Active equalization system for BMS

Abstract

The invention relates to an active equalization system for a BMS, and relates to the technical field of battery management, and the system comprises a state evaluation module which is used for carrying out the state evaluation of each battery cell in the BMS, and generating a state evaluation index of each battery cell; the path optimization module is used for optimizing an energy transmission path according to the state evaluation index of each battery cell and generating a path optimization result; the current equalization module is used for calculating self-adaptive equalization current based on the state evaluation index and the path optimization result of each battery cell; the loss compensation module is used for performing efficiency loss compensation on the BMS based on the self-adaptive equalization current and the path optimization result, and determining a corresponding efficiency loss compensation coefficient; and the active equalization module is used for generating an equalization control signal for the BMS based on the efficiency loss compensation coefficient, the path optimization result and the time attenuation factor. According to the system, the BMS load balancing accuracy and efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the manufacturing of charging, battery replacement and hydrogen refueling facilities, and more specifically to the field of battery management technology, in particular to an active balancing system, method, electronic device and non-transient computer-readable storage medium for BMS. Background Art

[0002] In today's Battery Management Systems (BMS), balancing the voltage differences between cells is often necessary to improve battery pack efficiency and extend their lifespan. Currently, widely used active balancing methods include inductive energy transfer, capacitive energy transfer, and transformer-coupled energy transfer.

[0003] However, traditional inductive or capacitive balancing circuits have complex structures and high costs. In multi-cell systems, the scheduling and control algorithms for the energy paths are complex, making it difficult to achieve fast and efficient balancing effects. On the other hand, some active balancing methods have additional energy losses during energy transmission and cannot balance efficiency and response speed. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides an active balancing system, method, electronic device and non-transitory computer-readable storage medium for a BMS, which can improve the accuracy and efficiency of BMS load balancing.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides an active balancing system for a BMS, the system comprising:

[0007] A status assessment module is used to assess the status of each battery cell in the BMS and generate a status assessment index for each battery cell;

[0008] A path optimization module, configured to optimize the energy transmission path according to the state evaluation index of each battery cell and generate a path optimization result;

[0009] A current balancing module, configured to calculate an adaptive balancing current based on a state evaluation index of each battery cell and the path optimization result;

[0010] a loss compensation module, configured to perform efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determine a corresponding efficiency loss compensation coefficient;

[0011] An active balancing module is configured to generate a balancing control signal for the BMS based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor.

[0012] Furthermore, the status assessment module is also used to:

[0013] Collecting the cell voltage, voltage change rate and state of charge of each cell;

[0014] Performing a square operation on the ratio of the cell voltage to the corresponding reference voltage to obtain the first evaluation factor;

[0015] performing an exponential operation on the absolute value of the voltage change rate as a second evaluation factor;

[0016] performing a nonlinear trigonometric function transformation on the state of charge to obtain a third evaluation factor;

[0017] The state evaluation index is generated by weighted combination of the first evaluation factor, the second evaluation factor, and the third evaluation factor.

[0018] Furthermore, the path optimization module is also used to:

[0019] Obtain the difference between the maximum state evaluation index and the current state index of each path;

[0020] Introducing a dynamic adjustment coefficient to perform real-time correction on the transmission efficiency of each path;

[0021] Comprehensively scoring each of the paths by combining an exponential decay function of the energy transmission distance of each path to obtain a path attenuation term;

[0022] The path optimization result is determined by the difference between the paths, the corrected transmission efficiency and the path attenuation item.

[0023] Furthermore, the current balancing module is further configured to:

[0024] The product of the reference current coefficient and the path optimization result is used as the basic current;

[0025] Obtain the periodic disturbance term representing the periodic fluctuation component;

[0026] Performing nonlinear gain adjustment according to the ratio of the maximum state evaluation index to the minimum state index to obtain a system imbalance amplification factor;

[0027] The periodic disturbance term and the system imbalance amplification factor are superimposed on the basic current to obtain the adaptive balancing current.

[0028] Furthermore, the loss compensation module is further configured to:

[0029] constructing an exponential compensation component related to the adaptive balancing current according to a compensation intensity coefficient, the adaptive balancing current and a reference current;

[0030] Associating the path optimization result with the compensation angle parameter through trigonometric functions to obtain a cosine modulation term;

[0031] The exponential compensation component and the cosine modulation term are adjusted based on a ratio of an average state evaluation index to the maximum state index to obtain the efficiency loss compensation coefficient.

