An adaptive power allocation method and system for a hybrid energy storage system

Abstract

The application belongs to the technical field of electric power energy storage and energy management, and relates to a self-adaptive power distribution method and system of a hybrid energy storage system. A dynamic efficiency balancing factor is calculated through a dynamic efficiency balancing factor prediction formula; based on the dynamic efficiency balancing factor and total system demand power, an instantaneous power distribution coefficient of a battery and an instantaneous power distribution coefficient of a super capacitor are determined through a real-time power distribution ratio calculation formula; based on the instantaneous power distribution coefficient of the battery, the instantaneous power distribution coefficient of the super capacitor, the total system demand power and the battery current of the last period, a battery current reference instruction and a super capacitor current reference instruction are calculated and issued to corresponding power converters; based on the actual output power and the actual loss power of the current period, the dynamic efficiency balancing factor is closed-loop corrected to obtain a corrected balancing factor, and the next period dynamic efficiency balancing factor is generated according to the corrected balancing factor.

Description

Technical Field

[0001] This invention belongs to the field of power energy storage and energy management technology, and relates to an adaptive power allocation method and system for a hybrid energy storage system. Background Technology

[0002] Hybrid Energy Storage Systems (HESS) typically combine high-energy-density batteries (such as lithium-ion batteries) with high-power-density, long-cycle-life supercapacitors to simultaneously meet the energy and power demands of loads. These systems have broad application prospects in renewable energy smoothing, microgrid support, electric vehicle powertrains, and grid frequency regulation. The core challenge lies in how to allocate power commands between these two distinct energy storage components in real time and rationally to maximize overall system performance, extend battery life, and improve overall operating efficiency.

[0003] Currently, power allocation strategies in this technology field can be mainly classified into the following categories: 1. Frequency-separation based filtering method: The basic idea is to use a low-pass filter (such as a first-order RC filter) to decompose the total power demand into low-frequency and high-frequency components, which are then allocated to the battery and supercapacitor, respectively. The battery responds to the slowly changing low-frequency component to provide steady-state energy support; the supercapacitor responds to the rapidly changing high-frequency component to smooth out power fluctuations. This type of method is simple to implement, but the choice of the filter cutoff frequency or time constant is crucial and is usually a fixed value.

[0004] 2. Rule-based threshold method: A series of static thresholds and rules are set based on the state of energy storage components (such as the state of charge of batteries or the voltage of supercapacitors) or the required power. The rules are logically intuitive, but the threshold setting depends on experience, and the combination of rules can be complex.

[0005] 3. Optimization-based allocation method: By establishing a system model (including loss model, aging model, etc.), the optimal power allocation is solved under operational constraints with the objective functions of maximizing efficiency, minimizing loss, or maximizing lifetime. While theoretically superior, this method has high computational complexity, is highly dependent on model accuracy, and is difficult to implement in real-time in low-cost controllers.

[0006] While the aforementioned existing technology achieves basic allocation functionality, it still suffers from the following drawbacks: 1. The static nature of the strategy and its lack of state adaptability: Both the cutoff frequency of the filter and the threshold parameters in the rules are usually set to fixed values ​​or switched under a limited number of operating conditions in existing technologies. They cannot respond in real time and continuously to the dynamic changes in the internal state of the battery and supercapacitor themselves.

[0007] 2. Neglecting the efficiency coupling characteristics: Existing methods (especially filtering-based methods) primarily rely on power spectrum characteristics or simple rules for "task decomposition," failing to consider the instantaneous efficiency characteristics of the two energy storage media at the current operating point as the core decision-making basis for power allocation. The efficiencies of batteries and supercapacitors are not constant but rather complex functions of their output current, internal resistance, and terminal voltage. Existing methods lack the ability to dynamically optimize for this "efficiency equilibrium point," and the allocated power solution is usually not the globally optimal solution.

[0008] 3. Lack of a closed-loop learning and optimization mechanism based on operational results: Most existing strategies are "feedforward" or "open-loop" control, meaning they generate instructions based on current measurements and fixed rules, lacking a reverse channel to fine-tune and optimize strategy parameters online based on the actual distribution effects of the previous cycle (such as actual losses and temperature rise). The system cannot learn from historical operating data. Furthermore, open-loop strategies cannot compensate for model mismatches or device parameter drift, resulting in poor robustness.

[0009] 4. The contradiction between accuracy and complexity in engineering implementation: While online optimization methods based on accurate loss models (such as real-time solving of nonlinear programming problems) can effectively address some of the aforementioned issues, they are computationally burdensome, demanding high-performance processors and difficult to implement stably on embedded platforms within millisecond-level control cycles. This limits the application of high-performance optimization algorithms in practical engineering, particularly in low-cost, high-reliability industrial scenarios. Summary of the Invention

[0010] The purpose of this invention is to provide an adaptive power allocation method and system for a hybrid energy storage system, in order to solve the technical problems of being unable to respond in real time and continuously to the dynamic changes in the internal state of the battery and supercapacitor during the power allocation process, not considering the instantaneous efficiency of energy storage elements, lacking adaptive learning in open-loop control, and high computational complexity.

[0011] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides an adaptive power allocation method for a hybrid energy storage system, comprising the following steps: Based on the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the total power demand of the system, the dynamic efficiency balance factor is calculated using the dynamic efficiency balance factor prediction formula. Based on the dynamic efficiency balancing factor and the total power demand of the system, the instantaneous power allocation coefficient of the battery and the instantaneous power allocation coefficient of the supercapacitor are determined by the real-time power allocation ratio calculation formula. Based on the instantaneous power allocation coefficient of the battery, the instantaneous power allocation coefficient of the supercapacitor, the total power demand of the system, the state parameters of the battery cell, the state parameters of the supercapacitor cell, and the battery current of the previous cycle, the battery current reference command and the supercapacitor current reference command are calculated and sent to the corresponding power converter. Based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle, the dynamic efficiency equalization factor is corrected in a closed loop to obtain the corrected equalization factor, and the dynamic efficiency equalization factor for the next cycle is generated based on the corrected equalization factor.

