Control strategy for multi-stage constant heating power vehicle battery graphene thermal insulation system

Abstract

The invention is suitable for the technical field of power battery protection, and provides a multi-stage constant heating power vehicle battery graphene thermal insulation system control strategy, which is characterized in that a multi-stage constant heating power (MCHP) combination is generated through offline optimization of a global genetic algorithm (GA), and the temperature rise time of a battery pack is restrained by taking the minimization of graphene heating energy consumption as a target; constructing a multi-stage power control table based on a temperature interval and storing the multi-stage power control table to a vehicle-mounted computer; and monitoring the temperature of the battery pack in real time, and dynamically switching the heating power according to a preset temperature interval until the temperature is stabilized in a target range (such as 20 DEG C + / -1 DEG C). The method has the beneficial effects that the temperature of the battery pack can be quickly increased (for example, the temperature is increased to 20 DEG C within 30 minutes) in a low-temperature environment, compared with a traditional control strategy, the energy consumption is reduced by 20%-30%, the temperature fluctuation range is reduced to be within + / -1 DEG C, and the method has high economy, stability and engineering applicability and is suitable for vehicle-mounted computing resource limited scenes.

Description

Technical Field

[0001] The present invention belongs to the technical field of power battery protection, and particularly relates to a control strategy for a graphene thermal insulation system of a vehicle battery with multi-level constant heating power. Background Art

[0002] To alleviate the global energy crisis and solve environmental pollution problems, new energy vehicles have become the focus of common concern around the world, and China has also successively introduced many policies to support the development of new energy vehicles. Compared with other types of batteries, lithium-ion batteries have the characteristics of high average output voltage, light weight, high energy density, long life, small size, high safety performance, and environmental friendliness. Therefore, with the rapid development of electric vehicles, lithium-ion batteries have become an important power source for electric vehicles and hybrid vehicles. Using lithium-ion batteries to achieve vehicle electrification is one of the main ways to achieve environmental friendliness and sustainable development. However, the problem of the decline in the working performance of lithium-ion power batteries at low temperatures in winter is an important factor restricting the popularization of electric vehicles. When lithium-ion batteries work at low temperatures, the electrochemical reactions inside the battery are unbalanced, resulting in poor working performance and shortened lifespan of the battery, affecting the use performance of electric vehicles.

[0003] In recent years, graphene materials have been widely used in electrothermal systems due to their excellent thermal conductivity. Therefore, based on the superior heating efficiency of graphene materials, we can develop a heating device for the low-temperature protection of power batteries of new energy vehicles. As an emerging engineering technology, the graphene heating device has been less studied at home and abroad in the field of new energy vehicles after our investigation. Developing a graphene electrothermal system with high heating efficiency, low energy consumption, and good control performance and applying it to the field of new energy vehicles is one of the main challenges at present. Summary of the Invention

[0004] The purpose of the present invention is to provide a control strategy for a graphene thermal insulation system of a vehicle battery with multi-level constant heating power, aiming to solve the technical problems existing in the prior art determined in the background art.

[0005] The present invention is implemented as follows. A control strategy for a graphene thermal insulation system of a vehicle battery with multi-level constant heating power, the control strategy includes:

[0006] S1. Generate a multi-level constant heating power combination through offline optimization by the global genetic algorithm. The GA optimization process includes:

[0007] Initialize the population and randomly generate an initial heating power combination;

[0008] Calculate the temperature change of the battery pack and the graphene heating energy consumption corresponding to each combination:

[0009] J = min(P GR )

[0010] Among them, J represents the target value of the fitness function. The optimization goal of J is to minimize P while satisfying the battery pack heating-up time constraint. GR P GR represents the total energy consumption of graphene heating;

[0011] With the goal of minimizing the energy consumption of graphene heating, through selection, crossover, and mutation iterations for optimization until the termination condition is met;

[0012] S2. Construct a multi-level heating power control table based on temperature intervals and store the control table in the vehicle-mounted computer;

[0013] S3. Real-time monitor the battery pack temperature, and dynamically switch the corresponding heating power according to the temperature range where the battery pack is located until the battery pack temperature reaches and stabilizes within the target range.

