Energy-saving management method and device for lithium battery

Abstract

The invention relates to the technical field of lithium battery energy-saving management, and discloses an energy-saving management method and device for a lithium battery. The method comprises the following steps: acquiring real-time dynamic data, temperature data and battery fixed parameters of a lithium battery, calculating battery power consumption through a temperature correction calculation model, and correcting the power consumption through a power consumption dynamic correction model; energy consumption parameters, energy efficiency and optimal power consumption are calculated through an energy consumption model and an energy efficiency model, and optimal energy consumption parameters and optimal energy efficiency are screened out; and calculating the optimized internal temperature and surface temperature according to the heat production model and Kalman filtering, generating a temperature adjustment instruction by combining the internal temperature difference and the surface temperature difference, and finally outputting an energy-saving target. The method has the following effects: multi-factor dynamic change can be comprehensively considered, accurate optimization of battery energy efficiency and temperature control is realized, energy consumption is effectively reduced, the service life of the battery is prolonged, and operation stability and safety under complex working conditions are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium batteries, and in particular to a lithium battery energy-saving management method and device. Background Art

[0002] Currently, lithium batteries have been widely used in electric vehicles, consumer electronic devices and energy storage systems. Due to their high energy density, long cycle life and environmental protection characteristics, they have become the preferred battery type for various applications. However, as the scope of lithium battery use continues to expand, its energy consumption management and performance optimization issues are becoming increasingly prominent.

[0003] In the prior art, energy-saving management methods for lithium batteries usually evaluate the operating status of the battery by collecting key parameters during the battery operation process, such as current, voltage, temperature, and internal resistance. Some methods rely on preset energy management rules or fixed models to optimize the performance of the battery, such as adjusting the charge and discharge rate according to the current temperature, or reducing energy consumption by controlling current and voltage.

[0004] However, existing technologies fail to comprehensively consider the dynamic changes of multiple factors such as temperature, current, internal resistance, and their mutual influence. Energy-saving management strategies mostly rely on static rules and lack the ability to flexibly adjust the battery operating status based on real-time data. At the same time, they lack specificity in temperature control and power management, and have low energy efficiency in complex environments. Summary of the invention

[0005] The present invention provides a lithium battery energy-saving management method, device, electronic device and storage medium to solve the problem that the prior art fails to comprehensively consider the dynamic changes of multiple factors such as temperature, current, internal resistance and their mutual influence, the energy-saving management strategy mostly relies on static rules, lacks the ability to flexibly adjust the battery operating status according to real-time data, and is not targeted enough in temperature control and power management, and has low energy efficiency in complex environments.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a lithium battery energy-saving management method, comprising: Obtain real-time dynamic data, temperature data and battery fixed parameters of lithium batteries; The energy efficiency of the battery at the current moment is calculated according to the real-time dynamic data, and the power consumption of the battery at the current moment is calculated according to the energy efficiency of the battery at the current moment and the temperature data through a temperature correction calculation model; Dynamically correct the battery power consumption at the current moment through a power consumption dynamic correction model to obtain optimized power consumption; According to the real-time dynamic data and the optimized power consumption, energy consumption parameters, energy efficiency and optimal power consumption are calculated respectively through an energy consumption model and an energy efficiency model, and optimal energy consumption parameters and optimal energy efficiency are screened out; Calculate the energy saving target at the current moment according to the optimal energy consumption parameter, the optimal energy efficiency, the optimal power consumption, the real-time dynamic data, and the battery fixed parameters, wherein the energy saving target includes a target current, a target power, a target voltage, a target power, and a target temperature; Perform deviation analysis on the target current in the energy-saving target, the target power in the energy-saving target, the target voltage in the energy-saving target, and the target power in the energy-saving target to obtain energy-saving control instructions on current, voltage, power, and power; Obtaining heat generation through a heat generation model according to the real-time dynamic data and the fixed parameters of the battery; According to the heat generation, the real-time dynamic data, and the fixed parameter data, an optimized internal temperature and an optimized surface temperature are obtained through a heat transfer model and a Kalman filter; According to the optimized internal temperature and the accurately optimized surface temperature, obtaining the internal temperature difference and the surface temperature difference by internal temperature difference calculation and surface temperature difference calculation respectively; According to the internal temperature difference and the surface temperature difference, the temperature is adjusted by a temperature control module to obtain energy-saving control instructions on the temperature.

[0007] Preferably, the energy efficiency of the battery at the current moment is calculated according to the real-time dynamic data, and the power consumption of the battery at the current moment is calculated according to the energy efficiency of the battery at the current moment and the temperature data through a temperature correction calculation model, including: The real-time dynamic data includes the current value of the battery at the current moment, the voltage value of the battery at the current moment, the current value provided by the charging module at the current moment, and the voltage value provided by the charging module at the current moment; The energy efficiency of the battery at the current moment is calculated using the following formula: in, is the energy efficiency of the battery at the current moment, is the output power of the battery, is the input power of the battery; is the current value of the battery at the current moment, is the voltage value of the battery at the current moment; is the current value provided by the charging module at the current moment, The voltage value provided by the charging module at the current moment; The current battery power consumption is obtained through the following temperature correction calculation model: in, The current battery consumption. is the time interval, is the current battery temperature, is the standard temperature of the battery under standard operating conditions, is a first temperature correction parameter, and the temperature data includes the battery temperature at the current moment and the standard temperature of the battery under standard operating conditions.

[0008] Preferably, the current battery power consumption is dynamically corrected by a power consumption dynamic correction model to obtain optimized power consumption, including: The current battery power consumption is dynamically corrected through the following power consumption dynamic correction model: in, To optimize power consumption, The remaining battery power at the last moment. is the remaining battery power at the current moment, is the current value of the battery at the current moment, is the battery temperature at the current moment, is the power correction parameter, is the current correction parameter, is the second temperature correction parameter, is a constant term.

