Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques
Abstract
:1. Introduction:
- (1)
- Coupling renewable energy systems with different generation characteristics in wider distribution via the transmission grids;
- (2)
- Responding to the demand by adapting consumption patterns;
- (3)
- Employing fossil-fueled utilities as a traditional back-up (either for meeting peak demand or providing spinning reserve); and
- (4)
- Equipping the grid with storage devices such as compressed air storage, battery storage, and hydro pump storage.
Motivation, Objective, and Innovation Contribution
- Employing an input time-delayed strategy to handle dynamic information of system.
- The Adaptive Neruo-fuzzy Inference System (ANFIS) and group method of data handling (GMDH) techniques are employed to analyze the relational grade between the SOH and selected features.
- Developing two data-driven frameworks to estimate the SOH. This article utilizes the fuzzy C-means clustering algorithm to tune and adjust the ANFIS parameter in advance, to create the initial rules.
- Accurate and effective validation of the framework in comparison to recently published articles and other methods.
2. Proposed Techniques
2.1. Group Method of Data Handling
2.2. Adaptive Neuro-Fuzzy Inference system
- Layer 1
- This layer is known as fuzzy-fication layer, which fuzzifies the input variables; every i node consists of a node function, which is , symbolized by , where is the linguistic label according to the node function, is the input to the node, and is the membership function of that, specifying the level for the assumed x. Hence, the membership function ascertains the membership level from the given input values. For a bell-shaped function, three parameters for each node should be defined, for which the maximum and minimum possible value are 1 and 0, respectively; where its generalized function can be mathematically described as follows:
- Layer 2
- Is called ‘fuzzy and’, because in this layer, only ‘AND’ operators are allowed. This layer is utilized to compute the firing robustness of every rule. It means product operation (see Equation [5]) referred to the weighting factor of the corresponding rule, is used.
- Layer 3
- Is known as ‘normalization’ term. The firing strength of each rule is normalized via computing the ration of each rule’s firing strength to the total of each rules. In Equation (6), is defined as the firing strength of each rule, as illustrated below:
- Layer 4
- Is recognized as ‘defuzzification’. This layer tries to compute the output of the previous layer, based on its node function; each node function is adaptive in accordance with the node function, as given by Equation (7).
- Layer 5
- Is called ‘aggregation’. This layer is utilized to compute the total of the outputs of all of the rules to produce the overall ANFIS output, whose equation is represented as follows:
3. Result and Discussion
3.1. Experimental Data
3.2. Short-Term State of Health Estimation
3.3. Long-Term State of Health Estimation
- While machine learning demonstrated an acceptable self-adaptation and high non-linearity modeling capability, a large amount of experimental data is required to obtain a high accuracy.
- Although the introduced SOH method is more predictable and accurate under charging and discharging processes, it is not a usable method for plug-in hybrid electric vehicles (PHEVs)/PEVs when they are connected to smart charging infrastructure.
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ANN | artificial neural network |
ANFIS | adaptive neruo-fuzzy inference system |
BMS | battery management system |
CC | constant current |
CV | constant voltage |
DG | distributed generation |
DGA | geometry based approach |
ESS | energy storage system |
EV | electric vehicle |
EKF | extended Kalman filter |
G2V | grid-to-vehicle |
GHG | greenhouse gas |
GMDH | group method of data handling |
GP | Gaussian process |
HRES | hybrid renewable energy system |
ITDNN | input time-delayed neural network |
KF | Kalman filter |
LS | least squares |
NN | neural network |
NEDC | new European driving cycle |
MSE | mean squared error |
PS | power system |
PF | particle filter |
QGPFR | quadratic polynomial mean function (GP) |
RMSE | root mean square error |
RBC | remaining battery capacity |
SG | smart grid |
SOC | state of charge |
SOH | state of health |
V2G | vehicle-to-grid |
NPF | nonlinear predictive filter |
MSE | mean square error |
OCV | open circuit voltage |
PHEV | plug-in hybrid electric vehicle |
Nomenclature
estimated values | |
expected values | |
number of input variables | |
pairs of input variables | |
membership function | |
linguistic label | |
Input variables | |
model coefficient |
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Rahbari, O.; Mayet, C.; Omar, N.; Van Mierlo, J. Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques. Appl. Sci. 2018, 8, 1301. https://doi.org/10.3390/app8081301
Rahbari O, Mayet C, Omar N, Van Mierlo J. Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques. Applied Sciences. 2018; 8(8):1301. https://doi.org/10.3390/app8081301
Chicago/Turabian StyleRahbari, Omid, Clément Mayet, Noshin Omar, and Joeri Van Mierlo. 2018. "Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques" Applied Sciences 8, no. 8: 1301. https://doi.org/10.3390/app8081301