Lithium-ion battery state of health estimation with short-term current pulse test and support vector machine

Meng, Jinhao; Cai, Lei; Luo, Guangzhao; Stroe, Daniel‐Ioan; Teodorescu, Remus

doi:10.1016/j.microrel.2018.07.025

Cited by 107 publications

(32 citation statements)

References 21 publications

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“…In order to improve the performance and prolong the battery's life, accurate knowledge of the state of health (SOH) for battery management system is essential and SOH is often expressed as the ratio between the present capacity and the nominal capacity [7]. As batteries are complex electrochemical systems, the capacity loss is difficult to measure in time and the accurate estimation of the battery's capacity is still an issue [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, with the development of big data technology, real-time monitored parameters like voltage, current, and temperature will be stored and processed, which also decreases the requirements of the microcontroller, and improves the estimation accuracy and robustness. Various data driven methods have been used to predict the battery SOH such as support vector machine [8], relevance vector machine [12], neural network [13], Gaussian process regression [14], etc.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

Sui

Stroe

et al. 2019

Applied Sciences

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It is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of batteries. In order to analyze the effect of voltage dataset selection on the accuracy of entropy-based estimation methods, six voltage datasets were collected, considering the current direction (i.e., charging or discharging) and the state of charge level. Furthermore, three kinds of entropies (approximate entropy, sample entropy, and multiscale entropy) were introduced, and the relationship between the entropies and the battery capacity was established by using first-order polynomial fitting. Finally, the interaction between the test conditions, entropy features, and estimation accuracy was analyzed. Moreover, the results can be used to select the correct voltage dataset and improve the estimation accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

Sui

Stroe

et al. 2019

Applied Sciences

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“…However, an ECM-based method is complicated owing to a large number of matrix computation and difficulty in the software/hardware configuration of the actual BMS [12]. A BBM-based method learns with large amounts of data and builds the model on its own, without relying on system parameters, like by the support vector regression (SVR) and neural network (NN) algorithm [14][15][16][17]. Compared to a model-based method, the computational complexity is low, but there are disadvantages in collecting various useful data for the correct learning of the BBM to obtain the desired result [15].…”

Section: Introductionmentioning

confidence: 99%

Incremental Capacity Curve Peak Points-Based Regression Analysis for the State-of-Health Prediction of a Retired LiNiCoAlO2 Series/Parallel Configured Battery Pack

Lee

Park

2019

Electronics

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To recycle retired series/parallel battery packs, it is necessary to know their state-of-health (SOH) correctly. Unfortunately, voltage imbalances between the cells occur repeatedly during discharging/charging. The voltage ranges for the discharge/charge of a retired series/parallel battery pack are reduced owing to the voltage imbalances between the cells. To determine the accurate SOH of a retired series/parallel battery pack, it is necessary to calculate the total discharge capacity using fully discharging/charging tests. However, a fully discharging/charging test is impossible owing to the reduced voltage range. The SOH of a retired series/parallel battery pack with a voltage imbalance should be estimated within the reduced discharging/charging voltage range. This paper presents a regression analysis of the peak point in the incremental capacity (IC) curve from the fresh state to a 100-cycle aging state. Moreover, the SOH of the considered retired series/parallel battery pack was estimated using a regression analysis model. The error in the SOHs of the retired series/parallel battery pack and linear regression analysis model was within 1%, and hence a good accuracy is achieved.

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“…39 Therefore, the breakthrough of the ECM modeling is especially important to the energy management, which is also an effective means to avoid the safety accidents. 44,45 The SoC is an important parameter reflecting its remaining available power. 41,42 The parameters such as ohmic internal resistance, polarization resistance, and capacitance in the ECM also need to be measured indirectly by the experimental means.…”

mentioning

confidence: 99%

“…43 Therefore, the mathematical description can only be achieved by using the external measurable parameters such as voltage, current, and temperature. 44,45 The SoC is an important parameter reflecting its remaining available power. The current integration, OCV, Kalman Filter (c) and its expansion algorithm, particle filtering (PF), and neural network (NN) have been explored gradually and applied to the SoC estimation of the LIBs.…”

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confidence: 99%

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

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The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

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Lithium-ion battery state of health estimation with short-term current pulse test and support vector machine

Cited by 107 publications

References 21 publications

The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

Incremental Capacity Curve Peak Points-Based Regression Analysis for the State-of-Health Prediction of a Retired LiNiCoAlO2 Series/Parallel Configured Battery Pack

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

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