Impedance measurements combined with the fuzzy logic methodology to assess the SOC and SOH of lithium-ion cells

Zenati, Ali; Desprez, Ph.; Razik, Hubert; Raël, Stéphane

doi:10.1109/vppc.2010.5729069

Cited by 25 publications

(15 citation statements)

References 16 publications

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“…Berecibar et al [9] reviewed the existing SOH estimation methods and classified in specific groups the different approaches. Adaptive methods like Kalman filter [10,11], neural networks [12,13], and many others are very accurate but computational resources and development are also complex [14]. On the opposite, empirical techniques mainly based on capacity and impedance measurements are simple and very effective methods but they are highly dependent on the operating conditions [15,16].…”

Section: Need Of Specific Methods For Determination Of Sohmentioning

confidence: 99%

Fast Electrical Characterizations of High-Energy Second Life Lithium-Ion Batteries for Embedded and Stationary Applications

et al. 2019

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This paper focuses on the fast characterization of automotive second life lithium-ion batteries that have been recently re-used in many projects to create battery storages for stationary applications and sporadically for embedded applications. Specific criteria dedicated to the second life are first discussed. After a short review of the available state of health indicators and their associated determination techniques, some electrical characterization tests are explored through an experimental campaign. This offline identification aims to estimate the remaining ability of the battery to store energy. Twenty-four modules from six different commercial electric vehicles are analyzed. Well-known methodologies like incremental capacity analysis (ICA) and constant voltage phase analysis during CC-CV charge highlight the difficulty—and sometimes the impossibility—to apply traditional tools on a battery pack or on individual modules, in the context of real second life applications. Indeed, the diversity of the available second life batteries induces a combination of aging mechanisms that leads to a complete heterogeneity from a cell to another. Moreover, due to the unknown first life of the battery, typical state of health determination methodologies are difficult to use. A new generic technique based on a partial coulometric counter is proposed and compared to other techniques. In the present case study, the partial coulometric counter allows a fast determination of the capacity aging. In conclusion, future improvements and working tracks are addressed.

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Section: Need Of Specific Methods For Determination Of Sohmentioning

confidence: 99%

Fast Electrical Characterizations of High-Energy Second Life Lithium-Ion Batteries for Embedded and Stationary Applications

et al. 2019

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“…However, the required models may need to be adjusted or rebuilt when battery characteristics change, and the complexity of the models will significantly affect their SOC-estimating performance. On the other hand, some researchers have proposed intelligent algorithms for SOC estimation, such as artificial neural network (ANN) [30][31][32], fuzzy logic [33][34][35], support vector [36,37], and adaptive network-based fuzzy inference system (ANFIS) [38][39][40]. These methods do not require complicated equivalent circuits or models for obtaining the nonlinear characteristics of batteries, instead it is done through their learning and inference capabilities.…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

2019

Processes

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In order to maximize the operating flexibility and optimize the system performance of a battery energy storage system (BESS), developing a reliable real-time estimation method for the state of charge (SOC) of a BESS is one of the crucial tasks. In practice, the accuracy of real-time SOC detection can be interfered with by various factors, such as battery's intrinsic nonlinearities, working current, temperature, and aging level, etc. Considering the feasibility in practical applications, this paper proposes a hybrid real-time SOC estimation scheme for BESSs based on an adaptive network-based fuzzy inference system (ANFIS) and Coulomb counting method, where a commercially available lead-acid battery-based BESS is used as the research target. The ANFIS allows effective learning of the nonlinear characteristics in charging and discharging processes of a battery. In addition, the'Coulomb counting method with an efficiency adjusting mechanism is simultaneously used in the proposed scheme to provide a reference SOC for checking the system reliability. The proposed estimating scheme was first simulated in a Matlab software environment and then implemented with an experimental hardware setup, where an industrial-grade digital control system using DS1104 as the control kernel and dSPACE Real-Time Interface (RTI) interface were used. Results from both simulation and experimental tests verify the feasibility and effectiveness of the proposed hybrid SOC estimation algorithm.Keywords: battery energy storage system (BESS); state of charge (SOC); adaptive network-based fuzzy inference system (ANFIS) Processes 2020, 8, 2 2 of 22 As a result, real-time SOC can be determined based on a given initial SOC value. This process can be expressed as (1):

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“…The most common one is the Coulomb-counting method, which is an open-loop system, and has the disadvantage of accumulating errors and not correcting them. 3) Adaptive methods such as fuzzy logic [25]- [27], artificial neural networks [28]- [30], and Kalman filters [31], [32] can avoid this issue by combining direct and indirect measurements and automatically adjusting to changing conditions. As lithium-based batteries are strongly affected by external factors and have nonlinear characteristics and behaviors, adaptive systems are a very good fit for an SOC determination algorithm.…”

Section: Application To State-of-charge Determinationmentioning

confidence: 99%

“…A matrix of partial derivatives (Jacobians) is computed and used instead to locally linearize the functions. 2) Mathematical Description: The EKF relies on two functions, i.e., a process equation (26), shown below, linking the previous state x k−1 to the new state x k and a measurement equation (27), shown below, i.e.,…”

Section: A Extended Kalman Filtersmentioning

confidence: 99%

Multiphysical Lithium-Based Battery Model for Use in State-of-Charge Determination

Watrin¹,

Ostermann

Blunier

et al. 2012

IEEE Trans. Veh. Technol.

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This paper presents a multiphysical battery pack model, along with a procedure to identify its parameters and its application to state-of-charge (SOC) determination using an extended Kalman filter (EKF). The model enables the reproduction of the electric and thermal behaviors of batteries with high accuracy. A methodology is proposed to identify the parameters of the model and optimize them. The model is experimentally validated with measurements run on high-energy-density ThunderSky cells. A comparison of measurements and model results shows that the electrical model error is below 1.6% and that the error of the thermal model is below 2.5%. The model is used as an EKF basis to reliably estimate the battery SOC. The results are also experimentally verified through measurements showing that the proposed model performs better than other simpler models and determination methods.Index Terms-Kalman filter, lithium-ion battery, multiphysical modeling, parameter identification, state-of-charge (SOC) determination.

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Impedance measurements combined with the fuzzy logic methodology to assess the SOC and SOH of lithium-ion cells

Cited by 25 publications

References 16 publications

Fast Electrical Characterizations of High-Energy Second Life Lithium-Ion Batteries for Embedded and Stationary Applications

Fast Electrical Characterizations of High-Energy Second Life Lithium-Ion Batteries for Embedded and Stationary Applications

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

Multiphysical Lithium-Based Battery Model for Use in State-of-Charge Determination

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