Energy management method applied to hybrid energy storage system and related device

Abstract

The invention relates to the technical field of vehicle battery energy management, in particular to an energy management method applied to a hybrid energy storage system and a related device. The method comprises the following steps: establishing a first constraint condition; establishing a second constraint condition; selecting a multi-target particle swarm optimization algorithm; after the multi-target particle swarm optimization algorithm passes through the second preset number of iteration rounds, obtaining a Pareto frontier solution set; selecting a solution with the maximum crowding degree and the minimum dominating count from the Pareto frontier solution set as a storage battery reference current and a super capacitor reference current; and performing energy management on the hybrid energy storage system according to the storage battery reference current and the super capacitor reference current. According to the invention, the optimal current can be found while the optimal power distribution is realized, the overall power consumption of the hybrid energy storage system is further reduced, the service life of the storage battery is prolonged, and the problems of poor energy management effect and high safety risk caused by high power density in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of energy management technology for pure electric vehicles, and in particular to an energy management method and related device applied to a hybrid energy storage system. Background Technology

[0002] In the field of pure electric vehicles, using a single battery as a power source often encounters instantaneous high-power loads. Prolonged high-power, high-current output can lead to battery overheating, and battery performance degrades significantly under high temperatures. Supercapacitors, on the other hand, offer high power density without the risk of overheating. Therefore, adding supercapacitors and batteries to form a hybrid energy storage system can effectively protect battery life by handling high-power loads. However, existing energy management methods are ineffective in distributing power between supercapacitors and batteries, resulting in a shorter battery lifespan. Summary of the Invention

[0003] This application provides an energy management method and related apparatus for hybrid energy storage systems, which can solve at least one of the above-mentioned technical problems.

[0004] According to one aspect of the embodiments of this application, an energy management method for a hybrid energy storage system is proposed, the energy management method for a hybrid energy storage system comprising: The first constraint condition is established based on the sum of the power of the battery and the power of the supercapacitor, as well as the operating conditions of the hybrid energy storage system. A second constraint condition is established based on the current of the battery and the current of the supercapacitor. A multi-objective particle swarm optimization algorithm is selected. The population size of the multi-objective particle swarm optimization algorithm is a first preset number of groups of population particles, and the number of iterations is a second preset number. Each group of population particles includes the current of the battery and the current of the supercapacitor obtained under the first constraint and the second constraint. After the multi-objective particle swarm optimization algorithm has undergone the second preset number of iterations, the Pareto front solution set is obtained; In the Pareto front solution set, the solution with the highest congestion and the lowest dominance count is selected as the battery reference current and the supercapacitor reference current. The battery reference current is the minimum current of the battery in each iteration round, and the supercapacitor reference current is the maximum current of the supercapacitor in each iteration round. Energy management of the hybrid energy storage system is performed based on the battery reference current and the supercapacitor reference current. The multi-objective particle swarm optimization algorithm includes the following steps in each iteration round: Determine the optimal solution in the current iteration round from each group of population particles, wherein the optimal solution corresponds to the optimal population particle in each group of population particles; If the optimal solution in the current iteration is better than the historical optimal solution in the previous iteration, then the optimal solution in the current iteration is updated to the historical optimal solution in the previous iteration, so as to perform iterative optimization in the next iteration. If the optimal solution in the current iteration round is not dominated by the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is added to the Pareto front solution set.

[0005] In the above scheme, establishing the first constraint condition based on the sum of the power of the battery and the power of the supercapacitor, and the operating conditions of the hybrid energy storage system, includes: Determine the power required by the hybrid energy storage system under its operating conditions; The first constraint condition is that the sum of the power of the battery and the power of the supercapacitor is equal to the power required by the hybrid energy storage system under its operating conditions.

[0006] In the above scheme, establishing the second constraint condition based on the current of the battery and the current of the supercapacitor includes: Determine the current limit range of the battery and the current limit range of the supercapacitor; The current limit range of the battery and the current limit range of the supercapacitor are used as the second constraint conditions. Wherein, the current of the battery obtained under the first constraint condition and the second constraint condition is limited to the current limit range of the battery, and the current of the supercapacitor obtained under the first constraint condition and the second constraint condition is limited to the current limit range of the supercapacitor.

[0007] In the above scheme, determining the optimal solution in the current iteration among the population particles in each group includes: Determine a first objective function related to the power of the hybrid energy storage system, and determine a second objective function related to the current of the battery; For each group of population particles, the current of the battery and the current of the supercapacitor obtained under the first and second constraints are input into the first objective function to obtain a first fitness value, and the current of the battery obtained under the first and second constraints is input into the second objective function to obtain a second fitness value. The optimal solution in the current iteration is determined from the first fitness value and the second fitness value corresponding to the population particles in each group.

