A method and system for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a super capacitor

Abstract

The present invention relates to a method and system for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a super capacitor. The system includes a lithium battery pack, a super capacitor, an energy management controller, a drive system, an on-vehicle remote terminal, an on-vehicle multimedia, and a cloud platform; a fuzzy controller is provided in the energy management controller; the energy management controller is respectively connected to the lithium battery pack, the super capacitor, the drive system, and the on-vehicle remote terminal; the on-vehicle multimedia is connected to the on-vehicle remote terminal for parameter download requests and data upload requests; the cloud platform is communicatively connected to the on-vehicle remote terminal for data invocation and storage. The present invention not only reasonably plans the energy usage and distribution of a vehicle with a dual energy source on the premise of meeting the vehicle driving requirements, but also proposes a system solution for jointly autonomously updating fuzzy parameters in the cloud, which greatly improves the applicability of this strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and particularly to a method and system for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a super capacitor. Background Art

[0002] Currently, automotive electrification technology is becoming more and more mature. Traditional electric vehicles usually use a power battery as the only energy source. This energy supply scheme has high requirements for the cold start time, the number of start cycles, and the load response speed of the power battery. Therefore, the power battery usually needs to work at a relatively low current level to reduce its own usage loss, which indirectly limits the performance of electric vehicles in terms of power performance (such as acceleration, climbing, etc.). Therefore, an energy supply scheme for electric vehicles combined with supercapacitors has been proposed. Supercapacitors have the characteristics of rapid discharge, high specific power, and long cycle life. When used in parallel with traditional batteries as the vehicle's energy source, they can well make up for the deficiency of the battery's discharge current. The battery-supercapacitor vehicle energy supply system is as Figure 1 shown, where the bidirectional power flow directions respectively represent the system supplying power to the drive system and the energy recovery of the drive system.

[0003] After introducing a dual energy source energy supply system including supercapacitors into electric vehicles, it is necessary to formulate an energy management strategy to allocate the working areas of the battery and the supercapacitor. Conventional energy management strategies usually divide the energy supply system state into several according to the component power in the system or the vehicle operating state, and establish a series of static parameters corresponding to each state of the system. During actual vehicle operation, the energy management controller will select these static parameters according to the vehicle state to achieve the energy distribution management of the vehicle.

[0004] In the prior art, Chinese Patent No. CN110834551A discloses an energy management control system for a pure electric vehicle. This patent elaborates in detail on the hardware composition of the control system, and divides the main relay into four working states according to the combination relationship between the key signals in the system and the battery pack voltage. And each state has a different system power supply scheme. However, the state division of the main relay mainly depends on the voltage range of the battery pack. The patent does not mention the value-taking method of the battery pack voltage, and the given fixed value may not be applicable to other battery systems.

[0005] A Chinese patent with the publication number CN110466388A discloses a method for energy management of a composite energy storage electric vehicle. By using the relationship between temperature rise, battery heat generation and battery internal resistance, this patent estimates the change of battery internal resistance and makes the discharge process of the battery tend to be stable through a battery management strategy, thus slowing down the battery aging speed and reducing costs. However, when formulating strategies such as extending the life of the energy system or reducing the cost of the energy system, it starts from within the energy supply system and does not consider the driving needs of the vehicle during actual operation, which is very likely to affect the driving experience of the driver.

[0006] A Chinese patent with the publication number CN110077389A discloses a method for energy management of a plug-in hybrid electric vehicle. In this patent, by collecting various vehicle parameters and environmental impact factors during the driving process of the vehicle and using a BP neural network to allocate the energy of the hybrid electric vehicle, the purpose of reducing energy consumption is achieved. However, the implementation of this technology requires the vehicle to be equipped with an energy management controller with sufficient computing power. Otherwise, the operation output based on the BP neural network may have a delay in matching with the input vehicle parameters, resulting in the inability to achieve the expected control effect. Summary of the Invention

[0007] In view of the above deficiencies of the prior art, the present invention provides a method and system for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a supercapacitor, which not only reasonably plans the energy use and distribution of a vehicle with a dual energy source on the premise of meeting the driving needs of the vehicle, but also proposes a system solution for jointly and autonomously updating fuzzy parameters in the cloud, greatly improving the applicability of this strategy.

