Online model parameter identification for supercapacitor based on weighting bat algorithm

Accepted Manuscript Online model parameter identification for supercapacitor based on weighting bat algorithm Geng Sun, Yanheng Liu, Ruizhi Chai, Fang Mei, Ying Zhang PII: DOI: Reference:

S1434-8411(17)32708-5 https://doi.org/10.1016/j.aeue.2018.02.015 AEUE 52242

To appear in:

International Journal of Electronics and Communications

Received Date: Accepted Date:

16 November 2017 12 February 2018

Please cite this article as: G. Sun, Y. Liu, R. Chai, F. Mei, Y. Zhang, Online model parameter identification for supercapacitor based on weighting bat algorithm, International Journal of Electronics and Communications (2018), doi: https://doi.org/10.1016/j.aeue.2018.02.015

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Online model parameter identification for supercapacitor based on weighting bat algorithmI,II Geng Suna,b,c , Yanheng Liua,b , Ruizhi Chaic , Fang Meia,b,∗, Ying Zhangc,∗∗ a

College of Computer Science and Technology, Jilin University, Changchun, 130012, China b Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China c School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA

Abstract To support real-time power management of the supercapacitor-powered embedded systems, an online model parameter identification method is proposed for predicting the supercapacitor behavior. In the proposed method, an optimization problem is formulated based on our previously developed supercapacitor model, and a weighting bat algorithm (WBA) with the weighting solution update method is proposed for solving this problem in each model parameter updating time window. Simulation and experimental results show that the proposed online model parameter identification method can accurately capture the terminal behavior of a supercapacitor, and the proposed WBA-based optimization method has better performance for the supercapacitor model parameter identification compared with other benchmark algorithms. Keywords: Supercapacitor, Real-time power management, Bat algorithm, I

This document is a collaborative effort. The second title footnote which is a longer longer than the first one and with an intention to fill in up more than one line while formatting. ∗ Corresponding author ∗∗ Co-corresponding author Email addresses: [email protected] (Geng Sun), [email protected] (Yanheng Liu), [email protected] (Ruizhi Chai), [email protected] (Fang Mei), [email protected] (Ying Zhang) II

Preprint submitted to Elsevier

February 8, 2018

Parameter identification 1. Introduction Supercapacitors have been employed as the energy buffer in many applications such as the energy harvesting sensor networks [1] and the electric vehicles [2, 3], due to their long charge-discharge cycles and high power density [4]. The terminal behavior of a supercapacitor can be affected by several factors such as the current, the voltage and the temperature [5], and its electrode structures, pore sizes and distributions may change over time [6]. Moreover, if the supercapacitor-based energy storage system is operated improperly, e.g., without considering the variation of working condition, it may limit the performance, accelerate the aging process and even destroy the system in practical applications [7]. Thus, to support power management of a supercapacitor-powered system, the model parameter values of a supercapacitor need to be updated online to accurately capture its terminal behavior and improve the system reliability. However, most of the parameter estimation methods require a large numbers of experiments and the accuracy of these methods are not very high [8, 9]. Moreover, running a complex model identification algorithm on an embedded hardware is difficult. Thus, these methods are not suitable for real-time applications using embedded systems. In this letter, we propose an online model parameter identification method for a supercapacitor to accurately capture its terminal behavior to support real-time power management of an embedded system. This method uses a weighting bat algorithm (WBA) to determine the optimal parameter values of the supercapacitor model within a time window based on the charging/discharging power and the supercapacitor terminal voltages measured in the previous time window, and the model parameter values are updated in the next time window based on the corresponding measurements in the current time window. Experiments verify that the proposed method provides a practical and accurate model updating approach for the supercapacitors, and the WBA-based optimization method has better performance than other methods. By using the proposed method, the terminal behavior of the supercapacitor can be accurately and timely predicted so that the power management system of a supercapacitor-based device is able to capture the status of the power source, thereby improving the performance of the system. For example, the power management system of a supercapacitor-powered sen-