[0012] In a second aspect, the present invention provides an adaptive power distribution system for a hybrid energy storage system, comprising: The equalization factor prediction module is used to calculate the dynamic efficiency equalization factor based on the state parameters of the battery cell, the state parameters of the supercapacitor cell, and the total power demand of the system, using the dynamic efficiency equalization factor prediction formula. The power allocation calculation module is used to determine the instantaneous power allocation coefficient of the battery and the instantaneous power allocation coefficient of the supercapacitor based on the dynamic efficiency balancing factor and the total power demand of the system, using the real-time power allocation ratio calculation formula. The current command calculation module is used to calculate and send battery current reference commands and supercapacitor current reference commands to the corresponding power converters based on the instantaneous power allocation coefficient of the battery, the instantaneous power allocation coefficient of the supercapacitor, the total power demand of the system, the state parameters of the battery cell, the state parameters of the supercapacitor cell, and the battery current of the previous cycle. The feedback correction module is used to perform closed-loop correction on the dynamic efficiency balance factor based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle, to obtain the corrected balance factor, and to generate the dynamic efficiency balance factor for the next cycle based on the corrected balance factor.

[0013] Compared with the prior art, the present invention has the following beneficial effects: This invention calculates a dynamic efficiency balancing factor based on the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the total system power demand using a dynamic efficiency balancing factor prediction formula. This prediction formula is used to balance the marginal efficiency loss of the battery and supercapacitor at the current operating point. By comprehensively considering multiple factors in calculating the dynamic efficiency balancing factor, a scientific and reasonable basis for subsequent power allocation can be provided, avoiding inefficiency caused by fixed strategies. Based on the dynamic efficiency balancing factor and the total system power demand, the instantaneous power allocation coefficients of the battery and supercapacitor are determined using a real-time power allocation ratio calculation formula. This allocation method fully considers the instantaneous efficiency characteristics of the energy storage components, enabling dynamic adjustment of power allocation and allowing the system to achieve more reasonable power allocation under different operating conditions, thus improving overall operating efficiency. Based on the instantaneous power allocation coefficients of the battery, the instantaneous power allocation coefficients of the supercapacitor, the total system power demand, the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the battery current of the previous cycle, battery current reference commands and supercapacitor current reference commands are calculated and sent to the corresponding power converters. By converting the power allocation results into specific current reference commands and accurately sending them to the corresponding power converters, it is ensured that the battery and supercapacitor can operate according to the calculated power allocation ratio. Consideration of the battery current in the previous cycle further improves the accuracy and feasibility of the commands. Based on the actual output / input power and actual power loss of the battery and supercapacitor in the current cycle, the dynamic efficiency balancing factor is closed-loop corrected to obtain the corrected balancing factor. The dynamic efficiency balancing factor for the next cycle is then generated based on the corrected balancing factor, constructing a closed-loop learning and optimization mechanism based on operational performance. This solves the problem of existing strategies lacking adaptive learning. By continuously optimizing the power allocation strategy, the allocation strategy is always close to the optimal, improving the robustness and long-term stability of the system. Through the synergistic effect of the above steps, this invention can respond in real-time and continuously to the dynamic changes in the internal state of the battery and supercapacitor, and perform reasonable power allocation based on the instantaneous efficiency characteristics of the energy storage elements, avoiding the limitations of static strategies. The closed-loop correction mechanism achieves adaptive learning and optimization based on operational performance, improving the robustness of the system. Furthermore, this method can be implemented in a low-cost controller while ensuring a certain level of accuracy, making it suitable for practical engineering applications.

[0014] The system of this invention includes an equalization factor prediction module, a power allocation calculation module, a current command solution module, and a feedback correction module. The equalization factor prediction module calculates the dynamic efficiency equalization factor based on the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the total system power demand using a dynamic efficiency equalization factor prediction formula. The power allocation calculation module determines the instantaneous power allocation coefficients of the battery and the supercapacitor based on the dynamic efficiency equalization factor and the total system power demand using a real-time power allocation ratio calculation formula. The current command solution module calculates and sends battery current reference commands and supercapacitor current reference commands to the corresponding power converters based on the instantaneous power allocation coefficients of the battery and the supercapacitor, the total system power demand, the state parameters of the battery cells and the supercapacitor cells, and the battery current of the previous cycle. The feedback correction module performs closed-loop correction on the dynamic efficiency equalization factor based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle, obtaining a corrected equalization factor, and generates the dynamic efficiency equalization factor for the next cycle based on the corrected equalization factor. The various modules work together to respond in real time to the dynamic changes in the internal state of the battery and supercapacitor. They can make reasonable power allocation based on the instantaneous efficiency characteristics of the energy storage elements, avoiding the limitations of static strategies and realizing adaptive learning and optimization based on operational performance. At the same time, they avoid the problem of high computational complexity of online optimization methods based on accurate loss models. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1: See Figure 1 This invention discloses an adaptive power allocation method for a hybrid energy storage system, comprising the following steps: S1 collects the state parameters of the battery cells and supercapacitor cells, as well as the total power demand of the system, and obtains the battery current from the previous cycle. This comprehensive acquisition of key system operation information provides an accurate data foundation for subsequent power allocation decisions.

[0019] In a preferred embodiment of the present invention, the state parameters of the battery cell include the battery's rated efficiency weighting coefficient, the battery's current state of charge, the battery's nominal state of charge reference point, the battery's current internal resistance, the battery's maximum allowable internal resistance, and the battery pack's average open-circuit voltage; the state parameters of the supercapacitor cell include the supercapacitor's rated efficiency weighting coefficient, the supercapacitor pack's bus terminal voltage, and the supercapacitor's maximum operating voltage.

[0020] S2, based on the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the total power demand of the system, calculates the dynamic efficiency balancing factor using a dynamic efficiency balancing factor prediction formula. This prediction formula is used to weigh the marginal efficiency loss of the battery and supercapacitor at the current operating point. By comprehensively considering multiple factors in calculating the dynamic efficiency balancing factor, it can respond in real-time and continuously to the dynamic changes in the internal states of the battery and supercapacitor, accurately weighing the marginal efficiency loss of the battery and supercapacitor at the current operating point. This provides a scientific and reasonable basis for subsequent power allocation, avoiding inefficiency problems caused by fixed strategies.

[0021] In a preferred embodiment of the present invention, the dynamic efficiency balance factor is calculated using a dynamic efficiency balance factor prediction formula based on the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the total power demand of the system, including: The rate of change of total power demand is obtained based on the total power demand of the system.