[0008] In order to achieve the above object and other related objects, the technical solutions provided by the present invention are as follows:

[0009] A method for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a supercapacitor, comprising the following steps:

[0010] M1: Based on standard road condition data, use the particle swarm optimization algorithm to optimize and output a set of standard fuzzy control parameters, initialize the fuzzy controller in the energy management controller, and output the power distribution factor K bat ;

[0011] M2: According to the power distribution factor K bat , perform output power distribution on the vehicle lithium battery pack and supercapacitor, and the distribution function is as follows:

[0012] W 锂 =K bat *p total , W 容 =(1 - K bat )*p total , where W 锂is the output power of the lithium battery pack, in W 容 is the power of the super capacitor, p total is the power required by the vehicle drive system;

[0013] M3: Obtain the power p required by the vehicle drive system in real time according to the energy management controller total , the current state of charge SOC of the lithium battery li and the current state of charge SOC of the super capacitor sc , and perform optimization iteration using the genetic algorithm to output optimized fuzzy control parameters;

[0014] M4: Input the optimized fuzzy control parameters into the fuzzy controller, update the parameters in the fuzzy controller, and output the corresponding power distribution factor K bat , according to the corresponding power distribution factor K bat , perform energy distribution on the vehicle lithium battery pack and the super capacitor.

[0015] Further, in step M1, the particle swarm algorithm includes the following steps:

[0016] M11: Initialization: Set the magnitudes of various vehicle control parameters, the initial position and initial speed of the vehicle;

[0017] M12: Calculate the objective function of each control parameter, and find the current extreme value of each control parameter and the current optimal solution P of all control parameters gd ;

[0018] M13: Update the values of each control parameter and the weights

[0019] M14: Whether the given threshold is reached. If not, return to step M12; if so, output the optimal solution.

[0020] Further, in step c), the update equation is:

[0021]

[0022]

[0023] where ω is the inertia factor, C 1 and C 2 are acceleration constants, random(0,1) is any number in the interval [0,1], is the current extreme value of each control parameter, P gd is the current optimal solution of all control parameters.

[0024] Further, in step M3, the steps for the genetic algorithm to perform optimization iteration are as follows:

[0025] M31: Taking the mathematical model of the vehicle's energy loss as the objective function and the fuzzy control parameters as variables, a mathematical model of the objective function is constructed;

[0026] M32: Inputting a set of parameter values into the fuzzy controller to generate fuzzy rules, performing operations in combination with the power demand data and the SOC value of the energy supply system, and outputting a power distribution factor;

[0027] M33: Substituting the output power distribution factor into the mathematical model serving as the objective function, calculating and selecting one or more parameters that minimize the vehicle's energy consumption for the next iteration until there is only one parameter value obtained by iteration or the algorithm reaches the set number of iterations, and outputting the optimized fuzzy control parameters.

[0028] To achieve the above and other related objectives, the present invention also provides an energy distribution management system for an electric vehicle with a dual energy source of a lithium battery and a super capacitor. The system includes a lithium battery pack, a super capacitor, an energy management controller, a drive system, an on-vehicle remote terminal, on-vehicle multimedia, and a cloud platform;

[0029] A fuzzy controller is provided inside the energy management controller;

[0030] The energy management controller is respectively connected to the lithium battery pack, the super capacitor, the drive system, and the on-vehicle remote terminal;

[0031] The cloud platform is communicatively connected to the on-vehicle remote terminal for data invocation and storage.

[0032] Further, the fuzzy controller outputs a power distribution factor to the energy management controller according to the vehicle drive demand power provided by the drive system, and the energy management controller controls the energy distribution of the lithium battery pack and the super capacitor.

