Multivariable battery equalization method and system based on adaptive fuzzy control

Abstract

The invention provides a multivariable battery equalization method and system based on adaptive fuzzy control, the equalization system comprises a battery state information acquisition module, a state estimation module and a fuzzy control module, and the equalization method based on the equalization system specifically comprises the following steps: acquiring battery information, and calculating a battery state variable based on the battery information; when the battery state variable meets the equalization starting condition, battery equalization is triggered; and based on the temperature information and the battery state variable in the battery information, adjusting the equalization current of the battery through adaptive fuzzy control of the duty ratio of battery equalization. According to the invention, the duty ratio can be used as the output of the equalization strategy, the current magnitude can be flexibly changed, the problem of repeated equalization is avoided, and the control weight of the state variable and the temperature in the battery equalization is adjusted through a self-adaptive fuzzy control mode, so that in the equalization process, the equalization time is shortened. The battery state variable and the temperature change of the battery can be considered at any time, and the equalization speed and the battery operation safety are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of battery balancing, and in particular to a multivariable battery balancing method and system based on adaptive fuzzy control. Background Art

[0002] Battery balancing is crucial to ensure the performance consistency of each battery in the battery pack, extend the overall service life, improve energy efficiency, ensure safe system operation and enhance user experience. It is a key link in the battery management system. In the existing balancing strategy, the voltage level, state of charge (SOC) or remaining capacity of the single cell is generally used as the main control variable for balancing. The state of charge indicates the percentage of the current power stored in the battery to the total power, which is a key parameter reflecting the battery status. Using the state of charge as the control variable of the balancing strategy is the current mainstream practice.

[0003] Temperature, as one of the key factors affecting battery performance, profoundly affects the accuracy of state-of-charge estimation. However, most existing balancing strategies use the battery state-of-charge as a single target for balancing, ignoring the impact of temperature on the battery state-of-charge. Instead, battery balancing will cause uneven temperature distribution inside the battery pack, further exacerbating the inconsistency between single cells. Summary of the invention

[0004] The purpose of the present invention is to overcome the disadvantage that in the prior art, when using the battery state of charge for battery balancing, the influence of the battery temperature on the battery state of charge is ignored, which will cause uneven temperature distribution inside the battery pack due to battery balancing, further aggravating the inconsistency between single cells. A multivariable battery balancing method and system based on adaptive fuzzy control is provided, which uses battery state variables and temperature information as control variables for balancing control to achieve adaptive fuzzy control of battery balancing. During the balancing process, the changes in the battery state variables and temperature of the battery can be considered at all times, so as to dynamically adjust the output of the duty cycle to flexibly change the current size. While accelerating the balancing, the battery temperature rise is limited, the battery service life is extended, the risk of battery fire is reduced, and the battery safety during the battery balancing process is improved.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A multivariable battery balancing method based on adaptive fuzzy control includes:

[0007] Collect battery information, and calculate battery state variables based on the battery information;

[0008] When the battery state variables meet the balancing start conditions, battery balancing is triggered;

[0009] Based on the temperature information and battery state variables in the battery information, the duty cycle of battery balancing is controlled by adaptive fuzzy control to adjust the battery balancing current.

[0010] Compared with the traditional balancing strategy that relies on mean and difference comparison and cannot flexibly change the current, the duty cycle is used as the output of the balancing strategy, which can flexibly change the current size, avoid the problem of repeated balancing, and improve the balancing speed. In addition, while using the battery state variable, that is, the battery state of charge as the control variable of battery balancing, the battery temperature information is also used as the control variable of battery balancing, and the control weights of the two control variables in battery balancing are adjusted by adaptive fuzzy control. In this way, during the balancing process, the changes in the battery state variable and temperature of the battery can be considered at all times, and the output of the duty cycle can be dynamically adjusted to limit the rise in battery temperature, extend the battery life, and reduce the risk of battery fire.

