CN114506247B - Active thermal management system of power battery controlled cooperatively by cloud - Google Patents
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- 238000007726 management method Methods 0.000 claims abstract description 55
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- 238000005516 engineering process Methods 0.000 description 4
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- 230000004927 fusion Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
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- 230000002411 adverse Effects 0.000 description 2
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
- B60L58/27—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries by heating
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/549—Current
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The invention relates to the field of power battery management, and discloses a cloud cooperative control power battery active heat management system, which comprises: the battery management system and the cloud server; the management method of the power battery active heat management system comprises the following steps: the battery management system collects state data of the power battery and sends the state data to the cloud; the state data comprise current, voltage and external temperature of the power battery; after receiving the state data of the power battery, the cloud server predicts the temperature of the power battery through a battery model fused with transfer learning, and judges whether the temperature of the power battery exceeds a threshold value; if the temperature of the power battery is higher than the upper threshold value, classifying risks according to the risk characteristics, and making different instructions for different risk classes; if the temperature of the power battery is lower than the lower threshold, adopting a multi-target tuna group optimization algorithm, and taking the service life, the heating rate and the heating time of the battery as optimization targets.
Description
Technical Field
The invention relates to the field of power battery thermal management, in particular to a cloud cooperative control power battery active thermal management system.
Background
At present, the rising of new energy industry promotes the vigorous development of electric automobiles. The management system of the power battery is an important ring in the development process of the electric automobile, and the thermal management system of the power battery also gradually reflects the eye curtains of people. When the temperature is too high, the power battery is easy to generate thermal runaway, so that adverse effects such as explosion and fire disaster are caused, and popularization of the electric automobile is hindered to a certain extent. In addition, too low ambient temperature also causes the influence of certain degree to the charge-discharge performance of battery, and mileage anxiety problem is comparatively serious, and low temperature charging can even lead to internal short circuit, causes explosion, conflagration etc. adverse effect. However, the battery system is a typical nonlinear and time-varying controlled object, and the existing control technology has the problems of insufficient sensor data, inaccurate observation data and the like, so that the estimation of the internal temperature of the battery is inaccurate, the identification of thermal risks is not timely, even damages such as explosion are generated, and the real-time, accurate and reliable thermal management of the power battery is extremely important.
Most of the existing power battery management systems are based on embedded systems, and the inherent storage space and computing capacity of a chip limit the capacity of processing non-stable change and multi-dimensional mass operation data in real time. Therefore, the existing power battery management system cannot monitor and analyze the battery state change trend in real time, and continuously and iteratively optimize battery management parameters. Thermal management systems, which are one of the important components of power cell management systems, typically employ a thermal conduction approach to thermal management of the power cells. The common heat conducting medium mainly comprises three types of air, liquid and phase change material, and the currently mainstream battery heat management system mostly adopts liquid as the heat conducting medium in terms of efficiency and cost. However, the current power battery thermal management system cannot identify thermal safety risks in advance and immediately handle safety problems in a high-temperature environment; under the low-temperature environment, the power battery cannot be preheated in advance, the preheating time is long, the temperature rise rate is low, and the performance of the power battery is seriously affected.
Disclosure of Invention
In order to solve the technical problems, the invention provides a power battery active heat management system with cloud cooperative control.
In order to solve the technical problems, the invention adopts the following technical scheme:
a cloud-co-controlled active thermal management system for a power battery, comprising: the battery management system and the cloud server; the management method of the power battery active heat management system comprises the following steps:
step one: the battery management system collects state data of the power battery and sends the state data to the cloud; the state data comprise current, voltage and external temperature of the power battery;
step two: after receiving the state data of the power battery, the cloud server predicts the temperature of the power battery through a battery model fused with transfer learning, and judges whether the temperature of the power battery exceeds a threshold value;
step three: if the temperature of the power battery does not exceed the upper threshold limit or the lower threshold limit, performing the step one;
step four: if the temperature of the power battery is higher than the upper threshold, extracting risk characteristics of the temperature change trend of the power battery, grading risks according to the risk characteristics, making different instructions for different risk grades, and executing the instructions to a power battery management system; if the temperature of the power battery is lower than the lower threshold, a multi-target tuna group optimization algorithm is adopted, battery life, heating rate and heating time are taken as optimization targets, the pareto front is calculated, the heating rate and heating time of the power battery are set, and the power battery is sent to a power battery management system for execution.
Specifically, the battery model is an electric-thermal coupling model that has completed training; and step two, when the battery model is fused with the transfer learning, taking the electric-thermal coupling model as a source domain of the transfer learning, taking the power battery state data acquired by the battery management system as the characteristics required by the transfer learning, and taking the power battery which is actually operated as a target domain.
