Active equalization system for a BMS

Abstract

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

Description

Technical Field

[0001] This invention relates to the manufacture of charging, battery swapping and hydrogen refueling facilities, and more particularly to the field of battery management technology, specifically to an active balancing system, method, electronic device and non-transitory computer-readable storage medium for a battery management system. Background Technology

[0002] Currently, in Battery Management Systems (BMS), to improve the efficiency of battery packs and extend their lifespan, it is usually necessary to balance the voltage differences between individual cells. The widely used active balancing methods mainly include inductive energy transfer, capacitive energy transfer, and transformer-coupled energy transfer.

[0003] However, traditional inductive or capacitive equalization circuits are complex and costly. In multi-cell systems, the energy path scheduling and control algorithms are complex, making it difficult to achieve fast and efficient equalization. On the other hand, some active equalization methods suffer additional energy loss during energy transmission, making it impossible to balance efficiency and response speed. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing an active load balancing system, method, electronic device, and non-transitory computer-readable storage medium for BMS that can improve the accuracy and efficiency of BMS load balancing.

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

[0006] This invention provides an active load balancing system for BMS, the system comprising:

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

[0008] The path optimization module is used to optimize the energy transmission path based on the state assessment index of each battery cell and generate path optimization results.

[0009] The current balancing module is used to calculate the adaptive balancing current based on the state assessment index of each cell and the path optimization result.

[0010] The loss compensation module is used to perform efficiency loss compensation on the BMS based on the adaptive equalization current and the path optimization result, and to determine the corresponding efficiency loss compensation coefficient.

[0011] An active balancing module is used 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 state assessment module is also used for:

[0013] Collect the cell voltage, voltage change rate, and state of charge of each of the aforementioned cells;

[0014] The ratio of the cell voltage to the corresponding reference voltage is squared and used as the first evaluation factor;

[0015] The absolute value of the voltage change rate is exponentially calculated and used as a second evaluation factor;

[0016] The charge state is subjected to a nonlinear trigonometric function transformation, which serves as a third evaluation factor.

[0017] The state assessment index is generated by weighting and combining the first assessment factor, the second assessment factor, and the third assessment factor.

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

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

[0020] A dynamic adjustment coefficient is introduced to correct the transmission efficiency of each path in real time.

[0021] The path attenuation term is obtained by comprehensively scoring each path based on the exponential decay function of the energy transmission distance of each path;

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

[0023] Furthermore, the current balancing module is also used for:

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

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

[0026] The nonlinear gain is adjusted based on the ratio of the maximum state evaluation index to the minimum state index to obtain the system imbalance amplification factor.

[0027] The adaptive equilibrium current is obtained by superimposing the periodic disturbance term and the system imbalance amplification factor onto the base current.

[0028] Furthermore, the loss compensation module is also used for:

[0029] Based on the compensation intensity coefficient, the adaptive equalization current, and the reference current, an exponential compensation component related to the adaptive equalization current is constructed.

[0030] The cosine modulation term is obtained by relating the path optimization result with the compensation angle parameter using trigonometric functions.

[0031] The efficiency loss compensation coefficient is obtained by adjusting the index compensation component and the cosine modulation term based on the ratio of the average state evaluation index to the maximum state index.

[0032] Furthermore, the active balancing module is also used for:

[0033] An inverse trigonometric function is introduced to perform a nonlinear mapping on the ratio of the path optimization result to the average state evaluation index, resulting in a nonlinear adjustment term;

[0034] The equalization control signal is obtained by superimposing the time decay exponential function, the efficiency loss compensation coefficient, and the adaptive equalization current onto the nonlinear adjustment term.

[0035] Furthermore, the active balancing module is also used for:

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

[0037] Introduce temperature gradient data into the path optimization function to add thermal equilibrium constraints.

[0038] A temperature compensation term is added to the overall condition index.

[0039] This invention also provides an active load balancing method for BMS, the method comprising:

[0040] The status of each cell in the BMS is assessed, and a status assessment index for each cell is generated.

[0041] Optimize the energy transfer path based on the state assessment index of each cell, and generate path optimization results;

[0042] Based on the state assessment index of each cell and the path optimization results, the adaptive balancing current is calculated.

[0043] Based on the adaptive equalization current and the path optimization results, the efficiency loss compensation of the BMS is performed to determine the corresponding efficiency loss compensation coefficient.

[0044] Based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor, an equalization control signal for the BMS is generated.

[0045] Furthermore, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby implementing an active load balancing method for a BMS as described above.

[0046] Furthermore, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements an active load balancing method for a BMS as described above.