[0008] In the above scheme, after determining the optimal solution in the current iteration among the population particles in each group, the method further includes: The first fitness value and the second fitness value of the corresponding population particles are determined based on the optimal solution in the current iteration round; The first and second fitness values ​​of the corresponding population particles are determined based on the historical best solution in the historical iteration rounds. If the first fitness value of the population particle corresponding to the optimal solution in the current iteration round is less than the first fitness value of the population particle corresponding to the historical optimal solution in the previous iteration round, and the second fitness value of the population particle corresponding to the optimal solution in the current iteration round is less than the second fitness value of the population particle corresponding to the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is determined to be better than the historical optimal solution in the previous iteration round. If the first fitness value of the population particle corresponding to the optimal solution in the current iteration is greater than or equal to the first fitness value of the population particle corresponding to the historical optimal solution in the previous iteration, or the second fitness value of the population particle corresponding to the optimal solution in the current iteration is greater than or equal to the second fitness value of the population particle corresponding to the historical optimal solution in the previous iteration, then it is determined that the optimal solution in the current iteration and the historical optimal solution in the previous iteration have a non-dominant relationship.

[0009] In the above scheme, after determining the optimal solution in the current iteration among the population particles in each group, the method further includes: Determine the position and velocity of the population particle corresponding to the optimal solution in the current iteration round; If the optimal solution in the current iteration is better than the historical optimal solution in a previous iteration, then the position and velocity of the population particle corresponding to the optimal solution in the current iteration are updated to the next position and the next velocity for the next iteration, so that optimization can be performed in the next iteration based on the next position and the next velocity.

[0010] According to one aspect of the embodiments of this application, an energy management device for a hybrid energy storage system is proposed, the device comprising: The first constraint unit is used to establish the first constraint conditions based on the sum of the power of the battery and the power of the supercapacitor and the operating conditions of the hybrid energy storage system. The second constraint unit is used to establish a second constraint condition based on the current of the battery and the current of the supercapacitor. The selection unit is used to select a multi-objective particle swarm optimization algorithm. The population size of the multi-objective particle swarm optimization algorithm is a first preset number of groups of population particles, and the number of iterations is a second preset number. Each group of population particles includes the current of the battery and the current of the supercapacitor obtained under the first constraint and the second constraint. An iterative unit is used to obtain the Pareto front solution set after the multi-objective particle swarm optimization algorithm has undergone the second preset number of iterations; The selection unit is used to select the solution with the highest congestion and the lowest dominance count from the Pareto front solution set as the battery reference current and the supercapacitor reference current. The battery reference current is the minimum current of the battery in each iteration round, and the supercapacitor reference current is the maximum current of the supercapacitor in each iteration round. An energy management unit is used to manage the energy of the hybrid energy storage system based on the reference current of the battery and the reference current of the supercapacitor. The multi-objective particle swarm optimization algorithm includes the following steps in each iteration round: Determine the optimal solution in the current iteration round from each group of population particles, wherein the optimal solution corresponds to the optimal population particle in each group of population particles; If the optimal solution in the current iteration is better than the historical optimal solution in the previous iteration, then the optimal solution in the current iteration is updated to the historical optimal solution in the previous iteration, so as to perform iterative optimization in the next iteration. If the optimal solution in the current iteration round is not dominated by the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is added to the Pareto front solution set.

[0011] According to one aspect of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the energy management method applied to a hybrid energy storage system as described above.

[0012] According to one aspect of the embodiments of this application, a computer program product is provided, the computer program product including a computer program that is read and executed by a processor of an electronic device, causing the electronic device to perform the energy management method applied to a hybrid energy storage system as described above.

[0013] The beneficial effects of this application are as follows: The hybrid energy storage system proposed in this application includes a battery and a supercapacitor. Based on these, a first constraint related to power and a second constraint related to current are established to build an energy optimization model. Specifically, a multi-objective particle swarm optimization algorithm is selected for iterative optimization, continuously searching for the optimal solution during the iteration process, and obtaining a Pareto front solution set. The Pareto front solution set includes multiple optimal solutions with non-dominated relationships. Therefore, the solution with the highest congestion and lowest dominance count is selected from the Pareto front solution set as the reference current for both the battery and the supercapacitor.

[0014] This application selects reducing the overall power consumption of the hybrid energy storage system and finding the minimum battery output current as optimization objectives, so that the multi-objective particle swarm optimization algorithm continuously searches for the optimal solution and finally obtains the Pareto front solution set, thereby obtaining the final battery reference current and supercapacitor reference current, which can achieve optimal power allocation and thus improve the energy management effect.

