive system (MRAS) is used to realize the real-time identification of the model parameters. The identification accuracy of the MRAS is found to be superior to that of the recursive extended least square algorithm. For a single hybrid pulse power characterization (HPPC), the PNGV model identified by the MRAS can achieve a high-precision terminal voltage estimation. For lithium iron phosphate, lithium titanate, and nickel-metal hydride batteries, the root mean square errors are 0.024, 0.048, and 0.020 V, respectively. Besides, the real-time state of charge (SOC) estimation can be realized by the identified open-circuit voltage (OCV). The average errors of the three batteries are only −0.02, −0.01, and −0.01, respectively. Since the PNGV model has a capacity of describing the change of OCV with the current accumulation effect, the model is only suitable for simulating single HPPC or positive-negative pulses with equal amplitude and not for other current pulses. This is a major drawback of the PNGV model. The real-time PNGV model parameters identification method proposed in this study can provide a solid foundation for various state estimation of a battery. Novelty Statement• Transformation of the PNGV model into a difference equation that can be directly identified.• Real-time identification of the PNGV model parameters via the MRAS.• The identified OCV realized the real-time SOC estimation and discussed the deficiencies of the PNGV model.

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“…The SOH is defined as the ratio between the cell's maximum capacity and the brand‐new cell's nominal capacity. It is known that the battery capacity and battery impedance are slow time‐varying parameters compared to battery SOC 22 . The capacity monitoring techniques can be categorized into data‐driven approaches and model‐based methods as well 23 .…”

Section: Introductionmentioning

confidence: 99%

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

et al. 2021

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Summary An accurate estimate of the battery state of charge and state of health is critical to ensure the lithium‐ion battery's efficiency and safety. The equivalent circuit model‐based methods and data‐driven models show the potential for robust estimation. However, the state of charge and state of health estimation system's performance with a parallel comparison has been rarely investigated. In this study, the performances of state of charge and state of health with equivalent circuit model‐based methods and data‐driven estimations are analyzed by different aged and capacity batteries through methods including extended Kalman filters, fully connected deep network with drop methods, and the combination (extended Kalman filters—fully connected deep network with drop methods). Besides the battery state of the voltage and current, the relationship between inner resistance, temperature, and capacity are also considered. Finally, a suggested method is promising for online state estimation of battery working at different temperatures and initial working state. The results indicate that the maximum state of charge estimation errors of the fully connected deep network with drop methods is 0.56% for the fully charged battery. Simultaneously, with an uncertain initial state of charge, the extended Kalman filter shows the lowest maximum state of charge estimation errors (1.4%). For the state of health estimation, the optimized method uses extended Kalman filters to do the monitor first; after 5 testing points, if the state of health drops to lower than 0.95, extended Kalman filters—fully connected deep network with drop methods is suggested. And finally, estimation errors for this method decreased from 30% to 2%.

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Lithium‐ionbatterySOC/SOHadaptive estimation via simplified single particle model

Cited by 62 publications

References 49 publications

Novel battery wear leveling method for large‐scale reconfigurable battery packs

Novel battery wear leveling method for large‐scale reconfigurable battery packs

Real‐time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

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