CN113281655A - Predictive control method and device for internal heating of power battery in low-temperature environment - Google Patents
Predictive control method and device for internal heating of power battery in low-temperature environment Download PDFInfo
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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Abstract
The invention discloses a method and a device for predictive control of internal heating of a power battery in a low-temperature environment, wherein the method comprises the following steps: the measuring system inputs the surface temperature, terminal voltage and current information of the power battery acquired in real time to the control system; the control system adopts an unscented Kalman filtering method to estimate the core temperature of the battery in the current state in real time, accesses an experimental database to obtain corresponding electric heating coupling model parameters, calculates the core temperature of the battery in a prediction time domain through a model-based prediction method, solves a multi-target optimization problem considering various heating performances, outputs the first pulse heating current in the control time domain as the reference current of PID control, and realizes bidirectional pulse current heating between the super capacitor and the power battery. And repeating the processes until the core temperature of the power battery reaches the target. The invention shortens the heating time of the power battery, reduces the energy and service life loss of the battery in the heating process, and effectively improves the endurance mileage of the electric automobile in a low-temperature environment.
Description
Technical Field
The invention belongs to the technical field of power battery management, and particularly relates to a method and a device for predictive control of internal heating of a power battery in a low-temperature environment.
Background
Under the low temperature environment, the performance of the lithium ion power battery is greatly reduced, so that the electric automobile suffers from serious mileage loss, and meanwhile, the running cost and the service life loss of the electric automobile are increased. Therefore, before the electric vehicle runs, the power battery needs to be heated to reach the normal working temperature range. How to realize the temperature rise of the power battery in a method with higher efficiency, more energy conservation and smaller service life loss under a low-temperature environment becomes a key problem of popularization of the electric automobile in cold regions.
In the prior art, external heating transfers heat generated by an external heat source to a battery pack through a heat transfer medium using a specially designed heat management system, which has low heat transfer efficiency, relatively long heating time, and non-uniform cell heating temperature. The internal heating strategy can well solve the problems, and a large amount of electrochemical heat is generated inside the battery by applying current by utilizing the characteristic that the battery has high impedance at low temperature. The existing internal heating technology usually needs an additional external power supply to provide heating current, and meanwhile, the change of parameters of a battery model under different temperatures, different charge states and different heating current rates cannot be considered, so that the thermodynamic battery cannot be heated most quickly and efficiently, and the service life loss of the battery is reduced.
Disclosure of Invention
The invention aims to provide a method and a device for predictive control of internal heating of a power battery in a low-temperature environment, aiming at the defects of the prior art, which can quickly and efficiently heat the power battery internally, reduce the cost of a thermal management system of an electric automobile and improve the service performance of the electric automobile in the low-temperature environment.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a predictive control method for internal heating of a power battery in a low-temperature environment comprises the following steps:
step S1, acquiring the initial state of charge of the battery;
step S2, setting the length of the prediction time domain, the length of the control time domain and the size of each control time window in the control time domain;
step S3, collecting the current surface temperature, terminal voltage and heating current of the power battery;
step S4, taking the surface temperature currently acquired by the power battery as observation data, taking the heating current and the environment temperature currently acquired by the power battery as reference factor data, and estimating the current core temperature of the power battery by adopting a Kalman filtering algorithm;
step S5, determining the current electric thermal coupling model parameters of the power battery according to the current state of charge of the power battery, the heating current obtained in step S3 and the core temperature of the power battery obtained in step S4;
step S6, performing rolling prediction on the state of charge and the core temperature of the power battery in the prediction time domain and updating the electrothermal coupling model parameters of the power battery in a rolling manner according to the state of charge S (k), the core temperature t (k), the electrothermal coupling model parameter Γ (k) and a heating current sequence u (j) given in the prediction time domain, wherein j is k +1, k +2, …, k + N at the current time point k of the power battery, and obtaining a core temperature sequence t (j) of the power battery in the prediction time domain, wherein j is k +1, k +2, …, k + N;
wherein the given heating current sequence is obtained by solving the following multi-objective optimization problem:
in the formula, alpha is a penalty factor of energy loss, IppcIs the maximum limit value of the heating current;
step S7, in the first control time window started at present, pulse heating current u (k +1) is executed to the power batteries;
and step S8, repeating the steps S3 to S7 until the core temperature of the power battery reaches the target value.