[0022] The dynamic efficiency balancing factor is calculated using the dynamic efficiency balancing factor prediction formula based on the battery's rated efficiency weighting coefficient, the battery's current state of charge, the battery's nominal state of charge reference point, the battery's current internal resistance, the battery's maximum permissible internal resistance, the supercapacitor's rated efficiency weighting coefficient, the supercapacitor bank's bus terminal voltage, and the supercapacitor's maximum operating voltage.

[0023] The prediction formula for the dynamic efficiency equilibrium factor is:

[0024] in, This is a dynamic efficiency balancing factor. This is the weighting factor for the rated efficiency of the battery. This is the weighting factor for the rated efficiency of the supercapacitor. This represents the battery's current state of charge. This is the reference point for the battery's nominal state of charge. The current internal resistance of the battery. This is the maximum permissible internal resistance of the battery. This refers to the bus terminal voltage of the supercapacitor bank. This is the maximum operating voltage of the supercapacitor. The rate of change of total demand power. The maximum allowable rate of change of power in the system. , , and All are normalized adjustment coefficients.

[0025] S3. Based on the dynamic efficiency balancing factor and the total system power demand, the instantaneous power allocation coefficients of the battery and the supercapacitor are determined using the real-time power allocation ratio calculation formula. The power allocation ratio between the battery and the supercapacitor is then rationally determined according to the dynamic efficiency balancing factor and the total system power demand. This allocation method fully considers the instantaneous efficiency characteristics of the energy storage components, enabling dynamic adjustment of power allocation. This allows the system to achieve a more reasonable power allocation under different operating conditions, improving overall operating efficiency and avoiding situations where a fixed allocation strategy might cause the battery or supercapacitor to operate in an inefficient range.

[0026] In a preferred embodiment of the present invention, the formula for calculating the real-time power allocation ratio is:

[0027]

[0028] in, This is the instantaneous power distribution coefficient of the battery. This represents the instantaneous power distribution factor of the supercapacitor. It is the hyperbolic tangent function. To assign sensitivity coefficients, This is a dynamic efficiency balancing factor. This represents the total power demand of the system. This is the system's maximum output power. It is a high power protection factor.

[0029] S4, based on the instantaneous power allocation coefficient of the battery, the instantaneous power allocation coefficient of the supercapacitor, the total power demand of the system, the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the battery current of the previous cycle, calculate and issue battery current reference commands and supercapacitor current reference commands to the corresponding power converters. By converting the power allocation results into specific current reference commands and accurately issuing them to the corresponding power converters, it ensures that the battery and supercapacitor can operate according to the calculated power allocation ratio. Considering the battery current of the previous cycle further improves the accuracy and feasibility of the commands, enabling power allocation to be effectively executed in actual equipment and ensuring the stable operation of the system.

[0030] The battery current reference command is calculated using the following formula:

[0031] in, This is a battery current reference command. The compensation coefficient is calculated for the battery current. The current internal resistance of the battery. This represents the battery current from the previous cycle. This is the instantaneous power distribution coefficient of the battery. This represents the total power demand of the system. The average open-circuit voltage of the battery pack. This is the sampling control period.

[0032] In a preferred embodiment of the present invention, the supercapacitor current reference command is calculated using the following formula:

[0033] in, This is a supercapacitor current reference command. The compensation coefficient is calculated for the capacitor current. This represents the instantaneous power distribution factor of the supercapacitor. This refers to the bus terminal voltage of the supercapacitor bank. This is the maximum operating voltage of the supercapacitor.

[0034] S5, based on the actual output / input power and actual power loss of the battery and supercapacitor in the current cycle, performs closed-loop correction on the dynamic efficiency balancing factor to obtain the corrected balancing factor, and generates the dynamic efficiency balancing factor for the next cycle based on the corrected balancing factor. This constructs a closed-loop learning and optimization mechanism based on operational performance, solving the problem of existing strategies lacking adaptive learning. By performing closed-loop correction on the dynamic efficiency balancing factor based on the actual operational performance (actual output power and actual power loss) in the current cycle, the system can learn from historical operational data, perceive and adapt to gradual processes such as battery aging and device parameter drift, continuously optimize the power allocation strategy, ensuring the allocation strategy always approaches optimal, and improving the system's robustness and long-term operational stability.

[0035] In a preferred embodiment of the present invention, the step of performing closed-loop correction on the dynamic efficiency equalization factor based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle to obtain the corrected equalization factor includes: Obtain the current cycle battery terminal voltage, current cycle battery current, current cycle supercapacitor terminal voltage, current cycle supercapacitor current, current cycle battery current, current battery internal resistance, and equivalent series resistance of the supercapacitor.

[0036] The actual output / input power of the battery in the current cycle is obtained based on the battery terminal voltage and the battery current in the current cycle.

[0037] The actual output / input power of the supercapacitor in the current cycle is obtained based on the current cycle supercapacitor terminal voltage and the current cycle supercapacitor voltage.

[0038] The actual power loss of the battery in the current cycle is obtained based on the current battery current and the current internal resistance of the battery.

[0039] The power loss of the supercapacitor in the current cycle is obtained based on the current cycle supercapacitor voltage and the equivalent series resistance of the supercapacitor.

[0040] The corrected equalization factor is obtained based on the actual output / input power of the battery in the current cycle, the actual output / input power of the supercapacitor in the current cycle, the actual power loss of the battery in the current cycle, and the power loss of the supercapacitor in the current cycle.

[0041] The formula for obtaining the corrected equilibrium factor is as follows:

[0042] in, This is the corrected equilibrium factor. This is a dynamic efficiency balancing factor. To provide feedback on the learning rate, This represents the actual power loss of the battery during the current cycle. This represents the actual output / input power of the battery in the current cycle. This represents the actual output / input power of the supercapacitor in the current cycle. This represents the power loss of the supercapacitor during the current cycle. This is the minimum protection constant. The sign function for power flow. This represents the total power demand of the system.

[0043] In a preferred embodiment of the present invention, generating the dynamic efficiency equilibrium factor for the next cycle based on the corrected equilibrium factor includes: The corrected equilibrium factor is weighted and fused with the new predicted equilibrium factor calculated by the dynamic efficiency equilibrium factor prediction formula for the next period to obtain the dynamic efficiency equilibrium factor for the next period.