2

Figure 1: Supercapacitor VLR model with charging/discharging currents

sor node can schedule the task dynamically during the operation process to improve the performance and efficiency of the node. The rest of this letter is as follows: Section 2 introduces the system model. Section 3 formulates the optimization problem. Section 4 proposes the algorithm. Section 5 shows the simulation results and Section 6 concludes the paper. 2. System models A supercapacitor variable leakage resistance (VLR) model shown in Fig. 1 is proposed in our previous work [1], to capture the behavior of the supercapacitor. By using this model, the charge redistribution process and self-discharge process are modeled as equivalent circuit components. In this model, the input and output regulators are neglected to facilitate the analysis. As can be seen from Fig. 1, the first branch of the VLR model consists of a resistor R1 , a constant capacitor C0 and a voltage dependent capacitor k2v ∗V1 , in which V1 is the voltage of the first branch capacitor and k2v represents the slope of the capacitance with respect to V1 . The total capacitance of the first branch can be calculated as C1 = C0 + K2v ∗ V1 . Moreover, the second branch of the VLR model is composed of a resistor R2 and a constant capacitor C2 . This branch models the long-term behavior of the supercapacitor, especially the charge-redistribution process. The time constant of the second branch 3

is on the order of minutes. In addition, the variable leakage resistance R3 characterizes the time varying self-discharge. According to Kirchhoff’s current law, the relationship of these three branch currents can be described as follows: I1 + I2 + I3 = IH − IC

(1)

where IH is the variable charging current and IC is the discharging current. For the first branch, we have: Vt (t) = I1 (t)R1 + V1 (t)

(2)

dV1 (t) (3) dt where V1 is the voltage of the first branch capacitor. The dynamics of the first branch can be represented as follows: I1 (t) = [C0 + KV V1 (t)]

dV1 (t) Vt (t) − V1 (t) = dt R1 C0 + R1 KV V1 (t)

(4)

The numerical solution is solved as: V1 [n] = V1 [n − 1] + T ∗

Vt [n − 1] − V1 [n − 1] R1 C0 + R1 KV V1 [n − 1]

(5)

Similarly, the second branch has Vt (t) = I2 (t)R2 + V2 (t) dV2 (t) dt The ordinary differential equation that describes the second branch is: I2 (t) = C2

dV2 (t) Vt (t) − V2 (t) = dt C2 R2

(6) (7)

(8)

The numerical solution is: T (Vt [n − 1] − V2 [n − 1]) R2 C2 Moreover, the third branch current can be calculated as: V2 [n] = V2 [n − 1] +

4

(9)

Vt (10) R3 where R3 is a piece wise linear function of terminal voltage Vt . The relationship between the supercapacitor terminal voltage Vt and voltages of the first and second branches V1 and V2 can be derived as: I3 =

Vt = V1 + I1 R1 = V1 + (−I2 − I3 + IH − IC )R1 Vt Vt − V2 = V1 + (− − )R1 + (IH − IC )R1 R3 R2 R1 R1 = V1 − Vt − (Vt − V2 ) + (IH − IC )R1 R3 R2 R1 R1 R1 = V1 + V2 − ( + )Vt + (IH − IC )R1 R2 R3 R2 Vt can be calculated by: Vt = RM [V1 +

R1 V2 + (IH − IC )R1 ] R2

(11)

(12)

R2 R3 where RM = R2 R3 +R is an intermediate variable and it is used to sim1 R2 +R1 R3 plify the equation. Since the charging/discharging current can be represented by IH (t) − Ic (t) = VPt(t) , Eq. (12) can be translated into: (t)

Vt2 (t) − RM (V1 (t) +

R1 )Vt (t) − RM R1 P (t) = 0 R2 V2 (t)

The solution of Eq. (13) can be calculated as follows:   RM R1 Vest (t) = V1 (t) + V2 (t) + 2 R2 s  2 1 2 R1 R V1 (t) + V2 (t) + RM R1 P (t) 4 M R2

(13)

(14)

