Battery module management system design method based on monomer high-sensitivity parameter estimation technology

Abstract

The invention relates to a battery module management system design method based on a monomer high-sensitivity parameter estimation technology, and belongs to the technical field of electric vehicle battery management, and the method comprises the following steps: S1, constructing a multi-level battery management system: designing a variable topological structure of a series-parallel circuit in a battery module; designing an MPC control strategy for hierarchical adaptive control of a single cell in the battery module; s2, based on the multi-level battery management system, establishing an online parameter identification method of a battery cell level, and meanwhile, realizing a dynamic equalization process of a module level under any environmental condition; and S3, designing and configuring a peripheral module, and constructing a complete module-cell multi-level cooperative control battery management system.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle battery management and relates to a battery module management system design method based on single cell high-sensitivity parameter estimation technology. Background Art

[0002] With electric vehicles now highly industrialized, transportation electrification is gradually expanding from initial pilot cities to nationwide. Battery systems must cope with alternating current and voltage loads, as well as environmental and internal heat loads. Battery management systems (BMSs) must monitor the multi-physical state of batteries in real time, efficiently, and accurately to cope with increasingly complex operating environments.

[0003] Building on years of advancements in battery management technology, today's BMSs now offer basic real-time sensing of the battery's internal state, including the overall state of charge (SoC) of the battery pack, temperatures at key nodes, and relatively accurate state of health (SoH). However, in the context of new developments and the longer-term prospects of the new energy industry, traditional BMSs face two challenges. First, the design and management strategies and BMS hardware architectures typically only provide effective battery management in specific environments. When the environment and operating conditions fluctuate drastically, overall battery system management is suboptimal. Second, when faced with multi-parameter coupled state estimation, traditional BMSs often overlook the interactions between different parameters, and traditional BMS architectures lack support for detailed parameter identification. Consequently, due to limitations in the battery management system architecture and the number of sensors, thermal management systems for large battery modules typically only monitor the overall state of the battery module, failing to provide detailed online sensing and identification at the individual cell level. Even if a cell problem is detected, adjustments are limited to a single cell within the module. Furthermore, due to the limitations of the battery system's physical architecture, traditional BMSs lack the necessary technology to decouple the coupled parameters during identification. This structural flaw also limits the adaptive development of management strategies, significantly weakening the functionality of the battery management system.

[0004] Based on the current application background of battery management systems and the difficulties they face, there is an urgent need for a parameter identification technology that is highly sensitive to any parameter and can be embedded in large-scale series-parallel coexisting battery modules with structural adaptability to achieve high-precision monitoring and management of battery cells in the module. Summary of the Invention

[0005] In view of this, an object of the present invention is to provide a battery module management system based on single cell high-sensitivity parameter estimation technology.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A battery module management system design method based on single cell high-sensitivity parameter estimation technology includes the following steps:

[0008] S1: Build a multi-level battery management system, including: designing the variable topology of the series and parallel circuits within the battery module; designing the MPC control strategy for hierarchical adaptive control of individual battery cells within the battery module;

[0009] S2: Based on the multi-level battery management system, an online parameter identification method at the battery cell level is established, and a dynamic balancing process at the module level is realized under any environmental conditions;

[0010] S3: Design and configure peripheral modules to build a complete battery management system with multi-level coordinated control of modules and cells.

[0011] Furthermore, the design of the variable topology structure of the series-parallel circuit inside the battery module includes the following steps:

[0012] S11: Design an isolated DC-DC circuit and implement the functions of series-parallel conversion within the battery module and adaptively connect each battery in the series circuit to the DC-DC control part; the adaptive selection circuit includes a main circuit external switch tube array and a current and voltage test module; the switch tubes in the main circuit external switch tube array are all MOSFETs or IGBTs with low current and high frequency control; the test module is used to test the voltage and current of each battery cell;

[0013] S12: Design the power electronic parameters in the adaptive control circuit, establish a virtual model, traverse different battery series and parallel situations, and verify the rationality of the parameter design. The power electronic parameters include parameters of the branch series and parallel conversion structure, the main line battery cell active selection structure, and the battery current control unit; the branch series and parallel conversion structure is a switch tube array connected to the battery module cell, and related parameters include the allowable current of the switch tube; the main line battery cell active selection structure is used to actively select a specific battery cell and connect it to the battery current control unit, and related parameters include the allowable current of the switch tube and the impedance of the test component; the battery current control unit is a phase-shifted full-bridge structure including a bidirectional DC-DC structure, and related parameters include parameter selection of the inductive and capacitive components in the DC-DC;

[0014] S13: Build a physical circuit of the virtual model using power electronic components, and conduct preliminary verification of functional feasibility under actual working conditions.