[0011] Furthermore, the battery balancing current is adjusted by adaptively fuzzy controlling the duty cycle of the battery balancing based on the temperature information and the battery state variable in the battery information, including:

[0012] Obtain a temperature control variable according to the temperature information in the battery information, use the temperature control variable as the input variable of the temperature fuzzy controller, and output a duty cycle of temperature balance;

[0013] Calculate the state control variable according to the battery state variable, use the state control variable as the input variable of the state fuzzy controller, and output the duty cycle of the state variable balance;

[0014] According to the average temperature of the battery as the input variable of the adaptive fuzzy controller, the duty cycle of temperature balance and the weight of the duty cycle of state variable balance are output.

[0015] In the battery balancing process, three parts are set up: temperature fuzzy control, battery state fuzzy control and adaptive fuzzy control. The battery balancing strategy considering battery temperature is determined by temperature fuzzy control, and the battery balancing strategy considering battery state variables is determined by battery state fuzzy control. Then, the average temperature of the battery is taken as a comprehensive consideration to determine whether battery balancing considering battery temperature or battery balancing considering battery state variables is dominant, so as to adjust the corresponding weights and realize battery balancing that considers both battery temperature and battery state variables.

[0016] Furthermore, the temperature control variable includes an average value of the battery temperature and a difference between a maximum value of the battery temperature and the average value.

[0017] The average value of the battery temperature can identify whether the battery is overheating or overcooling, while the difference between the maximum battery temperature and the average value can identify abnormal heating or poor heat dissipation of the battery. These two parameters are used as control parameters for temperature balance to accurately reflect the temperature conditions of the battery to ensure the accuracy of temperature balance.

[0018] Furthermore, the state control variable includes a range value of the battery state variable and an average value of the battery state variable.

[0019] The battery state variable, that is, the average value of the battery's state of charge, can reflect the overall charging state of the battery pack and is an important indicator for evaluating the remaining capacity and endurance of the battery pack. The range value can intuitively reflect the inconsistency between the single cells in the battery pack. When the range value is large, it means that there is a large performance difference in the battery pack and balancing control is required. Therefore, considering the range and average value of SOC at the same time can more comprehensively evaluate the state of the battery pack. The range value reflects the inconsistency in the battery pack, while the average value reflects the overall performance of the battery pack. Using these two control parameters for battery balancing can help improve the overall performance and energy utilization of the battery pack, and also help extend the service life of the battery pack and reduce maintenance costs.

[0020] Furthermore, when the battery state variable meets the balancing start condition, triggering battery balancing includes:

[0021] Obtain the maximum and minimum values ​​of the battery state variables, and calculate the range values ​​of the battery state variables;

[0022] When the extreme value of the battery state variable reaches a preset start-balancing threshold, it is determined that the balancing start condition is met, and battery balancing is triggered.

[0023] Since the essence of cell balancing is to reduce the inconsistency in the battery pack and improve the overall performance of the battery pack, and the extreme value can intuitively reflect the inconsistency between the single cells in the battery pack, therefore, in order to improve the efficiency of cell balancing, when the extreme value reaches the preset start-balancing threshold, it proves that the inconsistency in the battery pack is high at this time, there are large performance differences in the battery pack, and there is a need for balancing control, then the cell balancing is triggered to start.

[0024] Furthermore, the battery balancing method further includes:

[0025] Obtain the battery state variables of the battery in real time and calculate the extreme value of the battery state variables;

[0026] When the extreme value of the battery state variable is less than or equal to the stop balancing threshold, it is determined that the balancing stop condition is met and the battery balancing is stopped.

[0027] The purpose of battery balancing is to reduce the inconsistency between cells in the battery pack and improve the overall performance of the battery pack. However, if the balancing process lasts too long or the balancing degree is too large, the battery cells may be damaged or the performance may be degraded. Therefore, a corresponding stop balancing threshold is set so that the battery balancing can be stopped in time after the battery balancing reaches the predetermined target, to prevent over-balancing and protect the safety and performance of the battery cells.

[0028] Furthermore, the battery balancing method further includes:

[0029] The outputs of the temperature fuzzy controller, the state fuzzy controller and the adaptive fuzzy controller are defuzzified by the centroid method to obtain the corresponding temperature balanced duty cycle, state variable balanced duty cycle and temperature balanced duty cycle and state variable balanced duty cycle weights.

