An evolutionary framework for lithium-ion battery state of health estimation

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

doi:10.1016/j.jpowsour.2018.12.001

Cited by 84 publications

(51 citation statements)

References 32 publications

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“…Cai et al 19 proposed an evolutionary framework to estimate battery SOH using the support vector regression (SVR) and the aging features extracted from the short-term current pulse in a unified framework. In recent years, such methods have been widespread concerned due to their model-free characteristics and high flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…Based on Gaussian Process Regression (GPR), Yang et al 18 used four features of the charging process to estimate capacity. Cai et al 19 proposed an evolutionary framework to estimate battery SOH using the support vector regression (SVR) and the aging features extracted from the short-term current pulse in a unified framework. Besides, Wang et al 20 proposed a current-related parameter to reflect the SOH and proved the effectiveness using the aging data from NASA.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Currently, the science of Li-Ion batteries allows the use of the power bank of the VE to feed point loads (V2L) in addition to extended VE's autonomy. However, it has also been reported that there may be premature degradation ( [19][20][21][22][23][24][25]) that depends predominantly, to the authors' knowledge, on the temperature, state of charge (SoC), charge and discharge current rate (C-rate), and depth of discharge (DoD) during its usage and in some cases also during its storage. That is, although the State of Health of a battery (SoH) will inevitably decrease due (a) to the number of cycles during its usage (cycling aging) and (b) to its inherent expiration date (calendar aging), the SoH decay rate can be modeled as function of factors such as temperature, SoC, the DoD, and the C-rate during its regular use as well as the temperature and SoC during storage [18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Vehicle-to-Load Energy Management Strategy to Prevent Li-Ion Batteries Premature Degradation

Licea

Pérez-Pinal

Soriano-Sánchez

et al. 2019

Mathematical Problems in Engineering

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Today, electric vehicles available in the market aspire to offer different connections to the end user, for instance, Vehicle to Grid (V2G), Vehicle to Building (V2B), Vehicle to Home (V2H), Vehicle to Vehicle (V2V), and Vehicle to Load (V2L), among others. Notwithstanding these versatility options toward the development of a sustainable society, the additional degradation of the energy storage systems once those operate in extra discharge modes is inevitable. Therefore, in this paper, an energy management strategy (EMS) which operates autonomously and noninvasively as an additional layer to the battery management system (BMS) is proposed. The EMS limits the current flow avoiding high and low temperatures, low state of charge (SoC), high deep of discharge (DoD), noncentered DoD around an optimal SoC point, and high charge and discharge rates. The proposed EMS is evaluated by long-term simulations with a Li-Ion battery degradation model and realistic weather conditions, during standard driving cycles including the V2L operation. The effectiveness and simplicity of tuning of the proposed EMS allow estimating and increasing the life expectancy of the Li-Ion battery bank, by limiting the energy used for V2L operation.

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“…2 The power applications such as large-scale energy storage, new electric vehicle (EV), and drones must be safe and cost-effective in the whole life cycle. 5 The development of the power LIB application and safety management has entered an important period of difficult problems. However, its safety problem becomes prominent increasingly and more than 40 EV fires occurred in just 2018, 70% of which were caused by packing safety management problems.…”

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confidence: 99%

“…The accuracy of SoC estimation is difficult to be improved, which affects the effectiveness of BMS seriously and even attracts the safety accident. 5 The development of the power LIB application and safety management has entered an important period of difficult problems. 6 The equivalent modeling is the basis of obtaining the battery characteristics and mathematical expression in the working state estimation process.…”

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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An evolutionary framework for lithium-ion battery state of health estimation

Cited by 84 publications

References 32 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

Noninvasive Vehicle-to-Load Energy Management Strategy to Prevent Li-Ion Batteries Premature Degradation

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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