[0015] Furthermore, step S12 specifically includes the following steps:

[0016] S121: Design the main power electronic architecture of the branch series-parallel conversion structure to ensure that the series-parallel conversion between multiple branches of the battery is completed while ensuring the safety of the control unit and the battery cells themselves;

[0017] S122: Based on the battery's own current and voltage limits, ripple limits, and target signal control effect, design the specific parameters of the bidirectional DC-DC control unit, including the allocation and specific parameters of the MOSFET switches, inductors, capacitors, and resistors. The specific parameters of the bidirectional DC-DC control unit include the parameters of the inductor, capacitors, and current-limiting resistor. The basic design range of the inductor is:

[0018]

[0019] Where, d is the duty cycle of the PWM signal, U in is the input voltage of the bidirectional DCDC, ΔI L1 is the design ripple current, f PWM It is to design the PWM signal frequency;

[0020] The basic design range of capacitors is:

[0021]

[0022] Among them I o is the output current of the DCDC structure, ΔU C is the designed ripple voltage across capacitor C, I C is the current flowing through capacitor C, U req is the design effective voltage of capacitor C, satisfying U req >U battery , U battery is the maximum terminal voltage of the battery; the resistor is designed according to the specific circuit characteristics of the bidirectional DCDC

[0023] S123: Verify the feasibility of energy exchange between series-connected battery packs and high-frequency to low-frequency PWM generation in the constructed virtual circuit.

[0024] Furthermore, the MPC control strategy for hierarchical adaptive control of individual cells in the battery module is designed, including a PWM signal and a multi-state coordination control strategy between series-connected batteries, as follows:

[0025] S14: Design a control strategy for the MOSFET components in a bidirectional DC-DC converter, and use proportional-integral (PI) control to provide a specific PWM output method for multiple MOSFET switches.

[0026] S15: Design a dynamic access strategy for the series battery modules and the DCDC control module, use the MPC method to determine the dynamic current requirements of different battery modules, and achieve simultaneous parameter estimation and power balancing;

[0027] S16: Design the switching strategy for the series-parallel connection of the entire battery module.

[0028] Furthermore, step S15 specifically includes the following steps:

[0029] S151: Design the control variables, prediction domain, and control domain of the MPC control process; the control variable of the MPC is the current of each single cell in the series module;

[0030] S152: Design the cost function of the MPC algorithm:

[0031]

[0032] Where J represents the cost function, k is the time count in the prediction domain, and SoC obj Calculate the SoC value of the target battery, SoC avg is the average of the SoC calculated values ​​of all batteries in the series circuit;

[0033] S153: Design the control objectives of the MPC control process:

[0034] SoC obj (k)-SoC avg (k)≤ε

[0035] Among them, ε is the boundary value of the target, k represents the current time step that needs to be controlled;

[0036] The calculation method of battery SoC is as follows:

[0037]

[0038] Furthermore, step S2 specifically includes the following steps:

[0039] S21: Establish a dynamic evaluation algorithm for signal information based on the battery equivalent circuit model to evaluate the upper limit of the signal;

[0040] S22: Based on the calculation method of the lower limit of the signal information amount, the optimal signal frequency and amplitude for online estimation of the electrical parameters of a specific battery are given;

[0041] S23: Establish an online battery electrical parameter estimation algorithm for variable frequency signals.

[0042] Furthermore, step S21 specifically includes the following steps:

[0043] S211: For any battery system, use Bayes' theorem to generally characterize the estimation process of battery parameters:

[0044]

[0045] Among them, θ N is the parameter vector at time N, y is the battery terminal voltage, I is the battery current; θ N Expressed as [θ (1) …θ (i) … θ (M) ]; coefficient k represents time count, N is the upper limit of time count; p(θ N |y 1:N ,I 1:N ) represents the condition that the current and voltage from time 1 to time N are known. N The probability distribution of

[0046] S212: A set of signals (y 1:N ,I 1:N ) contains a parameter θ (i) The amount of information is expressed as:

[0047]

[0048] where σ V is the variance of the test values ​​of the battery voltage, Is the battery voltage calculated by the battery model, the specific coefficient matrix With parameter vector Θ k Determine based on the actual model;

[0049] S213: The upper bound of the amount of information in the actual signal is:

[0050]

[0051] in, Represents the initial estimated variance of the parameter, which is usually derived from experience with the parameter;

[0052] It is used to calculate the specific information content of a signal. After artificially giving a certain upper bound of expected information, the actual upper bound of a signal is represented as:

[0053]

[0054] Furthermore, step S22 specifically includes the following steps:

[0055] S221: Based on the parameter information amount given in step S213, a parameter information amount expression with cross information is established:

[0056]

[0057] S222: After a specific Fourier series signal expression is given, the battery parameter information at any frequency is calculated, and the optimal signal frequency is obtained based on the traversed information curve.