[0030] In fuzzy control, the output obtained by fuzzy reasoning is usually a fuzzy quantity, which cannot be directly used to control battery balancing. However, through defuzzification, the fuzzy output obtained by fuzzy reasoning can be converted into an accurate value, that is, the duty cycle of the output. The centroid method can comprehensively consider the membership function and output range of the fuzzy output, more accurately reflect the results of fuzzy reasoning, and thus improve the accuracy of subsequent control.

[0031] A multivariable battery balancing system based on adaptive fuzzy control, including:

[0032] A battery status information collection module is connected to each battery and is used to collect battery information of each battery;

[0033] A state estimation module, connected to the battery state information acquisition module, is used to calculate the battery state variable according to the battery information;

[0034] The fuzzy control module is used to adjust the battery balancing current by adaptive fuzzy control of the duty cycle of the battery balancing according to the battery state variables and temperature information.

[0035] Furthermore, the fuzzy control module includes a state fuzzy controller, a temperature fuzzy controller and an adaptive fuzzy controller, which are used to respectively obtain the duty cycle of state variable balance, the duty cycle of temperature balance and the weights of the duty cycle of temperature balance and state variable balance.

[0036] Furthermore, the battery status information acquisition module includes a temperature sensor, a voltage sensor and a current sensor, which are used to respectively acquire temperature information, voltage information and current information of each voltage cell.

[0037] The beneficial effects of the present invention are:

[0038] (1) Taking the duty cycle as the output of the balancing strategy can flexibly change the current size, avoid the problem of repeated balancing, and improve the balancing speed. In addition, while taking the battery state variable, that is, the battery state of charge, as the control variable of battery balancing, the battery temperature information is also taken as the control variable of battery balancing, and the control weights of the two control variables in battery balancing are adjusted by adaptive fuzzy control. Therefore, during the balancing process, the changes in the battery state variable and temperature of the battery can be considered at all times, and the output of the duty cycle can be dynamically adjusted to limit the battery temperature rise, extend the battery life, reduce the risk of battery fire, and improve the safety of the battery during the battery balancing process.

[0039] (2) Setting a corresponding stop balancing threshold so that the battery balancing can be stopped in time after the battery balancing reaches the predetermined target, thereby preventing over-balancing and protecting the safety and performance of the battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of a process of the present invention;

[0041] Figure 2 is the average value of the battery temperature shown in the embodiment of the present invention Schematic diagram of membership function;

[0042] Figure 3 Schematic diagram of the membership function of the difference ΔT between the maximum value and the average value of the battery temperature shown in the embodiment of the present invention;

[0043] Figure 4 is a schematic diagram of a membership function of a duty cycle DCt considering temperature balance shown in an embodiment of the present invention;

[0044] Figure 5 is a three-dimensional coordinate diagram of the corresponding relationship between the input variables and the output variables of the temperature fuzzy controller shown in the embodiment of the present invention;

[0045] Figure 6 is a schematic diagram of a membership function of an extreme difference value ΔSOC of a battery state variable shown in an embodiment of the present invention;

[0046] Figure 7 is the average value of the battery state variable shown in the embodiment of the present invention Schematic diagram of membership function;

[0047] Figure 8 is a schematic diagram of a membership function of a duty cycle DCsoc considering state variable balance shown in an embodiment of the present invention;

[0048] Fig. 9 It is a three-dimensional coordinate diagram of the corresponding relationship between the input variables and the output variables of the state fuzzy controller shown in the embodiment of the present invention;

[0049] Fig.10 is the average value of the battery temperature shown in the embodiment of the present invention Schematic diagram of membership function;

[0050] Fig.11 Schematic diagram of the membership function of the weight coefficient ω1 shown in the implementation of the present invention;

[0051] Fig.12 is a schematic diagram of a membership function of a weight coefficient v2 shown in an embodiment of the present invention;