[0032] Furthermore, the active balancing module is further configured to:

[0033] Introducing an inverse trigonometric function to perform nonlinear mapping on the ratio of the path optimization result to the average state evaluation index to obtain a nonlinear adjustment term;

[0034] The time decay exponential function and the product of the efficiency loss compensation coefficient and the adaptive balancing current are superimposed on the nonlinear adjustment item to obtain the balancing control signal.

[0035] Furthermore, the active balancing module is further configured to:

[0036] The surface temperature distribution of each battery cell is collected in real time through a wireless sensor network;

[0037] Introducing temperature gradient data into the path optimization function and adding thermal equilibrium constraints;

[0038] A temperature compensation term is added to the comprehensive status index.

[0039] The present invention also provides an active balancing method for a BMS, the method comprising:

[0040] Performing a status assessment on each battery cell in the BMS to generate a status assessment index for each battery cell;

[0041] Optimizing the energy transmission path according to the state evaluation index of each battery cell and generating a path optimization result;

[0042] Calculating an adaptive balancing current based on a state evaluation index of each of the battery cells and the path optimization result;

[0043] Performing efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determining a corresponding efficiency loss compensation coefficient;

[0044] A balancing control signal for the BMS is generated based on the efficiency loss compensation coefficient, the path optimization result, and a time decay factor.

[0045] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing the active balancing method for BMS as described above.

[0046] In addition, to achieve the above-mentioned purpose, the present invention further proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, the active balancing method for a BMS as described above is implemented.

[0047] The beneficial effects of the present invention are:

[0048] (1) The present invention introduces the PSI indicator, integrating the three-dimensional information of voltage level, rate of change and SOC; taking into account nonlinearity and dynamic changes, the state assessment is made more sensitive and accurate; it is conducive to the early identification of potential unbalanced cells and improves the judgment quality before balancing.

[0049] (2) The present invention combines transmission efficiency and distance attenuation to dynamically balance path selection; it can automatically avoid high-loss channels in a multi-path system; it effectively reduces heat consumption and power loss in energy scheduling, and improves the overall efficiency of the system.

[0050] (3) The present invention introduces periodic disturbance terms and PSI extreme difference factors; the balanced current is dynamically adjusted to quickly respond to state mutations; it ensures response speed while avoiding excessive intervention that causes energy waste or battery cell aging.

[0051] In summary, the present invention has the advantages of high accuracy, high energy efficiency, high responsiveness and strong adaptability, and can effectively improve the balancing quality and system life in actual battery systems (such as power batteries, energy storage batteries, UPS, etc.). BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A scenario diagram of an active balancing method for BMS provided by the present invention;

[0053] Figure 2 A schematic structural diagram of an active balancing system for BMS provided by the present invention;

[0054] Figure 3 A flow chart of an active balancing method for BMS provided by the present invention;

[0055] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0056] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0058] See also Figure 1 , Figure 1 This is a scene diagram of an active balancing method for BMS provided by the present invention. The active balancing system for BMS of the present invention belongs to the manufacturing of charging, battery replacement and hydrogenation facilities. Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. Terminals include, but are not limited to, portable devices such as mobile phones and tablets installed with various network platform applications, as well as fixed devices such as computers, kiosks, and advertising machines. The server provides various business services to users, including service push servers and user recommendation servers.

[0059] It should be noted that Figure 1 The scenario diagram of the active balancing method for a BMS shown is only an example. The terminal, server, and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not limit the technical solution provided by the embodiment of the present invention. It is known to those skilled in the art that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0060] Among them, the terminal can be used to:

[0061] Performing a status assessment on each battery cell in the BMS to generate a status assessment index for each battery cell;

[0062] Optimizing the energy transmission path according to the state evaluation index of each battery cell and generating a path optimization result;

[0063] Calculating an adaptive balancing current based on a state evaluation index of each of the battery cells and the path optimization result;

[0064] Performing efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determining a corresponding efficiency loss compensation coefficient;

[0065] A balancing control signal for the BMS is generated based on the efficiency loss compensation coefficient, the path optimization result, and a time decay factor.