[0015] Since each group of particles needs to meet the power requirements of the hybrid energy storage system's operating conditions (i.e., the first constraint) and the second constraint related to current, the battery current needs to be as low as possible to extend its lifespan. Supercapacitors, due to their high power density, have the ability to handle high currents. Therefore, while satisfying the first and second constraints, the battery current tends to be as low as possible during the iteration process, and the supercapacitor current tends to be as high as possible. Thus, the obtained battery reference current is the minimum battery current in each iteration round, and the supercapacitor reference current is the maximum supercapacitor current in each iteration round. This achieves optimized energy management while extending battery lifespan. Attached Figure Description

[0016] Figure 1 This is a system architecture diagram of the energy management method for hybrid energy storage systems provided in the embodiments of this application; Figure 2 A flowchart illustrating an energy management method for a hybrid energy storage system provided in an embodiment of this application; Figure 3 A schematic diagram of the spatial distribution of the Pareto front solution set obtained by the multi-objective particle swarm optimization algorithm provided in this application embodiment; Figure 4 A schematic diagram obtained from a single-objective particle swarm optimization algorithm provided in an embodiment of this application; Figure 5 A graph showing the SOC (energy capacity) of a lithium battery (storage battery) under WLTP conditions over time, provided in the embodiments of this application. Figure 6 A graph showing the SOC (state of charge) of a supercapacitor under WLTP conditions over time, provided in the embodiments of this application. Figure 7 A schematic diagram illustrating the change of vehicle acceleration over time under WLTP conditions, provided as an embodiment of this application. Figure 8 A block diagram of an energy management device applied to a hybrid energy storage system provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] It should be noted that while some processes described in the specification, claims, and accompanying drawings include multiple steps appearing in a specific order, it should be clearly understood that these steps may not be performed in the order they appear herein, or may be performed in parallel. The step numbers are merely used to distinguish different steps and do not themselves represent any execution order. Furthermore, descriptions such as "first," "second," or "objective" in this document are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. "Multiple" in this document refers to at least two.

[0019] It is worth noting that in the specific embodiments of this application, data related to vehicle current, power, etc., are involved. When the above embodiments of this application are applied to specific products or technologies, permission or consent from the target object is required, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards. For example, when an embodiment of this application needs to obtain vehicle current, power, etc., separate permission or consent from the target object can be obtained through pop-up windows or redirection to a confirmation page. After obtaining the target object's separate permission or consent, the necessary current, power, and other related data for enabling the embodiment of this application to operate normally can then be obtained.

[0020] Please see Figure 1 , Figure 1This is a system architecture diagram of the energy management method applied to a hybrid energy storage system provided in this application embodiment. It includes a terminal 140, an Internet connection 130, a gateway 120, a server 110, etc.

[0021] Terminal 140 can take various forms, including desktop computers, laptops, PDAs (personal digital assistants), mobile phones, vehicle terminals, and dedicated terminals. Furthermore, it can be a single device or a collection of multiple devices. For example, multiple desktop computers can be interconnected via a local area network, sharing a single monitor to work collaboratively, forming a single terminal 140. Terminal 140 can communicate with the Internet 130 via wired or wireless means to exchange data.

[0022] Server 110 refers to a computer system capable of providing certain services to terminal 140. Compared to ordinary terminal 140, server 110 has higher requirements in terms of stability, security, and performance. Server 110 can be a single high-performance computer in a network platform, a cluster of multiple high-performance computers, a portion of a single high-performance computer (e.g., a virtual machine), or a combination of portions of multiple high-performance computers (e.g., virtual machines). Server 110 can also communicate with the Internet 130 via wired or wireless means to exchange data.

[0023] Gateway 120, also known as an internetwork connector or protocol converter, is a computer system or device that acts as a translator, enabling network interconnection at the transport layer. It bridges the gap between two systems using different communication protocols, data formats, languages, or even completely different architectures. Gateways can also provide filtering and security functions. Messages sent from terminal 140 to server 110 are forwarded to the corresponding server 110 via gateway 120. Messages sent from server 110 to terminal 140 are also forwarded to the corresponding terminal 140 via gateway 120.

[0024] The following provides a detailed description of the specific implementation methods of the embodiments of this application: Please see Figure 2 , Figure 2 This is a flowchart illustrating an energy management method for a hybrid energy storage system provided in this application embodiment. The energy management method for a hybrid energy storage system can be implemented by server 110 and / or terminal 140. Figure 2 The energy management methods shown for hybrid energy storage systems include: Step 210: Establish the first constraint condition based on the sum of the power of the battery and the power of the supercapacitor and the operating conditions of the hybrid energy storage system. Step 220: Establish a second constraint condition based on the current of the battery and the current of the supercapacitor; Step 230: Select a multi-objective particle swarm optimization algorithm. The population size of the multi-objective particle swarm optimization algorithm is a first preset number of groups of population particles, and the number of iterations is a second preset number. Each group of population particles includes the current of the battery and the current of the supercapacitor obtained under the first constraint and the second constraint. Step 240: After the multi-objective particle swarm optimization algorithm has undergone the second preset number of iterations, the Pareto front solution set is obtained; Step 250: Select the solution with the highest congestion and the lowest dominance count from the Pareto front solution set as the reference current for the battery and the supercapacitor. Step 260: Perform energy management on the hybrid energy storage system based on the battery reference current and the supercapacitor reference current; The multi-objective particle swarm optimization algorithm includes the following steps in each iteration round: Determine the optimal solution in the current iteration round from each group of population particles, wherein the optimal solution corresponds to the optimal population particle in each group of population particles; If the optimal solution in the current iteration is better than the historical optimal solution in the previous iteration, then the optimal solution in the current iteration is updated to the historical optimal solution in the previous iteration, so as to perform iterative optimization in the next iteration. If the optimal solution in the current iteration round is not dominated by the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is added to the Pareto front solution set.

