Battery heating system and control method thereof

Abstract

A battery heating system and a control method thereof relate to the field of battery heating, and include a control host, a waste heat recovery module, a humidity sensor, and a temperature sensor. The control host internally includes a central processing system, a battery heating system, a data system, and a waste heat recovery module. The data system internally includes an external environment monitoring module, an internal environment monitoring module, and a cloud storage module. The data system acquires monitoring data from the humidity sensor and the temperature sensor, and generates an internal environment monitoring model and an external environment monitoring model based on the acquired data. The internal environment monitoring model and the external environment monitoring model are established through the external environment monitoring module and the internal environment monitoring module. The deep learning module uses the internal environment monitoring model and the external environment monitoring model as a benchmark, and then conducts deep learning on the battery temperature control data and the specific actual environment to establish an "adaptive adjustment model" to achieve adaptive heating of the battery.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery heating, and in particular relates to a battery heating system and a control method thereof. Background Art

[0002] The safety and range of batteries used in low-temperature environments have become the main shortcomings of new energy vehicles of major OEMs. Lithium batteries in low-temperature environments will lead to problems such as shortened range of electric vehicles and charging and discharging safety.

[0003] Currently, there are two main ways for electric vehicle onboard thermal management systems to heat batteries: using external devices to heat the batteries and using the battery's own discharge to heat the batteries.

[0004] After searching, the invention patent with Chinese patent number CN106602178A discloses a lithium battery heating system, including a temperature signal acquisition unit, a main control unit and a first heating unit, and the temperature signal acquisition unit and the first heating unit are respectively connected to the main control unit. The above system uses the temperature signal acquisition unit to collect the temperature parameters near the battery panel. If the temperature parameter is lower than the value set by the main control unit, the main control unit will drive the first heating unit to heat up to increase the temperature of the battery panel, thereby solving the problem of slow charging speed of lithium batteries in cold weather. However, in the process of aggregating and processing internal and external data during actual use, due to the lack of a monitoring model for the environment, the battery heating efficiency is reduced, and it is also difficult to perform effective heating regulation. Therefore, a battery heating system and a control method thereof are needed.

[0005] In addition, the battery heating method and device for battery swap cabinets disclosed in Chinese patent document CN114204646A, and the improved low-temperature automatic heating device for smart terminals disclosed in Chinese patent document CN114281122B, although they disclose an intelligent battery heating system that can obtain environmental information, do not take into account the influencing factor of humidity, so that the battery cannot achieve the expected heating effect. At the same time, they also lack dual monitoring of internal and external environments and cannot predict the temperature in different time periods. Summary of the Invention

[0006] In view of the technical problems existing in the background technology, the present invention provides a battery heating system and a control method thereof, which establish an internal environment monitoring model and an external environment monitoring model through the external environment monitoring module and the internal environment monitoring module. The deep learning module uses the internal environment monitoring model and the external environment monitoring model as a benchmark, and then conducts deep learning on the battery temperature control data and the specific actual environment to establish an "adaptive adjustment model" to achieve adaptive heating of the battery.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A battery heating system includes a central processing system, a battery heating system, a data system, a waste heat recovery module, a temperature sensor, and a humidity sensor;

[0009] The data system is used to obtain monitoring data from humidity sensors and temperature sensors, generate internal environment monitoring models and external environment monitoring models based on the acquired monitoring data, and synchronize the data within the central processing system in real time;

[0010] The central processing system is used to receive data from the data system and perform deep learning on the data, thereby establishing a temperature adaptive adjustment model and safely monitoring the battery temperature;

[0011] The battery heating system is used to heat and keep the battery warm, while transferring waste heat to the waste heat recovery module for absorption.

[0012] In a preferred solution, the data system is composed of an external environment monitoring module, an internal environment monitoring module and a cloud storage module;

[0013] The external environment monitoring module is used to conduct in-depth mining of the temperature and humidity data outside the vehicle, thereby establishing an external environment monitoring model;

[0014] The internal environment monitoring module is used to deeply mine the temperature and humidity data around the battery to establish an internal environment monitoring model;

[0015] The cloud storage module is used to obtain external battery temperature control data.

