# Battery Balancing Algorithm for an Agricultural Drone Using a State-of-Charge-Based Fuzzy Controller

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

#### 2.1. Battery Balancing Type

#### 2.2. Fuzzy Logic Controller Structures

#### 2.2.1. Fuzzification

#### 2.2.2. Rule Base

#### 2.2.3. Inference

Rule 2) IF x is S and y is N THEN u is FS

Rule 3) IF x is FS and y is FS THEN u is FL

Rule 4) IF x is FS and y is N THEN u is FL

Rule 2) MIN (uS (x = 1.3) = 0.3, uN (y = 0.8) = 0.2) = 0.2

Rule 3) MIN (uFS (x = 1.3) = 0.7, uFS (y = 0.8) = 0.8) = 0.7

Rule 4) MIN (uFS (x = 1.3) = 0.3, uN (y = 0.8) = 0.2) = 0.2

#### 2.2.4. Defuzzification

^{crisp}is the definite value of the output, b

_{i}is the center of the gravitational function indicating the degree of the output of rule i, and μ (i) is the area of the output accuracy value.

#### 2.3. Two-Phase Interleaved Bi-Directional DC/DC Converter

## 3. Development of the Proposed Agricultural Drone Battery Cell Balancing Algorithm

#### 3.1. SOC Estimation Algorithm with Improved Current Integration

#### 3.2. Battery Cell Balancing Algorithm

_{battN}≈ P

_{0N}

_{batt1}≈ i

_{o}v

_{o1}∝ v

_{o1}

P

_{batt1}≈ i

_{o}v

_{o2}∝ v

_{o2}

P

_{batt1}≈ i

_{o}v

_{oN}∝ v

_{oN}

_{battN}is the input power of the Nth module, P

_{oN}is the output power of the Nth converter, v

_{oN}is the output voltage of the Nth converter, and i

_{oN}is the output current of the Nth converter. The output voltage of each converter module is determined by the fuzzy controller.

#### 3.3. Fuzzy Controller Design for Battery Module Balancing

#### 3.3.1. Fuzzification

#### 3.3.2. Rule Base

_{d}is N, THEN V

_{0N}is VL

IF SOC is L and SOC

_{d}is N, THEN V

_{0N}is L

IF SOC is N and SOC

_{d}is N, THEN V

_{0N}is N

#### 3.3.3. Inference

_{d}= 11 are obtained by way of the attribution function and rule base.

_{d}is S, THEN V

_{0N}is VS

Rule 2: IF SOC is S and SOC

_{d}is N, THEN V

_{0N}is S

Rule 3: IF SOC is N and SOC

_{d}is S, THEN V

_{0N}is S

Rule 4: IF SOC is N and SOC

_{d}is N, THEN V

_{0N}is N

_{d}=11), = 0.2) = 0.2

Rule 2: MIN(μS (SOC = 55) = 0.3, μS (SOC

_{d}= 11), =0.8) = 0.3

Rule 3: MIN(μN (SOC = 55) = 0.6, μN (SOC

_{d}=11), =0.2) = 0.2

Rule 4: MIN(μN (SOC = 55) = 0.6, μN (SOC

_{d}=11), =0.8) = 0.6

#### 3.3.4. Defuzzification

_{0N}))

Rule 2: MIN (0.3, μS(V

_{0N}))

Rule 3: MIN (0.2, μS(V

_{0N}))

Rule 4: MIN (0.6, μN(V

_{0N}))

_{0N}to the final output value of each converter obtained using Equation (10), as shown in Equation (11).

_{0N}is the output command voltage value of each converter, V

_{0N}is the output value through the fuzzy control, and VDC-link is the DC-link value for holding the voltage constant. The output command voltage calculated using Equation (11) controls each switch using Equation (12) to determine the switching duty ratio.

## 4. Development of a Monitoring System

#### 4.1. RF Communication

#### 4.2. Design and Development

## 5. Development of an Agricultural Drone BMS

#### 5.1. Charge/Discharge Test to Verify the Performance of the System

- The purpose of this test is to compare the discharge efficiency between a typical battery versus the BMS when discharging at 0.2 and 6 C-rate with a 0.5 C-rate charge, and the test compares the discharge efficiency when only the battery and when the BMS are installed.
- In the comparison, no significant differences were observed in the discharge amounts, but the changes over time showed that the discharge time was longer in the BMS case.
- In particular, especially as shown in Table 6, a rapid voltage drop was observed at a voltage of 40 V, and it was confirmed that a discharge followed according to the gentle curve observed at approximately 4% when using the BMS.

