A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles

Yang, Dandan; Wang, Yujie; Pan, Rui; Chen, Ruiyang; Chen, Zonghai

doi:10.1016/j.egypro.2017.03.583

Cited by 132 publications

(77 citation statements)

References 13 publications

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“…Moreover, the inconsistency of the battery's environment and performance leads to its poor universality [8]. In the data-driven method, computational burden is usually caused [9,10], which reduces the estimated efficiency of the SoH and causes a higher request for CPU performance. ICA is another popular SoH estimation method, which shows a strong correlation with battery aging [11].…”

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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“…Yang et al 68 used a neural network to predict the SOH of Li-ion batteries for EVs. Taking in the voltage and current through a first-order ECM, a three-layer neural network could predict the SOH within 5%.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Predicting the state of charge and health of batteries using data-driven machine learning

et al. 2020

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W ith rising concerns about global warming, electrification of transport has recently emerged as an important vision in many countries. The successful development of electric vehicles (EVs) depends highly on the cycling performance, cost and safety of the batteries. Rechargeable lithium-ion (Li-ion) batteries are currently the best choice for EVs due to their reasonable energy density and cycle life 1 . Further research and development on Li-ion batteries will lead to even higher energy density and more complicated battery dynamics, where the efficiency and safety of such batteries will become a concern. An advanced battery management system (BMS) that can monitor and optimize battery behaviour and safety is thus essential for the entire electrification system 2 .Today, one of the major barriers to widespread adoption of EVs is range anxiety. The ability of a BMS to accurately determine the state of charge (SOC) and state of health (SOH) of batteries, and hence the estimated driving range, will alleviate this problem. In addition, reliable prediction of remaining useful life (RUL) will allow batteries to be used to their fullest potential and maximum life expectancy before replacement or disposal. Knowledge of the RUL of spent batteries will also enable their redeployment in less demanding, second-life applications such as stationary grid storage. If we are able to sort manufactured cells based on their expected lifetime using early-cycle data, we can further accelerate the testing, validation and development process of new batteries. In summary, accurate prediction of the current and future state of batteries will open up vast opportunities in battery manufacturing, usage and optimization 3,4 . SOC and SOH are the two most important parameters in battery management and are generally defined as:where C curr is the capacity of the battery in its current state, C full is the capacity of the battery in its fully charged state, C nom is the nominal capacity of the brand-new battery 2 . In essence, SOC denotes the capacity of the battery in its current state compared to the capacity in its fully charged state (equivalent of a fuel gauge), while SOH describes the capacity of the battery in its fully charged state compared to the nominal capacity when brand new. By convention, SOC is 100% when the battery is fully charged and 0% when it is empty, while SOH is 100% at the time of manufacture and reaches 80% at end of life (EOL). In the battery manufacturing industry, EOL is often defined as the point at which the actual capacity at full charge drops to 80% of its nominal value 2 . The remaining number of charge/discharge cycles until the battery reaches EOL is the RUL of the battery. Current BMSs can determine the SOC of Li-ion batteries within 0.6% to 6.5% 5 , but are unable to predict the SOH and RUL of batteries accurately 6 .The traditional methods for SOC estimation include ampere hour counting estimation, open-circuit voltage-based estimation, impedance-based estimation, model-based estimation, fuzzy logic, and Kal...

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“…Among all data-driven models, ML techniques particularly have appealed to diagnose battery health. ML methods can be considered as non-probabilistic such as artificial neural networks (ANN) [17] and support vector machine (SVM) and probabilistic methods including Gaussian process regression (GPR) [18] and relevant vector machine (RVM). Despite the convenient achievements of traditional ML methods in SoH estimation, they may not succeed in the context of noisy, nonlinear and complex data.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

et al. 2020

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The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

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A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles

Cited by 132 publications

References 13 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

Predicting the state of charge and health of batteries using data-driven machine learning

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

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