Specifically, if the temperature of the power battery exceeds the upper threshold, removing interference noise in the temperature change trend of the power battery in a wavelet signal analysis mode after the temperature of the power battery is predicted in the second step, extracting thermal signal characteristics from the temperature of the power battery in a time sequence by adopting a variation empirical mode decomposition method, and grading the thermal signal characteristics according to the time when the risk corresponding to the thermal signal characteristics comes to obtain a risk grade.
In the fourth step, when the temperature of the power battery is lower than the lower threshold, the service life of the battery, the heating rate and the heating time are used as optimization targets of a multi-target tuna group optimization algorithm.
Compared with the prior art, the invention has the beneficial technical effects that:
1. according to the invention, the cloud server is utilized to have almost unlimited storage capacity and calculation capacity, a full life cycle big data analysis model of the power battery is constructed based on a machine learning method, the battery state change trend is monitored and analyzed in real time, and the battery management parameters are optimized continuously and iteratively. In consideration of inaccurate temperature estimation caused by insufficient internal temperature data of a battery in the existing BMS, the cloud-based battery model fusion transfer learning method based on the cloud-based battery model fusion transfer learning can accurately and reliably estimate the internal temperature of the battery in real time by combining a small amount of information provided by a vehicle end, so that cloud-based collaborative thermal management control is realized.
2. The invention is based on a cloud battery model, combines a small amount of sensor information provided by a vehicle end, fuses a transfer learning technology, and predicts the temperature of the power battery accurately and reliably in real time; and then, removing the noise of the temperature signal by adopting wavelet signal analysis, extracting the hot air risk characteristic of the temperature change trend of the power battery by adopting an empirical mode decomposition method on a time sequence, forming an expert system integrating a knowledge graph according to the prior experience, classifying risks into a first class, a second class and a third class of risk classes, identifying the hot air safety risk in advance, and disposing the safety problem of an end according to the risk classes.
3. The invention is based on a cloud battery model, integrates a small amount of sensor information provided by a vehicle end, fuses a transfer learning technology, predicts the temperature of the power battery accurately and reliably in real time, and can actively preheat the battery by adopting a multi-target tuna group optimization algorithm in combination with a set temperature threshold and customer requirements, so that the highest temperature rise rate and the minimum battery damage are achieved, and the performance of the power battery at low temperature is greatly improved.
Drawings
FIG. 1 is a block diagram of a power cell active thermal management system of the present invention;
FIG. 2 is a flow chart of a management method of the active thermal management system of the power battery of the present invention;
FIG. 3 is a schematic diagram of the battery model fusion transfer learning of the present invention for estimating the internal temperature of a power battery;
FIG. 4 is a flow chart of the thermal signal denoising, feature extraction, and risk classification of the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the cloud cooperative control power battery active thermal management system in the invention comprises two parts: the cloud server is characterized by strong storage capacity and calculation capacity, is suitable for storing mass battery data, and monitors, analyzes and optimizes battery management parameters; the battery management system based on the embedded system is characterized by poor storage capacity and calculation capacity, is mainly used for collecting data of the battery, is uploaded to the cloud server and receives feedback control of the cloud server.
As shown in fig. 2, the management method of the power battery active thermal management system includes:
s1: the vehicle-end power battery management system collects state data (current, voltage, external temperature and the like) of the power battery and sends the state data to the cloud;
s2: after the cloud server receives the data, the cloud-based battery model fuses the migration learning to predict the power battery cells, the PACK group and the environmental temperature change curve accurately and reliably in real time; then, judging whether the temperature of the PACK group exceeds the upper threshold limit or the lower threshold limit;
s3: if not, returning to S1;
s4: if the temperature of the power battery is higher than the upper threshold, extracting risk characteristics of the temperature change trend of the power battery, grading risks according to the risk characteristics, making different instructions for different risk grades, and executing the instructions to a power battery management system; if the temperature of the power battery is lower than the lower threshold, adopting a multi-target tuna group optimization algorithm, taking the heating rate and the heating time as optimization targets, calculating the pareto front, setting the heating rate and the heating time of the power battery, and sending the heating rate and the heating time to a power battery management system for execution;
repeating S1-S4 until the process is finished. Wherein fig. 2 does not show the flow of the "power battery temperature below threshold lower limit" section.