[0066] See also Figure 2 , Figure 2This is a structural diagram of an active balancing system for BMS provided by the present invention.

[0067] like Figure 2 As shown, an active balancing system for a BMS proposed in an embodiment of the present invention includes:

[0068] A status assessment module 201 is configured to assess the status of each battery cell in the BMS and generate a status assessment index for each battery cell;

[0069] A path optimization module 202 is configured to optimize the energy transmission path according to the state evaluation index of each battery cell and generate a path optimization result;

[0070] A current balancing module 203 is configured to calculate an adaptive balancing current based on a state evaluation index of each battery cell and the path optimization result;

[0071] a loss compensation module 204 for performing efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determining a corresponding efficiency loss compensation coefficient;

[0072] The active balancing module 205 is configured to generate a balancing control signal for the BMS based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor.

[0073] Furthermore, the status assessment module 201 is further configured to:

[0074] Collect the cell voltage, voltage change rate and state of charge of each cell;

[0075] The ratio of the cell voltage to the corresponding reference voltage is squared as the first evaluation factor;

[0076] Performing an exponential operation on the absolute value of the voltage change rate as the second evaluation factor;

[0077] Perform nonlinear trigonometric function transformation on the state of charge as the third evaluation factor;

[0078] A state evaluation index is generated by weighted combination of the first evaluation factor, the second evaluation factor, and the third evaluation factor.

[0079] In some embodiments, the condition assessment index is expressed as:

[0080]

[0081] Among them, PSI is the battery status assessment index, V c is the cell voltage, V ref is the reference voltage, SOC is the state of charge, α, β, and γ are the first, second, and third weights respectively. is the voltage change rate.

[0082] Specifically, It is the first evaluation factor, which represents the normalized energy intensity of the cell voltage under the reference standard. It reflects whether the current voltage of the cell is close to the designed optimal voltage. The square term enhances the penalty for high voltage deviation.

[0083] It is the second assessment factor, capturing the severity of voltage changes. When the battery cell is under high load, fast charging or abnormal state, the voltage changes greatly and this item is rapidly amplified. It indicates the risk of battery cell stability.

[0084] It is the third evaluation factor, which maps the SOC (usually 0-1) to a sine curve from 0 to 1, enhancing the sensitivity to medium and high SOC; avoiding the insufficiency of the linear expression of SOC for high / low charge states.

[0085] α, β, and γ control the weights of the three physical quantities' influence on the total PSI value. They can be adjusted according to different application scenarios. For example, energy storage batteries can enhance their sensitivity to SOC (increase γ), while power batteries can emphasize voltage stability (increase β).

[0086] Set the parameters as follows:

[0087] V c =3.65V, V ref =3.70V, SOC=0.6,α、β、γare 0.5、

[0088] 0.3 and 0.2, the calculation process is as follows:

[0089]

[0090] PSI=0.487+0.305+0.162=0.954.

[0091] This calculation shows that under the current parameters, the cell's condition assessment index is 0.954 (the full score can be assumed to be between 1.5 and 2.0, depending on the weight configuration). The voltage deviation is small (relatively healthy), the rate of change is moderate (cell stability), the SOC is at a medium-to-high level, and the overall condition is good.

[0092] In some embodiments, the path optimization module 202 is further configured to:

[0093] Obtain the difference between the maximum state evaluation index and the current state index of each path;

[0094] Introducing dynamic adjustment coefficients to correct the transmission efficiency of each path in real time;

[0095] Combined with the exponential decay function of the energy transmission distance of each path, each path is comprehensively scored to obtain the path attenuation term;

[0096] The path optimization result is determined by the difference of each path, the corrected transmission efficiency and the path attenuation term.

[0097] In some embodiments, the path optimization result is represented as:

[0098]

[0099] Among them, TRF is the path optimization result, PSI max is the maximum status assessment index, PSI i is the current PSI value of the target cell on the i-th path, η i is the transmission efficiency of the i-th path, D i is the energy transmission distance of the i-th path, λ i is the dynamic adjustment coefficient of the i-th path, k i is the signal / energy attenuation coefficient of the i-th path.