[0052] Fig.13 is the average value of the weight coefficient ω1 of the adaptive fuzzy controller and the battery temperature shown in the embodiment of the present invention Three-dimensional coordinate diagram of the corresponding relationship;

[0053] Fig.14 is the average value of the weight coefficient v2 of the adaptive fuzzy controller and the battery temperature shown in the embodiment of the present invention Three-dimensional coordinate diagram of the corresponding relationship;

[0054] Fig.15 is a schematic diagram of changes in state variables of batteries B1-B4 shown in an embodiment of the present invention;

[0055] Fig.16 is a schematic diagram of changes in battery temperature of batteries B1-B4 shown in an embodiment of the present invention;

[0056] Fig.17 is a schematic diagram showing the variation trend of the weight coefficient ω1 and the weight coefficient ω2 with the average temperature of the batteries B1-B4 shown in the embodiment of the present invention;

[0057] Fig.18 It is a schematic diagram of the change trend of the duty ratio output by the three fuzzy controllers during the balancing process of the batteries B1-B4 shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0059] Example:

[0060] The existing balancing topologies mainly include passive balancing with energy consumption and active balancing without energy consumption. Among them, passive balancing mainly discharges high-energy batteries through resistors to keep the power of high-energy batteries and low-energy batteries equal, thereby achieving battery consistency. Active balancing mainly relies on energy storage components such as inductors and capacitors to control energy transfer between batteries, thereby achieving battery balancing. Compared with passive balancing, active balancing consumes less energy and can improve battery balancing efficiency.

[0061] like Figure 1 As shown, this embodiment is based on active balancing technology and proposes a multivariable battery balancing method based on adaptive fuzzy control, including:

[0062] Collect battery information, and calculate battery state variables based on the battery information;

[0063] When the battery state variables meet the balancing start conditions, battery balancing is triggered;

[0064] Based on the temperature information and battery state variables in the battery information, the duty cycle of battery balancing is controlled by adaptive fuzzy control to adjust the battery balancing current.

[0065] The battery state variable in this embodiment is the state of charge of the battery. The state of charge of the battery is used as a control variable for battery balancing to ensure the performance consistency of each single cell in the battery pack.

[0066] However, in the process of balancing with the battery state of charge as the target, the temperature of the battery will rise along with the change of the balancing current. It is easy to cause a high-temperature battery due to battery balancing. If the high-temperature battery continues to participate in balancing, it is likely to affect the life of the battery due to the high temperature, and even cause a fire in severe cases. Therefore, while taking the battery state of charge as the control variable of battery balancing, the battery temperature is also taken as the control variable of battery balancing. The weights of the two balancing are adjusted through the adaptive fuzzy control method to achieve flexible adjustment of the balancing current, ensure that the battery temperature is adjusted while achieving battery balancing, and ensure the safety of battery operation.

[0067] When the battery temperature is high, the battery balancing that takes the battery temperature into consideration can be used as the dominant battery balancing to reduce the impact of excessive battery temperature on battery operation. When the battery temperature is normal, the battery balancing that takes the battery state variables into consideration can be used as the dominant battery balancing to ensure the consistency of the battery pack.

[0068] Adaptive fuzzy control has self-learning and self-organizing capabilities. It can automatically adjust control strategies and parameters according to the real-time performance and temperature changes of the battery to adapt to the balancing requirements under different battery states and battery temperatures, which helps to improve balancing efficiency and reduce unnecessary energy loss.

[0069] Due to the high complexity and uncertainty of battery packs, it is difficult to establish an accurate mathematical model to describe the dynamic behavior of batteries. Adaptive fuzzy control does not require an accurate mathematical model, but uses fuzzy logic and adaptive adjustment mechanisms to control the battery balancing process, which can reduce the difficulty of control design.

[0070] During the battery balancing process, the balancing current is controlled by adjusting the duty cycle of the PWM signal. The larger the duty cycle, the longer the high level time is, and the greater the average current flowing through the inductor. Conversely, the smaller the duty cycle, the smaller the average current flowing through the inductor. Therefore, the balancing current can be controlled by setting the duty cycle, thereby achieving balancing of the battery pack.