[0016] In a preferred solution, the central processing system is composed of a deep learning module, an adaptive adjustment module and a security monitoring module;

[0017] The deep learning module is used to obtain battery temperature control data from the cloud storage module and use the internal and external environment monitoring models as benchmarks to conduct deep learning to establish an adaptive adjustment model.

[0018] The adaptive adjustment module integrates the adaptive adjustment model and performs adaptive adjustment to the changes in the internal and external environments.

[0019] The safety monitoring module monitors the battery temperature in real time and performs safety monitoring based on the acquired data.

[0020] In a preferred embodiment, the battery heating system is composed of a PTC heating system, a battery insulation module, a battery preheating module and a phase change insulation module; when the internal environment monitoring module detects that the temperature around the battery is lower than the safe temperature, the battery preheating module starts to work, and the battery preheating module and the PTC heating system cooperate to heat the battery and heat the temperature around the battery to a safe temperature. During the heating process, the battery insulation module and the phase change insulation module are used to keep the battery warm, thereby reducing heating consumption, and the waste heat generated during the battery heating process is recovered by the waste heat recovery module.

[0021] In a preferred embodiment, the control method of the battery heating system is characterized by comprising the following steps:

[0022] S1, data system judgment t n Whether the outside temperature and the outside humidity of the vehicle in the time period are less than the preset outside temperature and the preset outside humidity of the vehicle, if so, executing step S2, if not, the adaptive control method does not work;

[0023] S2, data system judgment t n Battery temperature during time period T n Is it less than the PTC starting temperature? If so, execute step S3; if not, the adaptive control method does not work;

[0024] S3, the battery preheating module preheats the battery. After the preheating is completed, the battery temperature rises to T1. The power consumption during the preheating process is Q1 = Q0 × (T1-T n ), where Q0 is the power consumption per unit temperature;

[0025] S4, continue heating, at t n The output power of the PTC heating system is controlled to be P=P n , continuously heating the battery;

[0026] If the battery temperature does not exceed the preset safety temperature T set , then the output power P should be adjusted accordingly in the next time period n+1 =(T set / T n ')×P n , and the temperature change during this period is ΔT=T n '-T n , the power consumption of the heating process Q2=(P n ×ΔT) / η, where η is the system operating efficiency;

[0027] If the battery temperature reaches the preset safety temperature T set , then stop heating, and start the battery insulation module and phase change insulation module to keep the battery warm, so that its temperature is stable in the range [0.98×T set , Tset ], the waste heat recovery system starts at the same time to collect excess heat Q3, and the output power P should also be adjusted in the next time period n+1 =C×P n ,in

[0028] C = (Q1 + Q2) / (Q1 + Q2 + Q3);

[0029] S5, set n=n+1, and execute step S1 again.

[0030] Preferably, in step S1, the outside temperature and the outside humidity are respectively obtained from a temperature sensor and a humidity sensor provided outside the vehicle; and the preset outside temperature and the preset outside humidity are derived from deep learning data.

[0031] Preferably, in step S2, the battery temperature T is set n The PTC start temperature is collected from the battery temperature sensor and is -25°C ± 1°C.

[0032] Preferably, in step S4, the preset safety temperature comes from deep learning data.

[0033] Preferably, the deep learning model is a long short-term memory neural network model, comprising an input layer, a hidden layer, and an output layer; the deep learning module learns based on the external environment monitoring module, the internal environment monitoring module, and the cloud storage module; the deep learning model preprocesses the historical data and real-time data collected by the temperature sensor and the humidity sensor, dividing them into a training set and a prediction set; group training is performed according to the internal environment monitoring module and the external environment monitoring module, the input layer comprises 7 neurons, the hidden layer is selected according to twice the number of neurons in the input layer, and is designed to include two hidden layers of 8 neurons and 6 neurons respectively; the output layer comprises two neurons, and the output data are the desired preset outside vehicle temperature and preset outside vehicle humidity;

[0034] The predicted t n The temperature and humidity of the internal and external environment monitoring modules within the time period are stored in the memory as preset reference values. Similarly, deep learning is performed on the temperature control data and environment in the cloud storage module. The input layer includes 3 neurons, the hidden layer includes 6 neurons, and the output layer includes 1 neuron. The output data is the temperature control data. The predicted t n The temperature control data within the time period is stored in the memory as a preset safety temperature value.