[0044] In a preferred embodiment of the present invention, the corrected equilibrium factor is weighted and fused with the new predicted equilibrium factor calculated by the dynamic efficiency equilibrium factor prediction formula for the next period. The weighted fusion is as follows:

[0045] in, The equilibrium factor after weighted fusion. As a new predictor of equilibrium factors, This is the corrected equilibrium factor.

[0046] In a preferred embodiment of the present invention, the method further includes: Battery state of charge protection: If the current state of charge of the battery is lower than the discharge cutoff limit or higher than the charging cutoff limit, the instantaneous power distribution coefficient of the battery will be set to zero or the preset minimum value. Voltage protection: If the bus terminal voltage of the supercapacitor pack is lower than the preset minimum operating voltage, the instantaneous power distribution coefficient of the battery will be forcibly increased to the preset value. Communication / sensor failure: If critical data is lost, switch to a fixed conservative allocation mode and trigger an alarm. The critical data includes at least: the current state of charge of the battery, the current internal resistance of the battery, the average open-circuit voltage of the battery pack, the bus terminal voltage of the supercapacitor pack, the total power demand of the system, the battery current of the previous cycle, and the supercapacitor current of the previous cycle.

[0047] This invention, through the synergistic effect of the above steps, can respond in real time and continuously to the dynamic changes in the internal state of the battery and supercapacitor, and perform reasonable power allocation based on the instantaneous efficiency characteristics of the energy storage elements, avoiding the limitations of static strategies. A closed-loop correction mechanism enables adaptive learning and optimization based on operational performance, improving the system's robustness. Simultaneously, while ensuring a certain level of accuracy, this method avoids the high computational complexity of online optimization methods based on precise loss models, enabling implementation in low-cost controllers and making it suitable for practical engineering applications. This improves the overall energy efficiency and economy of hybrid energy storage systems, extends battery life, and provides more effective technical support for the widespread application of hybrid energy storage systems in renewable energy smoothing, microgrid support, electric vehicle power systems, and grid frequency regulation.

[0048] See Figure 2 Based on the above method, the present invention also discloses an adaptive power distribution system for a hybrid energy storage system, comprising: The equalization factor prediction module is used to calculate the dynamic efficiency equalization factor based on the state parameters of the battery cell, the state parameters of the supercapacitor cell, and the total power demand of the system, using the dynamic efficiency equalization factor prediction formula. The power allocation calculation module is used to determine the instantaneous power allocation coefficient of the battery and the instantaneous power allocation coefficient of the supercapacitor based on the dynamic efficiency balancing factor and the total power demand of the system, using the real-time power allocation ratio calculation formula. The current command calculation module is used to calculate and send battery current reference commands and supercapacitor current reference commands to the corresponding power converters based on the instantaneous power allocation coefficient of the battery, the instantaneous power allocation coefficient of the supercapacitor, the total power demand of the system, the state parameters of the battery cell, the state parameters of the supercapacitor cell, and the battery current of the previous cycle. The feedback correction module is used to perform closed-loop correction on the dynamic efficiency balance factor based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle, to obtain the corrected balance factor, and to generate the dynamic efficiency balance factor for the next cycle based on the corrected balance factor.

[0049] The various modules of this invention cooperate with each other to respond continuously and in real time to the dynamic changes in the internal states of the battery and supercapacitor. It performs reasonable power allocation based on the instantaneous efficiency characteristics of the energy storage elements, avoiding the limitations of static strategies. Through a closed-loop correction mechanism, it achieves adaptive learning and optimization based on operational performance, improving the system's robustness. Simultaneously, while ensuring a certain level of accuracy, this method avoids the high computational complexity of online optimization methods based on precise loss models. It can be implemented in low-cost controllers, making it suitable for practical engineering applications. This improves the overall energy efficiency and economy of hybrid energy storage systems, extends battery life, and provides more effective technical support for the widespread application of hybrid energy storage systems in renewable energy smoothing, microgrid support, electric vehicle power systems, and grid frequency regulation.

[0050] Example 2: See Figure 1 This invention proposes a novel power allocation method based on real-time prediction and closed-loop optimization of dynamic efficiency equilibrium point. The core of this method consists of four control formulas with a strict mathematical and physical logical progression, which constitute a complete adaptive control closed loop from state perception to command generation, effect evaluation, and strategy correction. The dynamic efficiency equilibrium point is a scalar factor determined by the real-time state of the system, indicating the power allocation tendency that the battery and supercapacitor should follow at the current moment to achieve optimal overall system efficiency in the next control cycle.

[0051] The hybrid energy storage system of this invention includes: a DC bus, a main power interface, a battery energy storage unit, and a supercapacitor energy storage unit; The DC bus serves as the common node for system power exchange. The main power interface connects the DC bus to the load or an external power source (such as a photovoltaic array or grid converter). The battery energy storage unit consists of a lithium-ion battery pack, a battery management system, and a bidirectional DC-DC converter (hereinafter referred to as the battery converter). The supercapacitor energy storage unit consists of a supercapacitor module, voltage and current sensors, and another bidirectional DC-DC converter (hereinafter referred to as the capacitor converter). The central controller, which can be a digital signal processor (DSP), microcontroller unit (MCU), or field-programmable gate array (FPGA), is responsible for executing the power allocation algorithm of this invention and issuing current commands to the two converters.

[0052] The parameters of this invention are initialized as follows: When the system starts up, the central controller loads the following preset parameters, including: equipment rated parameters, algorithm control parameters, and state variable initialization.

[0053] The rated parameters of the equipment, obtained from the equipment datasheet, include: Battery pack nominal capacity Battery pack nominal voltage Maximum permissible internal resistance of battery pack Total capacity of the supercapacitor bank (Cuc), maximum operating voltage of the supercapacitor. Maximum allowable current of battery converter Maximum allowable current of capacitor converter and system maximum output / input power .

[0054] The algorithm control parameters are obtained through offline simulation, system identification, or empirical calibration. Example values ​​are shown below: Battery rated efficiency weighting factor: =0.65, Rated efficiency weighting coefficient of supercapacitors: =0.35. State normalization coefficient: =0.40, =0.25, =0.25, =0.10. Distribution sensitivity coefficient: ξ=3.0. High power protection coefficient: =0.08. Battery current calculation compensation coefficient. =0.03, compensation coefficient for capacitor current calculation =0.15. Feedback learning rate: =0.015. Minimum protection constant: =1×10 6 Battery nominal state of charge reference point: =0.55.