In a more preferred control method technical solution, the method for acquiring the initial state of charge of the battery in step S1 is as follows: establishing a voltage curve and a charge state curve by adopting a low-current charging and discharging method under different surface temperatures of the power battery in advance, wherein the voltage curve is used as an open-circuit voltage curve of the power battery, and further establishing a relation database of the charge state, the open-circuit voltage and the surface temperature of the battery; and then, searching the initial state of charge of the power battery in the established relational database according to the initial surface temperature and the open-circuit voltage measured by the power battery.
In a more preferred technical solution of the control method, the step S4 specifically includes:
s4.1, inputting the error between the surface temperature currently acquired by the power battery and the surface temperature estimated by the Kalman filter, the heating current currently acquired and the environment temperature into the Kalman filter;
s4.2, predicting state information and error covariance of the next moment by a Kalman filter, calculating Kalman gain, and updating Kalman estimation value and error covariance; the state information specifically refers to the surface temperature and the core temperature of the power battery;
and S4.3, continuously repeating the step S4.2 until an optimal estimated value of the battery core temperature is obtained.
In a more optimal control method technical scheme, a method for determining parameters of an electrothermal coupling model of a power battery comprises the following steps: obtaining a relational database of battery electrothermal coupling model parameters and battery core temperature, state of charge and current amplitude in advance in a hybrid power pulse capability characteristic test experiment at different temperatures, different battery states of charge and different current amplitudes; and then determining the current electric thermal coupling model parameters of the power battery in the relational database according to the current core temperature, the current state of charge and the heating current of the power battery.
In a more preferred control method, in step S6, the power battery state of charge prediction expression is:
where eta is coulombic efficiency, Δ t is sampling interval, CbAnd S (k) and S (k +1) are the states of charge of the power battery at sampling time points k and k +1 respectively, and u (k) is the heating current of the power battery at the sampling time point k.
In a more preferred control method, in step S6, the core temperature prediction expression of the power battery is:
in the formula, T (k) and T (k +1) are core temperatures of the power battery at sampling time points k and k +1 respectively, and m, c, A and h are mass, specific heat capacity, surface area and convection coefficient of the power battery respectively; q (k) is the heat generation power of the power battery at the sampling time point k, and is related to the heating current u (k).
In a more preferred control method, in step S7, the method for performing pulse heating current on each power battery is as follows: and (3) taking the heating current u (k +1) as a PID control reference signal, and outputting a control quantity through a negative feedback control principle to realize the current control between the super capacitor and the power battery.
The predictive control method for the internal heating of the power battery in the low-temperature environment is adopted to carry out the internal heating control on the power battery in the hybrid energy storage system in the low-temperature environment.
In a more preferred control device solution, the hybrid energy storage system includes: the power battery module, the super capacitor module and the bidirectional DC/DC converter are arranged on the power battery module;
the power battery module is formed by connecting a plurality of lithium batteries in series and in parallel and is used for providing main energy for the hybrid energy storage electric automobile;
the super capacitor module is formed by connecting a plurality of super capacitors in series and parallel and is used for providing secondary energy for the hybrid energy storage electric automobile and serving as auxiliary energy for pulse current heating of a power battery;
the bidirectional DC/DC converter is composed of two controllable MOSFETs and an inductor, and a given bidirectional pulse can be generated between the battery and the super capacitor by controlling the duty ratio of the two MOSFETs.
Advantageous effects
The method and the device have the following beneficial effects: because the hybrid energy storage system integrates the lithium ion power battery, the super capacitor and the bidirectional DC/DC converter, the internal heating of the power battery can be realized by utilizing two energy devices to carry out pulse charging and discharging, and the defects of low heat transfer efficiency, relatively long heating time, uneven heating temperature of single batteries and the like caused by external heating are avoided. Meanwhile, the super capacitor can provide heating current for the battery as auxiliary energy integrated in the hybrid energy storage system, so that other external power supplies do not need to be added, and the cost of the electric vehicle heat management system is reduced. The method has the advantages that a relational database of battery electric-heating coupling model parameters and battery core temperature, state of charge and current amplitude and a relational database of battery state of charge and open-circuit voltage and temperature are constructed, so that the characteristics of the power battery in a low-temperature environment can be more accurately represented, and a more accurate heating strategy can be designed. A model prediction control mechanism is introduced, model parameters of the battery are updated in real time, the core temperature and the charge state of the battery at the future moment are predicted, a multi-objective optimization problem considering various heating performances is solved, an optimal heating current sequence is obtained, the internal heating power battery is rapidly and efficiently heated, the service performance of the electric automobile in a low-temperature environment is improved, and the service life of the power battery is prolonged.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a hybrid energy storage system provided in an embodiment of the present invention;
FIG. 2 is a flow chart of a method according to an embodiment of the invention;
FIG. 3 is a block diagram of an algorithm for estimating the core temperature of the power battery according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a pulse current time window in the prediction time domain according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in detail, which are developed based on the technical solutions of the present invention, and give detailed implementation manners and specific operation procedures to further explain the technical solutions of the present invention.