[0071] Therefore, this embodiment sets the duty cycle as a specific control parameter output by the adaptive fuzzy control to achieve balancing of the battery pack.

[0072] The method of adjusting the battery balancing current by adaptively fuzzy controlling the duty cycle of the battery balancing based on the temperature information and the battery state variable in the battery information includes:

[0073] Obtain a temperature control variable according to the temperature information in the battery information, use the temperature control variable as the input variable of the temperature fuzzy controller, and output a duty cycle of temperature balance;

[0074] Calculate the state control variable according to the battery state variable, use the state control variable as the input variable of the state fuzzy controller, and output the duty cycle of the state variable balance;

[0075] According to the average temperature of the battery as the input variable of the adaptive fuzzy controller, the duty cycle of temperature balance and the weight of the duty cycle of state variable balance are output.

[0076] In this embodiment, the temperature fuzzy controller includes two input variables and one output variable, wherein the two input variables include the average value of the battery temperature The output variable is the duty cycle DCt considering temperature balance.

[0077] The two input variables are defined as:

[0078]

[0079]

[0080] Among them, T MAX is the maximum temperature of the battery in the battery pack, n is the number of single cells in the battery pack, T n is the battery temperature of the nth battery in the battery pack.

[0081] The above parameters collected are input into the temperature fuzzy controller, and the average value of the battery temperature is The ranges of the difference ΔT between the maximum value and the average value of the battery temperature are set to (20, 40) and (0, 10) respectively, and the range of the duty cycle DCt of the temperature balance is set to (0, 0.6).

[0082] The fuzzy control rule of the temperature fuzzy controller in this embodiment is: when the average value of the battery temperature When it is small, the battery temperature is generally low, and a larger duty cycle is used to generate a larger balancing current and reduce the balancing time. When the difference ΔT between the maximum and average battery temperatures is large, the battery temperature difference is large, and a smaller duty cycle is used to limit the battery temperature rise.

[0083] Based on the fuzzy control rules, the corresponding output can be obtained through fuzzy reasoning. This output is the average value of the input battery temperature. Under the conditions of the difference ΔT between the maximum and average battery temperature, the possible range of values ​​of the output variable and its possibility. This output variable is generally a fuzzy quantity, and the output quantity obtained needs to be defuzzified by the centroid method to obtain the temperature-balanced duty cycle DCt.

[0084] Specifically, based on the above temperature fuzzy rules, the fuzzy subsets in the temperature fuzzy controller are divided into five: VS (very small), S (small), M (medium), L (large), VL (very large), and the membership functions of the input variables and output variables are set, where the average value of the battery temperature is The difference between the maximum and average battery temperature ΔT and the membership function of the duty cycle DCt considering temperature balance are as follows: Figure 2 , Figure 3 and Figure 4 shown.

[0085] The core of the centroid method is to calculate the centroid of the area enclosed by the membership function curve and the horizontal axis. This centroid is the precise value after defuzzification. During the calculation, the corresponding area is integrated according to the given membership function, and the weighted average of the integral is calculated to obtain the area centroid, and then the horizontal coordinate value of the area centroid is obtained. This value is the precise value after defuzzification.

[0086] The expression of defuzzification processing is:

[0087]

[0088] Among them, v0 is the output value, that is, the output duty cycle DCt considering temperature balance, μ(z) is the output of fuzzy reasoning, and is the membership function of variable z.

[0089] The three-dimensional coordinate diagram of the corresponding relationship between the input variables and output variables of the temperature fuzzy controller obtained in this embodiment is as follows: Figure 5 shown.

[0090] In this embodiment, the state fuzzy controller includes two input variables and one output variable, wherein the two input variables include the extreme value ΔSOC of the battery state variable and the average value of the battery state variable The output variable is the duty cycle DCsoc taking into account the balance of state variables.

[0091] The two input variables are defined as:

[0092] ΔSOC=SOC MAX -SOC MIN ;

[0093]

[0094] Among them, SOC MAX is the maximum value of the battery state of charge in the battery pack, n is the number of single cells in the battery pack, SOC MAX SOC is the maximum value of the estimated state of charge of the single battery in the battery. MIN SOC is the minimum estimated value of the state of charge of the single battery in the battery. n The estimated state of charge of the nth battery in the battery pack.