[0055] The state variables are initialized as follows: Initial dynamic efficiency equilibrium factor: =0. Battery current in the previous cycle: =0. The power change rate filter status is cleared to zero.

[0056] The central controller of this invention uses a fixed sampling control cycle. (For example =10 ms) Execute the following complete process.

[0057] It should be noted that the previous cycle, current cycle, and next cycle in this invention are all relative to the fixed sampling time of the central controller. In other words, they correspond to , , time.

[0058] Step S100: Real-time data acquisition and preprocessing; S101, Collect battery cell status: Obtain the current total state of charge of the battery pack from the battery management system. ∈[0,1]. The battery management system estimates this value using the ampere-hour integration method combined with the open-circuit voltage method.

[0059] The equivalent series resistance of the battery pack at the current operating point (considering SOC and temperature) can be obtained from the battery management system or estimated online. This refers to the current internal resistance of the battery. It can be achieved using model-based Kalman filtering or a lookup table method.

[0060] Look up the table through the battery management system (based on...) (and temperature) to obtain the average open-circuit voltage of the battery pack , unit (V).

[0061] S102, Collect the status of the supercapacitor unit: The bus terminal voltage of the supercapacitor bank is directly measured using a voltage sensor. , unit (V).

[0062] The charge state of a supercapacitor is estimated using a current sensor and an ampere-hour integral.

[0063] S103, Obtain the total system power demand: The total power demand that the system needs to provide at the current moment can be obtained from the upper-level energy management system or through calculations using bus voltage and current. (W). Regulations: >0 indicates that the hybrid system needs to discharge to the bus (to meet load requirements). <0 indicates that the hybrid system needs to be charged from the bus (absorbing excess energy).

[0064] S104, Calculate the rate of change in power demand: Total power demand of the system at the current moment Discrete differentiation and low-pass filtering are performed to suppress measurement noise, resulting in a smoothed rate of change of power, i.e., the rate of change of total demand power. (W / s). This can be achieved using a first-order backward differential filter plus a first-order low-pass filter, as detailed below: First-order backward difference calculation of the original rate of change:

[0065] First-order low-pass filtering smoothing:

[0066] in, This represents the total power demand of the system. This represents the total system power demand at the previous moment. For sampling control period, This represents the original rate of change of power. For the current moment, For the next control cycle, This is the filtering time constant (e.g., 0.1s).

[0067] S105, retrieve the execution feedback data from the previous cycle: Read the battery current from the battery converter for the previous cycle. This refers to the average output current of the battery in the previous control cycle, expressed in amperes (A).

[0068] Calculate the actual output / input power of the battery and supercapacitor in the previous cycle:

[0069]

[0070] in, This represents the actual output / input power of the battery in the previous cycle. This represents the actual output / input power of the supercapacitor in the previous cycle. This refers to the battery terminal voltage from the previous cycle. This represents the battery current from the previous cycle. This is the voltage at the supercapacitor terminals in the previous cycle. This is the supercapacitor current from the previous cycle.

[0071] Estimate the power loss of each unit in the previous cycle:

[0072]

[0073] in, This represents the power loss of the battery in the previous cycle. This represents the power loss of the supercapacitor in the previous cycle. ESR (Equivalent Series Resistance, a constant) is the equivalent series resistance of the supercapacitor. For the auxiliary system power consumption of the battery cell, The power consumption of the auxiliary system for the supercapacitor cell is negligible, while the power consumption of the auxiliary system for the battery cell and the supercapacitor cell can be taken as a fixed value. This represents the battery's internal resistance in the previous cycle.

[0074] Step S200: Predict the dynamic efficiency equilibrium factor; S201, Substitute into the formula for calculation: Substitute the state parameters obtained from S101-S104 into the dynamic efficiency equilibrium factor prediction formula.

[0075]

[0076] Formula Origin and Derivation: This formula is inspired by the application of the "interior-point method" or "barrier function method" in constrained optimization problems (reference: Nesterov, Y., & Nemirovskii, A. (1994). Interior-point polynomial algorithms in convex programming. SIAM.). In classical methods, Lagrange multipliers or barrier parameters are used to balance the objective function and constraints. This invention extends this concept, creating a key factor for balancing the efficiency losses of batteries and supercapacitors, two energy storage media. .

[0077] in, As a dynamic efficiency balancing factor, at time... The predicted value, output quantity, is dimensionless.

[0078] This is the weighting factor for the rated efficiency of the battery. is the rated efficiency weighting factor for the supercapacitor. is an empirical constant used to calibrate the baseline efficiency importance of the two devices in the system. For example, it can be taken as... =0.7, =0.3, reflecting that battery efficiency has a greater impact on the overall system efficiency. Obtained through prior system efficiency calibration experiments.

[0079] This represents the current state of charge of the battery. The input value is estimated in real-time by the battery management system and ranges from [0,1].

[0080] This is a reference point for the battery's nominal (or healthy) state of charge, typically taken as 0.5 or 0.6 (corresponding to the higher efficiency region), and is a constant.

[0081] This represents the current internal resistance of the battery. This input value can be obtained through online impedance spectroscopy analysis or by looking up a table (based on the battery's state of charge and temperature), in Ω.

[0082] This is the battery's maximum permissible internal resistance (defined at the end of its lifespan). It's a constant, derived from the battery datasheet.

[0083] This is the bus terminal voltage of the supercapacitor bank. It's an input quantity, directly measured, in volts (V).

[0084] This is the maximum operating voltage of the supercapacitor. It is a constant, derived from the supercapacitor datasheet.

[0085] The rate of change of total power demand. Input quantity, obtained by analyzing the current total power demand. The derivative is obtained by performing differential or filtering, and the unit is W / s.

[0086] This represents the system's maximum permissible rate of power change. It is a constant, determined based on the system design.

[0087] , , and All are normalization adjustment coefficients. These are empirical constants to ensure that all terms in the formula are of similar order of magnitude and to make... It falls within the interval [-1, 1]. For example, it can take... =0.4, =0.3, =0.2, =0.1. Obtained by: Determined through simulation optimization using historical data.