[0033] Further, the lithium battery pack is connected in parallel with the super capacitor.

[0034] Further, the on-vehicle multimedia and the on-vehicle remote terminal are communicatively connected based on the full CAN network.

[0035] Further, the on-vehicle multimedia is connected to the on-vehicle remote terminal for parameter download requests and data upload requests.

[0036] Further, the energy management controller receives the lithium battery power information and the super capacitor power information in real time.

[0037] The present invention has the following positive effects:

[0038] 1. On the premise of meeting the vehicle driving requirements, the present invention reasonably plans the energy use and distribution of a vehicle with dual energy sources.

[0039] 2. The system solution proposed by the present invention for jointly updating fuzzy parameters autonomously in the cloud greatly improves the applicability of this strategy.

[0040] 3. In the fuzzy rule generation framework proposed by the present invention, different control requirements can be achieved by changing the algorithm objective function. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic flow chart of the method of the present invention;

[0042] Figure 2 is a schematic system framework diagram of the present invention;

[0043] Figure 3 is the input variable P of the present invention total membership function graph;

[0044] Figure 4 is the input variable SOC of the present invention li membership function graph;

[0045] Figure 5 is the input variable SOC of the present invention sc membership function graph;

[0046] Figure 6 is the output variable K of the present invention bat membership function graph. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1: As Figure 1 shown, a method for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a supercapacitor includes the following steps:

[0049] M1: Based on standard road condition data, a group of standard fuzzy control parameters are optimized by using the particle swarm algorithm, the fuzzy controller in the energy management controller is initialized, and the power distribution factor K is output bat ;

[0050] M2: According to the power distribution factor K bat, the output power of the vehicle's lithium battery pack and super capacitor is allocated, and the allocation function is as follows:

[0051] W 锂 = K bat * p total , W 容 = (1 - K bat ) * p total , where W 锂 is the output power of the lithium battery pack, and W 容 is the power of the super capacitor, and p total is the power required by the vehicle drive system;

[0052] M3: According to the energy management controller, the power required by the vehicle drive system p total , the current lithium battery state of charge SOC li and the current super capacitor state of charge SOC sc are obtained in real time, and genetic algorithm is used for optimization iteration to output optimized fuzzy control parameters;

[0053] M4: The optimized fuzzy control parameters are input into the fuzzy controller to update the parameters in the fuzzy controller, and the corresponding power distribution factor K bat is output. According to the corresponding power distribution factor K bat , the energy of the vehicle's lithium battery pack and the super capacitor is allocated.

[0054] In step M1, the particle swarm algorithm includes the following steps:

[0055] a) Initialization: Set the sizes of various vehicle control parameters, the initial position and initial speed of the vehicle;

[0056] b) Calculate the objective functions of various control parameters to find the current extreme values of various control parameters and the current optimal solution P gd of all control parameters;

[0057] c) Update the values and weights

[0058] d) Whether the given threshold is reached. If not, return to step b). If so, output the optimal solution.

[0059] In step c), the update equation is:

[0060]

[0061]

[0062] where ω is the inertia factor, C1 and C 2 is the acceleration constant, and random(0,1) is any number in the interval [0,1]. is the current extreme value of each control parameter, and P gd is the current optimal solution of all control parameters.

[0063] In step M3, the steps of the genetic algorithm for optimization iteration are as follows:

[0064] 1) Taking the mathematical model of the vehicle's energy loss as the objective function and the fuzzy control parameters as variables, a mathematical model of the objective function is constructed;

[0065] 2) Inputting a set of parameter values into the fuzzy controller to generate fuzzy rules, and performing operations in combination with the power demand data and the SOC value of the energy supply system to output the power distribution factor;

[0066] 3) Substituting the output power distribution factor into the mathematical model serving as the objective function, calculating and selecting one or more parameters that minimize the vehicle's energy consumption for the next iteration until the parameter values obtained by iteration are only one or the algorithm reaches the set number of iterations, and outputting the optimized fuzzy control parameters.

