Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

Li, Junfu; Wang, Lixin; Lyu, Chao; Zhang, Liqiang; Wang, Han

doi:10.1016/j.energy.2015.04.021

Cited by 32 publications

(7 citation statements)

References 39 publications

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“…Based on the above analysis, the indirect analysis method of the SOH estimation is hard to apply the SOH estimation of the power battery in the EVs. Some adaptive filtering algorithms are very suitable for solving the state estimation problem of nonlinear complex models, such as Kalman Filter and its improved algorithm, Extended Kalman Filter, Adaptive Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter . This algorithm can be applied to batteries of various chemical compositions.…”

Section: Introductionmentioning

confidence: 99%

“…Some adaptive filtering algorithms are very suitable for solving the state estimation problem of nonlinear complex models, such as Kalman Filter 6 and its improved algorithm, Extended Kalman Filter, 7,8 Adaptive Extended Kalman Filter, 9 Unscented Kalman Filter, 10 and Particle Filter. 11,12 This algorithm can be applied to batteries of various chemical compositions. However, such methods achieve SOH estimation by building a battery degradation model with SOH as the state quantity.…”

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confidence: 99%

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An online state of health estimation method based on battery management system monitoring data

Liu

et al. 2020

Int J Energy Res

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Summary A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

An online state of health estimation method based on battery management system monitoring data

Liu

et al. 2020

Int J Energy Res

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“…Those methods avoid understanding complex reaction mechanisms inside batteries to construct mathematical or physical models, thereby having been widely investigated by many researchers. For instance, Li et al [ 22 ] extracted four characteristic parameters from charging voltage curves and constructed a particle filter (PF) model to estimate discharge capacity. Cheng et al [ 23 ] applied visual cognition technique to build the capacity degradation model based on several geometrical features extracted from the current and voltage curves.…”

Section: Introductionmentioning

confidence: 99%

“…During everyday usage, batteries are seldom fully discharged to the 0% SOC level but are usually recharged from a partially discharged state to the 100% SOC level. This incomplete discharging process will affect the initial state (such as the initial voltage) and processed variables (such as the charging time) of the subsequent charging process, thereby restricting the extraction of external features depending on a deterministic and intact charging/discharging process, such as the time ratio of CC phase to CV phase [ 22 ], image information transformed from the entire discharging data [ 23 ], the CC charging duration or capacity [ 25 – 27 ], discharging cutoff voltage [ 27 ], voltage variation in the CC phase [ 22 , 24 , 28 ], and time interval between two predifined discharging voltage [ 29 ], etc. Unfortunately, to the best of our knowledge, little work has been performed to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile

et al. 2018

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Estimation of remaining capacity is essential for ensuring the safety and reliability of lithium-ion batteries. In actual operation, batteries are seldom fully discharged. For a constant current-constant voltage charging mode, the incomplete discharging process affects not only the initial state but also processed variables of the subsequent charging profile, thereby mainly limiting the applications of many feature-based capacity estimation methods which rely on a whole cycling process. Since the charging information of the constant voltage profile can be completely saved whether the battery is fully discharged or not, a geometrical feature of the constant voltage charging profile is extracted to be a new aging feature of lithium-ion batteries under the incomplete discharging situation in this work. By introducing the quantum computing theory into the classical machine learning technique, an integrated quantum particle swarm optimization–based support vector regression estimation framework, as well as its application to characterize the relationship between extracted feature and battery remaining capacity, are presented and illustrated in detail. With the lithium-ion battery data provided by NASA, experiment and comparison results demonstrate the effectiveness, accuracy, and superiority of the proposed battery capacity estimation framework for the not entirely discharged condition.

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“…These models are mostly based on complex physical and chemical processes that take into account the dynamic behavior of batteries [9][10][11], and the estimation performance is highly dependent on the accuracy of the models. In particular, these types of models are usually difficult to establish given the restrictions on acquisition of knowledge of the electrochemical parameters, aging mechanisms, and properties of batteries [12]. Moreover, these models are individually dependent on the specific type of battery in terms of production processes, electrolytes, and anode and cathode materials.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

2017

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This study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into multiple spatial-frequency channels with a set of orientation subbands by using nonsubsampled contourlet transform (NSCT). NSCT imitates the multichannel characteristic of the human visual system (HVS) that provides multiresolution, localization, directionality, and shift invariance. Third, several time-domain indicators of the NSCT coefficients are extracted to form an initial high-dimensional feature vector. Similarly, inspired by the HVS manifold sensing characteristic, the Laplacian eigenmap manifold learning method, which is considered to reveal the evolutionary law of battery performance degradation within a low-dimensional intrinsic manifold, is used to further obtain a low-dimensional feature vector. Finally, battery capacity degradation is estimated using the geodesic distance on the manifold between the initial and the most recent features. Verification experiments were conducted using data obtained under different operating and aging conditions. Results suggest that the proposed visual cognition approach provides a highly accurate means of estimating battery capacity and thus offers a promising method derived from the emerging field of cognitive computing.

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Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

Cited by 32 publications

References 39 publications

An online state of health estimation method based on battery management system monitoring data

An online state of health estimation method based on battery management system monitoring data

Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile

Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

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