Example 1
The embodiment discloses a method for predictive control of internal heating of a power battery in a low-temperature environment, which performs internal heating control on the power battery in a hybrid energy storage system in the low-temperature environment, where the hybrid energy storage system in the embodiment is shown in fig. 1 and includes: the power battery module, the super capacitor module and the bidirectional DC/DC converter are arranged on the power battery module; the power battery module is formed by connecting a plurality of lithium batteries in series and in parallel and is used for providing main energy for the hybrid energy storage electric automobile; the super capacitor module is formed by connecting a plurality of super capacitors in series and parallel and is used for providing secondary energy for the hybrid energy storage electric automobile and serving as auxiliary energy for pulse current heating of a power battery; the bidirectional DC/DC converter is composed of two controllable MOSFETs and an inductor, and a given bidirectional pulse can be generated between the battery and the super capacitor by controlling the duty ratio of the two MOSFETs.
Specifically, as shown in fig. 2, the method of this embodiment includes the following steps:
and step S1, measuring the low-temperature environment temperature and the initial temperature of the power battery, and estimating the initial state of charge of the battery.
In the step, a voltage curve of the battery from 2.5 to 4.2 volts is obtained by adopting a low-current charging and discharging (C/25) method under different surface temperatures of the power battery in advance, the voltage curve can be used as an open-circuit voltage curve of the battery, and a relational database of the state of charge of the battery, the open-circuit voltage and the temperature is established. The method comprises the steps that an environment temperature sensor and a thermocouple sensor tightly attached to the surface of a power battery respectively measure the temperature of a current low-temperature environment and the initial temperature of the power battery, the battery can be defaulted to reach thermal balance in the initial state, the core temperature of the battery is equal to the surface temperature, and finally the initial state of charge is estimated according to the current core temperature and the current open-circuit voltage of the battery.
Step S2, setting the length of the prediction time domain, the length of the control time domain and the size of each control time window in the control time domain;
in this step, the control time domain, the length of the prediction time domain and the size of the time window are input in a model predictive control algorithm of a computer according to the requirements of a user: setting the prediction time domain as N pulse periods, and setting the control time domain as F pulse periods. Since the thermodynamic model time constant of the battery is about tens of minutes, and the temperature rise caused by the pulse current of one cycle (usually 10HZ) is not obvious, the length of each control time window is set to be L pulse cycles, and the control time domain includes M (M ═ F/L) control time windows, and the adopted control laws are the same in the same time window. As shown in fig. 4.
Step S3, acquiring the current surface temperature, terminal voltage and flowing heating current information of the power battery;
in the step, a temperature sensor tightly attached to a power battery in a measurement system acquires the surface temperature of the battery in real time; the voltage sensor collects the terminal voltage and the open-circuit voltage of the battery in real time; the current sensor collects the pulse current flowing through the battery in real time and inputs the information to the computer through a serial port.
Step S4, taking the surface temperature currently acquired by the power battery as observation data, taking the heating current and the environment temperature currently acquired by the power battery as reference factor data, and estimating the current core temperature of the power battery by adopting a Kalman filtering algorithm; as shown in fig. 3, the method specifically includes:
s4.1, inputting the error between the surface temperature currently acquired by the power battery and the surface temperature estimated by the Kalman filter, the heating current currently acquired and the environment temperature into the Kalman filter;
s4.2, predicting state information and error covariance of the next moment by a Kalman filter, calculating Kalman gain, and updating Kalman estimation value and error covariance; the state information specifically refers to the surface temperature and the core temperature of the power battery;
and S4.3, continuously repeating the step S4.2 until an optimal estimated value of the battery core temperature is obtained.