[0009] Preferably, according to the real-time dynamic data and the optimized power consumption, energy consumption parameters, energy efficiency and optimal power consumption are calculated respectively through an energy consumption model and an energy efficiency model, and the optimal energy consumption parameters and optimal energy efficiency are screened out, including: The consumption parameters are calculated by the following formula: in, is the energy consumption parameter, is the remaining battery power at the current moment, is the current value of the battery at the current moment, is the battery temperature at the current moment, To optimize power consumption, is the standard temperature of the battery under standard operating conditions, , , , , is the weight parameter in the energy consumption model, is a constant term; Energy efficiency is calculated by the following formula: in, For energy efficiency, is the energy consumption parameter, Sensitivity factor for energy efficiency; The optimal energy efficiency is screened by the following formula: in, For optimal energy efficiency; According to the optimal energy efficiency, filtering out consumption parameters corresponding to the optimal energy efficiency; The optimal power consumption is calculated by the following formula: in, For optimal power consumption, is the maximum current tolerance coefficient of the battery at standard temperature, is the current ambient temperature, is the coefficient of the effect of temperature change on power consumption, is the internal resistance of the battery, including ohmic internal resistance and polarization internal resistance, is the energy conversion efficiency of the battery, It is the correction parameter for the battery open circuit voltage changing with temperature.

[0010] Preferably, the heat generation is obtained by a heat generation model according to the real-time dynamic data and the fixed parameters of the battery, including: The joule heat is calculated using the following formula: in, is the joule heat, is the output current of the battery at the current moment, is the internal resistance of the battery; The temperature-corrected heat is calculated using the following formula: in, Correct the heat for temperature, It is the correction parameter of the battery open circuit voltage changing with temperature; The heat production is calculated using the following formula: in, To produce heat, Correct the heat for temperature, The heat in joules.

[0011] Preferably, according to the heat generation, the real-time dynamic data, and the battery fixed parameter data, the optimized internal temperature and the optimized surface temperature are obtained through a heat transfer model and Kalman filtering, including: The temperature vector at the current moment is expressed by the following formula: in, is the temperature vector at the current moment, is the temperature inside the battery at the current moment, , They are the temperatures of the three surfaces of the battery at the current moment; The temperature vector at the next moment is expressed by the following formula: in, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature inside the battery at the next moment calculated initially, , The temperatures of the three surfaces of the battery at the next moment obtained by preliminary calculation; The temperature vector at the next moment after optimization is expressed by the following formula: in, is the temperature vector at the next moment after optimization, To optimize the temperature inside the battery at the next moment, The temperature of the three surfaces of the battery at the next moment after optimization; The temperature vector actually observed by the sensor is expressed by the following formula: in, is the temperature vector actually observed by the sensor, is the temperature inside the battery actually observed by the sensor at the next moment, is the temperature of the three surfaces of the battery actually observed by the sensor at the next moment; The control input vector is expressed by the following formula: in, is the control input vector, is the ambient temperature, To produce heat, is the specific heat capacity parameter of the battery, which is used to quantify the effect of heat on temperature changes; The state prediction formula is obtained through the heat transfer model: in, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature vector at the current moment, is the state transfer matrix, that is, the heat transfer relationship between the internal temperature and the surface temperature of the battery, is the input control matrix, i.e., the influence of heat source input and ambient temperature on the system; The forecast error covariance matrix is ​​updated by the following formula to quantify the uncertainty of the forecast: in, is the predicted state error covariance matrix, is the error covariance matrix of the previous moment, is the process noise covariance matrix; The Kalman gain is calculated by the following formula: in, is the Kalman gain, is the observation matrix, is the observation noise covariance matrix; The predicted state of the preliminary calculation is corrected by the following formula to obtain the accurately optimized internal temperature and surface temperature: in, is the temperature vector at the next moment after optimization, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature vector actually observed by the sensor, is the Kalman gain, is the observation matrix; The uncertainty of the corrected state is calculated by the following formula to update the error covariance matrix: in, is the corrected error covariance matrix, is the predicted state error covariance matrix, is the identity matrix, is the observation matrix, which describes the relationship between the actual observed temperature and the state variables. is the Kalman gain.

[0012] Preferably, according to the optimized internal temperature and the optimized surface temperature, the internal temperature difference and the surface temperature difference are obtained by internal temperature difference calculation and surface temperature difference calculation respectively, including: The internal temperature difference is calculated by the following formula: in, is the internal temperature difference, is the internal temperature at the current moment, is the corrected internal temperature at the next moment; The surface temperature difference is calculated by the following formula: in, is the surface temperature difference, is the ambient temperature, is the corrected surface temperature at the next moment.

[0013] In a second aspect, the present invention provides a lithium battery energy-saving management device, comprising: Data acquisition module, used to obtain real-time dynamic data and fixed parameters of lithium batteries; A battery power consumption calculation module, used to preliminarily calculate the battery power consumption at the current moment according to the real-time dynamic data and the battery fixed parameters through a temperature calculation model based on temperature correction, and dynamically correct the preliminarily calculated power consumption through a power consumption dynamic correction model to obtain optimized power consumption; The energy optimization module is used to calculate the energy consumption parameters and energy efficiency respectively through the energy consumption model and the energy efficiency model according to the real-time dynamic data, the battery fixed parameters and the optimized power consumption, and screen out the optimal energy consumption parameters and the optimal energy efficiency, and further calculate the optimal power consumption; The temperature control module is used to calculate the heat generation through the heat generation model according to the real-time dynamic data and the fixed parameters of the battery, optimize the internal temperature and the surface temperature by combining the heat transfer model and the Kalman filter, calculate the internal temperature difference and the surface temperature difference according to the optimized internal temperature and the surface temperature, generate the temperature adjustment instruction according to the internal temperature difference and the surface temperature difference, and regulate the temperature control module to execute the fast heating, fast heat dissipation or stable mode, and finally obtain the internal temperature and the surface temperature that meet the target temperature control requirements; The result output module is used to output the calculated target current, target voltage, target current, target power, and target temperature energy-saving control instructions, and adjust the operating state of the battery to meet the energy-saving target.