[0035] This patent can achieve the following beneficial effects:

[0036] 1. When the present invention is used, an internal environment monitoring model and an external environment monitoring model are established through the external environment monitoring module and the internal environment monitoring module. The deep learning module uses the internal environment monitoring model and the external environment monitoring model as a benchmark, and then conducts deep learning on the battery temperature control data and the specific actual environment to establish an "adaptive adjustment model" to achieve adaptive heating of the battery.

[0037] 2. When the present invention is used, the excess heat in the continuous heating process is recovered through the waste heat recovery module, and the battery insulation module and the phase change insulation module are used to keep the battery warm, thereby reducing energy consumption during the battery heating process.

[0038] 3. Considering the influence of humidity can better achieve the expected heating effect of the battery. At the same time, dual monitoring of the internal and external environments helps to improve the accuracy of the data and effectively adjust the heating. Combined with deep learning, the use of neural network prediction models can more effectively predict the temperature of each time period and guide the efficient implementation of adaptive adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0040] Figure 1 This is a system diagram of the present invention;

[0041] Figure 2 This is a control flow chart of the present invention.

[0042] In the figure: 1-Central processing system; 2-Battery heating system; 3-Data system; 4-Deep learning module; 5-Adaptive adjustment module; 6-Safety monitoring module; 7-External environment monitoring module; 8-Internal environment monitoring module; 9-Cloud storage module; 10-PTC heating system; 11-Battery insulation module; 12-Battery preheating module; 13-Phase change insulation module; 14-Waste heat recovery module; 15-Humidity sensor; 16-Temperature sensor. DETAILED DESCRIPTION

[0043] Example 1:

[0044] The preferred solution is Figures 1 to 2As shown, a battery heating system includes a control host, a waste heat recovery module 14, a humidity sensor 15, and a temperature sensor 16. The control host internally includes a central processing system 1, a battery heating system 2, a data system 3, and the waste heat recovery module 14. The data system 3 internally includes an external environment monitoring module 7, an internal environment monitoring module 8, and a cloud storage module 9. The data system 3 acquires monitoring data from the humidity sensor 15 and the temperature sensor 16, and generates internal and external environment monitoring models based on the acquired data. The data system 3 also synchronizes the data within the control host in real time and transmits the data to the central processing system 1. The humidity sensor 15 and the temperature sensor 16 are model TH600NXC.

[0045] Furthermore, the central processing system 1 includes a deep learning module 4, an adaptive adjustment module 5, and a safety monitoring module 6. The central processing system 1 performs deep learning on the received data to establish a temperature adaptive adjustment model and safely monitor the battery temperature.

[0046] The deep learning module 4 is used to obtain the battery temperature control data inside the cloud storage module, and uses the internal environment monitoring model and the external environment monitoring model as a benchmark to conduct deep learning to establish an adaptive adjustment model; specifically, the deep learning module 4 is selected as: Canaan Kendryte K510 CRB-KIT, development board RISC-V AI deep learning K210 module.

[0047] The adaptive adjustment module 5 integrates the adaptive adjustment model and performs adaptive adjustment on the changes of the internal and external environments.

[0048] The safety monitoring module 6 monitors the battery temperature in real time and performs safety monitoring based on the acquired data. Specifically, the safety monitoring module 6 is selected as: Dodd LH 5946 stop speed monitoring module.