#### 5.2. Cell Voltage Deviation and SOC Measurement

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Vardwaj, V.; Vishakha, V.; Jadoun, V.K.; Jayalaksmi, N.S.; Agarwal, A. Various methods used for battery balancing in electric vehicles: A comprehensive review. In Proceedings of the 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control, Mathura, India, 28–29 February 2020. [Google Scholar]
- Omariba, Z.B.; Zhang, L.; Sun, D. Review of battery Balancing methodologies for optimizing battery pack performance in electric vehicles. IEEE Access
**2019**, 7, 129335–129352. [Google Scholar] [CrossRef] - Yildirim, B.; Elgendy, M.; Smith, A.; Pickert, V. Evaluation and Comparison of Battery Cell Balancing Methods. In Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania, 29 September–2 October 2019. [Google Scholar]
- Lee, Y.S.; Duh, J.Y. Fuzzy-controlled individual-cell equalizer using discontinuous inductor current-mode Cuk convertor for lithium–ion chemistries. IEE Proc. Electr. Power Appl.
**2005**, 152, 1271–1282. [Google Scholar] - Li, X.; Hui, D.; Lai, X. Battery energy storage station (BESS)-based smoothing control of photovoltaic (PV) and wind power generation fluctuations, Sustainable Energy. IEEE Trans.
**2013**, 4, 464–473. [Google Scholar] - Ranjbar, H.; Banaci, A.; Khoobroo, A.; Fahimi, B. Online estimation of state of charge in Li-Ion batteries us-ing impulse response concept, Smart Grid. IEEE Trans.
**2012**, 3, 360–367. [Google Scholar] [CrossRef] - Yu, L.R.; Hsieh, Y.-C.; Liu, W.; Moo, C.S. Balanced discharging for serial battery power modules with boost converters. In Proceedings of the 2013 International Conference (ICSSE), Budapest, Hungary, 4–6 July 2013; pp. 449–453. [Google Scholar] [CrossRef]
- Bose, B.K. Modern Power Electronics and AC Drives; Prentice Hall PTR: Indianapolis, IN, USA, 2001. [Google Scholar]
- Thomas, S.; Zhu, W. A targeted equalizer for lithium ion battery packs. In Proceedings of the IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, USA, 7–10 September 2009; pp. 175–180. [Google Scholar]
- Lee, S.H.; Cho, S.E.; Moon, K. Fuzzy modeling and control of system interconnected photovoltaic power generation system. Int. Inf. Inst.
**2015**, 18, 1283–1292. [Google Scholar] - Lee, S.-H. Development of Fuzzy controller for battery cell balancing of agricultural drones. J. Instit. Internet Broadcast. Commun.
**2017**, 17, 199–208. [Google Scholar] - Lee, S.H.; Lee, S.J.; Moon, K.I. Application of Fuzzy Feedback Control for Warranty Claim, New Challenges for Intelligent Information and Database Systems; Springer: Berlin/Heidelberg, Germany, 2011; pp. 279–288. [Google Scholar] [CrossRef]
- Lavigne, L.; Sabatier, J.; Francisco, J.; Guillemard, F.; Noury, A. Lithium-ion Open Circuit Voltage (OCV) curve modelling and its ageing adjustment. J. Power Sour.
**2016**, 324, 694–703. [Google Scholar] [CrossRef] - Rivera-Barrera, J.P.; Muñoz-Galeano, N.; Sarmiento-Maldonado, H.O. SoC Estimation for Lithium-ion Batteries: Review and Future Challenges. Electronics
**2017**, 6, 102. [Google Scholar] [CrossRef] [Green Version] - Xiong, R.; Sun, F.; Hongwen, H.; Nguyen, T.D. A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles. Energy
**2013**, 63, 295–308. [Google Scholar] [CrossRef] - Tian, N.; Fang, H.; Chen, J.; Wang, Y. Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification, and Validation. IEEE Trans. Contr. Syst. Tech.
**2020**, 1–15. [Google Scholar] [CrossRef] [Green Version] - Meng, J.; Luo, G.; Ricco, M.; Swierczynski, M.; Stroe, D.; Teodorescu, R. Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles. Appl. Sci.
**2018**, 8, 659. [Google Scholar] [CrossRef] [Green Version] - Akyildiz, F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. Wireless Sensor Networks: A Survey. Elservier Comput. Netw.
**2002**, 38, 393–422. [Google Scholar] [CrossRef] [Green Version] - Lee, S.H.; Kim, S.B.; Yang, S.H. Design of drone battery usage monitoring system using wireless sensor network. In Proceedings of the 3rd International Integrated (Web & Offline) Conference & Concert on Convergence with Academic & Job (IICCC 2017, SCCTL 03), Lao-Korean College, Vientiane, Laos, 8–12 August 2017; pp. 161–164. [Google Scholar]