[0025] The complete embodiment of this application will be explained in detail below with reference to steps 210-260: This application proposes a hybrid energy storage system comprising a battery and a supercapacitor. The optimization objectives of this application are to reduce the overall power consumption of the hybrid energy storage system and to find the minimum rate of change of the battery output current and the supercapacitor current. While achieving optimal power distribution, it can also find the optimal current, that is, minimize the battery current, so that the supercapacitor can bear more current (i.e., bear more power output), further reducing the overall power consumption of the hybrid energy storage system and extending the battery life. This solves the problems of poor energy management and high safety risks caused by high power density in the prior art.

[0026] In step 210, a first constraint is established, which is related to the operating conditions of the hybrid energy storage system, i.e., the vehicle's operating conditions, such as WLTP and NEDC. The WLTP test cycle is a relatively new vehicle test procedure that includes four different test zones: low speed, medium speed, high speed, and ultra-high speed, simulating urban congestion, normal urban conditions, intercity expressways, and high-speed extreme conditions, with a total test mileage of 23.27 km. The WLTP test cycle simulates different levels of acceleration and deceleration, and then uses a weighted average method to sum the energy demand under different speed conditions, taking into account the impact of ambient temperature, load, and other factors on energy consumption. Compared with traditional testing methods, it more closely resembles real-world driving conditions.

[0027] The NEDC cycle is an earlier vehicle testing program that includes both urban and suburban test zones. The driving process is relatively smooth, with gentle acceleration and deceleration. This test condition is relatively idealized, ignoring real-world driving variables, and is often used to test a vehicle's fuel consumption.

[0028] This application takes the WLTP operating condition as an example. By determining the power required under the WLTP operating condition, a power balance condition is established. That is, the sum of the power of the battery and the power of the supercapacitor is equal to the power required under the operating condition of the hybrid energy storage system, which serves as the first constraint condition.

[0029] Refer to the following formula: This formula is the expression for the first constraint condition.

[0030] in, P For the power requirements of electric vehicles under specific operating conditions, P b It is the power provided by the battery. P sc The power is provided by the supercapacitor.

[0031] In step 220, a second constraint is established. The second constraint related to current is that the current of the battery and the current of the supercapacitor cannot exceed their actual safe upper limits. That is, the current limit range of the battery and the current limit range of the supercapacitor are determined; these limits are used as the second constraint. The battery current obtained under the first and second constraint conditions is limited to the battery current limit range, and the supercapacitor current obtained under the first and second constraint conditions is limited to the supercapacitor current limit range. Refer to the following formula: ; This formula is the expression for the second constraint condition.

[0032] in, I b For the current of the storage battery, I sc This refers to the current in the supercapacitor. I bmax This is the maximum current of the battery (i.e., the current limit range of the battery). I scmax This is the maximum current of the supercapacitor (i.e., the current limit range of the supercapacitor).

[0033] In step 230, a multi-objective particle swarm optimization algorithm is selected, and the first and second constraints are used as constraints to ensure that the current of the battery in each randomly generated group of particles is within the current limit of the battery, and the current of the supercapacitor in each group of particles is within the current limit of the supercapacitor. At the same time, since the current of the battery and the current of the supercapacitor affect the overall power of the hybrid energy storage system, the product of the current of the randomly generated battery and the terminal voltage of the battery and the product of the current of the supercapacitor and the terminal voltage of the supercapacitor must equal the power required by the hybrid energy storage system under the operating conditions. Therefore, each group of particles obtained satisfies the first and second constraints. Based on this, finding the optimal solution is to find the minimum battery current as the battery reference current, so that the power output it needs to bear is as small as possible, thereby extending the battery's service life.