[0047] The beneficial effects of this invention are:

[0048] (1) This invention introduces the PSI index, which integrates three dimensions of information: voltage level, rate of change and SOC; it considers nonlinearity and dynamic changes, making the state assessment more sensitive and accurate; it is conducive to identifying potential unbalanced cells in advance and improving the quality of judgment before equalization.

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

[0050] (3) This invention introduces a periodic disturbance term and a PSI range factor; the balanced current is dynamically adjusted, which can quickly respond to sudden changes in state; it ensures the response speed and avoids excessive intervention that could lead to energy waste or cell aging.

[0051] In summary, this 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.). Attached Figure Description

[0052] Figure 1 A scenario diagram illustrating an active load balancing method for a BMS provided by this invention;

[0053] Figure 2 This invention provides a schematic diagram of an active balancing system for a BMS.

[0054] Figure 3 A flowchart of an active load 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 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1 , Figure 1 This is a scenario diagram illustrating an active balancing method for a battery management system (BMS) provided by the present invention. The active balancing system for a BMS of the present invention pertains to the manufacture of charging, battery swapping, and hydrogen refueling facilities. Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. The terminal can include, but is not limited to, portable devices such as mobile phones and tablets with various network platform applications installed, as well as fixed terminals such as computers, kiosks, and advertising machines. The server provides users with various business services, including service push servers and user recommendation servers.

[0059] It should be noted that, Figure 1 The scenario diagram illustrating an active load balancing method for a BMS is merely an example. The terminals, servers, and application scenarios described in this embodiment are intended to more clearly illustrate the technical solutions of this embodiment and do not constitute a limitation on the technical solutions provided by this embodiment. As those skilled in the art will recognize, with the evolution of systems and the emergence of new business scenarios, the technical solutions provided by this embodiment are equally applicable to similar technical problems.

[0060] The terminal can be used for:

[0061] The status of each cell in the BMS is assessed, and a status assessment index for each cell is generated.

[0062] Optimize the energy transfer path based on the state assessment index of each cell, and generate path optimization results;

[0063] Based on the state assessment index of each cell and the path optimization results, the adaptive balancing current is calculated.

[0064] Based on the adaptive equalization current and the path optimization results, the efficiency loss compensation of the BMS is performed to determine the corresponding efficiency loss compensation coefficient.

[0065] Based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor, an equalization control signal for the BMS is generated.

[0066] Please see Figure 2 , Figure 2This is a schematic diagram of an active balancing system for BMS provided by the present invention.

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

[0068] The status assessment module 201 is used to assess the status of each cell in the BMS and generate a status assessment index for each cell.

[0069] Path optimization module 202 is used to optimize the energy transmission path according to the state evaluation index of each battery cell and generate path optimization results;

[0070] The current balancing module 203 is used to calculate the adaptive balancing current based on the state evaluation index of each of the battery cells and the path optimization results.

[0071] The loss compensation module 204 is used to perform efficiency loss compensation on the BMS based on the adaptive equalization current and the path optimization result, and to determine the corresponding efficiency loss compensation coefficient.

[0072] The active balancing module 205 is used 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 state assessment module 201 is also used for:

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

[0075] The square of the ratio of the cell voltage to the corresponding reference voltage is used as the first evaluation factor.

[0076] The absolute value of the voltage change rate is exponentially calculated and used as a second evaluation factor;

[0077] The state of charge is transformed by a nonlinear trigonometric function and used as a third evaluation factor.

[0078] A state assessment index is generated by weighting and combining the first, second, and third assessment factors.

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

[0080]

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

[0082] Specifically, It is the first evaluation factor, representing the normalized energy intensity of the cell voltage under the reference standard, reflecting whether the current voltage of the cell is close to the design optimal voltage, and the square term enhances the penalty for high voltage deviation.

[0083] It is the second evaluation factor, capturing the degree of voltage change; when the cell is under high load, fast charging or abnormal state, the voltage change is large, and this factor is rapidly amplified; indicating the cell stability risk.

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

[0085] α, β, and γ control the weights of the three physical quantities 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] The parameters are set as follows:

[0087] V c =3.65V, V ref =3.70V, SOC = 0.6, α, β, γ are 0.5, ...

[0088] For 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 weighting configuration). The voltage deviation is small (relatively healthy), the rate of change is moderate (cell stable), the SOC is at a medium-high level, and overall it is in good condition.

[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 for each path;

[0094] A dynamic adjustment coefficient is introduced to correct the transmission efficiency of each path in real time.