The battery system is a controlled object with typical nonlinearity and time variation, and the existing control technology has the problems of insufficient sensor data, inaccurate observation data and the like, so that the estimation of the internal temperature of the battery is inaccurate, and the thermal risk is not timely identified, and even damages such as explosion are generated. As shown in fig. 3, a trained power battery electric-thermal coupling model is deployed at the cloud end, the trained power battery electric-thermal coupling model is used as a source domain of transfer learning, sensor signals such as current, voltage, external temperature and the like acquired at the vehicle end are used as characteristics required by the transfer learning, and a power battery actually operated at the vehicle end is used as a target domain; through the transfer learning method, the internal actual temperature of the vehicle-end power battery can be predicted practically, accurately and reliably.
Aiming at different application scenes, the invention can actively realize autonomous heat control according to the change of the environment. In a high-temperature environment, as shown in fig. 4, the invention firstly adopts a wavelet signal analysis method to remove interference noise, adopts a variation empirical mode decomposition method to extract thermal signal characteristics from a power battery temperature signal on a time sequence, and classifies risks according to the coming time of predicted risks by an expert system which forms a fusion knowledge graph according to the prior experience so as to identify thermal safety risks in advance. Then, the safety problem of the power battery is actively and immediately disposed according to the risks of the first, second and third stages. Specifically, for the primary risk, reminding a user of red early warning, reporting the cloud management and control system, and starting a cooling function of the battery management system; for the secondary risk, reminding a user of orange early warning, and starting a cooling function of the battery management system; and reminding the user of yellow early warning for the third-level risk.
And in the low-temperature environment, a preheating instruction is sent in advance, and the power battery management system preheats the power battery actively in advance, so that the performance of the power battery in the low-temperature environment is greatly improved. In consideration of the difference of requirements among different users, the multi-target tuna group optimizing algorithm is utilized to convert the requirements (vehicle time and vehicle mileage) of the users into the required energy of the battery, so that the heating rate and the heating time are calculated, in addition, the defects of battery capacity attenuation and the like caused by the excessively fast heating rate are considered, the multi-target tuna group optimizing algorithm is adopted, the battery life, the heating rate and the heating time are taken as optimizing targets, and the pareto front is calculated, so that the heating rate and the heating time are reasonably selected, the purpose that the users can walk immediately is achieved, the battery life is protected, and the experience of the users is improved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a single embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to specific embodiments, and that the embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.
Claims (2)
1. The utility model provides a power battery active heat management system of high in clouds cooperative control which characterized in that includes: the battery management system and the cloud server; the management method of the power battery active heat management system comprises the following steps:
step one: the battery management system collects state data of the power battery and sends the state data to the cloud; the state data comprise current, voltage and external temperature of the power battery;
step two: after receiving the state data of the power battery, the cloud server predicts the temperature of the power battery through a battery model fused with transfer learning, and judges whether the temperature of the power battery exceeds a threshold value;
step three: if the temperature of the power battery does not exceed the upper threshold limit or the lower threshold limit, performing the step one;
step four: if the temperature of the power battery is higher than the upper threshold, extracting risk characteristics of the temperature change trend of the power battery, grading risks according to the risk characteristics, making different instructions for different risk grades, and executing the instructions to a power battery management system; if the temperature of the power battery is lower than the lower threshold, a multi-target tuna group optimization algorithm is adopted, battery life, heating rate and heating time are taken as optimization targets, the pareto front is calculated, the heating rate and heating time of the power battery are set, and the power battery is sent to a power battery management system for execution;
the battery model is an electric-thermal coupling model which has completed training; in the second step, when the battery model is fused with the transfer learning, the electric-thermal coupling model is used as a source domain of the transfer learning, the power battery state data acquired by the battery management system is used as a characteristic required by the transfer learning, and the power battery which is actually operated is used as a target domain;
and if the temperature of the power battery exceeds the upper threshold, removing interference noise in the temperature change trend of the power battery in a wavelet signal analysis mode after the temperature of the power battery is predicted to be obtained in the second step, extracting thermal signal characteristics from the temperature of the power battery in a time sequence by adopting a variation empirical mode decomposition method, and grading the thermal signal characteristics according to the time when the risk corresponding to the thermal signal characteristics comes to obtain a risk grade.
2. The cloud co-controlled active thermal management system of claim 1, wherein: and step four, when the temperature of the power battery is lower than the lower threshold, taking the service life of the battery, the heating rate and the heating time as optimization targets of a multi-target tuna group optimization algorithm.
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| CN114879062A (en) * | 2022-05-30 | 2022-08-09 | 东莞新能安科技有限公司 | Battery short circuit detection method, device and equipment |
| CN117872166B (en) * | 2024-03-11 | 2024-05-14 | 广东采日能源科技有限公司 | Method and device for detecting thermal runaway of energy storage battery and electronic equipment |
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