[0034] The multi-objective particle swarm optimization algorithm, also known as the MOPSO algorithm, has the following algorithm flow: Step 1: Particle swarm (population particles) parameter settings. The population size is the first preset number, for example, the first preset number is 50, and the variable dimension is 2-dimensional. I b , I sc The iteration rounds are a second preset number, for example, 100. The inertia weight is exemplarily set to 0.8, and the acceleration factor is exemplarily set to 1.5. The variables corresponding to the current of the lithium battery and the current of the supercapacitor are constrained, that is, constrained using the first and second constraints. (Selection) I b , I scAs a swarm particle, the iteration rounds are set to 100, and the swarm size is 50 groups. That is, all 50 groups of swarm particles need to go through 100 iterations, and the position and velocity of the initial swarm particles are randomly generated. It should be noted that the position and velocity of the swarm particles are commonly used expressions in multi-objective particle swarm optimization algorithms, and are not the physical position and velocity.

[0035] Step 2: Randomly initialize the positions and velocities of the population particles, select a fitness function (i.e., the first objective function and the second objective function), and calculate the initial (first round) fitness value, the individual optimal (the best group of particles in each iteration round, e.g., the optimal solution of particle A in 100 iterations), and the population optimal (the best among the 50 groups of particles during the iteration process). The first objective function is... F 1. The second objective function is F 2. Refer to the following formula: ; ;

[0036] in, R b This is the internal resistance of the battery. R sc This is the internal resistance of the supercapacitor.

[0037] Population optimality can be understood as follows: for example, after 3 rounds of iteration, the optimal group of population particles is selected from the 3 rounds of iteration, which is to say, the population optimality is selected from 3×50 groups.

[0038] Step 3: For each population particle X i Calculate the fitness value on the objective function, that is F 1 and F 2, where F1 represents the fitness value on the first objective function. F 2 represents the fitness value on the second objective function.

[0039] Step 4: Based on the position and velocity of the particles in the current round, as well as the global optimal solution (i.e., the swarm optimal) and the individual optimal solution (i.e., the individual optimal), update the position and velocity of the swarm particles using the following formula:

[0040] Where, t This represents the current iteration round number; ω Inertial weights; c 1. c 2 is the acceleration factor; r 1.r 2 is a random number between (0, 1), and Pi is the number of digits. i The historical individual optimal solution of each particle (i.e., the individual optimal solution, which is determined according to the current iteration round). G It is the globally optimal solution (i.e., the population optimal solution, which depends on the current iteration round).

[0041] Step 5: For each group of particles, compare the current best solution obtained in the current iteration with the historical best solutions. If the fitness value of the current best solution is better than that of the historical best solution, it means the fitness value of the current best solution is smaller than that of the historical best solution. In this case, the current best solution will be used as the historical best solution in the next iteration. Based on the updated particle position and velocity, proceed to the next iteration. That is, repeat steps 3 to 4 until the maximum number of iterations is reached, then stop the iteration and output the result.

[0042] Step 6: That is, in step 240, record the non-dominated solution set obtained in each iteration round. After a second preset number of iteration rounds (100 rounds), generate the Pareto front solution set and plot it. F 1- F 2. Two-dimensional spatial distribution (e.g.) Figure 3 (As shown). In step 250, the solution with the highest congestion and lowest dominance count in the Pareto front is selected as the reference current for the battery and the supercapacitor.

[0043] If it is a single-objective PSO algorithm, that is, a single-objective particle swarm optimization algorithm, the optimal current it finds... I b For 170.07A (e.g.) Figure 4 (As shown). The optimal algorithm found by the multi-objective particle swarm optimization algorithm, MOPSO, is... I b It is 170.37A. I sc It is 85.38A.

[0044] In step 260, energy management of the hybrid energy storage system is performed based on the reference current of the battery and the reference current of the supercapacitor. Taking WLTP operation as an example, the required power is calculated. The energy-carrying ratio of the battery and supercapacitor is determined, and the DC / DC power converter parameters are selected. Then, PWM is modulated to obtain the corresponding duty cycle. After energy allocation is completed, the battery current is optimized. Instantaneous high power is carried out by the supercapacitor, and the power allocation strategy is implemented by using the magnitude of acceleration as a logic switch. Battery current. I b Supercapacitor current I scThese data are input as variables into a multi-objective particle optimization algorithm to obtain the optimal solution. The PID controller adjusts the current in the battery and supercapacitor circuit to provide the vehicle with the required power. Furthermore, based on the magnitude of the vehicle's acceleration, the system can intelligently switch to different operating modes to achieve optimal power distribution.

[0045] This application proposes a control strategy using a multi-objective particle swarm optimization (PSO) algorithm to achieve power allocation in a hybrid energy storage system. Independent variables (battery current and supercapacitor current), optimization objectives (reducing overall power consumption and minimizing battery current), and constraints (first and second constraints) are combined to construct an energy optimization model, which is then input into the PSO algorithm. Simulation software is used to calculate the Pareto front solution set composed of the battery reference current and the supercapacitor reference current. Finally, the solution with the highest congestion and lowest dominance from the Pareto front is selected as the final optimal solution.