[0067] As Figure 2 shown, in order to achieve the above and other related purposes, the present invention also provides an energy distribution management system for an electric vehicle with a dual energy source of a lithium battery and a supercapacitor. The system includes a lithium battery pack, a supercapacitor, an energy management controller, a drive system, an in-vehicle remote terminal, in-vehicle multimedia, and a cloud platform; a fuzzy controller is provided in the energy management controller;

[0068] The energy management controller is respectively connected to the lithium battery pack, the supercapacitor, the drive system, and the in-vehicle remote terminal;

[0069] The in-vehicle multimedia is connected to the in-vehicle remote terminal and is used for parameter download requests and data upload requests; the cloud platform is communicatively connected to the in-vehicle remote terminal and is used for data invocation and storage.

[0070] The fuzzy controller outputs a power distribution factor to the energy management controller according to the vehicle drive demand power provided by the drive system, and the energy management controller controls the energy distribution of the lithium battery pack and the supercapacitor.

[0071] Embodiment 2: According to the description in the overall technical solution, the key control parameter of the strategy is the power distribution factor Kbat, which determines the energy supply distribution of each energy source under different power demands of the vehicle. To obtain this parameter, it is necessary to combine the real-time P total and SOCli and the SOC sc It is obtained through data operation. Therefore, the fuzzy subsets of the input variables and output variables of the fuzzy controller can be defined as follows:

[0072] P total : {NB, NM, NS, ZE, PS, PM, PB}

[0073] SOC li : {LE, ME, GE}

[0074] SOC sc : {LE, ME, GE}

[0075] K bat : {NB, NM, NS, ZE, PS, PM, PB}.

[0076] Figure 4 — Figure 6 In it, Qi is the parameter of the membership function and needs to be obtained through the iterative operation of the genetic algorithm. Figure 4 — Figure 6 In it, the universes of discourse of the input variables are [-3, 3], [1, 3] and [1, 3] in sequence, and the universe of discourse of the output variable is [1, 5]. The upper and lower limits of the interval represent the maximum and minimum values mapped by their physical values respectively. Taking K bat as an example, '1' represents the minimum value 0 of the battery power distribution ratio, and '5' represents the maximum value 1. Among them, the negative value of the input variable P total interval represents the energy feedback of the vehicle.

[0077] Combined with the membership function, the following input-output rules can be formulated according to the fuzzy control principle:

[0078] Table 1 is the output rule of K sc when the SOC bat is LE

[0079]

[0080] Table 2 is the output rule of K sc when the SOC bat is ME

[0081]

[0082] Table 3 is the output rule of K sc when the SOC bat is GE

[0083]

[0084] The above fuzzy rules can be adjusted according to the actual conditions of the vehicle. To obtain the membership function parameter Qi, a genetic algorithm operation needs to be carried out with the minimum vehicle energy consumption as the objective function. The vehicle energy consumption can be judged according to the SOC value of the on-vehicle energy source. Therefore, the algorithm flow is described as follows:

[0085] ① Encode Qi as the algorithm variable, determine the search range of the algorithm according to the domain size of the membership function, and randomly generate several groups of initial data;

[0086] ② Use the standard genetic algorithm iteration formula for iteration;

[0087] ③ Decode the data obtained by iteration and bring it into the fuzzy controller. Based on the dual-energy-source pure electric vehicle model established on the ADVISOR platform, perform simulation. Use the data of a cycle condition to simulate the change value of the energy source SOC. Compare the group of parameters with the smallest current SOC change value with the parameters of the historical minimum SOC change value and update them as the current optimal solution of the algorithm.

[0088] ④ Repeat steps ② and ③ until the maximum number of iterations is reached or the improvement step of the current optimal value of the algorithm is less than the specified threshold. Output the current optimal solution of the algorithm as the corresponding parameters of the membership function and the fuzzy rules.

[0089] In summary, the present invention not only reasonably plans the energy use allocation of vehicles with dual energy sources on the premise of meeting the vehicle driving requirements, but also proposes a system solution for jointly and autonomously updating fuzzy parameters in the cloud, which greatly improves the applicability of this strategy.