[0095] By combining the exponential decay function of the energy transmission distance of each path, a comprehensive score is obtained for each path, resulting in a path decay term;

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

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

[0098]

[0099] Where TRF is the path optimization result, PSI is the path optimization result. max It is the Maximum State Assessment Index, PSI i η is the current PSI value of the target cell on the i-th path. i D is the transmission efficiency of the i-th path. i λ is the energy transmission distance of the i-th path. i It is the dynamic adjustment coefficient for the i-th path, k i It is the signal / energy attenuation coefficient of the i-th path.

[0100] In specific implementation, (PSI) max -PSI i η represents the state difference between the current i-th target cell and the cell with the best system state; the larger the difference, the more "lagging" the target cell is on that path, and the more energy it needs; this provides a basis for priority energy scheduling. i This represents the energy retention ratio of the i-th path during transmission; the closer it is to 1, the smaller the loss. exp(-k i ·D i ) is the path attenuation term, the path loss factor, which varies with the transmission distance D. i Increases and decays exponentially; decay coefficient k i It can be dynamically set according to factors such as circuit material, temperature, and impedance. λ i It 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 there are 3 transmission paths (i = 1, 2, 3), as shown in the table below:

[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 this invention, the current system path optimization function value is 0.1704, which will be used in the next step to calculate the equalization current IB. From the contribution of each path, path 2 has the highest weight (0.0794), indicating that this path has the greatest transmission significance. Although it is inefficient and has a long distance, it has the largest target cell state difference. This function reflects multi-dimensional weighting characteristics, which helps the system to achieve fine-grained scheduling and minimum loss equalization based on 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 is adjusted based on the ratio of the maximum state evaluation index to the minimum state index to obtain the system imbalance amplification factor.

[0123] By superimposing a periodic disturbance term and a system imbalance amplification factor onto the base current, an adaptive equilibrium current is obtained.

[0124] The adaptive balancing current can be expressed as:

[0125]

[0126] Where IB is the adaptive equalization current, μ is the reference current coefficient, ω is the fluctuation adjustment factor, controlling the amplitude of the periodic disturbance, θ is the sine wave phase coefficient, t is the time variable, and PSI is the constant. min It is the minimum PSI value of the battery cell in the system.

[0127] Specifically, IB stands for adaptive equalization current, which is the dynamic equalization current of the actual output.

[0128] μ is the reference current coefficient, which represents 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 disturbances and the amplitude of sinusoidal disturbances. Its value is generally between 0 and 1.

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

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

[0133] PSI min It is the minimum PSI value of the battery cell in the system.

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

[0135] Set 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 to the static control current, it has the following characteristics: the TPF reflects the effectiveness of the path, ensuring that the current distribution prioritizes the best path; the fluctuation term gives the control a "soft start" characteristic, improving the dynamic response and stability of the system; the PSI range factor enhances the imbalance identification capability, and the current automatically increases the response when the system difference intensifies.

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

[0142] Based on the compensation intensity coefficient, adaptive equalization current and reference current, an exponential compensation component related to the adaptive equalization current is constructed.

[0143] The cosine modulation term is obtained by combining the path optimization results of trigonometric functions with the compensation angle parameters.

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

[0145] 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 This is the reference current, φ is the compensation angle parameter, and PSI. avg It is the Average State Assessment Index, PSI max It is the maximum state evaluation index, and TPF is the path optimization result.

[0148] In practical implementation, CF is the efficiency loss compensation coefficient, used to adjust energy efficiency compensation during the equalization control process. ρ is the compensation intensity coefficient, the fundamental coefficient of control compensation capability, with a unit of 1. ref φ is the reference current, the system's set base current, measured in amperes (A). φ is the compensation angle parameter, controlling the cosine modulation frequency or compensating for periodic changes, measured in rads. -1 TPF is the result of path optimization, characterizing the quality of path adjustment, and its unit is dimensionless. PSI avg It is the average PSI value of all cells in the system, which is the arithmetic mean of the PSI values ​​of all cells in the system. PSI maxIt is the maximum state evaluation index, used for normalization calculations to suppress overcompensation.

[0149] It is a nonlinear enhancement mechanism. It is the exponential compensation component. The larger IB is, the more obvious the compensation; similar to a saturation curve, it avoids overcompensation in the low current stage. cos(φ·TPF) is the cosine modulation term, which introduces periodic adjustment due to path changes; it prevents some paths from being over-amplified due to excessively high TPF. It is a PSI term used to dynamically adjust the compensation intensity. The more unbalanced the system, the smaller this value, and the lower the compensation intensity will be, reflecting the actual energy efficiency capability.