[0014] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements any one of the above-mentioned lithium battery energy-saving management methods when executing the computer program.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned lithium battery energy-saving management methods.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The lithium battery energy-saving management method and device provided by the present invention comprehensively considers the real-time dynamic data and fixed parameters of the battery, including key factors such as current, voltage, temperature and internal resistance, and proposes an energy management method based on multi-factor collaborative optimization, overcoming the limitations of single factor optimization in the prior art. By combining the energy consumption model with the energy efficiency model, the present invention can comprehensively evaluate the energy consumption and energy efficiency of lithium batteries, and improve the accuracy and adaptability of energy-saving management in complex environments.

[0017] In the specific implementation process of the technical solution, the present invention introduces a dynamic correction model for power consumption, and uses the remaining power of the battery at the previous moment, the current value and temperature data at the current moment to correct the power consumption parameters of the battery in real time, thereby improving the flexibility and dynamic response capability of battery energy management. Compared with the energy-saving management method that relies on static rules in the prior art, the present invention can dynamically adjust the battery operating state according to real-time data, significantly reducing the adjustment lag phenomenon, and meeting the demand for efficient energy-saving management under complex operating conditions.

[0018] The present invention also achieves accurate optimization of the internal temperature and surface temperature of the battery through the organic combination of the heat transfer model and the Kalman filter. The heat distribution during the operation of the battery is predicted by the heat transfer model, and the preliminary predicted temperature value is corrected by the Kalman filter, and more accurate temperature parameters are obtained by combining the real-time observation data. By calculating the internal temperature difference and the surface temperature difference, the temperature control module is further guided to perform rapid heating, rapid heat dissipation or stable operation mode, thereby achieving a dynamic balance between the internal and external temperatures of the battery, significantly improving the accuracy and energy efficiency of temperature control.

[0019] In addition, the present invention adopts a joint optimization method of optimal energy consumption parameters and optimal energy efficiency to screen out the optimal power consumption, and dynamically adjusts energy-saving targets, including target current, target voltage, and target temperature, to ensure that the battery achieves the lowest energy consumption while meeting performance requirements. This power consumption optimization strategy significantly reduces the operating energy loss of the battery and improves the overall energy efficiency of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the energy-saving management process of a lithium battery provided by the first embodiment of the present invention; Figure 2It is a schematic diagram of the energy-saving management structure of a lithium battery provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 The first embodiment of the present invention provides a lithium battery energy saving management method, comprising the following steps: S1, obtain real-time dynamic data, temperature data and battery fixed parameters of lithium batteries; S2, calculating the energy efficiency of the battery at the current moment according to the real-time dynamic data, and calculating the power consumption of the battery at the current moment according to the energy efficiency of the battery at the current moment and the temperature data through a temperature correction calculation model; S3, dynamically correcting the battery power consumption at the current moment through a power consumption dynamic correction model to obtain optimized power consumption; S4, according to the real-time dynamic data and the optimized power consumption, respectively calculating energy consumption parameters, energy efficiency and optimal power consumption through an energy consumption model and an energy efficiency model, and screening out optimal energy consumption parameters and optimal energy efficiency; S5, calculating the energy saving target at the current moment according to the optimal energy consumption parameter, the optimal energy efficiency, the optimal power consumption, the real-time dynamic data, and the battery fixed parameters, the energy saving target including target current, target power, target voltage, target power, and target temperature; S6, performing deviation analysis on the target current in the energy-saving target, the target power in the energy-saving target, the target voltage in the energy-saving target, and the target power in the energy-saving target to obtain energy-saving control instructions on current, voltage, power, and power; S7, obtaining heat generation through a heat generation model according to the real-time dynamic data and the fixed parameters of the battery; S8, obtaining an optimized internal temperature and an optimized surface temperature through a heat transfer model and Kalman filtering according to the heat generation, the real-time dynamic data, and the fixed parameter data; S9, obtaining an internal temperature difference and a surface temperature difference by internal temperature difference calculation and surface temperature difference calculation respectively according to the optimized internal temperature and the accurately optimized surface temperature; S10, adjusting the temperature through a temperature control module according to the internal temperature difference and the surface temperature difference, and obtaining an energy-saving control instruction on the temperature.

[0023] In step S1, real-time dynamic data, temperature data and battery fixed parameters of the lithium battery are obtained, including: The real-time dynamic data of the lithium battery includes battery current, battery temperature, battery output voltage, battery remaining power, battery internal resistance, battery output power, battery input power, and battery energy efficiency; which can be measured and calculated by sensors inside and outside the battery.

[0024] The temperature data includes ambient temperature, temperature distribution, standard temperature of the battery under standard working conditions, temperature correction parameters, and ambient temperature correction parameters; the temperature data can be measured by an ambient temperature sensor, and the standard temperature is provided by the battery manufacturer.

[0025] The temperature distribution includes the internal temperature of the battery and the surface temperature of the battery; The fixed parameters of the battery include the standard current of the battery under standard working conditions, the standard voltage of the battery under standard working conditions, the maximum energy efficiency at standard temperature, the current correction parameter, the energy loss correction parameter, the heat dissipation correction parameter, the open circuit voltage correction parameter, the nominal capacity of the battery, the dynamic response time parameter, the heat dissipation model parameter, and the thermal capacity parameter. The standard current and voltage, and the nominal capacity can be provided by the battery manufacturer, and the other parameters are determined through multiple experimental tests.