[0090] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirits of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a super capacitor, characterized in that, the method includes the following steps: M1. Based on the standard road condition data, a particle swarm optimization algorithm is used to optimize and output a set of standard fuzzy control parameters to initialize the fuzzy controller in the energy management controller, and the output power distribution factor K is output bat ; M2. According to the power distribution factor K bat , the output power of the vehicle lithium battery pack and the super capacitor is distributed, and the distribution function is as follows: W 锂 = K bat * p total ,W 容 = (1 - K bat ) * p total ,where W 锂 is the output power of the lithium battery pack, Wcap is the power of the super capacitor, and p total is the power required by the vehicle drive system; M3. Obtain the required power p of the vehicle drive system in real time according to the energy management controller total , the current state of charge (SOC) of the lithium battery li and the current state of charge (SOC) of the super capacitor sc , and use the genetic algorithm for optimization iteration to output the optimized fuzzy control parameters; M4. Input the optimized fuzzy control parameters into the fuzzy controller to update the parameters in the fuzzy controller and output the corresponding power distribution factor K bat , according to the corresponding power distribution factor K bat , perform energy distribution on the vehicle lithium battery pack and the super capacitor; In step M1, the particle swarm optimization algorithm includes the following steps: M11. Initialization: Set the magnitudes of various vehicle control parameters, the initial position and initial speed of the vehicle; M12. Calculate the objective function of each control parameter and find the current extreme value of each control parameter and the current optimal solution P of all control parameters gd ; M13. Update the values of each control parameter and weights ; M14. Whether the given threshold is reached. If not, return to step M12. If so, output the optimal solution; In step M13, the update equation is, ， ， where ω is the inertia factor, C 1 and C 2 are acceleration constants, random(0,1) is any number in the interval [0,1], is the current extreme value of each control parameter, P gd is the current optimal solution of all control parameters; In step M3, the steps for the genetic algorithm to perform optimization iteration are: M31. Take the mathematical model of the overall vehicle energy loss as the objective function and the fuzzy control parameters as variables to construct the objective function mathematical model; M32. Input a set of parameter values into the fuzzy controller to generate fuzzy rules, perform operations in combination with the power demand data and the SOC value of the energy supply system, and output the power distribution factor; M33. Substitute the output power distribution factor into the mathematical model serving as the objective function, calculate and select one or more parameters that minimize the overall vehicle energy consumption for the next iteration until the parameter values obtained by iteration are only one or the algorithm reaches the set number of iterations, and output the optimized fuzzy control parameters.

2. A system for implementing the method for energy distribution management of an electric vehicle with a dual energy source of a lithium battery and a super capacitor according to claim 1, characterized in that: the system includes a lithium battery pack, a super capacitor, an energy management controller, a drive system, an in-vehicle remote terminal, in-vehicle multimedia, and a cloud platform; a fuzzy controller is provided inside the energy management controller; the energy management controller is respectively connected to the lithium battery pack, the super capacitor, the drive system, and the in-vehicle remote terminal; the cloud platform is communicatively connected to the in-vehicle remote terminal for data invocation and storage.

3. The system according to claim 2, characterized in that: the fuzzy controller outputs a power distribution factor to the energy management controller according to the vehicle drive demand power provided by the drive system, and the energy management controller controls the energy distribution of the lithium battery pack and the super capacitor.

4. The system according to claim 2, characterized in that: the lithium battery pack and the super capacitor are connected in parallel.

5. The system according to claim 2, characterized in that: the in-vehicle multimedia and the in-vehicle remote terminal are communicatively connected based on the full CAN network.

6. The system according to claim 2, characterized in that: the in-vehicle multimedia is connected to the in-vehicle remote terminal for parameter download requests and data upload requests.

7. The system according to claim 2, characterized in that: the energy management controller receives the lithium battery power information and the super capacitor power information in real time.

Images