[0150] Assume the parameters are as follows:

[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 equalization current in the final control strategy will be amplified by approximately 20.63% to offset path losses and uneven energy efficiency. This dynamic compensation mechanism enables the system to: avoid excessive amplification when the current is low, maintaining system stability; moderately suppress compensation through cosine modulation when the transmission path is unfavorable; and maintain stability even when the overall system state is poor (PSI). avg (Low) Automatic reduction compensation to prevent overload.

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

[0159] By introducing an inverse trigonometric function to nonlinearly map the ratio of the path optimization result to the average state evaluation index, a nonlinear adjustment term is obtained.

[0160] The equalization control signal is obtained by superimposing the time decay exponential function, the efficiency loss compensation coefficient, and the adaptive equalization current onto the nonlinear adjustment term.

[0161] The equalization control signal can be expressed as:

[0162]

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

[0164] In practice, FBC control is ultimately applied to the system's current dispatch signal, where CF represents the dynamically adjusted energy compensation value, and σ is used to amplify the sensitivity of the control response. The ratio of path optimization intensity to the overall state of the cell controls the response intensity. τ controls the decay of the control quantity over time, where t is the current time point and the unit is seconds.

[0165] IB·CF refers to the base equalization current after compensation adjustment. It 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, causing the system to gradually approach a steady state over time.

[0166] Assuming 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 equalization control quantity FBC = 0.2751 (in A or equivalent control signal), which means that the system output at the 5th second is approximately 0.2751 unit control strength.

[0173] In summary, this invention utilizes dynamic nonlinear response: controlling the amplitude of the ratio change through arctan control to avoid over-excitation. This invention exhibits time stability, introducing an exponential decay term to ensure stable system convergence. This invention achieves a compensated collaborative closed loop, which can be linked with IB and CF to form a complete scheduling and 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] Introduce temperature gradient data into the path optimization function to add thermal equilibrium constraints.

[0177] A temperature compensation term is added to the overall condition index.

[0178] In practice, the system can monitor the temperature distribution on the surface of each battery cell in real time and obtain thermal state data through the deployment of wireless temperature sensor nodes, providing 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 certain path is too high, its energy transfer priority is reduced, thereby avoiding heat accumulation and improving system safety.

[0180] Temperature-related compensation terms (such as...) can be added to the PSI (Condition Assessment Index). This allows for a more comprehensive assessment of cell condition, taking into account both electrical and thermal performance, thereby improving the accuracy and reliability of the balancing strategy.

[0181] Please see Figure 3 The present invention provides a flowchart of an active load balancing method for BMS, comprising the following steps:

[0182] Step 301: Perform a status assessment on each cell in the BMS and generate a status assessment index for each cell;

[0183] Step 302: Optimize the energy transmission path based on the state assessment index of each cell, and generate path optimization results;

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

[0185] Step 304: Based on the adaptive equalization current and the path optimization results, perform efficiency loss compensation on the BMS and determine the corresponding efficiency loss compensation coefficient;

[0186] Step 305: Based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor, generate an equalization control signal for the BMS.

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

[0188] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... 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, it performs the following steps:

[0189] The status of each cell in the BMS is assessed, and a status assessment index for each cell is generated.

[0190] Optimize the energy transfer path based on the state assessment index of each cell, and generate path optimization results;

[0191] Based on the state assessment index of each cell and the path optimization results, the adaptive balancing current is calculated.

[0192] Based on the adaptive equalization current and the path optimization results, the efficiency loss compensation of the BMS is performed to determine the corresponding efficiency loss compensation coefficient.

[0193] Based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor, an equalization control signal for the BMS is generated.

[0194] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... 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, it performs the following steps:

[0195] The status of each cell in the BMS is assessed, and a status assessment index for each cell is generated.

[0196] Optimize the energy transfer path based on the state assessment index of each cell, and generate path optimization results;

[0197] Based on the state assessment index of each cell and the path optimization results, the adaptive balancing current is calculated.

[0198] Based on the adaptive equalization current and the path optimization results, the efficiency loss compensation of the BMS is performed to determine the corresponding efficiency loss compensation coefficient.

[0199] Based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor, an equalization control signal for the BMS is generated.