[0100] In the specific implementation, (PSI max -PSI i ) represents the state difference between the current i-th target cell and the optimal cell in the system state; the larger the difference, the more "lagging" the target cell in this path is, and the more energy support is needed; it provides a basis for priority energy scheduling. η i It represents the energy retention ratio of the i-th path during transmission; the closer it is to 1, the smaller the loss. i ·D i ) is the path attenuation term, the path loss factor, which increases with the transmission distance D i Increases and decays exponentially; the decay coefficient k i It can be set dynamically according to circuit material, temperature, impedance, etc. i Indicates the importance, policy preference, or real-time scheduling priority of the path; it can be used to introduce strategic control, such as avoiding overheated lines and encouraging efficient paths.

[0101] Assume that there are three transmission paths (i=1, 2, 3), as shown in the following table:

[0102]

[0103] Path-by-path calculation:

[0104] Path 1:

[0105] ΔPSI=0.95-0.85=0.10;

[0106] exp(-0.4·1.2)=exp(-0.48)≈0.619;

[0107] TPF1=1.0·0.10·0.92·0.619=0.0569.

[0108] Path 2:

[0109] ΔPSI=0.95-0.65=0.30;

[0110] exp(-0.5·2.0)=exp(-1.0)≈0.619;

[0111] TPF2=0.9·0.30·0.80·0.368=0.0794.

[0112] Path 3:

[0113] ΔPSI=0.95-0.90=0.05;

[0114] exp(-0.35·1.0)=exp(-0.35)≈0.705;

[0115] TPF3=1.1·0.05·0.88·0.705=0.0341.

[0116] The total TPF value is:

[0117] TPF1+TPF2+TPF3=0.0569+0.0794+0.0341=0.1704;

[0118] In summary, in the example of the present invention, the path optimization function value of the current system is 0.1704, which is used in the next step to regulate the calculation of the balancing current IB; from the perspective of the contribution of each path, path 2 has the highest weight (0.0794), indicating that this path has the greatest transmission significance. Although the efficiency is not high and the distance is long, the target battery cell state difference is the largest; this function reflects the multi-dimensional weighted characteristics, which helps the system achieve refined scheduling and minimum loss balancing according to the actual state.

[0119] In some embodiments, the current balancing module 203 is further configured to:

[0120] The base current is the product of the reference current coefficient and the path optimization result;

[0121] Obtain the periodic disturbance term representing the periodic fluctuation component;

[0122] The nonlinear gain adjustment is performed according to the ratio of the maximum state evaluation index to the minimum state index to obtain the system imbalance amplification factor;

[0123] The periodic disturbance term and the system imbalance amplification factor are superimposed on the basic current to obtain the adaptive balanced current.

[0124] Among them, the adaptive balancing current can be expressed as:

[0125]

[0126] Where IB is the adaptive balancing current, μ is the reference current coefficient, ω is the fluctuation adjustment factor, which controls the amplitude of periodic disturbance, θ is the sine wave phase coefficient, t is the time variable, and PSI min Is the minimum PSI value of the cells in the system.

[0127] Specifically, IB is the adaptive balancing current, which is the actual output dynamic balancing current.

[0128] μ is the reference current coefficient, which indicates the basic current level of the system and can be set according to the system design.

[0129] TPF is the path optimization result, which is the result of the previous optimization calculation.

[0130] ω is the fluctuation adjustment factor, which controls the amplitude of periodic disturbance and the amplitude of sinusoidal disturbance, and its value is generally between 0 and 1.

[0131] θ is the sine wave phase coefficient, which controls the sine wave frequency (time scale).

[0132] t is a time variable, and periodic disturbances are introduced to prevent the system from falling into the equilibrium dead zone.

[0133] PSI min Is the minimum PSI value of the cells in the system.

[0134] μ·TPF is the basic current regulation, which adjusts the current intensity according to the comprehensive scheduling demand derived from the path optimization function. [1+ω·sin(θ·t)] is the periodic disturbance term representing the periodic fluctuation component. It makes the current fluctuate periodically within a safe range, improves the dynamic responsiveness of the system, and avoids "rigid control". It is the system imbalance amplification factor. The larger the PSI gap, the larger this item is, indicating that the system needs forced balance.

[0135] Set the input parameters:

[0136] μ=4A, TPF=0.1704, ω=0.3, t = 5s, PSI max =0.95, PSI min =0.60.