Here, Vt (t) is replaced by Vest (t), which is used to represent the estimated terminal voltage value. 3. Problem formulation A time window is introduced for the parameter updating of the supercapacitor model. At the beginning of each step, an optimization problem 5

is formulated as below based on the charging/discharging power and the terminal voltage measured in the previous time window. The determined parameters are used to predict the terminal behavior of the supercapacitor in the next time window. At the beginning of the next step, a new optimization problem based on the new measurements is solved over a shifted time window. The optimization problem formulation in each window is similar to that in [10], and the online model updating is introduced to improve the accuracy of the model prediction. Accordingly, we define the fitness function as follows: tend window

X

fˆ(R1 , R2 , C0 , KV , C2 ) =

 i 2 i Vest (t) − Vmea (t)

(15)

i=tstart window end where tstart window and twindow are the start and end time of a time window, respectively, and Vmea is the measured voltage from the experiment. Thus, the optimization problem within each time window can be formulated as:

min fˆ(R1 , R2 , C0 , KV , C2 ) s.t. R1 ∈ [ESR − δ, ESR + δ] C0 < Crated , KV C0 + × Vrated + C2 > Crated , 2 C2 < C0 , (C0 + KV × Vi ) × R1 < 0.01 C2 R2

(16a) (16b) (16c) (16d) (16e) (16f)

where ESR is the equivalent serial resistance and its value can be found from the data sheet, δ can be chosen as 0.02 ∗ ESR because R1 is very similar to the value of ESR. The constraints (16c) and (16d) are for the first branch capacitance because C0 corresponds to the minimum capacitance and C0 + KV × Vrated + C2 corresponds to the maximum capacitance of the supercapacitor. The constraint (16e) is for the second branch capacitance due to the fact that the redistributed charge is only a small portion of the total charge. Because of the finite conductance of electrolyte and the small size of micro and meso pores, charge stored in these pores are limited. Therefore, C2 , which represents its ability to store electric charges, must be smaller 6

than C0 . Moreover, the constraint (16f) limits the time constants of the first and the second branches by guaranteeing that the ratio of the time constant of these two branches is smaller than 0.01, thereby the two branches represent the immediate dynamics and delayed dynamics of the supercapacitor, respectively. 4. Algorithm 4.1. Conventional bat algorithm The bat algorithm (BA) is inspired by the echolocation process of the bats. In this algorithm, A bat xi is encoded as a solution to an optimization problem. The idealized rules suggested in BA are as follows [11]: • Each bat uses the echolocation system to sense the distance and to measure how far the prey is against the surroundings. • A bat flies randomly with the velocity vi at position xi with a fixed frequency ranging from fmin to fmax with various wavelength λ and loudness A0 to search for prey. • The bats can automatically adjust the wavelength (or frequency) of their emitted pulses and the pulse emission rate r in the range of [0, 1], depending on the proximity of their target. • Although the loudness can vary in many ways, we assume that the loudness varies from a large (positive) A0 to a minimum constant value Amin . In BA, the fitness function of each bat is evaluated and the current best value should be found. When the bats are searching for the prey, the loudness Ai , the frequency fi and the pulse rate ri are continuously varied. The new solution is generated by updating the values of velocity vi at position xi , and the detailed solution updating methods are as follows: fi = fmin + (fmax − fmin ) · β

(17)


viiter+1 = viiter + xiter − x∗ · f i i

(18)

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xiter+1 = xiter + viiter+1 i i

(19)

where iter is the current iteration, fi is the frequency of the ith bat, fmin and fmax are the minimum and maximum frequencies, respectively. β ∈ [0, 1] is a random vector drawn from a uniform distribution. x∗ is the current global best location (solution) which is located after comparing all the solutions among all n bats. vi is the velocity of the ith bat. Moreover, for the local search part, the algorithm will select a solution among the best solutions according to a certain probability, and use a local search operator to generate a new solution. The local search operator is as follows: xnew = xold + εAiter