[0200] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0201] Those skilled in the art will understand that embodiments of the present invention can be provided as systems, methods, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0202] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0203] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0205] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0206] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An active load balancing system for BMS, characterized in that, The system includes: The status assessment module is used to assess the status of each cell in the BMS and generate a status assessment index for each cell. The path optimization module is used to optimize the energy transmission path based on the state assessment index of each cell, generate path optimization results, and is also used to: obtain the difference between the maximum state assessment index and the current state index of each path; introduce a dynamic adjustment coefficient to correct the transmission efficiency of each path in real time; combine the exponential decay function of the energy transmission distance of each path to perform a comprehensive score on each path, and obtain a path decay term; determine the path optimization result through the difference of each path, the corrected transmission efficiency, and the path decay term. The path optimization result is expressed as: ;in, It is the result of path optimization. It is the maximum state assessment index. It is the current PSI value of the target cell on the i-th path. It is the transmission efficiency of the i-th path. It is the energy transmission distance of the i-th path. It is the dynamic adjustment coefficient of the i-th path. Signal / energy attenuation coefficient for the i-th path; The current balancing module is used to calculate the adaptive balancing current based on the state assessment index of each cell and the path optimization result. The loss compensation module is used to perform efficiency loss compensation on the BMS based on the adaptive equalization current and the path optimization result, and to determine the corresponding efficiency loss compensation coefficient. An active balancing module is used 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 load balancing system for BMS according to claim 1, characterized in that, The status assessment module is also used for: Collect the cell voltage, voltage change rate, and state of charge of each of the aforementioned cells; The ratio of the cell voltage to the corresponding reference voltage is squared and used as the first evaluation factor; The absolute value of the voltage change rate is exponentially calculated and used as a second evaluation factor; The charge state is subjected to a nonlinear trigonometric function transformation, which serves as a third evaluation factor. The state assessment index is generated by weighting and combining the first assessment factor, the second assessment factor, and the third assessment factor.

3. The active load balancing system for BMS according to claim 1, characterized in that, The current balancing module is also used for: The base current is the product of the reference current coefficient and the path optimization result; Obtain the periodic disturbance term representing the periodic fluctuation component; The nonlinear gain is adjusted based on the ratio of the maximum state evaluation index to the minimum state index to obtain the system imbalance amplification factor. The adaptive equilibrium current is obtained by superimposing the periodic disturbance term and the system imbalance amplification factor onto the base current.

4. The active load balancing system for BMS according to claim 3, characterized in that, The loss compensation module is also used for: Based on the compensation intensity coefficient, the adaptive equalization current, and the reference current, an exponential compensation component related to the adaptive equalization current is constructed. The cosine modulation term is obtained by relating the path optimization result with the compensation angle parameter using trigonometric functions. The efficiency loss compensation coefficient is obtained by adjusting the index compensation component and the cosine modulation term based on the ratio of the average state evaluation index to the maximum state index.

5. The active load balancing system for BMS according to claim 4, characterized in that, The active balancing module is also used for: An inverse trigonometric function is introduced to perform a nonlinear mapping on the ratio of the path optimization result to the average state evaluation index, resulting in a nonlinear adjustment term; The equalization control signal is obtained by superimposing the time decay exponential function, the efficiency loss compensation coefficient, and the adaptive equalization current onto the nonlinear adjustment term.

6. The active load balancing system for BMS according to claim 5, characterized in that, The active balancing module is also used for: The surface temperature distribution of each battery cell is collected in real time through a wireless sensor network. Introduce temperature gradient data into the path optimization function to add thermal equilibrium constraints. A temperature compensation term is added to the overall condition index.

7. An active load balancing method for a BMS, wherein the method implements the system as described in claim 1, characterized in that, The method includes: performing a status assessment on each cell in the BMS and generating a status assessment index for each cell; The energy transmission path is optimized based on the state assessment index of each battery cell, generating path optimization results. This includes: obtaining the difference between the maximum state assessment index and the current state index for each path; introducing a dynamic adjustment coefficient to correct the transmission efficiency of each path in real time; comprehensively scoring each path by combining the exponential decay function of the energy transmission distance of each path to obtain a path decay term; and determining the path optimization result using the difference between each path, the corrected transmission efficiency, and the path decay term. The path optimization result is expressed as follows: in, It is the result of path optimization. It is the maximum state assessment index. It is the current PSI value of the target cell on the i-th path. It is the transmission efficiency of the i-th path. It is the energy transmission distance of the i-th path. It is the dynamic adjustment coefficient of the i-th path. Signal / energy attenuation coefficient for the i-th path; Based on the state assessment index of each cell and the path optimization results, the adaptive balancing current is calculated. Based on the adaptive equalization current and the path optimization results, the efficiency loss compensation of the BMS is performed to determine the corresponding efficiency loss compensation coefficient. Based on the efficiency loss compensation coefficient, the path optimization result, and the time decay factor, an equalization control signal for the BMS is generated.

Images