[0137] 1+ω·sin(θ·t)=1+0.3·1=1.3;

[0138]

[0139] IB=4·0.1704·1.3·1.258≈4·0.1704·1.635≈4·0.2788=1.1152A.

[0140] In summary, the current calculated adaptive balancing current is approximately 1.12A. Compared with the static control current, it has the following characteristics: the TPF reflects the effectiveness of the path, ensuring that current distribution prioritizes high-quality paths; the fluctuation term gives the control a "soft start" feature, improving the system's dynamic response and stability; the PSI range factor enhances the imbalance identification ability. When the system difference intensifies, the current automatically increases the response.

[0141] In some embodiments, the loss compensation module 204 is further configured to:

[0142] constructing an exponential compensation component related to the adaptive balancing current according to the compensation intensity coefficient, the adaptive balancing current and the reference current;

[0143] The cosine modulation term is obtained by correlating the path optimization result with the compensation angle parameter through trigonometric functions;

[0144] The efficiency loss compensation coefficient is obtained by adjusting the exponential compensation component and the cosine modulation term based on the ratio of the average state evaluation index to the maximum state index.

[0145] Among them, the efficiency loss compensation coefficient can be expressed as:

[0146]

[0147] Where CF is the efficiency loss compensation coefficient, ρ is the compensation intensity coefficient, IB is the adaptive balancing current, and I ref is the reference current, φ is the compensation angle parameter, PSI avg is the mean status assessment index, PSI max is the maximum state evaluation index, and TPF is the path optimization result.

[0148] In the specific implementation, CF is the efficiency loss compensation coefficient, which is used to adjust the energy efficiency compensation in the balance control process. ρ is the compensation intensity coefficient, which is the basic coefficient of the control compensation capability, with a unit of 1. ref is the reference current, which is the base current set by the system, and the unit is A. φ is the compensation angle parameter, which controls the cosine modulation frequency or compensates for periodic changes, and the unit is rad -1 TPF is the result of path optimization, which represents the quality of path adjustment and is dimensionless. avg Is the average PSI value of all cells in the system, which is the arithmetic mean of the PSI of all cells in the system. PSI maxIt is the maximum state evaluation index, which is used for normalization calculation and suppression of over-compensation.

[0149] It is a nonlinear enhancement mechanism. This is the exponential compensation component. The larger IB is, the more pronounced the compensation becomes. Similar to a saturation curve, this term prevents overcompensation during low current stages. cos(φ·TPF) is the cosine modulation term, which introduces periodic adjustments due to path variations and prevents certain paths from being over-amplified due to excessively high TPF. It is a PSI normalized term used to dynamically adjust the compensation intensity. The more unbalanced the system is, the smaller the value will be, and the compensation intensity will decrease, reflecting the actual energy efficiency capability.

[0150] Assume the following parameters:

[0151] ρ=1.2,IB=1.1152A,I ref =4A, φ=π, TPF=0.1704, PSI avg =0.78, PSI max =0.95.

[0152] The calculation process is as follows:

[0153]

[0154] cos(φ·TPF)=cos(π·0.1704)=cos(0.535)≈0.860;

[0155]

[0156] CF=1.2·0.2434·0.860·0.8211≈1.2·0.2434·0.706=1.2·0.1719=0.2063;

[0157] In summary, the compensation coefficient CF≈0.2063 calculated in this example of the present invention indicates that the balanced current in the final control strategy will be amplified by about 20.63% to offset the path loss and energy efficiency unevenness. This dynamic compensation mechanism enables the system to: not over-amplify when the current is small, maintaining system stability; moderately suppress compensation through cosine modulation when the transmission path is unfavorable; and when the overall system state is poor (PSI avg Low) automatically reduces compensation to prevent overload.

[0158] In some embodiments, the active balancing module 205 is further configured to:

[0159] The inverse trigonometric function is introduced to perform nonlinear mapping on the ratio of the path optimization result to the average state evaluation index to obtain the nonlinear adjustment term.

[0160] The time decay exponential function and the product of the efficiency loss compensation coefficient and the adaptive balancing current are superimposed on the nonlinear adjustment term to obtain the balancing control signal.

[0161] Among them, the equalization control signal can be expressed as:

[0162]

[0163] Where FBC is the balancing control signal, σ is the control gain coefficient, τ is the time decay coefficient, IB is the adaptive balancing current, CF is the efficiency loss compensation coefficient, and t is the time variable.