(20)

where ε ∈ [−1, 1] is a random number, while Aiter =< Aiter > is the average i loudness of all bats at the iterth iteration [11, 12]. The loudness Ai and the pulse emission rate ri should also be updated accordingly as the iterations proceed, and they are updated by: Aiter = αiiter−1 , riiter+1 = ri0 [1 − e−γiter ] i

(21)

where α and γ are constants. For any 0 < α < 1 and γ > 0, we have aiter → 0, riiter → ri0 , as iter → ∞ i

(22)

The main steps of the conventional BA can be found in reference [11]. 4.2. Weighting bat algorithm For real-time power management of the supercapacitor-powered embedded systems, it is very important to improve the convergence rate and the accuracy of the algorithm. Thus, we use a weighting solution update methodas described below: xiter+1 = witer · xiter + viiter+1 i i 8

(23)

witer =

wmax − (wmax − wmin ) · iter itermax

(24)

where wmax and wmin are the maximum and minimum values of the weighting factor, itermax is the maximum iteration. witer is larger in the earlier iterations so that the algorithm has a better global search ability. On the contrary, witer is smaller in the later iterations, and hence it has a better local search performance. Thus, by using the weighting solution update method, the exploration and exploitation of the algorithm can be balanced. Accordingly, the steps of the WBA is shown in Algorithm 1. A solution xi = (R1 , R2 , C0 , KV , C2 ) of the fitness function (2) can be considered as a bat, and then the optimal solution can be obtained by the proposed WBA. Algorithm 1: WBA 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18

Define the fitness function fˆ(x), x = (x1 , x2 , ..., xn )T ; Initialize the bat population xi and vi ; Define pulse frequency fi ; Initialize pulse rates ri and loudness Aiter ; while t < itermax do Generate new solutions by Eqs. (23) and (24); if rand < ri then Select a solution among the best solutions; Generate a local solution around the selected best solution by Eq. (20); end Generate a new solution by flying randomly; if rand < Li & f (xi ) < f (x∗ ) then Accept the new solutions; Increase ri and reduce Aiter ; end Rank the bats and find the current best x∗ ; end Return the best solution;

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5. Verification 5.1. Experimental setup A supercapacitor (310 F, 2.7 V) produced by Maxwell is used to validate the proposed WBA-based online parameter identification method. Before the experiment, the supercapacitor is charged by a constant voltage source. The supercapacitor is charged to 2.7 V for 2 hours using a Maccor testing device (Model 4300) to make sure the charge redistribution process has completed. Thus, the initial values of V1 and V2 are both 2.7 V. Then, the Maccor testing system, shown in Fig. 2, is used to perform the charging/discharging experiments and measure the supercapacitor terminal voltage. The parameters of the proposed WBA include two parts: the parameters of the conventional BA, and the newly added parameters wmax and wmin . For the parameters of BA, their values are the same as references [11, 12] because these values have been demonstrated to achieve the best performance for most fitness functions and applications. For the newly added parameters wmax and wmin , they will be chosen based on the parameter tuning tests. In the tests, the values of these two parameters vary from 0 to 1, and the steps are both 0.1. The tuning tests are repeated for 50 times and the average value are presented in Fig. 3. As can be seen, when wmax = 0.9 and wmin = 0.5, the algorithm achieves the lowest fitness function value. However, the tuning results also show that the selected parameters do not show significant improvement over other parameter values as all parameter values achieve similar results. This may be because that the formulated optimization problem does not have a high-dimensional search space, and therefore is not sensitive to the parameter values. The parameter values of WBA are listed in Table 1. 5.2. Online identification results An experiment with 100 independent trials is conducted on Matlab to validate the effectiveness and the stability of the proposed WBA-based online model identification method. The CPU of the computer used for the simulation is Intel Core i7 with a frequency of 3.30 GHz, and the RAM is 4 GB. And, the operating system is Windows 7. For comparison, the genetic algorithm (GA) [13], the firefly algorithm (FA) [14] and the conventional BA are selected as the benchmark algorithms. Note that the parameter values used in each algorithm typically affect the optimization performance. However, in the optimization problem formulated in this paper, the result is not 10