[0046] Based on this, the optimal solution found can be used to achieve optimal power allocation, for example... Figure 5 As shown, initially, the SOC of the lithium battery (i.e., the storage battery described in this application) is 0.9. Throughout the WLTP cycle, the SOC of the lithium battery continuously decreases until it reaches 0.75. The rate of change is low throughout the process, which is beneficial for extending the lifespan of the lithium battery. For example... Figure 6 As shown, the initial SOC of the supercapacitor is 0.99. From 0 to 200 seconds, it mainly shows a decreasing trend, remaining constant for a short period, indicating that the supercapacitor receives no energy feedback during this period. From 200 to 800 seconds, the supercapacitor's SOC decreases, then increases, then decreases again, indicating repeated charging and discharging. Supercapacitors have high power density and a high number of charge / discharge cycles. Based on power allocation, in urban congestion scenarios, this high-frequency charging and discharging behavior should be handled by the supercapacitor. Simulation results also confirm that this allocation strategy is the superior strategy. From 800 to 1200 seconds, the supercapacitor's SOC continuously increases, indicating a charging state. Combined with WLTP acceleration, as... Figure 7 If we define high power as an absolute acceleration value greater than or equal to 2, then during this time period, the electric vehicle only experiences high power fluctuations around the 1100th second. Therefore, the energy supply is primarily provided by the lithium battery (i.e., by increasing the current of the lithium battery). I b During this period, the lithium battery simultaneously charges the supercapacitor, keeping it at a high SOC to cope with sudden large power fluctuations. The SOC changes significantly and frequently during this process, and the power distribution to it is in line with the characteristics of the supercapacitor itself.

[0047] In summary, this application addresses the problem of high power output demand from a single battery under urban operating conditions by proposing a power allocation strategy for the coordinated control of dual power sources in electric vehicles, optimized by a multi-objective particle swarm optimization algorithm. The matching parameters of the battery and supercapacitor are selected by calculating the required power under operating conditions (i.e.,...). I b and I sc A hybrid energy storage system (HESS) combining batteries and supercapacitors was established based on their complementary characteristics. Power allocation in the HESS was simulated using Matlab / Simulink. The simulation model parameters were optimized using a multi-objective particle swarm optimization algorithm. From an economic perspective, minimizing battery current was the primary optimization objective, while reducing overall power loss was a secondary objective. Battery and supercapacitor currents were used as decision variables, and corresponding fitness functions were established to obtain the optimal solution. Power allocation for the HESS was then performed based on the operating conditions. Validation under WLTP conditions demonstrated that the proposed energy management strategy reduced frequent battery charging and discharging, effectively improving battery range and extending battery life.

[0048] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an energy management device 300 applied to a hybrid energy storage system according to an embodiment of this application. The energy management device 300 applied to the hybrid energy storage system is applied to a computer device, and the energy management device 300 applied to the hybrid energy storage system may include: The first constraint unit 301 is used to establish a first constraint condition based on the sum of the power of the battery and the power of the supercapacitor and the operating conditions of the hybrid energy storage system. The second constraint unit 302 is used to establish a second constraint condition based on the current of the battery and the current of the supercapacitor. The selection unit 303 is used to select a multi-objective particle swarm optimization algorithm. The population size of the multi-objective particle swarm optimization algorithm is a first preset number of groups of population particles, and the number of iterations is a second preset number. Each group of population particles includes the current of the battery and the current of the supercapacitor obtained under the first constraint and the second constraint. The iteration unit 304 is used to obtain the Pareto front solution set after the multi-objective particle swarm optimization algorithm has gone through the second preset number of iterations; Selection unit 305 is used to select the solution with the largest congestion and the smallest dominance count from the Pareto front solution set as the battery reference current and the supercapacitor reference current. Energy management unit 306 is used to perform energy management on the hybrid energy storage system based on the battery reference current and the supercapacitor reference current. The multi-objective particle swarm optimization algorithm includes the following steps in each iteration round: Determine the optimal solution in the current iteration round from each group of population particles, wherein the optimal solution corresponds to the optimal population particle in each group of population particles; If the optimal solution in the current iteration is better than the historical optimal solution in the previous iteration, then the optimal solution in the current iteration is updated to the historical optimal solution in the previous iteration, so as to perform iterative optimization in the next iteration. If the optimal solution in the current iteration round is not dominated by the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is added to the Pareto front solution set.