[0164] In the specific implementation, FBC control is ultimately applied to the current scheduling signal of the system, CF represents the dynamically adjusted energy compensation value, and σ is used to amplify the sensitivity of the control response. The ratio of the path optimization intensity to the overall state of the battery cell controls the reaction intensity, τ controls the attenuation of the control amount over time, and t is the current time point in seconds.

[0165] IB·CF refers to the basic balancing current after compensation adjustment. is a nonlinear adjustment term. The more aggressive the path optimization, the more sensitive the output. exp(-τ·t) is a time-decaying exponential function that causes the system to gradually approach a steady state over time.

[0166] Assume that the aforementioned parameters are known:

[0167] IB=1.1152A, CF=0.2063, σ=1.5, TPF=0.1704, PSI avg =0.78, τ=0.02, t=5s.

[0168] The calculation process is as follows:

[0169]

[0170] exp(-τ·t)=exp(-0.02·5)=exp(-0.1)≈0.9048;

[0171] FBC=1.1152·0.2063·1.3225·0.9048=0.2751;

[0172] The final balanced control amount FBC=0.2751, with the unit being A or an equivalent control signal), indicating that the system currently outputs a unit control strength of approximately 0.2751 at the 5th second.

[0173] In summary, the present invention utilizes dynamic nonlinear response: arctan control of the ratio variation avoids overly aggressive control. It also exhibits temporal stability, introducing an exponential decay term to ensure stable system convergence. This invention achieves a closed-loop compensation coordination mechanism, which can be integrated with IB and CF to form a complete scheduling control chain.

[0174] In some embodiments, the active balancing module 205 is further configured to:

[0175] The surface temperature distribution of each battery cell is collected in real time through a wireless sensor network;

[0176] Introducing temperature gradient data into the path optimization function and adding thermal equilibrium constraints;

[0177] A temperature compensation term is added to the comprehensive status index.

[0178] In specific implementation, through the deployment of wireless temperature sensor nodes, the system can monitor the temperature distribution on the surface of each battery cell in real time, obtain thermal status data, and provide a basis for subsequent control.

[0179] A temperature gradient term can be embedded in the path optimization function (TPF) as an additional adjustment factor. If the cell temperature corresponding to a path is too high, the energy transmission priority of that path is reduced, thereby avoiding heat accumulation and improving system safety.

[0180] Temperature related compensation items (such as ), making the cell status assessment more comprehensive, taking into account both electrical and thermal performance, thereby improving the accuracy and reliability of the balancing strategy.

[0181] See also Figure 3 , provides a flow chart of an active balancing method for a BMS of the present invention, comprising the following steps:

[0182] Step 301: Evaluate the status of each battery cell in the BMS and generate a status evaluation index for each battery cell;

[0183] Step 302: Optimize the energy transmission path according to the state evaluation index of each battery cell and generate a path optimization result;

[0184] Step 303: Calculate an adaptive balancing current based on the state evaluation index of each battery cell and the path optimization result;

[0185] Step 304: Compensate the BMS for efficiency loss based on the adaptive balancing current and the path optimization result, and determine a corresponding efficiency loss compensation coefficient;

[0186] Step 305 : Generate a balancing control signal for the BMS based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor.

[0187] It should be noted that, for the specific embodiments and beneficial effects of the above steps 301-305, please refer to the above description of modules 201-205, which will not be repeated here.

[0188] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0189] Performing a status assessment on each battery cell in the BMS to generate a status assessment index for each battery cell;

[0190] Optimizing the energy transmission path according to the state evaluation index of each battery cell and generating a path optimization result;

[0191] Calculating an adaptive balancing current based on a state evaluation index of each of the battery cells and the path optimization result;

[0192] Performing efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determining a corresponding efficiency loss compensation coefficient;

[0193] A balancing control signal for the BMS is generated based on the efficiency loss compensation coefficient, the path optimization result, and a time decay factor.

[0194] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0195] Performing a status assessment on each battery cell in the BMS to generate a status assessment index for each battery cell;

[0196] Optimizing the energy transmission path according to the state evaluation index of each battery cell and generating a path optimization result;

[0197] Calculating an adaptive balancing current based on a state evaluation index of each of the battery cells and the path optimization result;

[0198] Performing efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determining a corresponding efficiency loss compensation coefficient;

[0199] A balancing control signal for the BMS is generated based on the efficiency loss compensation coefficient, the path optimization result, and a time decay factor.