Figure 2: The Maccor testing device (Model 4300) used in this work

−0.4329 wmax=0.9 w =0.5

−0.432

−0.4329

min

fitness function=−0.4332 −0.4329

Fitness function

−0.4325 −0.4329 −0.433

−0.4329

−0.433 −0.4335 −0.433 −0.434 1

1 0.8

−0.433

0.8 0.6

0.6 0.4 0.2

wmax

−0.433

0.4 0.2 0

0

−0.433

wmin

Figure 3: Joint parameter tuning results of wmax and wmin

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Table 1: Parameter values of the proposed WBA

Parameters Loudness, Ai Pulse emission rate, ri Minimum frequency, fmin Maximum frequency, fmin The two constant parameters, α and γ Maximum weighting factor, wmax Minimum weighting factor, wmin

Values 0.95 0.1 0 1 0.95 and 0.95 0.9 0.5

very sensitive to the parameter values of each algorithm, which can be reflected in Fig. 3. Thus, in this work, we use the common parameter values of GA, FA and BA presented in references [13], [14] and [12], respectively. Moreover, the maximum iteration and the population size of each algorithm are chosen to be 50 and 20, respectively. In addition, the time window for model updating is 60 s and the maximum execution time is 2000 s in each trial. Thus, each algorithm will be run about 30 times in each trial. Fig. 4(a) shows the dynamic charging/discharging power. Fig. 4(b) shows the measured terminal output voltages of the supercapacitor (blue curve) and the terminal voltage predicted by the supercapacitor model (green curve), which is updated online using the proposed approach. For comparison, the terminal voltages predicted by a supercapacitor model that is not updated online are also shown in the figure (red curve). Note that the parameter values of the model that is not updated online are determined using the measured output voltages and charging/discharging power in the first 60 s, and it will not be updated online dynamically. As can be seen from Fig. 4(b), the output terminal voltages of the supercapacitor predicted by the proposed method match very well with the measured results. In contrast, the voltage predicted by the model without the online updating is similar to the measured value only in the early stage of the operation process, and shows significant deviation from the measurement in the later stage. Thus, the model using the proposed online model updating method can accurately predict the terminal behavior of the supercapacitor, and has much better performance than the fixed model. Fig. 5(a) shows the convergence rates of these algorithms for the prediction trials. Each result is the average values of the 100 independent trials. 12

Input Power, W

4 2 0 −2 −4 −6

0

200

400

600

800

(a)

1000

1200

3.5

Output Voltage, V

1400

1600

1800

2000

Time, s measured Non−real−time prediced Real−time predicted

3 2.5 2 1.5 1 0.5

0

(b)

200

400

600

800

1000

1200

1400

1600

1800

2000

Time, s

Figure 4: Prediction results obtained by different methods. (a) Input power. (b) Output voltage comparisons.

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Fig. 5(b) shows the fitness values obtained by different algorithms of the 100 independent trials. As can be seen, the proposed WBA has better performance in terms of the convergence rate and the accuracy compared with other algorithms. The statistical results of these trials are listed in Table 2. It can be seen from the table that WBA has the best performance not only in accuracy but also in stability. Moreover, the average CPU time of each algorithm in each time window (50 iterations) of the 100 trials, implemented in Matlab, is also shown in Table 2. It can be seen that the proposed WBA uses the shortest CPU time while achieving the best solution. Furthermore, Fig. 5(a) also indicates that WBA converges to the lowest fitness value within about 5 iterations, which means that the CPU time can be further reduced in the practical application by using less iterations. Since the supercapacitors may be used with some embedded systems, e.g., as the source power of a sensor node, the proposed online parameter identification algorithm should be able to run on an embedded environment. Thus, to demonstrate the practicability of the proposed method in a real embedded system, the proposed method for the supercapacitor model parameter identification is implemented in an embedded development platform. The processor of the platform is ARM v8 with a frequency of 1.2 GHz, the RAM of the platform is 128M, and the algorithm is written in C-code. The average optimization result (fitness value) obtained by the embedded system (ARM v8, C-code) is 0.432867, which is very similar to that of the Matlab platform (Core i7, Matlab-code). This is because the principle of the proposed WBA is the same for both the embedded system and the Matlab platform, and they have the same initial data for the first round prediction (the measured data of the voltage for predicting the parameter values in the initial stage is the same). However, since WBA is a heuristic search strategy and has certain randomness, the results from the embedded system and the Matlab are not exactly same. In addition, the main difference between these two platforms is the CPU time. The average CPU time for running WBA in each time window (50 iterations) is 0.01424 s. This demonstrates that the proposed WBA is suitable for the practical online supercapacitor parameter identification. As we known, the C-code is much more efficient than the Matlab-code. Therefore, the CPU time of the embedded system is shorter than that of the Matlab.