[0088] Physical meaning: It is a quantitative prediction of the optimal condition that the marginal efficiency loss of the battery and the supercapacitor are equal at the next moment.

[0089] Numerical meaning: >0 indicates that the current system state tends to favor the battery output, which is more beneficial to overall efficiency. A value less than 0 indicates a tendency to make the supercapacitor output more power.

[0090] Step S300 calculates the real-time power allocation ratio.

[0091] Step S301, substitute into the formula to calculate: the dynamic efficiency balancing factor output from S202. The total system power demand obtained from S103 Substitute into the real-time power allocation ratio calculation formula.

[0092]

[0093]

[0094] Formula Origin and Derivation: This formula is based on the weighting concept in power allocation, but creatively incorporates the dynamic efficiency balancing factor. Through a smooth hyperbolic tangent function ( This is mapped to the power distribution ratio between the battery and the supercapacitor. The function originates from mathematics (hyperbolic function), has a range of (-1,1), and has a central saturation property, making it very suitable for smoothly converting decision factors into allocation instructions.

[0095] This is the instantaneous power distribution coefficient of the battery. This represents the instantaneous power distribution coefficient of the supercapacitor. It is the output quantity, dimensionless, and... + =1.

[0096] The hyperbolic tangent function is used to determine the dynamic efficiency balancing factor. Smooth mapping to (-1,1).

[0097] The sensitivity coefficient is a constant used to adjust the allocation strategy. The degree of sensitivity, The larger the value, the better the allocation strategy. The more radical the changes, the better. =2.5. Acquisition method: Adjusted according to system dynamic response requirements.

[0098] The total power demand of the system (load power + charging power) is an input quantity, which can be directly measured or given by the upper-level controller, in watts (W).

[0099] This represents the system's maximum output power. It is a constant.

[0100] This is a high-power protection factor, a small constant, such as 0.1. Its function is to slightly reduce the battery's power allocation ratio when the system's power demand approaches its maximum, preventing the battery from overloading or experiencing a sharp drop in efficiency due to sudden high-power surges, and transferring more of these peak power spikes to the supercapacitor.

[0101] S302, Output and Limiting: Obtain the instantaneous power distribution coefficient of the battery. Instantaneous power distribution factor of supercapacitors The sum of the two is 1.

[0102] Immediately implement amplitude limiting protection: ensure , If it exceeds the limit, clamp it to the boundary value.

[0103] Physical meaning: This represents the total power demand. The proportion of batteries planned to be allocated in the formula. The function implements from The smooth, saturated mapping of the allocation ratio avoids abrupt changes in the setpoint. The high-power protection item in parentheses ensures automatic fine-tuning of the allocation ratio under extreme high-power demands, allowing the supercapacitor to handle more peak loads and protecting the battery.

[0104] By obtaining the total power demand that the system needs to provide at the current moment. This transforms abstract decision factors into concrete, directly usable power partitioning instructions.

[0105] Step S400: Solve the current reference command; S401, the instantaneous power distribution coefficient of the battery output from S302. Instantaneous power distribution factor of supercapacitors The relevant data of S101, S102, and S105 are substituted into the current command calculation formula.

[0106] For the battery side:

[0107] For the supercapacitor side:

[0108] in, The compensation coefficient is calculated for the battery current. The compensation coefficient is calculated for the capacitor current.

[0109] S402, Output and Final Limiting: Obtains a battery current reference command that can be directly sent to the converter. and supercapacitor current reference command .

[0110] Final safety limit: Must ensure | |≤ And | |≤ If it exceeds the limit, it will be scaled proportionally or according to priority.

[0111] This step transforms the power distribution command, combined with the real-time electrical characteristics of the device, into a high-precision, directly executable current control command.

[0112] Step S500, Command issuance and power converter control: The S501 central controller sends battery current reference commands and supercapacitor current reference commands to the local controllers of the battery converter and capacitor converter respectively via a communication bus (such as CAN (Controller Area Network) or SPI (Serial Peripheral Interface)) or PWM analog signals.

[0113] S502, Current Closed-Loop Control: Upon receiving a current command, both converters immediately use it as a setpoint in their local control loop (typically a proportional-integral (PI) controller or a proportional-resonant (PR) controller). The controller adjusts the duty cycle of the power switches to ensure that the inductor current (i.e., battery or supercapacitor current) tracks the reference value quickly and accurately.

[0114] S503, Data Recording for This Cycle: At the end of the current control cycle, measure and record the following data to prepare for feedback correction in the next cycle: Current cycle battery terminal voltage Current cycle battery current Current cycle supercapacitor terminal voltage Current cycle of supercapacitor electricity .

[0115] Using these values, calculate the actual output / input power of the battery in the current cycle according to the method described in S105. The actual output / input power of the supercapacitor in the current cycle Current cycle battery power loss Current cycle supercapacitor power loss .

[0116] Step S600: Feedback correction of dynamic efficiency balance factor; S601, Substitute into the formula for calculation: Before the start of the next control cycle (or during the calculation idle time at the end of this cycle), use the actual operating data of this cycle recorded in S503 to calculate the dynamic efficiency balancing factor used in S202. The feedback correction formula for the dynamic efficiency balance factor is as follows: (This is used for online correction.)

[0117] in, The corrected equilibrium factor is used to predict the equilibrium factor in the next cycle. The output is also the input to the dynamic efficiency equilibrium factor prediction formula at the next moment, either as initialization or fused with the new prediction value.

[0118] The feedback learning rate (correction step size) is a very small normal number, such as 0.01, to control the correction magnitude and ensure system stability.

[0119] This represents the actual power loss of the battery during the current cycle. This represents the power loss of the supercapacitor in the current cycle, expressed as an input quantity in watts (W). The actual power loss of the battery and the power loss of the supercapacitor in the current cycle are obtained as follows: .in, This represents the measured port current for the corresponding energy storage element (battery / supercapacitor) during the current cycle. The internal resistance is obtained through online estimation or table lookup. Auxiliary losses (such as thermal management power consumption) can be ignored or taken as a fixed value.

[0120] This represents the actual output / input power of the supercapacitor in the current cycle. This represents the power loss and input of the supercapacitor in the current cycle, in watts (W). The actual output / input power and power loss of the supercapacitor in the current cycle are obtained as follows: , For actual measured port voltage, This represents the measured port current for the corresponding energy storage element (battery / supercapacitor) during the current cycle.