Step S5, determining the current electric thermal coupling model parameters of the power battery according to the current state of charge of the power battery, the heating current obtained in step S3 and the core temperature of the power battery obtained in step S4;
in the step, a relational database of battery electrothermal coupling model parameters and battery core temperature, state of charge and current amplitude is obtained in advance in a test experiment of hybrid power pulse capability characteristics of different temperatures, different battery states of charge and different current amplitudes; and then, reading the relation data of the battery electric thermal coupling model parameters, the battery core temperature, the state of charge and the current amplitude in the database by combining the heating current, the core temperature and the current state of charge information, and obtaining the current electric thermal coupling model parameters.
Step S6, according to the state of charge S (k) of the power battery at the current time point k, the core temperature t (k), the electrothermal coupling model parameter Γ (k), and the heating current sequence u (j) given in the prediction time domain (step S2, the prediction time domain includes N pulse periods), where j is k +1, k +2, …, k + N, performing rolling prediction on the state of charge and the core temperature of the power battery in the prediction time domain, and performing rolling update on the electrothermal coupling model parameter of the power battery to obtain the core temperature sequence t (j) of the power battery in the prediction time domain, where j is k +1, k +2, …, k + N;
the state of charge prediction expression of the power battery is as follows:
where eta is coulombic efficiency, Δ t is sampling interval, CbAnd S (k) and S (k +1) are the states of charge of the power battery at sampling time points k and k +1 respectively, and u (k) is the heating current of the power battery at the sampling time point k.
The core temperature prediction expression of the power battery is as follows:
in the formula, T (k) and T (k +1) are core temperatures of the power battery at sampling time points k and k +1 respectively, and m, c, A and h are mass, specific heat capacity, surface area and convection coefficient of the power battery respectively; q (k) is the heat generation power of the power battery at the sampling time point k, and is related to the heating current u (k).
The given heating current sequence is obtained by solving the following multi-objective optimization problem:
wherein, the first term represents the change value of the core temperature of the battery in the prediction time domain, and the second term represents the energy loss of the battery in the prediction time domain; α is a penalty factor for energy loss, IppcIs the maximum limit value of the heating current. Therefore, the invention solves the multi-objective optimization problem of the heating current sequence and comprehensively considers the heating speed and the energy loss.
Specifically, the multi-objective optimization problem can be converted into a concave function under a convex set in a computer, and a cutting plane method is operated to obtain an optimal heating current sequence as a given heating current sequence, as shown in fig. 4.
Step S7, in the first control time window started at present, pulse heating current u (k +1) is executed to the power batteries; namely, the pulse current of the first time window in the optimal pulse sequence is used as a reference signal and input to a PID controller on a DSP control board, and the controller outputs the duty ratio of a bidirectional DC/DC converter through a negative feedback control principle, so that the pulse current in the hybrid energy storage system is the same as the first term in the optimal heating sequence.
And step S8, repeating the steps S3 to S7 until the core temperature of the power battery reaches the target value. The target temperature value needs to be set in advance and must meet the output power requirement of the power battery.
Example 2
The embodiment provides a predictive control device for internal heating of a power battery in a low-temperature environment, which performs internal heating control on the power battery in a hybrid energy storage system in the low-temperature environment by using the predictive control method for internal heating of the power battery in the low-temperature environment described in embodiment 1.
Specifically, the prediction control device for internal heating of a power battery in a low-temperature environment according to the embodiment includes, as shown in fig. 5, a hybrid energy storage system, a measurement system, a control system, and a database. The hybrid energy storage system is a controlled object of the patent and is used for realizing bidirectional pulse current between the super capacitor and the power battery, and the current flows through an internal joule heat and polarization heat heating battery generated by the battery; the measuring system is used for measuring the external environment and the state information of each component of the hybrid energy storage system in real time, and mainly comprises temperature information and voltage and current information; the main functions of the control system comprise receiving measurement information, operating an algorithm to estimate real-time model parameters, operating a prediction algorithm to predict future state information of the hybrid energy storage system, solving a multi-objective optimization problem and outputting an optimal control law to the hybrid energy storage system. The database is used for storing relation data of the battery charge state, the open-circuit voltage and the temperature and relation data of battery electric heating coupling model parameters, the battery core temperature, the charge state and the current amplitude, wherein the relation data are obtained by basic experiments.