[0049] Furthermore, the battery heating system 2 includes a PTC heating system, a battery insulation module 11, a battery preheating module 12 and a phase change insulation module 13. The central processing system 1 transmits the processed data to the battery heating system 2, and the battery is heated and insulated by the battery heating system 2, while the waste heat is absorbed by the waste heat recovery module 14.

[0050] When the internal environment monitoring module detects that the temperature around the battery is lower than the safe temperature, the battery preheating module 12 starts to work. The battery preheating module 12 cooperates with the PTC heating system 10 to heat the battery and heat the temperature around the battery to a safe temperature. During the heating process, the battery insulation module 11 and the phase change insulation module 13 are used to keep the battery warm, thereby reducing heating consumption. The waste heat generated during the battery heating process is recovered by the waste heat recovery module.

[0051] Specifically, the battery insulation module is selected as follows: 6V lithium battery guard plate, charged balancing source module, and built-in thermal insulation control protection.

[0052] The battery preheating module is selected as: TP5000 charging board module, voltage level: 3.6 / 4.2V.

[0053] Phase change insulation module: perlite particles.

[0054] The waste heat recovery module is selected as: plate heat exchanger.

[0055] PTC heating system selection: graphene PTC heating film.

[0056] Furthermore, the data system is composed of an external environment monitoring module 7, an internal environment monitoring module 8 and a cloud storage module 9;

[0057] The external environment monitoring module 7 is used to deeply mine the temperature and humidity data outside the vehicle, so as to establish an external environment monitoring model; the external environment monitoring module selected is: Dodd LH 5946.

[0058] The internal environment monitoring module 8 is used to deeply mine the temperature and humidity data around the battery, thereby establishing an internal environment monitoring model; the selected model is: Dodd LH 5946.

[0059] The cloud storage module 9 is used to obtain external battery temperature control data. The selected model is: CVR DS-A80316S / DS-A81116S.

[0060] The working principle of this system is as follows:

[0061] During operation, the humidity sensor 15 monitors the humidity outside the battery and the humidity outside the vehicle, and the temperature sensor 16 monitors the temperature around the battery and the temperature outside the vehicle. The external environment monitoring module 7 deeply mines the external temperature and humidity data to establish an external environment monitoring model. The internal environment monitoring module 8 deeply mines the temperature and humidity data around the battery to establish an internal environment monitoring model.

[0062] The external environment monitoring module 7 acquires external battery temperature control data, and the deep learning module 4 acquires battery temperature control data from the cloud storage module 9. Using the internal and external environment monitoring models as benchmarks, the module conducts deep learning to establish an "adaptive adjustment model."

[0063] The adaptive adjustment module 5 integrates the "adaptive adjustment model" to adaptively adjust to changes in the internal and external environments. The safety monitoring module 6 monitors the battery temperature in real time and performs safety monitoring based on the acquired data. When the temperature around the battery is detected to be below the safe temperature, the battery preheating module 12 starts working. The battery preheating module 12 works in conjunction with the PTC heating system to heat the battery to a safe temperature, facilitating subsequent battery charging / discharging.

[0064] During the heating process, the battery heat preservation module 11 and the phase change heat preservation module 13 are used to keep the battery warm, thereby reducing heating consumption. The waste heat generated during the battery heating process is recovered by the waste heat recovery module 14 .

[0065] Preferably, a method for controlling a battery heating system includes the following steps:

[0066] S1, data system judgment t n Whether the outside temperature and the outside humidity of the vehicle in the time period are less than the preset outside temperature and the preset outside humidity of the vehicle, if so, executing step S2, if not, the adaptive control method does not work;

[0067] S2, data system judgment t n Battery temperature during time period T n Is it less than the PTC starting temperature? If so, execute step S3; if not, the adaptive control method does not work;

[0068] S3, the battery preheating module preheats the battery. After the preheating is completed, the battery temperature rises to T1. The power consumption during the preheating process is Q1 = Q0 × (T1-T n ), where Q0 is the power consumption per unit temperature;