[0026] In step S2, the energy efficiency of the battery at the current moment is calculated according to the real-time dynamic data, and the battery power consumption at the current moment is calculated according to the energy efficiency of the battery at the current moment and the temperature data through a temperature correction calculation model, including: The real-time dynamic data includes the current value of the battery at the current moment, the voltage value of the battery at the current moment, the current value provided by the charging module at the current moment, and the voltage value provided by the charging module at the current moment; The energy efficiency of the battery at the current moment is calculated using the following formula: in, is the energy efficiency of the battery at the current moment, is the output power of the battery, is the input power of the battery; is the current value of the battery at the current moment, is the voltage value of the battery at the current moment; is the current value provided by the charging module at the current moment, The voltage value provided by the charging module at the current moment; The current battery power consumption is obtained through the following temperature correction calculation model: in, The current battery consumption. is the time interval, is the current battery temperature, is the standard temperature of the battery under standard operating conditions, is a first temperature correction parameter, and the temperature data includes the battery temperature at the current moment and the standard temperature of the battery under standard operating conditions.

[0027] It is worth noting that the temperature correction parameter needs to be determined through experiments or simulations, and it reflects the impact of temperature changes on battery power consumption.

[0028] In step S3, the current battery power consumption is dynamically corrected by using a power consumption dynamic correction model to obtain optimized power consumption, including: The current battery power consumption is dynamically corrected through the following power consumption dynamic correction model: in, To optimize power consumption, The remaining battery power at the last moment. is the remaining battery power at the current moment, is the current value of the battery at the current moment, is the battery temperature at the current moment, is the power correction parameter, is the current correction parameter, is the second temperature correction parameter, is a constant term.

[0029] It should be noted that, in step S3, the battery power consumption at the next moment may be further predicted by the power consumption prediction model, including: The battery power consumption at the next moment is predicted using the following power consumption prediction model: in, is the predicted value of the remaining battery power at the next moment, is the remaining battery power at the current moment, The remaining battery power at the last moment. is the predicted value of the battery current at the next moment, is the predicted temperature value of the battery at the next moment, Correct parameters for the power consumption prediction model.

[0030] It is worth noting that the various correction coefficients in this step can be estimated and adjusted through real-time monitoring data provided by the battery management system (BMS).

[0031] In step S4, according to the real-time dynamic data and the optimized power consumption, energy consumption parameters, energy efficiency and optimal power consumption are calculated through an energy consumption model and an energy efficiency model, and the optimal energy consumption parameters and optimal energy efficiency are screened out, including: The consumption parameters are calculated by the following formula: in, is the energy consumption parameter, is the remaining battery power at the current moment, is the current value of the battery at the current moment, is the battery temperature at the current moment, To optimize power consumption, is the standard temperature of the battery under standard operating conditions, , , , , is the weight parameter in the energy consumption model, is a constant term; Energy efficiency is calculated by the following formula: in, For energy efficiency, is the energy consumption parameter, Sensitivity factor for energy efficiency; The optimal energy efficiency is screened by the following formula: in, For optimal energy efficiency; According to the optimal energy efficiency, filtering out consumption parameters corresponding to the optimal energy efficiency; The optimal power consumption is calculated by the following formula: in, For optimal power consumption, is the maximum current tolerance coefficient of the battery at standard temperature, is the current ambient temperature, is the coefficient of the effect of temperature change on power consumption, is the internal resistance of the battery, including ohmic internal resistance and polarization internal resistance, is the energy conversion efficiency of the battery, It is the correction parameter for the battery open circuit voltage changing with temperature.

[0032] It is worth noting that the maximum current tolerance factor at standard temperature Obtained from the battery specification or data provided by the manufacturer; the coefficient of the effect of temperature change on power consumption The coefficient is determined by experimental determination, such as measuring the power consumption of the battery at different temperatures, and then determining it by data fitting, which is not limited in the embodiment of the present invention. The correction parameter of the battery open circuit voltage changing with temperature is obtained by experimentally measuring the open circuit voltage of the battery at different temperatures, and then calculating the derivative of the voltage with respect to the temperature.

[0033] In step S5, the energy saving target at the current moment is calculated according to the optimal energy consumption parameter, the optimal energy efficiency, the optimal power consumption, the real-time dynamic data, and the battery fixed parameters. The energy saving target includes target current, target power, target voltage, target power, and target temperature, including: The target current is calculated using the following formula: in, is the target current, is the standard current of the battery under standard working conditions, is the current value of the battery at the current moment, It is the correction factor describing the current deviation to the target current; The target power is calculated using the following formula: in, is the target power, is the remaining battery power at the current moment, is the power correction step coefficient, is the power correction value; The target voltage is calculated using the following formula: in, is the target voltage, is the standard voltage of the battery under standard working conditions, is the voltage correction factor, is the internal resistance of the battery at the current moment; The target power is calculated by the following formula: in, is the target power, It is the highest energy efficiency that a battery can achieve under theoretical optimal conditions, usually obtained from historical data, experimental calibration or theoretical calculation. is the power consumption correction factor.

[0034] It should be further explained that, in step S5, the power correction value can be calculated by the following formula: in, is the power correction value, It is the highest energy efficiency that a battery can achieve under theoretical optimal conditions, usually obtained from historical data, experimental calibration or theoretical calculation. A correction parameter to describe the effect of energy loss on efficiency, It is the actual energy efficiency of the battery in its current state, reflecting the actual operating performance of the battery.