[0058] Furthermore, step S23 specifically includes the following steps:

[0059] S231: Determine a battery online parameter estimation method, and calculate the battery electrical parameters using least squares based on the first-order RC equivalent circuit model of the battery:

[0060]

[0061] θ (1) represents the prior parameter vector, θ (2) is the posterior parameter vector, is a coefficient matrix given by the first-order RC equivalent circuit model of the battery; P(t) represents the covariance matrix in the process of estimating the battery parameter matrix to be identified using the least squares principle; λ represents the forgetting factor;

[0062] S232: Determine the calculation equation and observation equation for the battery state of charge (SOC), implement automatic iteration of the battery SOC over time, and establish a relationship between the SOC and observable variables:

[0063]

[0064] where x t is the actual observation parameter, which is a vector containing SOC; ω t represents the process noise, that is, in x t Error value in the process of automatic update over time; v t Represents the test noise, that is, through x t The error generated in the process of calculating the battery terminal voltage V; calculate the Kalman gain and correct the average battery SOC at the current moment:

[0065]

[0066] P t represents the covariance matrix in the SOC estimation process, is the first updated P at time t t The value of P represents the second updated t The value of P at time t t Output value; K t is the Kalman gain; R w is the process noise ω tThe variance, R v is the test noise v t The variance of e t is the difference between the test terminal voltage and the terminal voltage.

[0067] Further, step S3 specifically includes:

[0068] Design a signal acquisition module in the control board to collect the terminal voltage of each connected battery cell in the series trunk line and indirectly measure the current signal flowing through each battery cell through the set overcurrent resistor. Key parameters that need to be designed include the structural configuration of the signal acquisition module and the resistance value of the overcurrent resistor.

[0069] A driver module designed to drive a switch array in a battery management system, the driver module being capable of outputting a corresponding start-up level according to the type of switch device;

[0070] Design a voltage conversion module suitable for power electronics to convert the external DC power supply voltage into the different voltage levels required by the drive module and the battery management system core chip;

[0071] An electrical isolation module is configured to achieve physical electrical isolation between the high-voltage side and the low-voltage control side of the battery management system, while supporting the safe transmission of signals or energy;

[0072] Select the core chip of the battery management system, which is used to store the control algorithm program and output the switch tube array control signal and high-frequency PWM control signal.

[0073] The beneficial effects of the present invention are as follows: the present invention addresses the problems of poor intervention capability of traditional battery management systems, difficulty in decoupling coupling parameter estimation in traditional BMS structures, and insufficient sensitivity. It develops a battery module management system based on single-cell high-sensitivity parameter estimation technology, designs a power electronic hardware structure with high degrees of freedom for battery modules with series-parallel coupling configurations, realizes accurate and independent control of the access signal of any battery, specifically improves the sensitivity of the signal to any battery parameter, and completes the parallel control of state perception and balance. The specific advantages of the present invention are:

[0074] 1) The present invention realizes independent control of any battery cell in a series-parallel coupled battery module, and can realize independent control of battery cell signals by using only a small number of bidirectional DCDC control units without affecting the main output.

[0075] 2) The present invention can realize the screening and loading of highly sensitive signals of coupling parameters through the DCDC control center, and realize the online accurate identification of multiple parameters of the battery in the physical structure without introducing complex algorithms to increase the computing pressure of the BMS chip.

[0076] 3) The present invention can realize high-precision estimation of battery parameters and energy transfer between cells within the module in parallel. On the one hand, the high-precision estimation improves the accuracy of battery energy balancing and prevents the accumulation of power unevenness within the module. On the other hand, the transfer of energy between different batteries also reduces the energy consumption of the additional signal access process.