[0121] This is a minimum protection constant used to avoid the denominator being zero, such as 1e-6.

[0122] This is the power flow sign function. It is positive during discharging and negative during charging. Its purpose is to ensure correct correction direction in both charging and discharging modes, as the impact of losses on efficiency may differ during charging and discharging.

[0123] S602, Correction value fusion and transfer: Obtain the corrected factor.

[0124] Fusion Strategy (Example): To avoid fluctuations from a single feedback loop, a first-order low-pass filter or weighted averaging can be used to fuse the corrected value with the new predicted value calculated by the dynamic efficiency balance factor prediction formula for the next period, serving as the final decision factor. For example:

[0125] This step is based on a correction for instantaneous efficiency deviation. The difference in parentheses reflects the actual difference in the rate of loss between the battery and the supercapacitor in the previous cycle. If the battery's rate of loss is relatively higher, the difference is positive during discharge ( If positive, the correction term will decrease K, thereby reducing the battery allocation ratio in the real-time power allocation ratio calculation formula for the next cycle. This enables adaptive optimization.

[0126] This step enables the method of the present invention to form a closed-loop learning mechanism, allowing the prediction model to be continuously fine-tuned based on the actual operating results, gradually approaching the global optimal efficiency point.

[0127] Step S700, Cycle and Advanced Protection; S701, Return Loop: After completing all calculations, the central controller waits for the next control cycle, then jumps to step S100 to begin a new round of data acquisition and control. The dynamic efficiency balancing factor in the next round... Replace with .

[0128] S702, global protection logic (independent of the main loop, running in parallel or triggered by an interrupt): Battery state of charge protection: If the battery's current state of charge... If the value is below the discharge cutoff limit or above the charge cutoff limit, the algorithm result will be forcibly overridden, and the instantaneous power allocation coefficient of the battery will be adjusted. Set to zero or the minimum value.

[0129] Voltage protection: If the bus terminal voltage of the supercapacitor bank... If the voltage is below the minimum operating voltage, the instantaneous power distribution factor of the battery will be forcibly increased. .

[0130] Communication / sensor failure: If critical data is lost, switch to a fixed, conservative allocation mode (e.g., ...). =0.5) and alarm.

[0131] Compared with existing technologies, the advantages of this invention are as follows: When the battery has a high state of charge and low internal resistance, more power is automatically allocated to the battery to make full use of its high-efficiency range.

[0132] When the load power fluctuates drastically, more power is automatically allocated to the supercapacitor, giving full play to its advantages of high power density and fast response.

[0133] When the battery's internal resistance increases due to aging, the system will automatically and gradually reduce its power allocation ratio through the combined effect of the dynamic efficiency balance factor prediction formula and the dynamic efficiency balance factor feedback correction formula, thereby optimizing long-term operating economy.

[0134] The entire allocation process is smooth, shock-free, and fully adaptive, requiring no manual intervention to adjust parameters.

[0135] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. An adaptive power allocation method for a hybrid energy storage system, characterized in that, Includes the following steps: Based on the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the total power demand of the system, the dynamic efficiency balance factor is calculated using the dynamic efficiency balance factor prediction formula, including: The rate of change of total power demand is obtained based on the total power demand of the system. The dynamic efficiency balancing factor is calculated using the dynamic efficiency balancing factor prediction formula based on the battery's rated efficiency weighting coefficient, the battery's current state of charge, the battery's nominal state of charge reference point, the battery's current internal resistance, the battery's maximum allowable internal resistance, the supercapacitor's rated efficiency weighting coefficient, the supercapacitor bank's bus terminal voltage, and the supercapacitor's maximum operating voltage. The prediction formula for the dynamic efficiency equilibrium factor is: in, This is a dynamic efficiency balancing factor. This is the weighting factor for the rated efficiency of the battery. This is the weighting factor for the rated efficiency of the supercapacitor. This represents the battery's current state of charge. This is the reference point for the battery's nominal state of charge. The current internal resistance of the battery. This is the maximum permissible internal resistance of the battery. This refers to the bus terminal voltage of the supercapacitor bank. This is the maximum operating voltage of the supercapacitor. The rate of change of total demand power. The maximum allowable rate of change of power in the system. , , and All are normalized adjustment factors; Based on the dynamic efficiency balancing factor and the total power demand of the system, the instantaneous power allocation coefficient of the battery and the instantaneous power allocation coefficient of the supercapacitor are determined by the real-time power allocation ratio calculation formula. The formula for calculating the real-time power allocation ratio is: in, This is the instantaneous power distribution coefficient of the battery. This represents the instantaneous power distribution factor of the supercapacitor. It is the hyperbolic tangent function. To assign sensitivity coefficients, This is a dynamic efficiency balancing factor. This represents the total power demand of the system. This is the system's maximum output power. High power protection factor; Based on the instantaneous power allocation coefficient of the battery, the instantaneous power allocation coefficient of the supercapacitor, the total power demand of the system, the state parameters of the battery cell, the state parameters of the supercapacitor cell, and the battery current of the previous cycle, the battery current reference command and the supercapacitor current reference command are calculated and sent to the corresponding power converter. Based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle, the dynamic efficiency equalization factor is corrected in a closed loop to obtain the corrected equalization factor, and the dynamic efficiency equalization factor for the next cycle is generated based on the corrected equalization factor.

2. The adaptive power allocation method for a hybrid energy storage system according to claim 1, characterized in that, The state parameters of the battery cell include the rated efficiency weighting coefficient of the battery, the current state of charge of the battery, the reference point of the nominal state of charge of the battery, the current internal resistance of the battery, the maximum allowable internal resistance of the battery, and the average open circuit voltage of the battery pack. The state parameters of the supercapacitor unit include the rated efficiency weighting coefficient of the supercapacitor, the bus terminal voltage of the supercapacitor bank, and the maximum operating voltage of the supercapacitor.