[0035] It is worth noting that the correction factor of the current deviation to the target current is The power correction step coefficient is obtained by statistically analyzing the operating data of the battery under different current deviation conditions and combining the experimentally measured relationship between the current change and the energy efficiency. After experimentally determining the response relationship between different residual power changes and energy consumption, the voltage correction coefficient is determined using the fitting optimization method. By recording the voltage deviation under different current loads and fitting the internal resistance characteristics, the power consumption correction factor is obtained. By collecting experimental data on the impact of power consumption on energy efficiency and combining it with theoretical model calculations, the correction parameters for the impact of energy loss on efficiency are obtained. By recording the dynamic relationship between energy efficiency and energy loss and determining it through fitting optimization analysis, the parameters involved in this step are derived from experimental and actual operation data, and can be dynamically adjusted according to the application scenario to improve the model adaptability and accuracy.

[0036] In step S6, deviation analysis is performed on the target current in the energy-saving target, the target power in the energy-saving target, the target voltage in the energy-saving target, and the target power in the energy-saving target to obtain energy-saving control instructions on current, voltage, power, and power, including: The current deviation is calculated by the following formula: in, is the current deviation, is the current value of the battery at the current moment, is the target current, when the current deviation When the current is greater than 0, the current output should be increased to ensure stable power supply. When it is less than 0, the current output should be reduced to avoid overcurrent loss.

[0037] The power deviation is calculated by the following formula: in, is the power deviation, is the remaining battery power at the current moment, is the target power, when the power deviation When it is greater than 0, the charging power should be reduced or the discharge load should be increased to avoid overcharging. When it is less than 0, the charging power should be increased to restore the target power as soon as possible; The voltage deviation is calculated by the following formula: in, is the voltage deviation, is the voltage value of the battery at the current moment, is the target voltage. When the target voltage When the target voltage is greater than 0, the output voltage should be reduced to avoid overvoltage loss. When it is less than 0, the output voltage should be increased to ensure that the load requirements are met.

[0038] The power deviation is calculated by the following formula: in, is the power deviation, is the current output power, The target power is the output power. When the target power is greater than 0, the output power should be reduced. The power consumption can be reduced by reducing the output current or output voltage to avoid energy waste. When the target power is less than 0, the output power should be increased. The load demand can be met by increasing the output current or output voltage to ensure stable power supply.

[0039] It is worth noting that in actual applications, the rate of each deviation correction can be dynamically adjusted according to the application scenario. For example, by introducing a deviation correction coefficient, the adjustment rate of current, power, voltage and power can be optimized to adapt to different operating requirements and operating conditions.

[0040] In step S7, the heat generation is obtained by using a heat generation model according to the real-time dynamic data and the battery fixed parameters, including: The joule heat is calculated using the following formula: in, is the joule heat, is the output current of the battery at the current moment, is the internal resistance of the battery; The temperature-corrected heat is calculated using the following formula: in, Correct the heat for temperature, It is the correction parameter of the battery open circuit voltage changing with temperature; The heat production is calculated using the following formula: in, To produce heat, Correct the heat for temperature, The heat in joules.

[0041] It is worth noting that the correction parameter of the battery open circuit voltage changes with temperature It is obtained by experimentally measuring the change of open circuit voltage of the battery under different temperature conditions. The specific methods include: Under constant current or static state, place the battery at different ambient temperatures, record the corresponding open circuit voltage value, and then calculate the derivative of voltage with respect to temperature to obtain the correction parameter. In addition, the accuracy of this parameter has a significant impact on the calculation result of temperature-corrected heat, so it is necessary to combine the chemical characteristics of the battery and the actual operation data for multiple measurements and fitting to ensure its applicability and accuracy.

[0042] At the same time, in practical applications, this parameter can be further dynamically adjusted according to the long-term operating status of the battery. For example, through the change trend of open circuit voltage and temperature in historical data, linear fitting or polynomial fitting methods are used to dynamically update the correction parameters to adapt to the changes in battery performance under different aging degrees and usage scenarios.

[0043] In step S8, according to the heat generation, the real-time dynamic data, and the fixed parameter data, an optimized internal temperature and an optimized surface temperature are obtained through a heat transfer model and Kalman filtering, including: The temperature vector at the current moment is expressed by the following formula: in, is the temperature vector at the current moment, is the temperature inside the battery at the current moment, , They are the temperatures of the three surfaces of the battery at the current moment; The temperature vector at the next moment is expressed by the following formula: in, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature inside the battery at the next moment calculated initially, , The temperatures of the three surfaces of the battery at the next moment obtained by preliminary calculation; The temperature vector at the next moment after optimization is expressed by the following formula: in, is the temperature vector at the next moment after optimization, To optimize the temperature inside the battery at the next moment, The temperature of the three surfaces of the battery at the next moment after optimization; The temperature vector actually observed by the sensor is expressed by the following formula: in, is the temperature vector actually observed by the sensor, is the temperature inside the battery actually observed by the sensor at the next moment, is the temperature of the three surfaces of the battery actually observed by the sensor at the next moment; The control input vector is expressed by the following formula: in, is the control input vector, is the ambient temperature, To produce heat, is the specific heat capacity parameter of the battery, which is used to quantify the effect of heat on temperature changes; The state prediction formula is obtained through the heat transfer model: in, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature vector at the current moment, is the state transfer matrix, that is, the heat transfer relationship between the internal temperature and the surface temperature of the battery, is the input control matrix, i.e., the influence of heat source input and ambient temperature on the system; The forecast error covariance matrix is ​​updated by the following formula to quantify the uncertainty of the forecast: in, is the predicted state error covariance matrix, is the error covariance matrix of the previous moment, is the process noise covariance matrix; The Kalman gain is calculated by the following formula: in, is the Kalman gain, is the observation matrix, is the observation noise covariance matrix; The predicted state of the preliminary calculation is corrected by the following formula to obtain the accurately optimized internal temperature and surface temperature: in, is the temperature vector at the next moment after optimization, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature vector actually observed by the sensor, is the Kalman gain, is the observation matrix; The uncertainty of the corrected state is calculated by the following formula to update the error covariance matrix: in, is the corrected error covariance matrix, is the predicted state error covariance matrix, is the identity matrix, is the observation matrix, which describes the relationship between the actual observed temperature and the state variables. is the Kalman gain.