[0069] S4, continue heating, at t n The output power of the PTC heating system is controlled to be P=P n , continuously heating the battery;

[0070] If the battery temperature does not exceed the preset safety temperature T set , then the output power P should be adjusted accordingly in the next time period n+1 =(T set / T n ')×P n , and the temperature change during this period is ΔT=T n'-T n , the power consumption of the heating process Q2=(P n ×ΔT) / η, where η is the system operating efficiency;

[0071] If the battery temperature reaches the preset safety temperature T set , then stop heating, and start the battery insulation module and phase change insulation module to keep the battery warm, so that its temperature is stable in the range [0.98×T set , T set ], the waste heat recovery system starts at the same time to collect excess heat Q3, and the output power P should also be adjusted in the next time period n+1 =C×P n ,in

[0072] C = (Q1 + Q2) / (Q1 + Q2 + Q3);

[0073] S5, set n=n+1, and execute step S1 again.

[0074] Furthermore, in step S1, the outside temperature and the outside humidity of the vehicle are respectively obtained from a temperature sensor and a humidity sensor provided outside the vehicle; and the preset outside temperature and the preset outside humidity of the vehicle are derived from deep learning data.

[0075] Furthermore, in step S2, the battery temperature T is set n The PTC start temperature is collected from the battery temperature sensor and is -25°C ± 1°C.

[0076] Furthermore, in step S4, the preset safety temperature comes from deep learning data.

[0077] Furthermore, the deep learning model is a long-short-term memory neural network model, including an input layer, a hidden layer, and an output layer. The deep learning module learns based on the external environment monitoring module, the internal environment monitoring module, and the cloud storage module. The deep learning model preprocesses the historical and real-time data collected by the temperature sensor and the humidity sensor, dividing them into a training set and a prediction set. Training is performed in groups according to the internal environment monitoring module and the external environment monitoring module. The input layer includes 7 neurons, and the hidden layer is selected to be twice the number of neurons in the input layer, and is designed to include two hidden layers, 8 neurons and 6 neurons respectively. The output layer includes two neurons, and the output data are the required preset outside temperature and preset outside humidity.

[0078] The predicted t nThe temperature and humidity of the internal and external environment monitoring modules within the time period are stored in the memory as preset reference values. Similarly, deep learning is performed on the temperature control data and environment in the cloud storage module. The input layer includes 3 neurons, the hidden layer includes 6 neurons, and the output layer includes 1 neuron. The output data is the temperature control data. The predicted t n The temperature control data within the time period is stored in the memory as a preset safety temperature value.

[0079] The above embodiments are merely preferred technical solutions of the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention shall be the technical solutions set forth in the claims, including equivalent alternatives to the technical features of the technical solutions set forth in the claims. In other words, equivalent alternatives and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A battery heating system, characterized in that: Includes central processing system, battery heating system, data system, waste heat recovery module, temperature sensor and humidity sensor; The data system is used to obtain monitoring data from humidity sensors and temperature sensors, generate internal environment monitoring models and external environment monitoring models based on the acquired monitoring data, and synchronize the data within the central processing system in real time; The central processing system is used to receive data from the data system and perform deep learning on the data, thereby establishing a temperature adaptive adjustment model and safely monitoring the battery temperature; The battery heating system is used to heat and keep the battery warm, while transferring waste heat to the waste heat recovery module for absorption; The data system consists of an external environment monitoring module, an internal environment monitoring module, and a cloud storage module; The external environment monitoring module is used to conduct in-depth mining of the temperature and humidity data outside the vehicle, thereby establishing an external environment monitoring model; The internal environment monitoring module is used to deeply mine the temperature and humidity data around the battery to establish an internal environment monitoring model; The cloud storage module is used to obtain external battery temperature control data; The central processing system consists of a deep learning module, an adaptive adjustment module, and a security monitoring module; The deep learning module is used to obtain battery temperature control data from the cloud storage module and use the internal and external environment monitoring models as benchmarks to conduct deep learning to establish an adaptive adjustment model. The adaptive adjustment module integrates the adaptive adjustment model and performs adaptive adjustment to the changes in the internal and external environments. The safety monitoring module monitors the battery temperature in real time and performs safety monitoring based on the acquired data.