[0095] The above parameters collected are input into the state controller, and the extreme value ΔSOC of the battery state variable and the average value of the battery state variable are calculated. The ranges of are set to (0%, 50%) and (0, 100%) respectively, and the range of the duty cycle DCsoc of the state variable balance is set to (0, 0.6).

[0096] The fuzzy control rule of the state controller in this embodiment is:

[0097] when When ΔSOC is large or small, in order to protect the battery, a smaller duty cycle is used to generate a smaller balancing current to avoid excessive charge and discharge of the battery. When ΔSOC is large, the duty cycle is appropriately increased to speed up the balancing rate. When the load is moderate, a larger duty cycle is used to generate a larger balancing current, which can quickly improve the inconsistency of the battery.

[0098] Based on the above temperature fuzzy rules, the fuzzy subsets in the state fuzzy controller are divided into five: VS (very small), S (small), M (medium), L (large), VL (very large), and the membership functions of the input variables and output variables are set, among which the extreme value ΔSOC of the battery state variable and the average value of the battery state variable are The membership functions of the duty cycle DCsoc considering the balance of state variables are as follows: Figure 6 , Figure 7 and Figure 8 shown.

[0099] The defuzzification process is performed using the centroid method, and the three-dimensional coordinate diagram of the corresponding relationship between the input variables and output variables of the state fuzzy controller is obtained as follows: Fig. 9 shown.

[0100] After the outputs of the temperature fuzzy controller and the state fuzzy controller are determined, the weights of the outputs of the two fuzzy controllers are set by an adaptive fuzzy controller to output a desired duty cycle for battery balancing.

[0101] Specifically, the adaptive fuzzy controller includes one input and two outputs. The input is the average value of the battery temperature. The output is the weight coefficients of the duty cycle, ω1 and ω2, which are the weights of the state fuzzy controller and the temperature fuzzy controller, respectively. After the weights are assigned by the adaptive fuzzy controller, the output desired duty cycle DC is shown as follows:

[0102] DC=ω1DCsoc+ω2DCt.

[0103] The duty cycle outputs of the two fuzzy controllers are recombined through weight coefficients so that the coordinated balance of the battery state of charge and temperature is considered at any time during the balancing process. The state of charge balance or temperature balance can be selected to dominate according to the average temperature of the battery pack in the current state.

[0104] The average value of the collected battery temperature Enter the adaptive fuzzy controller, which sets the average value of the battery temperature The range of is (20, 40), and the range of weight coefficients ω1 and ω2 is set to (0, 1).

[0105] The fuzzy control rules of the adaptive fuzzy controller in this embodiment are: when the average temperature is between 20 and 30 degrees, the state variable balance is considered to be dominant, the temperature balance weight gradually increases with the temperature rise, the balance current can be larger within the allowable temperature range, and the balance speed is accelerated. When the average temperature is between 30 and 40 degrees, the temperature balance is considered to be dominant, the state variable balance weight gradually decreases with the temperature rise, the balance current is small, and the battery temperature rise speed is limited.

[0106] According to the fuzzy control rules of the adaptive fuzzy controller, the fuzzy subsets in the adaptive fuzzy controller are divided into five: VS (very small), S (small), M (medium), L (large), VL (very large), and the membership functions of the input variables and the output variables are set. The average value of the battery temperature The membership functions of weight coefficient ω1 and weight coefficient ω2 are as follows: Fig.10 , Fig.11 and Fig.12 shown.

[0107] The center of gravity method is used to perform defuzzification, and the two-dimensional coordinate diagrams of the corresponding relationship between the input variables and output variables of the adaptive fuzzy controller are as follows: Fig.13 and Fig.14 shown.

[0108] When the battery state variable meets the balancing start condition, triggering battery balancing includes:

[0109] Obtain the maximum and minimum values ​​of the battery state variables, and calculate the range values ​​of the battery state variables;

[0110] When the extreme value of the battery state variable reaches a preset start-balancing threshold, it is determined that the balancing start condition is met, and battery balancing is triggered.