[0077] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0079] Figure 1 This is a simplified diagram of the steps for implementing a battery module management system based on a single cell high-sensitivity parameter estimation technology according to the present invention;

[0080] Figure 2 It is a variable series-parallel power electronic structure for multi-branch modules;

[0081] Figure 3 A schematic diagram of a bidirectional DCDC control unit and its internal structure;

[0082] Figure 4 This is a schematic diagram of the power electronics hardware module;

[0083] Figure 5 are the battery parameter estimation results, where (a) is the parameter estimation result under general low-power conditions, and (b) is the parameter identification result under short-term high-power conditions. DETAILED DESCRIPTION

[0084] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0085] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0086] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0087] Example 1:

[0088] like Figure 1 As shown, the present invention provides a battery module management system design method based on single cell high-sensitivity parameter estimation technology, designs a variable battery management system hardware structure and its related battery management algorithm, establishes high-information signal access for coupling parameters and high-sensitivity parameter online identification technology, and realizes the control of any single cell in a large battery module. The specific steps are as follows:

[0089] S1: Design the variable topology of the series-parallel circuit inside the battery module, specifically including: S11: Design the isolated DCDC circuit and realize the functions of the series-parallel conversion inside the battery module and the adaptive access of each battery in the series circuit to the DCDC control part.

[0090] The adaptive selection circuit includes a trunk external switch tube array and a current and voltage test module;

[0091] The switch tubes in the main circuit external switch tube array are all MOSFET or IGBT low current high frequency control switches;

[0092] The test module is used to test the voltage and current of each battery cell.

[0093] S12: Design the power electronic parameters in the adaptive control circuit, establish a virtual model, traverse different battery series and parallel situations, and verify the rationality of the parameter design. This includes the series-parallel conversion structure, the main line battery cell active selection structure, and the parameters of the battery current control unit; the branch series-parallel conversion structure is a switch tube array connected to the battery module cell, and the relevant parameters include the allowable current of the switch tube, etc.; the main line battery cell active selection structure is used to actively select a specific battery cell and connect it to the battery current control unit, and the relevant parameters include the allowable current of the switch tube, the impedance of the test element, etc.; the battery current control unit is a phase-shifted full-bridge structure including a bidirectional DCDC structure, and the relevant parameters include the parameter selection of the inductive and capacitive components in the DCDC; the specific design steps are as follows:

[0094] S121: If Figure 2 As shown, the main power electronic architecture of the branch series-parallel conversion structure is designed to ensure the conversion of the series-parallel relationship between multiple branches of the battery under the safety of the control unit and the battery cell itself;

[0095] S122: Based on the battery's own current and voltage limits, ripple limits, and target signal control effects, design the specific parameters of the bidirectional DCDC inside the control unit, including the allocation and specific parameters of the MOSFET switches, inductors, capacitors, and resistors. Use bidirectional DCDC as a control unit, such as Figure 3 As shown in the figure, the parameters that need to be calculated include the parameters of the inductor, capacitor and current limiting resistor. Set the PWM signal duty cycle d to 0.25 and the input voltage U in Set to 3.2V, design ripple current ΔI L1 Set to 0.0001A, PWM signal frequency f PWM If the frequency is set to 50kHz, the basic design range of the inductor is:

[0096]

[0097] Set the current I of the output capacitor of the DCDC structure o The ripple voltage ΔU across capacitor C2 is 2A. C = 4V*0.05=0.2V, PWM frequency and duty cycle are the same as above, based on these data, we can calculate the lower limit of capacitor C2. At the same time, assuming that the current I C2 To ensure that the capacitor C2 can discharge to the output terminal, the effective voltage U of the capacitor C2 is usually required to meet the maximum terminal voltage of the battery. req Greater than the maximum terminal voltage U of the battery battery, and when C2 is used as voltage compensation, the filtered current frequency is already low-frequency current, taking f = 30Hz. In addition, the design effective voltage U of capacitor C req If it is set to 3.4V, the basic design range of the capacitor is:

[0098]

[0099] That is, it should satisfy:

[0100] 68μF≤C2≤84μF

[0101] In addition, for Figure 3 The capacitor C1 in the figure can have the same value as C2.

[0102] The specific design method of the resistor needs to be further determined according to the specific circuit characteristics of the bidirectional DCDC. The resistor R1 must meet the maximum current of the battery is not higher than 2C. Assuming that the maximum battery voltage V max,battery is 4.2V, the maximum current I max,battery is 6.4A, the impedance R of the inductor L L is 0.3Ω, then the resistor R1 should satisfy:

[0103]

[0104] Maximum transient voltage ΔU:

[0105]

[0106] Among them, U C is the voltage across the capacitor, f * is the frequency, C p is the capacitance, I C is the current flowing through the capacitor, which can be expressed as follows according to Ohm’s law:

[0107]

[0108] Among them, X L is the inductive reactance, X C Yes, the resistor R2 needs to satisfy the requirement that the fluctuation of ΔU is less than ξ, so it is easy to deduce:

[0109]

[0110] Assume that X L For X C = ξ=0.01V, current I is 3.2A, C p 70μF, f * If the frequency is 50kHz, then the resistor R2 should satisfy:

[0111]

[0112] S123: Verify the feasibility of energy exchange between series-connected battery packs and high-frequency to low-frequency PWM generation in the constructed virtual circuit.