[0014] The beneficial effects of the present invention are:

[0015] The present invention intelligently controls the graphene thermal insulation system for vehicle power batteries through the MCHP strategy based on global GA optimization, and keeps the thermal insulation system within the high-efficiency range during the heating process. While ensuring the working temperature required by the power battery in a low-temperature environment, it realizes the reduction of the energy consumption of the thermal insulation system and reduces the use cost of the thermal insulation system. Compared with other control strategies, the MCHP controller has smaller heating power fluctuations and is easy to apply in practical engineering. Description of the Drawings

[0016] Figure 1 is the flowchart of global GA optimization;

[0017] Figure 2 is the working principle diagram of the MCHP controller. Detailed Embodiments

[0018] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] A control strategy for a multi-level fixed heating power graphene thermal insulation system for vehicle batteries is provided. The specific implementation effects are as Figure 2 shown. The system used in the implementation strategy of the present invention includes the following components:

[0020] Battery Management System (BMS): Real-time monitor the battery pack temperature (T pack ) and the battery state, and transmit the data to the MCHP controller.

[0021] MCHP Controller: Built-in multi-stage heating power control table optimized offline, and switches to the corresponding power level (HP1 - HP5) according to the current temperature.

[0022] Graphene heating device: Distributed on the surface of the battery pack, enabling rapid heating through current adjustment.

[0023] High-precision temperature sensor: Deployed at key positions of the battery pack, with a measurement error ≤ ±0.5°C.

[0024] In-vehicle computer: Stores the control table and executes the MCHP strategy.

[0025] For the MCHP controller: The heating power of the graphene thermal insulation system varies with the temperature of the vehicle power battery pack. At the very beginning of the heating process, the temperature T1 of the battery pack is very low, and a relatively high heating power HP1 should be used to increase the temperature of the battery pack. When the battery pack temperature rises to T2, the heating power switches to HP2, where HP2 is lower than HP1. Based on this rule, when the battery pack temperature is between the initial temperature T1 and T2, the heating power is adjusted to HP1. When the battery pack temperature is between T2 and T3, the heating power is adjusted to HP2. When the battery pack temperature is higher than T N , the heating power is adjusted to HP N . As the battery pack temperature rises, the heating power also decreases.

[0026] Theoretically, the heating power can be any value between the minimum heating power and the maximum heating power. In addition, T2, T3, and T N can be any value between the initial battery pack temperature and the target battery pack temperature. Different heating powers corresponding to the battery pack temperature have different battery pack heating processes and different energy consumptions. A high heating power means a strong temperature rise ability and can quickly reach the target battery pack temperature, but at the same time, the power consumption will also be higher. Through GA optimization, the present invention obtains the optimal combination of the proposed MCHP control strategy.

[0027] Global GA optimization method:

[0028] Since different heating power combinations correspond to different temperature rise processes and different energy consumptions, the present invention proposes an MCHP based on global GA optimization to reduce energy consumption while ensuring the thermal comfort of the occupants. The global GA optimization process is as Figure 1 shown. Its optimization process includes the following steps:

[0029] Step 1: Initialize the population to define the initial heating power. In this genetic algorithm optimization process, the initial population size is 50.

[0030] Step 2: Apply the initial heating power to the thermal insulation system and calculate the change in battery pack temperature.

[0031] Step 3: Select operators with higher fitness, that is, while meeting the battery pack temperature requirements, the graphene heating energy consumption PGR is minimized. Breed and mutate these operators to generate new operators and assign them to the heating power.

[0032] Step 4: When the input heating power does not meet the battery pack temperature requirements or P GR is not the minimum value or the maximum number of generations has not been reached, perform the selection step.

[0033] Step 5: When the battery pack temperature requirements, the minimum graphene heating energy consumption P GR and the maximum number of generations are reached simultaneously, the optimization program stops and outputs the optimal MCHP.

[0034] Implementation steps:

[0035] Step 1: Offline global GA optimization

[0036] 1.1 Parameter settings:

[0037] Population size: 50

[0038] Crossover probability: 0.8

[0039] Mutation probability: 0.1

[0040] Maximum number of iterations: 100

[0041] Target heating-up time: ≤ 30 minutes

[0042] 1.2 Definition of fitness function:

[0043] J = min(P GR );

[0044] where J represents the target value of the fitness function. The optimization goal of J is to minimize P GR while meeting the battery pack heating-up time constraint; P GR represents the total energy consumption of graphene heating, and needs to meet the time constraint for the battery pack to heat up from the initial temperature (such as -10 °C) to the target temperature (20 °C).;

[0045] 1.3 Optimization process:

[0046] Randomly generate an initial heating power combination (such as HP1 = 500W, HP2 = 400W,..., HP5 = 100W).