[0049] Reference Figure 9 , Figure 9 To implement the structural block diagram of a portion of the terminal 140 in this application embodiment, the terminal 140 includes: a radio frequency (RF) circuit 710, a memory 715, an input unit 730, a display unit 740, a sensor 750, an audio circuit 760, a wireless fidelity (WiFi) module 770, a processor 780, and a power supply 790, among other components. Those skilled in the art will understand that... Figure 9 The terminal 140 structure shown does not constitute a limitation on a mobile phone or computer, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0050] The RF circuit 710 can be used to receive and transmit signals during information transmission or calls. In particular, it receives downlink information from the base station and processes it with the processor 780; in addition, it transmits uplink data to the base station.

[0051] The memory 715 can be used to store software programs and modules. The processor 780 executes various functional applications of the terminal and energy management processing applied to the hybrid energy storage system by running the software programs and modules stored in the memory 715.

[0052] The input unit 730 can be used to receive input numeric or character information, and to generate key signal inputs related to the terminal's settings and function control. Specifically, the input unit 730 may include a touch panel 731 and other input devices 732.

[0053] The display unit 740 can be used to display input or provided information, as well as various menus of the terminal. The display unit 740 may include a display panel 741.

[0054] Audio circuitry 760, speaker 761, and microphone 762 provide an audio interface.

[0055] In this embodiment, the processor 780 included in the terminal 140 can execute the energy management method applied to the hybrid energy storage system in the previous embodiment.

[0056] The terminal 140 in this application embodiment includes, but is not limited to, mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle terminals, and aircraft. This application embodiment can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, and assisted driving.

[0057] Figure 10 This is a partial structural block diagram of a server 110 implementing an embodiment of this application. The server 110 can vary significantly due to different configurations or performance characteristics, and may include one or more central processing units (CPUs) 822 (e.g., one or more processors) and memory 832, and one or more storage media 830 (e.g., one or more mass storage devices) for storing application programs 842 or data 844. The memory 832 and storage media 830 can be temporary or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the server 110. Furthermore, the CPU 822 may be configured to communicate with the storage media 830 and execute the series of instruction operations in the storage media 830 on the server 110.

[0058] Server 110 may also include one or more power supplies 826, one or more wired or wireless network interfaces 850, one or more input / output interfaces 858, and / or one or more operating systems 841, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0059] The central processing unit 822 in server 110 can be used to execute the energy management method applied to a hybrid energy storage system according to the embodiments of this application.

[0060] This application also provides a computer-readable storage medium for storing program code for executing the energy management methods applied to hybrid energy storage systems described in the foregoing embodiments.

[0061] This application also provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, causing the computer device to perform the energy management method described above for a hybrid energy storage system.

[0062] Furthermore, the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0063] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0064] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0066] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0067] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0068] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0070] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0071] The above is a detailed description of the embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An energy management method applied to a hybrid energy storage system, characterized in that, The hybrid energy storage system includes a battery and a supercapacitor, and the method includes: The first constraint condition is established based on the sum of the power of the battery and the power of the supercapacitor, as well as the operating conditions of the hybrid energy storage system. A second constraint condition is established based on the current of the battery and the current of the supercapacitor. A multi-objective particle swarm optimization algorithm is selected. The population size of the multi-objective particle swarm optimization algorithm is a first preset number of groups of population particles, and the number of iterations is a second preset number. Each group of population particles includes the current of the battery and the current of the supercapacitor obtained under the first constraint and the second constraint. After the multi-objective particle swarm optimization algorithm has undergone the second preset number of iterations, the Pareto front solution set is obtained; In the Pareto front solution set, the solution with the highest congestion and the lowest dominance count is selected as the battery reference current and the supercapacitor reference current. The battery reference current is the minimum current of the battery in each iteration round, and the supercapacitor reference current is the maximum current of the supercapacitor in each iteration round. Energy management of the hybrid energy storage system is performed based on the battery reference current and the supercapacitor reference current. The multi-objective particle swarm optimization algorithm includes the following steps in each iteration round: Determine the optimal solution in the current iteration round from each group of population particles, wherein the optimal solution corresponds to the optimal population particle in each group of population particles; If the optimal solution in the current iteration is better than the historical optimal solution in the previous iteration, then the optimal solution in the current iteration is updated to the historical optimal solution in the previous iteration, so as to perform iterative optimization in the next iteration. If the optimal solution in the current iteration round is not dominated by the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is added to the Pareto front solution set.

2. The energy management method for a hybrid energy storage system according to claim 1, characterized in that, The establishment of the first constraint condition based on the sum of the power of the battery and the power of the supercapacitor, and the operating conditions of the hybrid energy storage system, includes: Determine the power required by the hybrid energy storage system under its operating conditions; The first constraint condition is that the sum of the power of the battery and the power of the supercapacitor is equal to the power required by the hybrid energy storage system under its operating conditions.