[0113] S13: Using power electronic components to build a physical circuit of the virtual model, and perform preliminary verification of functional feasibility under actual working conditions;

[0114] S2: Design an MPC control strategy for hierarchical adaptive control of individual cells within the battery module. This step mainly proposes specific control strategies for different components based on the structural design of step S1, including PWM signals and multi-state coordination control strategies between series-connected batteries:

[0115] S21: Design a control strategy for the MOSFET components in a bidirectional DC-DC converter. Use Proportional-Integral (PI) control to provide a specific PWM output method for multiple MOSFET switches to ensure that the designed signal can be correctly output.

[0116] S22: Design a dynamic access strategy for the series battery modules and the DCDC control module, and use the MPC method to determine the dynamic current requirements of different battery modules to achieve accurate parameter estimation and simultaneous power balancing. Specifically, the following steps are included:

[0117] S221: Design the control variables, prediction domain, and control domain of the MPC control process. In this invention, the variable that can be directly controlled structurally in the battery module is the battery current. Therefore, the control variable of the MPC used in S2 is the current of each single cell in the series module. Considering the time-varying nature of the battery SoC, the prediction domain is 10s, and the control domain is 1s.

[0118] S222: Design the cost function of the MPC algorithm. For the battery balancing problem, the goal is to gradually approach the SoC between batteries during the control process. Therefore, the cost function is also a function of SoC:

[0119]

[0120] Where J represents the cost function, k is the time count in the prediction domain, and SoC obj Calculate the SoC value of the target battery, SoC avg is the average of the SoC calculated values ​​of all batteries in the series circuit;

[0121] S223: Design the control target of the MPC control process. The control target is also a function of the battery SoC:

[0122] SoC obj(k)-SoC avg (k)≤ε

[0123] Among them, ε is the boundary value of the target and needs to be specifically designed. At the same time, k in this step specifically refers to the current time step that needs to be controlled;

[0124] S224: The calculation method of the battery SoC in steps S222 to S223 is as follows:

[0125]

[0126] S23: Design a switching strategy for the series-parallel connection of the entire battery module.

[0127] S3: Based on the multi-level battery management system, an online parameter identification method at the battery cell level is established, and a dynamic balancing process at the module level under any environmental conditions is implemented, including the following steps:

[0128] S31: Establish a dynamic signal information evaluation algorithm based on the battery equivalent circuit model to evaluate the upper limit of the signal. Specifically, it includes:

[0129] S311: For any battery system, the battery parameter estimation process can be generally characterized using Bayesian theorem as follows:

[0130]

[0131] Among them, θ N is the parameter vector at time N, y is the battery terminal voltage, and I is the battery current. N It can be expressed as [θ (1) … θ (i) … θ (M) The coefficient k represents the time count, and N is the upper limit of the time count. N |y 1:N ,I 1:N ) represents the condition that the current and voltage from time 1 to time N are known. N The probability distribution of is essentially a general expression of parameter estimation.

[0132] S312: A set of signals (y 1:N ,I 1:N ) contains a parameter θ (i) The amount of information can be expressed as:

[0133]

[0134] where σ V is the variance of the test values ​​of the battery voltage, Is the battery voltage calculated by the battery model, the specific coefficient matrix With parameter vector Θ k It needs to be determined based on the actual model.

[0135] S313: Step S311 can provide an upper bound on the amount of information in the actual signal:

[0136]

[0137] in, Represents the initial estimate variance of the parameter, which is usually derived from experience with the parameter.

[0138] S314: It can be used to calculate the specific information content of a signal. After artificially giving a certain upper bound of expected information, the actual upper bound of a signal can be represented as:

[0139]

[0140] Among them, in the equivalent circuit model, and θ can be expressed as:

[0141]

[0142] θ=[θ (1) θ (2) θ (3) ]=[R s R t τ]

[0143] Where OCV is the open circuit voltage, I is the current, and R s and R t are ohmic internal resistance and polarization internal resistance respectively, and τ is the time constant. Then, R s For example, The first-order partial derivative of can be further derived as:

[0144]

[0145] Further second-order partial derivatives are:

[0146]

[0147] akin, R t The second-order partial derivatives of and τ can be derived as:

[0148]

[0149] S32: Based on the calculation method of the lower limit of the signal information amount, the optimal signal frequency and amplitude for online estimation of the electrical parameters of a specific battery are provided, including the following steps:

[0150] S321: Based on the parameter information amount given in step S313, a parameter information amount expression with cross information can be further established:

[0151]

[0152] S322: After a specific Fourier series signal expression is given, the battery parameter information at any frequency can be calculated, and the optimal signal frequency can be obtained based on the traversed information curve.

