A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity

Chen, Lin; Liu, Zhiqiang; Lin, Weilong; Li, Junzi; Pan, Haihong

doi:10.1016/j.measurement.2017.11.016

Cited by 189 publications

(78 citation statements)

References 27 publications

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“…In Chen et al, 29 the ohmic resistances in different SOC ranges are calculated, and the differences in different SOC ranges are less than 1 milliohm. Thus, it is necessary to analyze and evaluate the model and RLS-based method in view of these uncertain factors such as SOC, SOH, loading profiles, and operating ambient temperature.…”

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

“…Thevenin model contains an open circuit voltage (OCV), ohmic resistance (R 0 ), and a resistor-capacitor (R 1 //C 1 ) network, as shown in Figure 4A. Based on the recursive least-squares (RLS) method, which is an effective system identification algorithm, 13,29 the results of parameter identification using one randomly selected dynamic loading profile are shown in Figure 4B-F.…”

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

“…Thus, it is necessary to analyze and evaluate the model and RLS-based method in view of these uncertain factors such as SOC, SOH, loading profiles, and operating ambient temperature. In Chen et al, 29 the ohmic resistances in different SOC ranges are calculated, and the differences in different SOC ranges are less than 1 milliohm. Thus, the ohmic resistance can be approximately equal to the average resistance of 80% to 30% in SOC ranges.…”

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

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A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

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Summary Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.

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Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

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A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

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“…The estimation methods in the SOH are mainly divided into two categories: One is to start with the characteristic parameters of the battery, and the other is to analyze the aging characteristics and electrochemical reaction characteristics of the battery [12]. The former mainly uses the direct measurement method, obtaining the current SOH by obtaining aging characteristic parameters such as capacity and ohmic internal resistance [8,13]. There are also methods such as neural networks [14] and fuzzy logic [15], which can directly estimate the SOH of the battery through data training without an a priori model.…”

Section: Of 15mentioning

confidence: 99%

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

et al. 2019

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In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.

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“…10 While in the study of Eftekhar, a decreasing quantity of active substance caused the decline of the diffusion coefficient and led to the impact on charging and discharging rate. 11 Moreover, internal resistance 12,13 and thermal behaviour 14,15 are considered as another two factors contributing to the variation of operating state of battery during its aging process. In addition, charging time is considered to be another variable suit for measuring SOC and SOH of batteries.…”

Section: Introductionmentioning

confidence: 99%

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Garg

Gao

et al. 2019

Int J Energy Res

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Summary In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters‐based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.

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A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity

Cited by 189 publications

References 27 publications

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

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