Wherein, hybrid energy storage system includes: the device comprises a lithium ion power battery module, a super capacitor module and a bidirectional DC/DC converter. The lithium ion power battery module is formed by connecting a plurality of batteries in series and in parallel, provides main energy for the hybrid energy storage electric automobile, and is the most critical equipment for ensuring the effective driving of the electric automobile; the super capacitor module is formed by connecting a plurality of super capacitors in series and parallel, provides secondary energy for the hybrid energy storage electric automobile, and is auxiliary energy for realizing pulse current heating of the battery; the bidirectional DC/DC converter is composed of two controllable metal-oxide-semiconductor field effect transistors (MOSFETs) and an inductor, and a given bidirectional pulse can be generated between a battery and a super capacitor by controlling the duty ratio of the two MOSFETs.
In this embodiment, the measurement system further includes: the device comprises a voltage sensor, a current sensor, a temperature sensor and a communication serial port. The voltage sensor is used for acquiring terminal voltage and open-circuit voltage values of the power battery and the super capacitor; the current sensor is used for acquiring pulse current amplitude and frequency information input and output by the power battery and the super capacitor; the temperature sensor is used for measuring the ambient temperature and the surface temperature of the power battery; the communication serial port is used for transmitting the measurement information of the sensor to the control system.
In this embodiment, the control system further includes: computer and DSP control panel. The computer is used for operating an algorithm to estimate real-time model parameters, operating a prediction algorithm to predict future state information of the hybrid energy storage system, solving a multi-objective optimization problem and reading parameter data in a database; the DSP control board is used for receiving the optimal given heating current sequence solved by the computer, realizing negative feedback control and outputting a control quantity (MOSFET duty ratio) to the bidirectional DC/DC converter.
In short, in this embodiment, by setting the prediction time domain, the control time domain, the time window length, and the target core temperature, the measurement system transmits the acquired real-time temperature, voltage, and current information to the computer, and the computer sequentially runs the parameter identification algorithm, reads the database, runs the prediction algorithm, solves the heating multi-objective optimization problem, and outputs the optimal heating current sequence. The DSP control board executes optimal current setting, internal heating power batteries are quickly and efficiently carried out, the use performance of the electric automobile in a low-temperature environment is improved, and the service life of the power batteries is prolonged.
The above embodiments are preferred embodiments of the present application, and those skilled in the art can make various changes or modifications without departing from the general concept of the present application, and such changes or modifications should fall within the scope of the claims of the present application.
Claims (9)
1. A predictive control method for internal heating of a power battery in a low-temperature environment is characterized by comprising the following steps:
step S1, acquiring the initial state of charge of the battery;
step S2, setting the length of the prediction time domain, the length of the control time domain and the size of each control time window in the control time domain;
step S3, collecting the current surface temperature, terminal voltage and heating current of the power battery;
step S4, taking the surface temperature currently acquired by the power battery as observation data, taking the heating current and the environment temperature currently acquired by the power battery as reference factor data, and estimating the current core temperature of the power battery by adopting a Kalman filtering algorithm;
step S5, determining the current electric thermal coupling model parameters of the power battery according to the current state of charge of the power battery, the heating current obtained in step S3 and the core temperature of the power battery obtained in step S4;
step S6, performing rolling prediction on the state of charge and the core temperature of the power battery in the prediction time domain and updating the electrothermal coupling model parameters of the power battery in a rolling manner according to the state of charge S (k), the core temperature t (k), the electrothermal coupling model parameter Γ (k) and a heating current sequence u (j) given in the prediction time domain, wherein j is k +1, k +2, …, k + N at the current time point k of the power battery, and obtaining a core temperature sequence t (j) of the power battery in the prediction time domain, wherein j is k +1, k +2, …, k + N;
wherein the given heating current sequence is obtained by solving the following multi-objective optimization problem:
in the formula, alpha is a penalty factor of energy loss, IppcIs the maximum limit value of the heating current;
step S7, in the first control time window started at present, pulse heating current u (k +1) is executed to the power batteries;
and step S8, repeating the steps S3 to S7 until the core temperature of the power battery reaches the target value.
2. The method of claim 1, wherein the step S1 comprises the steps of: establishing a voltage curve and a charge state curve by adopting a low-current charging and discharging method under different surface temperatures of the power battery in advance, wherein the voltage curve is used as an open-circuit voltage curve of the power battery, and further establishing a relation database of the charge state, the open-circuit voltage and the surface temperature of the battery; and then, searching the initial state of charge of the power battery in the established relational database according to the initial surface temperature and the open-circuit voltage measured by the power battery.