3. The energy management method applied to a hybrid energy storage system according to claim 1, characterized in that, The establishment of the second constraint condition based on the current of the battery and the current of the supercapacitor includes: Determine the current limit range of the battery and the current limit range of the supercapacitor; The current limit range of the battery and the current limit range of the supercapacitor are used as the second constraint conditions. Wherein, the current of the battery obtained under the first constraint condition and the second constraint condition is limited to the current limit range of the battery, and the current of the supercapacitor obtained under the first constraint condition and the second constraint condition is limited to the current limit range of the supercapacitor.

4. The energy management method applied to a hybrid energy storage system according to claim 1, characterized in that, Determining the optimal solution in the current iteration among the population particles in each group includes: Determine a first objective function related to the power of the hybrid energy storage system, and determine a second objective function related to the current of the battery; For each group of population particles, the current of the battery and the current of the supercapacitor obtained under the first and second constraints are input into the first objective function to obtain a first fitness value, and the current of the battery obtained under the first and second constraints is input into the second objective function to obtain a second fitness value. The optimal solution in the current iteration is determined from the first fitness value and the second fitness value corresponding to the population particles in each group.

5. The energy management method for a hybrid energy storage system according to claim 4, characterized in that, After determining the optimal solution in the current iteration among the population particles in each group, the method further includes: The first fitness value and the second fitness value of the corresponding population particles are determined based on the optimal solution in the current iteration round; The first and second fitness values ​​of the corresponding population particles are determined based on the historical best solution in the historical iteration rounds. If the first fitness value of the population particle corresponding to the optimal solution in the current iteration round is less than the first fitness value of the population particle corresponding to the historical optimal solution in the previous iteration round, and the second fitness value of the population particle corresponding to the optimal solution in the current iteration round is less than the second fitness value of the population particle corresponding to the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is determined to be better than the historical optimal solution in the previous iteration round. If the first fitness value of the population particle corresponding to the optimal solution in the current iteration is greater than or equal to the first fitness value of the population particle corresponding to the historical optimal solution in the previous iteration, or the second fitness value of the population particle corresponding to the optimal solution in the current iteration is greater than or equal to the second fitness value of the population particle corresponding to the historical optimal solution in the previous iteration, then it is determined that the optimal solution in the current iteration and the historical optimal solution in the previous iteration have a non-dominant relationship.

6. The energy management method applied to a hybrid energy storage system according to claim 5, characterized in that, After determining the optimal solution in the current iteration among the population particles in each group, the method further includes: Determine the position and velocity of the population particle corresponding to the optimal solution in the current iteration round; If the optimal solution in the current iteration is better than the historical optimal solution in a previous iteration, then the position and velocity of the population particle corresponding to the optimal solution in the current iteration are updated to the next position and the next velocity for the next iteration, so that optimization can be performed in the next iteration based on the next position and the next velocity.

7. An energy management device for use in a hybrid energy storage system, characterized in that, The hybrid energy storage system includes a battery and a supercapacitor, and the device includes: The first constraint unit is used to establish the first constraint conditions based on the sum of the power of the battery and the power of the supercapacitor and the operating conditions of the hybrid energy storage system. The second constraint unit is used to establish a second constraint condition based on the current of the battery and the current of the supercapacitor. The selection unit is used to select a multi-objective particle swarm optimization algorithm. The population size of the multi-objective particle swarm optimization algorithm is a first preset number of groups of population particles, and the number of iterations is a second preset number. Each group of population particles includes the current of the battery and the current of the supercapacitor obtained under the first constraint and the second constraint. An iterative unit is used to obtain the Pareto front solution set after the multi-objective particle swarm optimization algorithm has undergone the second preset number of iterations; The selection unit is used to select the solution with the highest congestion and the lowest dominance count from the Pareto front solution set as the battery reference current and the supercapacitor reference current. The battery reference current is the minimum current of the battery in each iteration round, and the supercapacitor reference current is the maximum current of the supercapacitor in each iteration round. An energy management unit is used to manage the energy of the hybrid energy storage system based on the reference current of the battery and the reference current of the supercapacitor. The multi-objective particle swarm optimization algorithm includes the following steps in each iteration round: Determine the optimal solution in the current iteration round from each group of population particles, wherein the optimal solution corresponds to the optimal population particle in each group of population particles; If the optimal solution in the current iteration is better than the historical optimal solution in the previous iteration, then the optimal solution in the current iteration is updated to the historical optimal solution in the previous iteration, so as to perform iterative optimization in the next iteration. If the optimal solution in the current iteration round is not dominated by the historical optimal solution in the previous iteration round, then the optimal solution in the current iteration round is added to the Pareto front solution set.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the energy management method for hybrid energy storage systems as described in any one of claims 1 to 6.

9. A computer program product, the computer program product comprising a computer program, characterized in that, The computer program is read and executed by the processor of the electronic device, causing the electronic device to perform the energy management method for a hybrid energy storage system as described in any one of claims 1 to 6.

Images