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Battery groups are widely used in production and life. Optimal charging can not only shorten the charge time, but also improve the performance and life of the battery pack. A constant current or constant voltage charging method is commonly used. This type of method cannot adjust the charge capacity in time according to the change of charging capacity of storage battery, and the charge performance is not high. This paper designs a fuzzy PID controller. In the case of variable load and interference, the battery group can still be charged by the optimal charging current. Through the simulation results, the fuzzy PID controller works well and verifies the feasibility of the charging controller.

The battery pack can supply power from the power supply system and can also provide buffer for the power grid when it is charged. The total electrochemical reaction equation of the most commonly used lead-acid battery during the charging process is [

During the actual charge, the late charging is often accompanied by a lot of water electrolysis reaction. The reaction equation is:

The chemical reaction of the lead-acid battery during the charging process is shown in

1) Charge acceptance

Charge acceptance refers to the maximum charge current the electrolyte can accept under the premise of producing only a small amount of gas [

2) Temperature

The voltage of the battery cell will drop by about 4 mV for every 1˚C increase in temperature [

3) Battery capacity

The battery capacity represents the ability of the battery to store electricity. During charging, the charging capacity of the battery can be:

where i is the charging current (A); t is the charging time (h); K is the charging coefficient of the lead-acid battery.

The working process of the battery pack is non-linear and its characteristics vary with the working time and the environment. When the battery characteristics change, the charging current must be adjusted in order to get a good control effect. The traditional constant current or constant voltage charging does not

have this “adaptive” capability, and its parameter setting is dependent on the experience of the technician, making it difficult to guarantee timeliness and continuity [

Battery pack management system is a complex nonlinear time-varying system and can establish a precise mathematical model, making it suitable for fuzzy control [

1) Fuzzification

Take the subordinating degree function of the input (e, ec) and the output (K_{P}, K_{I}, K_{D}) as the triangular function [

The variation range of e (the input error) and ec (the error rate of change) is defined as the universe of discourse on the fuzzy set:

_{ }are: {NB, NM, NS, Z, PS, PM, PB}2) Fuzzy rulesThrough repeated experiments and literature review [

ec_{ } e | NB | NM | NS | Z | PS | PM | PB |
---|---|---|---|---|---|---|---|

NB | PS | PS | PM | PM | PM | PS | PS |

NM | PM | PM | PB | PB | PB | PM | PS |

NS | PB | PS | NS | NS | NS | NS | Z |

Z | PS | PS | NS | Z | NS | NS | Z |

PS | PS | PS | NS | NS | NS | NS | Z |

PM | PS | PM | PB | PB | PB | PM | PS |

PB | PB | PM | PB | PB | PM | PS | PS |

ec_{ } e | NB | NM | NS | Z | PS | PM | PB |
---|---|---|---|---|---|---|---|

NB | NB | Z | PB | PB | PB | PS | PS |

NM | NB | PS | PB | PB | PS | Z | Z |

NS | Z | PS | PS | PM | PM | PS | PS |

Z | Z | PM | PS | PM | PM | PS | Z |

PS | Z | PS | PS | PM | PM | PM | Z |

PM | NS | Z | PM | PS | PS | NS | NS |

PB | NS | NS | Z | PS | NS | NS | NS |

ec_{ } e | NB | NM | NS | Z | PS | PM | PB |
---|---|---|---|---|---|---|---|

NB | PS | NB | NB | NS | NB | NB | PS |

NM | Z | NB | NB | NS | NB | NB | PS |

NS | Z | NM | NM | NS | NM | NM | PS |

Z | Z | NS | NS | Z | NS | NS | Z |

PS | Z | PM | PS | Z | PS | PM | Z |

PM | Z | PB | PS | PS | PB | PB | PS |

PB | PS | PB | PM | PM | PB | PB | PS |

3) Fuzzy reasoning

Take K_{P} as an example, as calculated by the fuzzy inference synthesis rule:

Fuzzy relations:

4) Defuzzification

The maximum subordinative degree method is adopted for defuzzification.

5) Simulation results

In the simulation of MATLAB, there are dedicated Fuzzy modules, which can be simulated only by inputting the designed parameters. Chart of Fuzzy rule is shown in

Response comparison during the load response is shown in

When the system load suddenly changes under the fuzzy PID control, the overshoot generated is about 12 V and the system transition time is less than 0.1 s; and when the system load suddenly changes under the traditional PID control,

the overshoot generated is about 20 V and the system transition time is about 0.25 s. It can be seen from the results that when the load suddenly changes, the fuzzy PID control effect is significantly better than the traditional PID control.

When the interference occurs, the fuzzy PID can make quick adjustment to ensure a stable charging voltage. Comparison of the anti-interference capability is shown in

The charging process and charging method of the battery are analyzed in this paper, combined with the latest research results and simulation experiments. A fuzzy PID controller is designed to meet the requirement of battery charging control. The simulation results show that the output of the fuzzy PID controller can be quickly returned to the setting value when the setting value changes. The overshoot of fuzzy PID controller is very small, and oscillation phenomenon is rare. The fuzzy PID controller, which has improve rapidity and effectiveness of battery charging, has better control effect than PID control.

Xu, X.Y. (2017) Research on Fuzzy PID Charging Control of Battery Pack. World Journal of Engineering and Technology, 5, 421-427. https://doi.org/10.4236/wjet.2017.53036