3. The method according to claim 1, wherein the step S4 specifically includes:
s4.1, inputting the error between the surface temperature currently acquired by the power battery and the surface temperature estimated by the Kalman filter, the heating current currently acquired and the environment temperature into the Kalman filter;
s4.2, predicting state information and error covariance of the next moment by a Kalman filter, calculating Kalman gain, and updating Kalman estimation value and error covariance; the state information specifically refers to the surface temperature and the core temperature of the power battery;
and S4.3, continuously repeating the step S4.2 until an optimal estimated value of the battery core temperature is obtained.
4. The method according to claim 1, wherein the parameters of the electrothermal coupling model of the power battery are determined by: obtaining a relational database of battery electrothermal coupling model parameters and battery core temperature, state of charge and current amplitude in advance in a hybrid power pulse capability characteristic test experiment at different temperatures, different battery states of charge and different current amplitudes; and then determining the current electric thermal coupling model parameters of the power battery in the relational database according to the current core temperature, the current state of charge and the heating current of the power battery.
5. The method according to claim 1, wherein in step S6, the power battery state of charge prediction expression is:
where eta is coulombic efficiency, Δ t is sampling interval, CbAnd S (k) and S (k +1) are the states of charge of the power battery at sampling time points k and k +1 respectively, and u (k) is the heating current of the power battery at the sampling time point k.
6. The method according to claim 1, wherein in step S6, the core temperature prediction expression of the power battery is:
in the formula, T (k) and T (k +1) are core temperatures of the power battery at sampling time points k and k +1 respectively, and m, c, A and h are mass, specific heat capacity, surface area and convection coefficient of the power battery respectively; q (k) is the heat generation power of the power battery at the sampling time point k, and is related to the heating current u (k).
7. The method of claim 1, wherein in step S7, the method for performing pulse heating current for each power battery is as follows: and (3) taking the heating current u (k +1) as a PID control reference signal, and outputting a control quantity through a negative feedback control principle to realize the current control between the super capacitor and the power battery.
8. A prediction control device for internal heating of a power battery in a low-temperature environment is characterized in that the prediction control method for internal heating of the power battery in the low-temperature environment according to any one of claims 1 to 7 is adopted to perform internal heating control on the power battery in a hybrid energy storage system in the low-temperature environment.
9. The control device of claim 8, wherein the hybrid energy storage system comprises: the power battery module, the super capacitor module and the bidirectional DC/DC converter are arranged on the power battery module;
the power battery module is formed by connecting a plurality of lithium batteries in series and in parallel and is used for providing main energy for the hybrid energy storage electric automobile;
the super capacitor module is formed by connecting a plurality of super capacitors in series and parallel and is used for providing secondary energy for the hybrid energy storage electric automobile and serving as auxiliary energy for pulse current heating of a power battery;
the bidirectional DC/DC converter is composed of two controllable MOSFETs and an inductor, and a given bidirectional pulse can be generated between the battery and the super capacitor by controlling the duty ratio of the two MOSFETs.
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| CN114400388A (en) * | 2022-01-20 | 2022-04-26 | 阳光电源股份有限公司 | Energy storage system and battery heating method thereof |
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| CN114400388A (en) * | 2022-01-20 | 2022-04-26 | 阳光电源股份有限公司 | Energy storage system and battery heating method thereof |
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| CN114506247A (en) * | 2022-03-16 | 2022-05-17 | 中国科学技术大学 | Power battery active heat management system based on cloud cooperative control |
| CN114506247B (en) * | 2022-03-16 | 2024-02-09 | 中国科学技术大学 | Active thermal management system of power battery controlled cooperatively by cloud |
| CN115123027A (en) * | 2022-06-09 | 2022-09-30 | 中国第一汽车股份有限公司 | Power battery pulse heating method and device, terminal and storage medium |
| CN115659790A (en) * | 2022-10-13 | 2023-01-31 | 厦门宇电自动化科技有限公司 | Real-time temperature detection method for power battery pack |
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| CN116205053A (en) * | 2023-02-02 | 2023-06-02 | 上海玫克生储能科技有限公司 | Method and device for solving simulation output working condition sequence |
| CN116149398A (en) * | 2023-04-18 | 2023-05-23 | 杭州博峰实业有限公司 | Temperature controller accurate control system and method based on data feedback |
| CN116149398B (en) * | 2023-04-18 | 2023-07-14 | 杭州博峰实业有限公司 | Temperature controller accurate control system and method based on data feedback |
| CN116826254A (en) * | 2023-08-17 | 2023-09-29 | 中南大学 | Battery direct-current self-heating method, system, medium and terminal under low temperature of Ad hoc network of heavy-duty freight train |
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