[0044] It should be further explained that the Kalman filter combines sensor measurement data and model prediction data, and balances the contribution of prediction error and observation error to the optimization of temperature calculation through the Kalman gain, which significantly improves the accuracy and robustness of temperature prediction. In addition, it is worth noting that the state transfer matrix in the state prediction formula obtained by the heat transfer model and the control input matrix The state transfer matrix can be obtained based on the battery heat distribution characteristics and experimental data fitting. and the control input matrix Can be used to describe the heat transfer relationship between the internal and surface temperatures of the battery and the influence of environmental factors; Error covariance matrix It is obtained through the uncertainty analysis of temperature changes in the experiment, reflecting the level of thermal disturbance in the system process; the observation noise covariance matrix used in calculating the Kalman gain is obtained by analyzing the sensor measurement error characteristics.

[0045] In step S9, according to the optimized internal temperature and the accurately optimized surface temperature, the internal temperature difference and the surface temperature difference are obtained by internal temperature difference calculation and surface temperature difference calculation respectively, including: The internal temperature difference is calculated by the following formula: in, is the internal temperature difference, is the internal temperature at the current moment, is the corrected internal temperature at the next moment; The surface temperature difference is calculated by the following formula: in, is the surface temperature difference, is the ambient temperature, is the corrected surface temperature at the next moment.

[0046] In step S10, the temperature is adjusted by a temperature control module according to the internal temperature difference and the surface temperature difference to obtain an energy-saving control instruction on the temperature, including: The heating power or cooling power based on the internal temperature difference is calculated by the following formula: in, is the heating power or cooling power based on the internal temperature difference, is the proportional coefficient of the internal temperature difference PID control, is the integral coefficient of the internal temperature difference PID control, is the differential coefficient of the internal temperature difference PID control, is the internal temperature difference; The heating power or cooling power based on the surface temperature difference is calculated by the following formula: in, is the heating power or cooling power based on the surface temperature difference, is the proportional coefficient of the surface temperature difference PID control, is the integral coefficient of the surface temperature difference PID control, is the differential coefficient of the surface temperature difference PID control, is the surface temperature difference; The overall heating power or cooling power is calculated by the following formula: in, It is the overall heating power or heat dissipation power.

[0047] It should be noted that in step S10, when the deviation trends of the internal temperature and the surface temperature are consistent (for example, both need to increase or decrease the temperature), the consistent adjustment suggestions are executed first. When the adjustment requirements of the internal temperature and the surface temperature conflict, the internal temperature adjustment is given priority. At this time, the internal temperature control is assisted by appropriately adjusting the surface heat dissipation strategy.

[0048] In addition, it is worth noting that in the temperature control process, the values ​​of each PID control parameter are crucial to the temperature control accuracy and response speed. These parameters can be determined by experimental debugging or optimization algorithms. For example, under experimental conditions, the PID parameters are adjusted by fitting multiple sets of temperature difference response curves to achieve the best adaptation to different temperature control requirements. A dynamic adjustment mechanism can also be introduced to adaptively adjust the PID parameters by real-time analysis of the rate and amplitude of change of internal temperature difference and surface temperature difference to improve the response flexibility of the temperature control system under different working conditions.

[0049] At the same time, when the adjustment requirements of the internal temperature difference and the surface temperature difference conflict (for example, the internal temperature needs to be increased while the surface temperature needs to be reduced), the adjustment requirements of the internal temperature are met first because the internal temperature has a more significant impact on the battery's chemical reaction and performance. In this case, more energy allocation can be reserved for heating or cooling the internal temperature by reducing the surface heat dissipation power, ensuring that the battery's chemical activity is maintained within the optimal range.

[0050] In addition, the overall heating power or cooling power can also be optimized in combination with energy efficiency. For example, when the internal temperature difference and the surface temperature difference are small, the heating or cooling power can be reduced to reduce unnecessary energy consumption; when the temperature difference is large and the adjustment requirements are consistent, the power can be appropriately increased to accelerate the temperature control process, thereby improving the response efficiency of the system.

[0051] In extended applications, the ambient temperature can also be incorporated into the adjustment strategy. For example, in a low-temperature environment, heating is prioritized to increase the internal temperature; in a high-temperature environment, heat dissipation is enhanced to avoid overheating damage. In addition, by real-time monitoring of the battery's energy consumption status, the temperature control strategy can be further optimized so that the overall temperature control process can meet the temperature regulation requirements while minimizing energy loss.

[0052] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0053] In one implementation, the heating power or cooling power calculated based on the internal temperature difference and the surface temperature difference can be further dynamically optimized in combination with the actual operating state of the current battery. For example, based on the power load demand and heat distribution during battery operation, the internal temperature is preferentially adjusted to the target temperature control range, thereby quickly improving the operating efficiency of the battery. When the internal temperature difference is small, the system will appropriately reduce the heating or cooling power to avoid unnecessary energy consumption and achieve higher energy saving effects.

[0054] In addition, by introducing a dynamic PID control parameter adjustment mechanism, the values ​​of the proportional, integral, and differential coefficients are automatically adjusted according to the temperature difference change rate, making the heating or cooling power adjustment more precise and meeting the dual needs of fast response and stable control.

[0055] In another implementation, comprehensive optimization can be performed in combination with the ambient temperature and the current discharge curve of the battery. When the ambient temperature is low, the heating power based on the internal temperature difference is increased first to ensure the normal chemical reaction rate of the battery; when the ambient temperature is high, the heat dissipation power based on the surface temperature difference is appropriately increased to prevent the battery from overheating and causing performance degradation.