[0111] The essence of cell balancing is to reduce the inconsistency in the battery pack and improve the overall performance of the battery pack. The extreme value can directly reflect the inconsistency between the single cells in the battery pack. Therefore, in order to improve the efficiency of cell balancing, when the extreme value reaches the preset start-balancing threshold, it proves that the inconsistency in the battery pack is high, there are large performance differences in the battery pack, and there is a need for balancing control, then the cell balancing is triggered.

[0112] In this embodiment, the start balancing threshold is set to 0.02.

[0113] If the balancing process lasts too long or the balancing degree is too large, the battery cells may be damaged or their performance may be degraded. Therefore, the battery state variables of the battery are obtained in real time, and the extreme value of the battery state variables is calculated;

[0114] When the extreme value of the battery state variable is less than or equal to the stop balancing threshold, it is determined that the balancing stop condition is met and the battery balancing is stopped.

[0115] After the battery balancing reaches the predetermined target, the battery balancing can be stopped in time to prevent over-balancing and protect the safety and performance of the battery cells.

[0116] In this embodiment, the stop balancing threshold is set to 0.002.

[0117] In another aspect of this embodiment, a multivariable battery balancing system based on adaptive fuzzy control is provided, including:

[0118] A battery status information collection module is connected to each battery and is used to collect battery information of each battery;

[0119] A state estimation module, connected to the battery state information acquisition module, is used to calculate the battery state variable according to the battery information;

[0120] The fuzzy control module is used to adjust the battery balancing current by adaptive fuzzy control of the duty cycle of the battery balancing according to the battery state variables and temperature information.

[0121] The balancing system of this embodiment is proposed based on the topological structure of the inductive active device. After obtaining two weight coefficients through the adaptive fuzzy controller, the desired duty cycle can be obtained by combining the duty cycles output by the two fuzzy controllers, thereby obtaining the corresponding PWM pulse waveform. After the PWM pulse waveform is input into the gate of the thyristor that controls the current parameter of the battery pack, its universal time can be controlled, thereby changing the size of the balancing current in the battery pack, while speeding up the balancing speed, limiting the battery temperature rise and ensuring the safe operation of the battery.

[0122] The battery status information acquisition module includes a temperature sensor, a voltage sensor and a current sensor, which are used to respectively acquire the temperature information, voltage information and current information of each voltage cell.

[0123] The state estimation module is provided with relevant algorithms for battery state estimation, which can estimate the state of a single battery through the ampere-hour integration method, a method based on characterization parameters, a model-based method or a data-driven method to obtain the battery state variables.

[0124] The fuzzy control module includes a state fuzzy controller, a temperature fuzzy controller and an adaptive fuzzy controller, which are used to respectively obtain the duty cycle of state variable balance, the duty cycle of temperature balance and the weights of the duty cycle of temperature balance and the duty cycle of state variable balance.

[0125] Take a battery pack containing four batteries as an example. The four batteries are B1, B2, B3 and B4, and their initial state variables are 52%, 53%, 60%, 42% respectively. The initial temperature is set to 25°C (without heat exchange with the outside world) for balancing.

[0126] The change diagram of the state variables of batteries B1-B4 is as follows Fig.15 As shown in the figure, SOC1, SOC2, SOC3 and SOC4 are the state variables of B1, B2, B3 and B4 respectively, and their equilibrium time is 800s; the temperature changes as shown in Fig.16 As shown in the figure, T1, T2, T3 and T4 are the battery temperatures of B1, B2, B3 and B4 respectively; the weight coefficient ω1 and the weight coefficient ω2 are the average values ​​of the battery temperature The changing trend of Fig.17 As shown in the figure, the duty cycle, DC, DCsoc and DCt output by the three fuzzy controllers have different trends. Fig.18 shown.

[0127] Combination Fig.16 and Fig.17It can be seen that during the balancing process, the duty cycle output by the adaptive fuzzy controller will be dynamically adjusted with the inconsistency of the state variables of the battery pack and the change of temperature, which can effectively limit the temperature rise of the battery while shortening the balancing time.