**Figure 8.**Definition of the membership function: (

**a**) Membership function for the battery cell SOC. (

**b**) Membership function for the SOC difference between battery cells. (

**c**) Membership function for the output Von of the converter.

**Figure 11.**Testing prototype (

**a**) and the transmission/reception module when mounted on an agricultural drone (

**b**).

**Figure 12.**Drone load measurement by system: Communications -RF 424 MHz, -Transmission output 10 dBm, Receive Sensitivity −124 dBm, Current Sensor: -ACS759, ~200 A/Micom: -Arduino Mega 2560. (The part labeled (

**a**) is the receiver, (

**b**) is the receiver data display, and (

**c**) is the transmitter.).

**Figure 19.**Cell change comparison in the discharge interval of the drones ((

**a**): BMS, (

**b**): Battery only).

Language Description | Language Variable | Applied Value |
---|---|---|

Very Large | VL | +3 |

Large | L | +2 |

Few Large | FL | +1 |

Normal | N | 0 |

Few Small | FS | −1 |

Small | S | −2 |

Very Small | VS | −3 |

Input (u) | Deviation (x) | |||||||
---|---|---|---|---|---|---|---|---|

VL | L | FL | N | FS | S | VS | ||

Deviation Variation Rate (y) | VL | VS | VS | NS | S | FS | S | N |

L | VS | VS | S | FS | N | FS | FL | |

FL | VS | S | FS | N | FL | N | FL | |

N | S | FS | FS | FL | FL | FL | L | |

FS | FS | FS | N | FL | L | L | VL | |

S | FS | N | FL | L | VL | VL | VL | |

VS | N | FL | FL | VL | VL | VL | VL |

Language Variable | Applied Value | Language Description |
---|---|---|

VL | 80 | Very Large |

L | 65 | Large |

N | 50 | Normal |

S | 35 | Small |

VS | 20 | Very Small |

Language Variable | Applied Value | Language Description |
---|---|---|

VL | 20 | Very Large |

L | 15 | Large |

N | 10 | Normal |

S | 5 | Small |

VS | 0 | Very Small |

Input (u) | Deviation (x) | |||||
---|---|---|---|---|---|---|

VL | L | N | S | VS | ||

SOC difference between battery cells (SOC _{d}) | VL | VL | VL | VL | VL | NL |

N | VL | VL | N | N | S | |

L | VL | L | N | S | VS | |

S | VL | N | S | VS | VS | |

VS | VS | L | N | VS | VS |

**Table 6.**Comparing the discharge of the battery alone versus that with the battery management system (BMS).

Division | Only Battery | BMS | Remarks | |
---|---|---|---|---|

0.2 C | Discharge amount (Ah) | 21.77 | 22 | |

Discharge amount (Ah) | 15,463 | 18,565 | ||

6 C | Discharge amount (Ah) | 22 | 22 | |

Discharge amount (Ah) | 810 | 840 | ||

Cell voltage deviation (mV) | 120 | 50 | ||

Discharge rate (%) | 94.6 | 95.6 |

Battery Charge Voltage | Charge/Discharge Measurement Value | BMS Measurement Value | Deviation |
---|---|---|---|

45 V | 2.62 Ah | 2.428 Ah | −7.47 |

* Calculation formula: ((BMS measurement value-charge/discharge measurement value)/(charge/discharge) measurement value) × 100 |

Cell No. | Measured Voltage | Cell No. | Measured Voltage |
---|---|---|---|

1 | 3.715 9 | 7 | 3.715 7 |

2 | 3.715 7 | 8 | 3.714 9 |

3 | 3.715 8 | 9 | 3.716 0 |

4 | 3.716 0 | 10 | 3.715 8 |

5 | 3.715 7 | 11 | 3.716 3 |

6 | 3.716 4 | 12 | 3.715 6 |

Maximum deviation | Maximum deviation |

Division | Only Battery | BMS | Improvement Efficiency | Measuring Instrument |
---|---|---|---|---|

Charging time | 240 min | 210 min | 14% | Charge-discharge Device |

Discharge time | 9 min | 12 min | 33% | |

Flight time 9 min | 550 s | 580 s | 5% | Agricultural Drone |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Kim, S.-B.; Lee, S.-H.
Battery Balancing Algorithm for an Agricultural Drone Using a State-of-Charge-Based Fuzzy Controller. *Appl. Sci.* **2020**, *10*, 5277.
https://doi.org/10.3390/app10155277

**AMA Style**

Kim S-B, Lee S-H.
Battery Balancing Algorithm for an Agricultural Drone Using a State-of-Charge-Based Fuzzy Controller. *Applied Sciences*. 2020; 10(15):5277.
https://doi.org/10.3390/app10155277

**Chicago/Turabian Style**

Kim, Sang-Bum, and Sang-Hyun Lee.
2020. "Battery Balancing Algorithm for an Agricultural Drone Using a State-of-Charge-Based Fuzzy Controller" *Applied Sciences* 10, no. 15: 5277.
https://doi.org/10.3390/app10155277