[0200] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0201] It will be understood by those skilled in the art that embodiments of the present invention may be provided as systems, methods, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0202] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0203] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0205] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0206] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An active balancing system for BMS, characterized in that: The system comprises: A status assessment module is used to assess the status of each battery cell in the BMS and generate a status assessment index for each battery cell; A path optimization module, configured to optimize the energy transmission path according to the state evaluation index of each battery cell and generate a path optimization result; A current balancing module, configured to calculate an adaptive balancing current based on a state evaluation index of each battery cell and the path optimization result; a loss compensation module, configured to perform efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determine a corresponding efficiency loss compensation coefficient; An active balancing module is configured to generate a balancing control signal for the BMS based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor.

2. The active balancing system for BMS according to claim 1, characterized in that: The status assessment module is further configured to: Collecting the cell voltage, voltage change rate and state of charge of each cell; Performing a square operation on the ratio of the cell voltage to the corresponding reference voltage to obtain the first evaluation factor; performing an exponential operation on the absolute value of the voltage change rate as a second evaluation factor; performing a nonlinear trigonometric function transformation on the state of charge to obtain a third evaluation factor; The state evaluation index is generated by weighted combination of the first evaluation factor, the second evaluation factor, and the third evaluation factor.

3. The active balancing system for BMS according to claim 2, characterized in that: The path optimization module is also used for: Obtain the difference between the maximum state evaluation index and the current state index of each path; Introducing a dynamic adjustment coefficient to perform real-time correction on the transmission efficiency of each path; Comprehensively scoring each of the paths by combining an exponential decay function of the energy transmission distance of each path to obtain a path attenuation term; The path optimization result is determined by the difference between the paths, the corrected transmission efficiency and the path attenuation item.

4. The active balancing system for BMS according to claim 3, characterized in that: The current balancing module is further configured to: The product of the reference current coefficient and the path optimization result is used as the basic current; Obtain the periodic disturbance term representing the periodic fluctuation component; Performing nonlinear gain adjustment according to the ratio of the maximum state evaluation index to the minimum state index to obtain a system imbalance amplification factor; The periodic disturbance term and the system imbalance amplification factor are superimposed on the basic current to obtain the adaptive balancing current.

5. The active balancing system for BMS according to claim 4, characterized in that: The loss compensation module is further configured to: constructing an exponential compensation component related to the adaptive balancing current according to a compensation intensity coefficient, the adaptive balancing current and a reference current; Associating the path optimization result with the compensation angle parameter through trigonometric functions to obtain a cosine modulation term; The exponential compensation component and the cosine modulation term are adjusted based on a ratio of an average state evaluation index to the maximum state index to obtain the efficiency loss compensation coefficient.

6. The active balancing system for BMS according to claim 5, characterized in that: The active balancing module is further configured to: Introducing an inverse trigonometric function to perform nonlinear mapping on the ratio of the path optimization result to the average state evaluation index to obtain a nonlinear adjustment term; The time decay exponential function and the product of the efficiency loss compensation coefficient and the adaptive balancing current are superimposed on the nonlinear adjustment item to obtain the balancing control signal.

7. The active balancing system for BMS according to claim 6, characterized in that: The active balancing module is further configured to: The surface temperature distribution of each battery cell is collected in real time through a wireless sensor network; Introducing temperature gradient data into the path optimization function and adding thermal equilibrium constraints; A temperature compensation term is added to the comprehensive status index.

8. An active balancing method for BMS, which implements the system according to claim 1, characterized in that: The method comprises: Performing a status assessment on each battery cell in the BMS to generate a status assessment index for each battery cell; Optimizing the energy transmission path according to the state evaluation index of each battery cell and generating a path optimization result; Calculating an adaptive balancing current based on a state evaluation index of each of the battery cells and the path optimization result; Performing efficiency loss compensation on the BMS based on the adaptive balancing current and the path optimization result, and determining a corresponding efficiency loss compensation coefficient; A balancing control signal for the BMS is generated based on the efficiency loss compensation coefficient, the path optimization result, and a time decay factor.

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