2. A battery heating system according to claim 1, characterized in that: The battery heating system consists of a PTC heating system, a battery insulation module, a battery preheating module and a phase change insulation module; when the internal environment monitoring module detects that the temperature around the battery is lower than the safe temperature, the battery preheating module starts working, and the battery preheating module works together with the PTC heating system to heat the battery and heat the temperature around the battery to a safe temperature. During the heating process, the battery insulation module and the phase change insulation module are used for insulation to reduce heating consumption, and the waste heat generated during the battery heating process is recovered through the waste heat recovery module.

3. The control method of a battery heating system according to claim 1, characterized in that The following steps are involved: S1, data system judgment t n Whether the outside temperature and the outside humidity of the vehicle in the time period are less than the preset outside temperature and the preset outside humidity of the vehicle, if so, executing step S2, if not, the adaptive control method does not work; S2, data system judgment t n Battery temperature during time period T n Is it less than the PTC starting temperature? If so, execute step S3; if not, the adaptive control method does not work; S3, the battery preheating module preheats the battery. After the preheating is completed, the battery temperature rises to T1. The power consumption during the preheating process is Q1 = Q0 × (T1-T n ), where Q0 is the power consumption per unit temperature; S4, continue heating, at t n The output power of the PTC heating system is controlled to be P=P n , continuously heating the battery; If the battery temperature does not exceed the preset safety temperature T set , then the output power P should be adjusted accordingly in the next time period n+1 =(T set / T n ')×P n , and the temperature change during this period is ΔT=T n '-T n , the power consumption of heating process Q2=(P n ×ΔT) / η, where η is the system operating efficiency; If the battery temperature reaches the preset safety temperature T set , then stop heating, and start the battery insulation module and phase change insulation module to keep the battery warm, so that its temperature is stable in the range [0.98×T set , T set ], the waste heat recovery system starts at the same time to collect excess heat Q3, and the output power P should also be adjusted in the next time period n+1 =C×P n , where C=(Q1+Q2) / (Q1+Q2+Q3); S5, set n=n+1, and re-execute step S1.

4. The control method of a battery heating system according to claim 3, characterized in that: In step S1, the outside temperature and the outside humidity are respectively obtained from a temperature sensor and a humidity sensor provided outside the vehicle; the preset outside temperature and the preset outside humidity are derived from deep learning data.

5. The control method of a battery heating system according to claim 4, characterized in that: In step S2, the battery temperature T n The PTC start temperature is collected from the battery temperature sensor and is -25°C ± 1°C.

6. The control method of a battery heating system according to claim 5, characterized in that: In step S4, the preset safety temperature comes from deep learning data.

7. The control method of a battery heating system according to claim 6, characterized in that: The deep learning model is a long short-term memory neural network model, including an input layer, a hidden layer, and an output layer. The deep learning module learns based on the external environment monitoring module, the internal environment monitoring module, and the cloud storage module. The deep learning model preprocesses historical and real-time data collected by temperature and humidity sensors, dividing them into training and prediction sets. Training is performed in groups based on internal and external environment monitoring modules. The input layer includes seven neurons, and the hidden layer is designed to have two hidden layers, eight neurons and six neurons, respectively, chosen to be twice the number of neurons in the input layer. The output layer includes two neurons, and the output data are the desired preset outside temperature and humidity. The predicted t n The temperature and humidity of the internal and external environment monitoring modules within the time period are stored in the memory as preset reference values. Similarly, deep learning is performed on the temperature control data and environment in the cloud storage module. The input layer includes 3 neurons, the hidden layer includes 6 neurons, and the output layer includes 1 neuron. The output data is the temperature control data. The predicted t n The temperature control data within the time period is stored in the memory as a preset safety temperature value.

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