[0056] In this implementation, the temperature regulation efficiency of the battery can also be evaluated through historical data to dynamically adjust the overall heating or cooling strategy. For example, when the temperature adjustment efficiency is detected to be low multiple times, the system will give priority to enhancing the surface temperature regulation capability, thereby improving the overall temperature control effect.

[0057] The following describes the working process of the present invention using a common scenario as an example. Figure 2 , which is Figure 1 Schematic diagram of the working scenario of the method.

[0058] During the operation of electric vehicles, when the battery is in a high-speed discharge state, the internal temperature and surface temperature will rise rapidly due to the heat of the battery. At this time, the real-time temperature data is obtained through the sensor to calculate the internal temperature difference and the surface temperature difference. For example, when the internal temperature difference is 15°C and the surface temperature difference is 10°C, the system calculates the heat dissipation power based on the internal temperature difference and the heat dissipation power based on the surface temperature difference based on these two temperature differences, and superimposes the two to obtain the overall heat dissipation power.

[0059] In this scenario, since the internal temperature difference is large and the trend of change is consistent with the surface temperature difference (both need heat dissipation), the system prioritizes the implementation of a consistent heat dissipation strategy to quickly reduce the internal and surface temperatures of the battery to the target temperature control range (for example, 40°C). During the heat dissipation process, the temperature control response is made more accurate and stable by dynamically adjusting parameters.

[0060] If in another situation, when the external ambient temperature is low when the electric vehicle is running, and the adjustment requirements of the internal temperature difference and the surface temperature difference conflict (for example, the internal temperature is low and needs to be heated, while the surface temperature is high and needs to be cooled), the system will give priority to meeting the internal temperature adjustment requirements, and assist in the adjustment of the internal temperature by appropriately reducing the surface heat dissipation power to ensure the chemical reaction activity of the battery.

[0061] This scenario example clearly demonstrates the flexibility and accuracy of the present invention in practical applications. By dynamically adjusting the heating and heat dissipation power, it meets the temperature control requirements under complex working conditions and achieves the goal of high efficiency and energy saving.

[0062] In summary, the second embodiment of the present invention provides a lithium battery energy-saving management device, including: Data acquisition module, used to obtain real-time dynamic data and fixed parameters of lithium batteries; A battery power consumption calculation module, used to preliminarily calculate the battery power consumption at the current moment according to the real-time dynamic data and the battery fixed parameters through a temperature calculation model based on temperature correction, and dynamically correct the preliminarily calculated power consumption through a power consumption dynamic correction model to obtain optimized power consumption; The energy optimization module is used to calculate the energy consumption parameters and energy efficiency respectively through the energy consumption model and the energy efficiency model according to the real-time dynamic data, the battery fixed parameters and the optimized power consumption, and screen out the optimal energy consumption parameters and the optimal energy efficiency, and further calculate the optimal power consumption; The temperature control module is used to calculate the heat generation through the heat generation model according to the real-time dynamic data and the fixed parameters of the battery, optimize the internal temperature and the surface temperature by combining the heat transfer model and the Kalman filter, calculate the internal temperature difference and the surface temperature difference according to the optimized internal temperature and the surface temperature, generate the temperature adjustment instruction according to the internal temperature difference and the surface temperature difference, and regulate the temperature control module to execute the fast heating, fast heat dissipation or stable mode, and finally obtain the internal temperature and the surface temperature that meet the target temperature control requirements; The result output module is used to output the calculated target current, target voltage, target current, target power, and target temperature energy-saving control instructions, and adjust the operating state of the battery to meet the energy-saving target.

[0063] Preferably, the data acquisition module further includes a current acquisition unit, a voltage acquisition unit and a temperature sensor, which are used to respectively acquire the real-time current value, voltage value, internal temperature and surface temperature of the lithium battery, and transmit the acquired data to the battery power consumption calculation module for processing in real time. The data collected by the high-precision sensor can ensure the accuracy and real-time performance of the entire energy-saving management process.

[0064] Preferably, the battery power consumption calculation module further includes a temperature correction calculation unit and a dynamic correction unit, wherein: The temperature correction calculation unit makes a preliminary correction to the power consumption by combining the temperature parameters of the battery at the current moment through a calculation model based on temperature correction; The dynamic correction unit uses the dynamic correction model of power consumption to dynamically correct the initially calculated power consumption data in combination with multiple real-time parameters such as the remaining battery power at the previous moment, the current current, temperature, etc., to improve the prediction accuracy of power consumption.

[0065] Preferably, the energy optimization module further comprises a consumption parameter calculation unit and an efficiency optimization unit, wherein: The consumption parameter calculation unit calculates the energy consumption parameter of the battery at the current moment through the energy consumption model; The efficiency optimization unit uses the energy efficiency model to screen out the optimal energy consumption parameters and optimal energy efficiency, and calculates the optimal power consumption based on this, thus providing an optimization basis for the formulation of subsequent energy-saving goals.

[0066] Preferably, the temperature control module further includes a heat generation calculation unit, a temperature difference analysis unit and a temperature control unit, wherein: The heat generation calculation unit calculates the heat generation of the battery at the current moment through the heat generation model based on real-time dynamic data and fixed parameters; The temperature difference analysis unit combines the Kalman filter results to calculate the internal temperature difference and the surface temperature difference; The temperature control unit generates temperature control instructions based on the temperature difference analysis results, and ensures the temperature balance between the inside and surface of the battery by dynamically adjusting the heating power or heat dissipation power.

[0067] Preferably, the result output module further comprises a target parameter generating unit and a control signal sending unit, wherein: The target parameter generation unit generates energy-saving targets including target current, target voltage, target power and target temperature according to the calculation results of the energy optimization module and the temperature control module; The control signal sending unit outputs a control instruction according to the energy-saving target, which is used to adjust the battery operation state and ensure the realization of the energy-saving target.