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Figure 5: Performance comparisons of different algorithms. (a) Average convergence rates. (b) Average fitness values of different algorithms of the 100 independent trials. Table 2: Statistical results of different algorithms

Best fval Worst fval Average SD Median CPU time (simulation)

GA 0.4381 0.4390 0.4388 0.00024 0.4388 0.0529 s

FA 0.4390 0.4382 0.4388 0.00022 0.4389 0.0503 s

BA 0.4341 0.4350 0.4348 0.00024 0.4348 0.0451 s

WBA 0.4328 0.4330 0.4329 0.000063 0.4329 0.0450 s

6. Conclusion A WBA-based online parameter identification method is proposed to determine the model parameters of the supercapacitor. An optimization problem is formulated in this method, and the WBA is proposed for solving this problem. WBA uses the weighting solution update method to improve the accuracy and the convergence rate of the conventional BA, thereby making it suitable for the real-time systems. Validation results show that the proposed WBA-based online parameter identification method has better performances in terms of the accuracy, the convengence rate, the stability and the CPU time compared with other benchmark algorithms.

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7. Acknowledgments This study was supported in part by the National Natural Science Foundation of China (Grant No. 61373123), the National Science Foundation under Grant CNS-1253390, the Chinese Scholarship Council (No. [2016] 3100) and the Graduate Innovation Fund of Jilin University (No. 2017016). In addition, Geng Sun wants to thank, in particular, the patience, the support, the care and the waiting from Shuang Liang over the passed years. Will you marry me? [1] Ruizhi Chai and Ying Zhang. ”A practical supercapacitor model for power management in wireless sensor nodes.” IEEE Transactions on Power Electronics 30.12 (2015): 6720-6730 (DOI: 10.1109/TPEL.2014.2387113). [2] El Mejdoubi, Asmae, et al. ”Online supercapacitor diagnosis for electric vehicle applications.” IEEE Transactions on Vehicular Technology 65.6 (2016): 4241-4252 (DOI: 10.1109/TVT.2015.2454520). [3] El Mejdoubi, Asmae, et al. ”Prediction aging model for supercapacitor’s calendar life in vehicular applications.” IEEE Transactions on Vehicular Technology 65.6 (2016): 4253-4263 (DOI: 10.1109/TVT.2016.2539681). [4] Kumar, Mano Ranjan, Subhojit Ghosh, and Shantanu Das. ”Chargedischarge energy efficiency analysis of ultracapacitor with fractionalorder dynamics using hybrid optimization and its experimental validation.” AEU-International Journal of Electronics and Communications 78 (2017): 274-280. (DOI:10.1016/j.aeue.2017.05.011) [5] Chaoui, H., El Mejdoubi, A., Oukaour, A., and Gualous, H. ”Online system identification for lifetime diagnostic of supercapacitors with guaranteed stability.” IEEE Transactions on Control Systems Technology 24.6 (2016): 2094-2102 (DOI: 10.1109/TCST.2016.2520911). [6] Li, Zhang, S., Yao, Y., Zhang, Y., Hu, Y., & Li, Y. (2016). Mission profile based parameter estimation of supercapacitors for reliability improvement in energy storage systems. Industrial Electronics Society, IECON 2016 -, Conference of the IEEE ( pp.6830-6835). IEEE. (DOI: 10.1109/IECON.2016.7793336)

