A fast estimation algorithm for lithium-ion battery state of health

Tang, Xiaopeng; Zou, Changfu; Yao, Ke; Chen, Guohua; Liu, Boyang; He, Zhenwei; Gao, Furong

doi:10.1016/j.jpowsour.2018.06.036

Cited by 265 publications

(114 citation statements)

References 19 publications

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“…For traditional ICA methods, IC peaks are the primary premise to estimate the aging state of battery. However, there are only a limited number of peaks that can be used to estimate SoH [16][17][18][19], that is, the sample range available for SoH estimation is too narrow, and there is no guarantee that the voltage corresponding to the IC peak will be passed in each charging process. Moreover, since IC peaks are the extreme values of the incremental capacity (dQ) during the entire battery charging process, the time to calculate dQ is also the longest, accordingly.…”

Section: Introductionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

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Section: Introductionmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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“…where C n0 is the capacity calibrated at the beginning of the service life under a temperature specified by the manufacturer, which is usually 25 • C. Under the same temperature, C n (k) can be calculated through the current integration method or estimated from the measured battery current and voltage [30]. We consider k to be sampled at each battery operating cycle.…”

Section: Model Formulationmentioning

confidence: 99%

“…The experimental equipment mainly includes a UPower battery testing system and commercial Li-ion cells, as described in [30]. As the most widely used charging method, the constantcurrent constant-voltage (CCCV) strategy was adopted here to charge the cells, where the maximum voltage denotes V max and the cut-off current is always 0.05C.…”

Section: Experimental Settingsmentioning

confidence: 99%

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

et al. 2019

Self Cite

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This paper develops a new prediction method for the aging trajectory of lithium-ion batteries with significantly reduced experimental tests. This method is driven by data collected from two types of battery operation modes. The first type is accelerated aging tests that are performed under stress factors, such as overcharging, over-discharging and large current rates, and cover most of the battery lifespan. In the second operation mode, the same kinds of cells are aged at normal speeds to generate a partial aging profile. An accelerated aging model is developed based on the first type of data and is then migrated as a new model to describe the normalspeed aging behavior. Under the framework of Bayesian Monte Carlo algorithms, the new model is parameterized based on the second type of data and is used for prediction of the remaining battery aging trajectory. The proposed prediction method is validated on three types of commercial batteries and also compared with two benchmark algorithms. The sensitivity of results to the number of cycles is investigated for both modes. Illustrative results demonstrate that based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories (up to 500 cycles) at a root-mean-square error of less than 2.5% for all considered scenarios. When only using the first five-cycle data for model training, such a prediction error is bounded by 5% for aging trajectories of all the tested batteries.

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“…29 A new parameter identification method was proposed by considering electrochemical properties. 35 The degradation state prediction was explored in the whole climate, 36 and the identification was conducted for different ECM parameters along with the global optimization. 32 A practical model was also constructed for the energy and voltage response state prediction including temperature and aging effects.…”

mentioning

confidence: 99%

“…33 The parameter estimation and sensitivity analysis were carried out by using the electrochemical models for the LIBs, 34 which was coupled with the thermal model and used to describe the thermal performance during the cycling charge-discharge process. 35 The degradation state prediction was explored in the whole climate, 36 and the identification was conducted for different ECM parameters along with the global optimization. 37 As the complex series-parallel combination is used in the LIB packs to break for the limitations of cell voltage and capacity, there are differences in cell-to-cell consistency and interaction.…”

mentioning

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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A fast estimation algorithm for lithium-ion battery state of health

Cited by 265 publications

References 19 publications

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

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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