[0153] S33: Establishing an online battery electrical parameter estimation algorithm for the variable frequency signal, specifically including:

[0154] S331: Determine a battery online parameter estimation method, and calculate the battery electrical parameters using least squares based on the first-order RC equivalent circuit model of the battery:

[0155]

[0156] θ (1) represents the prior parameter vector, θ (2) is the posterior parameter vector, is a coefficient matrix given by the first-order RC equivalent circuit model of the battery; P(t) represents the covariance matrix in the process of estimating the battery parameter matrix to be identified using the least squares principle; λ represents the forgetting factor;

[0157] S332: Determine the calculation equation and observation equation for the battery state of charge (SOC), implement automatic iteration of the battery SOC over time, and establish the relationship between the SOC and observable variables:

[0158]

[0159] where x t is the actual observation parameter, which is a vector containing SOC; ω t represents the process noise, that is, in x t Error value in the process of automatic update over time; v t Represents the test noise, that is, through x t The error generated in the process of calculating the battery terminal voltage V; calculate the Kalman gain and correct the average battery SOC at the current moment:

[0160]

[0161] P t represents the covariance matrix in the SOC estimation process, is the first updated P at time t t The value of P represents the second updated t The value of P at time t t Output value; K t is the Kalman gain; R w is the process noise ω in step S322 t The variance, R v is the test noise v in step S332 t The variance of e t is the difference between the test terminal voltage and the terminal voltage calculated in step S322.

[0162] S4: Construct a multi-level battery management system module to obtain a complete battery management system with multi-level coordinated control of modules and cells, including:

[0163] Design the signal acquisition module in the control board to directly collect the terminal voltage of each connected battery cell in the series trunk line, and indirectly measure the current signal flowing through each battery cell through the set overcurrent resistor. The key parameters that need to be designed include the structural configuration of the signal acquisition module and the resistance value of the overcurrent resistor. The principle of voltage and current acquisition is as follows Figure 4 As shown, the OPA4277PA chip can be used to complete the task of digital-to-analog conversion;

[0164] The driving module is designed to drive the switch array in the battery management system. The driving module can output the corresponding start level according to the type of switch device to ensure the normal operation of each type of switch tube. The driving principle of the switch array is generally as follows: Figure 5 As shown, TLP250(F) can be used to drive the MOSFET switch tube;

[0165] Design a voltage conversion module suitable for the power electronics mechanism to convert the external DC power supply voltage into the different voltage levels required by the drive module and the battery management system core chip, ensuring stable power supply to all parts of the system. The power conversion module can use the RO-1215S_HP chip.

[0166] An electrical isolation module is configured to achieve physical electrical isolation between the high-voltage side and the low-voltage control side of the battery management system, while supporting the secure transmission of signals and energy, improving system safety and anti-interference capabilities. Optocoupler isolation of PWM high-frequency signals is also implemented using the TLP250 chip. The drive isolation power module uses the QA121C2 chip.

[0167] Select the core chip for the battery management system. This chip stores the control algorithm and outputs the switch array control signals and high-frequency PWM control signals. Chip selection should consider requirements such as the number of control pins, PWM output accuracy, and system control logic. Given the matrix operations involved in the algorithm, a DSP28335 chip is recommended.

[0168] It should also be noted that the parameter estimation results of the above battery management system are as follows: Figure 5 As shown in (a)-(b), Figure 5 (a) shows the parameter identification results of the battery management system for ohmic internal resistance, polarization internal resistance, and time constant under general low-power operating current conditions; Figure 5 (b) shows the parameter identification results of ohmic internal resistance, polarization internal resistance and time constant under short-term high-power current conditions.

[0169] Example 2:

[0170] An electronic device comprising a memory and a processor;

[0171] The memory is used to store computer programs;

[0172] The processor is configured to implement the method described in Example 1 when executing the computer program.

[0173] Example 3:

[0174] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in Example 1 is implemented.

[0175] Example 4:

[0176] A computer program product includes a computer program, which implements the method described in embodiment 1 when executed by a processor.