3. The adaptive power allocation method for a hybrid energy storage system according to claim 2, characterized in that, The battery current reference command is calculated using the following formula: in, This is a battery current reference command. The compensation coefficient is calculated for the battery current. The current internal resistance of the battery. This represents the battery current from the previous cycle. This is the instantaneous power distribution coefficient of the battery. This represents the total power demand of the system. The average open-circuit voltage of the battery pack. This is the sampling control period; The supercapacitor current reference command is calculated using the following formula: in, This is a supercapacitor current reference command. The compensation coefficient is calculated for the capacitor current. This represents the instantaneous power distribution factor of the supercapacitor. This refers to the bus terminal voltage of the supercapacitor bank. This is the maximum operating voltage of the supercapacitor.

4. The adaptive power allocation method for a hybrid energy storage system according to claim 2, characterized in that, The dynamic efficiency equalization factor is corrected by performing closed-loop correction based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle, resulting in the corrected equalization factor, including: Obtain the current cycle battery terminal voltage, current cycle battery current, current cycle supercapacitor terminal voltage, current cycle supercapacitor current, current cycle battery current, current battery internal resistance, and equivalent series resistance of the supercapacitor; The actual output / input power of the battery in the current cycle is obtained based on the current cycle battery terminal voltage and the current cycle battery current; The actual output / input power of the supercapacitor in the current cycle is obtained based on the current cycle supercapacitor terminal voltage and the current cycle supercapacitor voltage. The actual power loss of the battery in the current cycle is obtained based on the current battery current and the current internal resistance of the battery. The power loss of the supercapacitor in the current cycle is obtained based on the current cycle supercapacitor current and the equivalent series resistance of the supercapacitor. The corrected equalization factor is obtained based on the actual output / input power of the battery in the current cycle, the actual output / input power of the supercapacitor in the current cycle, the actual power loss of the battery in the current cycle, and the power loss of the supercapacitor in the current cycle. The formula for obtaining the corrected equilibrium factor is as follows: in, This is the corrected equilibrium factor. This is a dynamic efficiency balancing factor. To provide feedback on the learning rate, This represents the actual power loss of the battery during the current cycle. This represents the actual output / input power of the battery in the current cycle. This represents the actual output / input power of the supercapacitor in the current cycle. This represents the power loss of the supercapacitor during the current cycle. This is the minimum protection constant. The sign function for power flow. This represents the total power demand of the system.

5. The adaptive power allocation method for a hybrid energy storage system according to claim 4, characterized in that, The step of generating the dynamic efficiency balance factor for the next cycle based on the corrected balance factor includes: The corrected equilibrium factor is weighted and fused with the new predicted equilibrium factor calculated by the dynamic efficiency equilibrium factor prediction formula for the next period to obtain the dynamic efficiency equilibrium factor for the next period.

6. The adaptive power allocation method for a hybrid energy storage system according to claim 5, characterized in that, The corrected equilibrium factor is then weighted and fused with the new predicted equilibrium factor calculated using the dynamic efficiency equilibrium factor prediction formula for the next period. The weighted fusion is as follows: in, The equilibrium factor after weighted fusion. As a new predictor of equilibrium factors, This is the corrected equilibrium factor.

7. The adaptive power allocation method for a hybrid energy storage system according to claim 1, characterized in that, The method further includes: If the battery's current state of charge is below the discharge cutoff limit or above the charging cutoff limit, the battery's instantaneous power distribution coefficient is set to zero or the preset minimum value. If the bus terminal voltage of the supercapacitor pack is lower than the preset minimum operating voltage, the instantaneous power distribution coefficient of the battery will be forcibly increased to the preset value. If critical data is lost, the system will switch to a fixed conservative allocation mode and trigger an alarm. The critical data includes at least the following: current state of charge of the battery, current internal resistance of the battery, average open-circuit voltage of the battery pack, bus terminal voltage of the supercapacitor pack, total power demand of the system, battery current of the previous cycle, and supercapacitor current of the previous cycle.

8. An adaptive power distribution system for a hybrid energy storage system, characterized in that, include: The equalization factor prediction module is used to calculate the dynamic efficiency equalization factor based on the state parameters of the battery cells, the state parameters of the supercapacitor cells, and the total power demand of the system, using the dynamic efficiency equalization factor prediction formula. This includes: The rate of change of total power demand is obtained based on the total power demand of the system. The dynamic efficiency balancing factor is calculated using the dynamic efficiency balancing factor prediction formula based on the battery's rated efficiency weighting coefficient, the battery's current state of charge, the battery's nominal state of charge reference point, the battery's current internal resistance, the battery's maximum allowable internal resistance, the supercapacitor's rated efficiency weighting coefficient, the supercapacitor bank's bus terminal voltage, and the supercapacitor's maximum operating voltage. The prediction formula for the dynamic efficiency equilibrium factor is: in, This is a dynamic efficiency balancing factor. This is the weighting factor for the rated efficiency of the battery. This is the weighting factor for the rated efficiency of the supercapacitor. This represents the battery's current state of charge. This is the reference point for the battery's nominal state of charge. The current internal resistance of the battery. This is the maximum permissible internal resistance of the battery. This refers to the bus terminal voltage of the supercapacitor bank. This is the maximum operating voltage of the supercapacitor. The rate of change of total demand power. The maximum allowable rate of change of power in the system. , , and All are normalized adjustment factors; The power allocation calculation module is used to determine the instantaneous power allocation coefficient of the battery and the instantaneous power allocation coefficient of the supercapacitor based on the dynamic efficiency balancing factor and the total power demand of the system, using a real-time power allocation ratio calculation formula. The real-time power allocation ratio calculation formula is as follows: in, This is the instantaneous power distribution coefficient of the battery. This represents the instantaneous power distribution factor of the supercapacitor. It is the hyperbolic tangent function. To assign sensitivity coefficients, This is a dynamic efficiency balancing factor. This represents the total power demand of the system. This is the system's maximum output power. High power protection factor; The current command calculation module is used to calculate and send battery current reference commands and supercapacitor current reference commands to the corresponding power converters based on the instantaneous power allocation coefficient of the battery, the instantaneous power allocation coefficient of the supercapacitor, the total power demand of the system, the state parameters of the battery cell, the state parameters of the supercapacitor cell, and the battery current of the previous cycle. The feedback correction module is used to perform closed-loop correction on the dynamic efficiency balance factor based on the actual output / input power and actual power loss of the battery and the supercapacitor in the current cycle, to obtain the corrected balance factor, and to generate the dynamic efficiency balance factor for the next cycle based on the corrected balance factor.

Images