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[7] Elwakil, Ahmed S., et al. ”Further experimental evidence of the fractional-order energy equation in supercapacitors.” AEU-International Journal of Electronics and Communications 78 (2017): 209-212. (DOI: 10.1016/j.aeue.2017.03.027) [8] Chaoui, Hicham, and Hamid Gualous. ”Online Lifetime Estimation of Supercapacitors.” IEEE Transactions on Power Electronics 32.9 (2017): 7199-7206 (DOI: 10.1109/TPEL.2016.2629440). [9] Reichbach, Noam, and Alon Kuperman. ”Recursive-least-squaresbased real-time estimation of supercapacitor parameters.” IEEE Transactions on Energy Conversion 31.2 (2016): 810-812 (DOI: 10.1109/TEC.2016.2521324). [10] Chai, Ruizhi, H. Ying, and Y. Zhang. ”Supercapacitor charge redistribution analysis for power management of wireless sensor networks.” IET Power Electronics 10.2(2017):169-177 (DOI: 10.1049/iet-pel.2015.1029). [11] Yang, Xin-She. ”A new metaheuristic bat-inspired algorithm.” Nature inspired cooperative strategies for optimization (NICSO 2010) (2010): 65-74 (DOI: 10.1007/978-3-642-12538-6 6). [12] Kaur, G., Rattan, M., & Jain, C. (2017). Optimization of swastika slotted fractal antenna using genetic algorithm and bat algorithm for s-band utilities. Wireless Personal Communications(4), 1-13. (DOI: 10.1007/s11277-017-4495-6). [13] Grewal, Narwant Singh, M. Rattan, and M. S. Patterh. ”A NonUniform Circular Antenna Array Failure Correction Using Firefly Algorithm.” Wireless Personal Communications 97.1(2017):845-858. (DOI: 10.1007/s11277-017-4540-5). [14] Hamamoto, Anderson Hiroshi, et al. ”Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic.” Expert Systems with Applications 92(2017). (DOI: 10.1016/j.eswa.2017.09.013). Geng Sun received BS degree in Communication Engineering from Dalian Polytechnic University, China in 2011. He is currently a Ph.D. candidate in College of Computer Science and Technology at Jilin University, and a visiting researcher at Georgia Institute of Technology, USA. His research interests 17

include wireless sensor networks and collaborative beamforming. Yanheng Liu received MSc and Ph.D. degrees in computer science from Jilin University, China. He is currently a professor in Jilin University, China. His primary research interests are in network security, network management, mobile computing network theory and applications, etc. Ruizhi Chai received the M.S. degree in automation from Shanghai Jiao Tong University, Shanghai, China, in 2013, and the M.S. degree in electrical and computing engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 2013, where he is currently working toward the Ph.D. degree at the Department of Electrical and Computer Engineering, Georgia Institute of Technology. His research interests include developing new techniques of energy conservation for WSN and embedded systems. Fang Mei received M.S. degree and Ph.D degree in computer science and technology from Jilin University at Changchun in 2005 and 2010 respectively. She is working as an Lecture in the Department of Computer Science and technology , Jilin University. Her research interests are in the areas of vehicular communication network, software defined network, intelligent information processing. Ying Zhang received a M.S. degree in materials engineering from the University of Illinois at Chicago, a M.S. degree in electrical engineering from the University of Massachusetts Lowell, and the Ph.D. degree in systems engineering from the University of California at Berkeley, in 2001, 2002, and 2006, respectively. She is working as an Associate Professor in the School of Electrical and Computer Engineering, Georgia Institute of Technology. Her research interests are in the areas of sensors and smart wireless sensing systems, power management for energy harvesting wireless sensor networks.

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