[0047] Calculate P GR and the heating-up time for each combination through simulation, and screen out individuals with high fitness for crossover and mutation.

[0048] Iteratively optimize until the termination condition (P GRMinimize, time ≤ 30 minutes, iteration up to 100 generations).

[0049] 1.4 Output results:

[0050] Optimized multi - stage heating power combination and corresponding temperature range:

[0051] HP1 = 500W (T pack <0 °C)

[0052] HP2 = 400W (0 °C ≤ T pack < 5 °C)

[0053] HP3 = 300W (5 °C ≤ T pack <10 °C)

[0054] HP4 = 200W (10 °C ≤ T pack <15 °C)

[0055] HP5 = 100W (T pack ≥15 °C)

[0056] Step 2: Real - time control and power switching

[0057] 2.1 Initialization stage:

[0058] The BMS collects the initial temperature (such as - 10 °C), and the MCHP controller calls HP1 (500W) to start heating.

[0059] 2.2 Dynamic switching stage:

[0060] When T pack rises to 0 °C, switch to HP2 (400W);

[0061] When T pack rises to 5 °C, switch to HP3 (300W);

[0062] And so on, until T pack stabilizes at 20 °C ± 1 °C.

[0063] 2.3 Maintenance stage:

[0064] Use HP5 (100W) to maintain the temperature and avoid over - heating.

[0065] Effect of the embodiment:

[0066] In a low - temperature environment of - 10 °C, the battery pack heats up to 20 °C within 25 minutes by this strategy, the total energy consumption is reduced by 28% compared with the traditional PID control, and the temperature fluctuation range is controlled within ±1 °C.

[0067] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0068] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

[0069] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A control strategy for a multi-stage constant heating power graphene thermal insulation system of a vehicle battery, characterized in that, The control strategy includes: S1. Generate a multi-level constant heating power combination through offline optimization by the global genetic algorithm. The GA optimization process includes: Initialize the population and randomly generate an initial heating power combination; Calculate the battery pack temperature change and graphene heating energy consumption corresponding to each combination: J = min(P GR ); Among them, J represents the target value of the fitness function. The optimization objective of J is to minimize P while satisfying the battery pack heating-up time constraint. GR P GR represents the total energy consumption of graphene heating; Aim at minimizing the graphene heating energy consumption and perform iterative optimization through selection, crossover, and mutation until the termination conditions are met; S2. Construct a multi-level heating power control table based on temperature intervals and store the control table in the vehicle-mounted computer; S3. Monitor the battery pack temperature in real time and dynamically switch the corresponding heating power according to the temperature interval where the battery pack temperature is located until the battery pack temperature reaches and stabilizes within the target range.

2. The control strategy according to claim 1, wherein The GA optimization parameters include a population size of 50, a crossover probability of 0.8, a mutation probability of 0.1, and a maximum number of iteration generations of 100.

3. The control strategy according to claim 1, wherein The multi-level heating power combination includes 5 power levels, namely HP1, HP2, HP3, HP4, and HP5, which respectively correspond to the following temperature intervals: HP1 is used when the battery pack temperature < 0°C; HP2 is used when 0°C ≤ battery pack temperature < 5°C; HP3 is used when 5°C ≤ battery pack temperature < 10°C; HP4 is used when 10°C ≤ battery pack temperature < 15°C; HP5 is used when the battery pack temperature ≥ 15°C.

4. The control strategy according to claim 1, wherein The termination conditions are that both of the following are satisfied: The time for the battery pack to heat up to the target temperature does not exceed 30 minutes; The graphene heating energy consumption reaches the minimum value; The maximum number of iteration generations of 100 is reached.

5. The control strategy according to claim 1, characterized in that, When dynamically switching the heating power, when the battery pack temperature rises to the threshold of the next temperature interval, the power is reduced to the corresponding level until the temperature stabilizes within 20°C ± 1°C.

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