[0128] The above-described embodiment is only a preferred solution of the present invention and does not limit the present invention in any form. There are other variations and modifications without exceeding the technical solution described in the claims.

Claims

1. A multivariable battery balancing method based on adaptive fuzzy control, characterized in that: include: Collect battery information, and calculate battery state variables based on the battery information; When the battery state variables meet the balancing start conditions, battery balancing is triggered; Based on the temperature information and battery state variables in the battery information, the duty cycle of battery balancing is controlled by adaptive fuzzy control to adjust the battery balancing current.

2. The multivariable battery balancing method based on adaptive fuzzy control according to claim 1, characterized in that: The method of adjusting the battery balancing current by adaptively fuzzy controlling the duty cycle of the battery balancing based on the temperature information and the battery state variable in the battery information includes: Obtain a temperature control variable according to the temperature information in the battery information, use the temperature control variable as the input variable of the temperature fuzzy controller, and output a duty cycle of temperature balance; Calculate the state control variable according to the battery state variable, use the state control variable as the input variable of the state fuzzy controller, and output the duty cycle of the state variable balance; According to the average temperature of the battery as the input variable of the adaptive fuzzy controller, the duty cycle of temperature balance and the weight of the duty cycle of state variable balance are output.

3. The multivariable battery balancing method based on adaptive fuzzy control according to claim 2, characterized in that: The temperature control variable includes an average value of the battery temperature and a difference between a maximum value of the battery temperature and the average value.

4. The battery multivariable balancing method based on adaptive fuzzy control according to claim 2, characterized in that: The state control variable includes a range value of the battery state variable and an average value of the battery state variable.

5. The multivariable battery balancing method based on adaptive fuzzy control according to claim 1, characterized in that: When the battery state variable meets the balancing start condition, triggering battery balancing includes: Obtain the maximum and minimum values ​​of the battery state variables, and calculate the range values ​​of the battery state variables; When the extreme value of the battery state variable reaches a preset start-balancing threshold, it is determined that the balancing start condition is met, and battery balancing is triggered.

6. The multivariable battery balancing method based on adaptive fuzzy control according to claim 1, characterized in that: Also includes: Obtain the battery state variables of the battery in real time and calculate the extreme value of the battery state variables; When the extreme value of the battery state variable is less than or equal to the stop balancing threshold, it is determined that the balancing stop condition is met and the battery balancing is stopped.

7. The multivariable battery balancing method based on adaptive fuzzy control according to claim 2, characterized in that: Also includes: The outputs of the temperature fuzzy controller, the state fuzzy controller and the adaptive fuzzy controller are defuzzified by the centroid method to obtain the corresponding temperature balanced duty cycle, state variable balanced duty cycle and temperature balanced duty cycle and state variable balanced duty cycle weights.

8. A multivariable battery balancing system based on adaptive fuzzy control, used to execute the multivariable battery balancing method based on adaptive fuzzy control according to any one of claims 1 to 7, characterized in that: include: A battery status information collection module is connected to each battery and is used to collect battery information of each battery; A state estimation module, connected to the battery state information acquisition module, is used to calculate the battery state variable according to the battery information; The fuzzy control module is used to adjust the battery balancing current by adaptive fuzzy control of the duty cycle of the battery balancing according to the battery state variables and temperature information.

9. The multivariable battery balancing system based on adaptive fuzzy control according to claim 8, characterized in that: The fuzzy control module includes a state fuzzy controller, a temperature fuzzy controller and an adaptive fuzzy controller, which are used to respectively obtain the duty cycle of state variable balance, the duty cycle of temperature balance and the weights of the duty cycle of temperature balance and the duty cycle of state variable balance.

10. The multivariable battery balancing system based on adaptive fuzzy control according to claim 8, characterized in that: The battery status information acquisition module includes a temperature sensor, a voltage sensor and a current sensor, which are used to respectively acquire the temperature information, voltage information and current information of each voltage cell.

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