[0177] In the above embodiments, references to "this embodiment" in the specification indicate that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily refer to the same embodiment.

[0178] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the present invention are intended to encompass all such alternatives, modifications, and variations that fall within the broad scope of the appended claims.

[0179] Regarding the computer-readable storage medium in this embodiment, those skilled in the art will appreciate that all or part of the steps in the aforementioned method embodiments can be implemented using hardware associated with the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps in the aforementioned method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0180] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used for communication, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes the various steps of the above method.

[0181] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0182] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0183] The present invention can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above.

[0184] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A battery module management system design method based on single cell high-sensitivity parameter estimation technology, characterized by: Including the following step: S1: Build a multi-level battery management system, including: designing the variable topology of the series and parallel circuits within the battery module; designing the MPC control strategy for hierarchical adaptive control of individual battery cells within the battery module; S2: Based on the multi-level battery management system, an online parameter identification method at the battery cell level is established, and a dynamic balancing process at the module level is realized under any environmental conditions; S3: Design and configure peripheral modules to build a complete battery management system with multi-level coordinated control of modules and cells.

2. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 1 is characterized by: The design of a variable topology structure of the series-parallel circuit within the battery module includes the following steps: S11: Design an isolated DC-DC circuit and implement the functions of series-parallel conversion within the battery module and adaptively connect each battery in the series circuit to the DC-DC control part; the adaptive selection circuit includes a main circuit external switch tube array and a current and voltage test module; the switch tubes in the main circuit external switch tube array are all MOSFETs or IGBTs with low current and high frequency control; the test module is used to test the voltage and current of each battery cell; S12: Design the power electronic parameters in the adaptive control circuit, establish a virtual model, traverse different battery series and parallel situations, and verify the rationality of the parameter design. The power electronic parameters include parameters of the branch series and parallel conversion structure, the main line battery cell active selection structure, and the battery current control unit; the branch series and parallel conversion structure is a switch tube array connected to the battery module cell, and related parameters include the allowable current of the switch tube; the main line battery cell active selection structure is used to actively select a specific battery cell and connect it to the battery current control unit, and related parameters include the allowable current of the switch tube and the impedance of the test component; the battery current control unit is a phase-shifted full-bridge structure including a bidirectional DC-DC structure, and related parameters include parameter selection of the inductive and capacitive components in the DC-DC; S13: Build a physical circuit of the virtual model using power electronic components, and conduct preliminary verification of functional feasibility under actual working conditions.

3. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 2 is characterized by: Step S12 specifically includes the following steps: S121: Design the main power electronic architecture of the branch series-parallel conversion structure to ensure that the series-parallel conversion between multiple branches of the battery is completed while ensuring the safety of the control unit and the battery cells themselves; S122: Based on the battery's own current and voltage limits, ripple limits, and target signal control effect, design the specific parameters of the bidirectional DC-DC control unit, including the allocation and specific parameters of the MOSFET switches, inductors, capacitors, and resistors. The specific parameters of the bidirectional DC-DC control unit include the parameters of the inductor, capacitors, and current-limiting resistor. The basic design range of the inductor is: Where, d is the duty cycle of the PWM signal, U in is the input voltage of the bidirectional DCDC, ΔI L1 is the design ripple current, f PWM It is to design the PWM signal frequency; The basic design range of capacitors is: Among them I o is the output current of the DCDC structure, ΔU C is the designed ripple voltage across capacitor C, I C is the current flowing through capacitor C, U req is the design effective voltage of capacitor C, satisfying U req >U battery , U battery is the maximum terminal voltage of the battery; the resistor is designed according to the specific circuit characteristics of the bidirectional DCDC S123: Verify the feasibility of energy exchange between series-connected battery packs and high-frequency to low-frequency PWM generation in the constructed virtual circuit.

4. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 1 is characterized in that: The MPC control strategy for hierarchical adaptive control of individual cells in a battery module, including PWM signals and multi-state coordination control strategy between series-connected batteries, is specifically as follows: S14: Design a control strategy for the MOSFET components in a bidirectional DC-DC converter, and use proportional-integral (PI) control to provide a specific PWM output method for multiple MOSFET switches. S15: Design a dynamic access strategy for the series battery modules and the DCDC control module, use the MPC method to determine the dynamic current requirements of different battery modules, and achieve simultaneous parameter estimation and power balancing; S16: Design the switching strategy for the series-parallel connection of the entire battery module.

5. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 4 is characterized in that: Step S15 specifically includes the following steps: S151: Design the control variables, prediction domain, and control domain of the MPC control process; the control variable of the MPC is the current of each single cell in the series module; S152: Design the cost function of the MPC algorithm: Where J represents the cost function, k is the time count in the prediction domain, and SoC obj Calculate the SoC value of the target battery, SoC avg is the average of the SoC calculated values ​​of all batteries in the series circuit; S153: Design the control objectives of the MPC control process: SoC obj (k)-SoC avg (k)≤ε Among them, ε is the boundary value of the target, k represents the current time step that needs to be controlled; The calculation method of battery SoC is as follows:

6. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 1 is characterized by: Step S2 specifically includes the following steps: S21: Establish a dynamic evaluation algorithm for signal information based on the battery equivalent circuit model to evaluate the upper limit of the signal; S22: Based on the calculation method of the lower limit of the signal information amount, the optimal signal frequency and amplitude for online estimation of the electrical parameters of a specific battery are given; S23: Establish an online battery electrical parameter estimation algorithm for variable frequency signals.

7. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 6 is characterized in that: Step S21 specifically includes the following steps: S211: For any battery system, use Bayes' theorem to generally characterize the estimation process of battery parameters: Among them, θ N is the parameter vector at time N, y is the battery terminal voltage, I is the battery current; θ N Expressed as [θ (1) ...θ (i) … θ (M) ]; coefficient k represents time count, N is the upper limit of time count; p(θ N |y 1:N ,I 1:N ) represents the condition that the current and voltage from time 1 to time N are known. N The probability distribution of S212: A set of signals (y 1:N ,I 1:N ) contains a parameter θ (i) The amount of information is expressed as: where σ V is the variance of the test values ​​of the battery voltage, Is the battery voltage calculated by the battery model, the specific coefficient matrix With parameter vector Θ k Determine based on the actual model; S213: The upper bound of the amount of information in the actual signal is: in, Represents the initial estimated variance of the parameter, which is usually derived from experience with the parameter; It is used to calculate the specific information content of a signal. After artificially giving a certain upper bound of expected information, the actual upper bound of a signal is represented as:

8. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 6, characterized in that: Step S22 specifically includes the following steps: S221: Based on the parameter information amount given in step S213, a parameter information amount expression with cross information is established: S222: After a specific Fourier series signal expression is given, the battery parameter information at any frequency is calculated, and the optimal signal frequency is obtained based on the traversed information curve.

9. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 6, characterized in that: Step S23 specifically includes the following steps: S231: Determine a battery online parameter estimation method, and calculate the battery electrical parameters using least squares based on the first-order RC equivalent circuit model of the battery: θ (1) represents the prior parameter vector, θ (2) is the posterior parameter vector, is a coefficient matrix given by the first-order RC equivalent circuit model of the battery; P(t) represents the covariance matrix in the process of estimating the battery parameter matrix to be identified using the least squares principle; λ represents the forgetting factor; S232: Determine the calculation equation and observation equation for the battery state of charge (SOC), implement automatic iteration of the battery SOC over time, and establish a relationship between the SOC and observable variables: where x t is the actual observation parameter, which is a vector containing SOC; ω t represents the process noise, that is, in x t Error value in the process of automatic update over time; v t Represents the test noise, that is, through x t The error generated in the process of calculating the battery terminal voltage V; calculate the Kalman gain and correct the average battery SOC at the current moment: P t represents the covariance matrix in the SOC estimation process, is the first updated P at time t t The value of P represents the second updated t The value of P at time t t Output value; K t is the Kalman gain; R w is the process noise ω t The variance, R v is the test noise v t The variance of e t is the difference between the test terminal voltage and the terminal voltage.

10. The battery module management system design method based on single cell high sensitivity parameter estimation technology according to claim 1, characterized in that: Step S3 specifically includes: Design a signal acquisition module in the control board to collect the terminal voltage of each connected battery cell in the series trunk line and indirectly measure the current signal flowing through each battery cell through the set overcurrent resistor. Key parameters that need to be designed include the structural configuration of the signal acquisition module and the resistance value of the overcurrent resistor. A driver module designed to drive a switch array in a battery management system, the driver module being capable of outputting a corresponding start-up level according to the type of switch device; Design a voltage conversion module suitable for power electronics to convert the external DC power supply voltage into the different voltage levels required by the drive module and the battery management system core chip; An electrical isolation module is configured to achieve physical electrical isolation between the high-voltage side and the low-voltage control side of the battery management system, while supporting the safe transmission of signals or energy; Select the core chip of the battery management system, which is used to store the control algorithm program and output the switch tube array control signal and high-frequency PWM control signal.

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