[0068] It should be noted that the energy-saving management device for a lithium battery provided in an embodiment of the present invention is used to execute all the process steps of the energy-saving management method for a lithium battery in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0069] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an energy optimization program. When the processor executes the computer program, the steps in the above-mentioned lithium battery energy-saving management method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0070] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0071] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0072] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0073] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0074] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0075] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0076] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A lithium battery energy-saving management method, characterized in that: include: Obtain real-time dynamic data, temperature data and battery fixed parameters of lithium batteries; The energy efficiency of the battery at the current moment is calculated according to the real-time dynamic data, and the power consumption of the battery at the current moment is calculated according to the energy efficiency of the battery at the current moment and the temperature data through a temperature correction calculation model; Dynamically correct the battery power consumption at the current moment through a power consumption dynamic correction model to obtain optimized power consumption; According to the real-time dynamic data and the optimized power consumption, energy consumption parameters, energy efficiency and optimal power consumption are calculated respectively through an energy consumption model and an energy efficiency model, and optimal energy consumption parameters and optimal energy efficiency are screened out; Calculate the energy saving target at the current moment according to the optimal energy consumption parameter, the optimal energy efficiency, the optimal power consumption, the real-time dynamic data, and the battery fixed parameters, wherein the energy saving target includes a target current, a target power, a target voltage, a target power, and a target temperature; Perform deviation analysis on the target current in the energy-saving target, the target power in the energy-saving target, the target voltage in the energy-saving target, and the target power in the energy-saving target to obtain energy-saving control instructions on current, voltage, power, and power; Obtaining heat generation through a heat generation model according to the real-time dynamic data and the fixed parameters of the battery; According to the heat generation, the real-time dynamic data, and the battery fixed parameter data, an optimized internal temperature and an optimized surface temperature are obtained through a heat transfer model and a Kalman filter; According to the optimized internal temperature and the optimized surface temperature, obtaining the internal temperature difference and the surface temperature difference through internal temperature difference calculation and surface temperature difference calculation respectively; According to the internal temperature difference and the surface temperature difference, the temperature is adjusted by a temperature control module to obtain energy-saving control instructions on the temperature.

2. The energy-saving management method of lithium batteries according to claim 1, characterized in that: The energy efficiency of the battery at the current moment is calculated according to the real-time dynamic data, and the power consumption of the battery at the current moment is calculated according to the energy efficiency of the battery at the current moment and the temperature data through a temperature correction calculation model, include: The real-time dynamic data includes the current value of the battery at the current moment, the voltage value of the battery at the current moment, the current value provided by the charging module at the current moment, and the voltage value provided by the charging module at the current moment; The energy efficiency of the battery at the current moment is calculated using the following formula: in, is the energy efficiency of the battery at the current moment, is the output power of the battery, is the input power of the battery; is the current value of the battery at the current moment, is the voltage value of the battery at the current moment; is the current value provided by the charging module at the current moment, The voltage value provided by the charging module at the current moment; The current battery power consumption is obtained through the following temperature correction calculation model: in, The current battery consumption. is the time interval, is the current battery temperature, is the standard temperature of the battery under standard operating conditions, is a first temperature correction parameter, and the temperature data includes the battery temperature at the current moment and the standard temperature of the battery under standard operating conditions.

3. The energy-saving management method of lithium batteries according to claim 1, characterized in that: The current battery power consumption is dynamically corrected by using a power consumption dynamic correction model to obtain optimized power consumption, including: The current battery power consumption is dynamically corrected through the following power consumption dynamic correction model: in, To optimize power consumption, The remaining battery power at the last moment. is the remaining battery power at the current moment, is the current value of the battery at the current moment, is the battery temperature at the current moment, is the power correction parameter, is the current correction parameter, is the second temperature correction parameter, is a constant term.

4. The energy-saving management method of lithium batteries according to claim 1, characterized in that: According to the real-time dynamic data and the optimized power consumption, energy consumption parameters, energy efficiency and optimal power consumption are calculated respectively through an energy consumption model and an energy efficiency model, and optimal energy consumption parameters and optimal energy efficiency are screened out, including: The consumption parameters are calculated by the following formula: in, is the energy consumption parameter, is the remaining battery power at the current moment, is the current value of the battery at the current moment, is the battery temperature at the current moment, To optimize power consumption, is the standard temperature of the battery under standard operating conditions, , , , , is the weight parameter in the energy consumption model, is a constant term; Energy efficiency is calculated by the following formula: in, For energy efficiency, is the energy consumption parameter, Sensitivity factor for energy efficiency; The optimal energy efficiency is screened by the following formula: in, For optimal energy efficiency; According to the optimal energy efficiency, filtering out consumption parameters corresponding to the optimal energy efficiency; The optimal power consumption is calculated by the following formula: in, For optimal power consumption, is the maximum current tolerance coefficient of the battery at standard temperature, is the current ambient temperature, is the coefficient of the effect of temperature change on power consumption, is the internal resistance of the battery, including ohmic internal resistance and polarization internal resistance, is the energy conversion efficiency of the battery, It is the correction parameter for the battery open circuit voltage changing with temperature.

5. The energy-saving management method of lithium batteries according to claim 1, characterized in that: According to the real-time dynamic data and the fixed parameters of the battery, the heat generation is obtained through a heat generation model, including: The joule heat is calculated using the following formula: in, is the joule heat, is the output current of the battery at the current moment, is the internal resistance of the battery; The temperature-corrected heat is calculated using the following formula: in, Correct the heat for temperature, It is the correction parameter of the battery open circuit voltage changing with temperature; The heat production is calculated using the following formula: in, To produce heat, Correct the heat for temperature, The heat in joules.

6. The energy-saving management method of lithium batteries according to claim 2, characterized in that: According to the heat generation, the real-time dynamic data, and the battery fixed parameter data, an optimized internal temperature and an optimized surface temperature are obtained through a heat transfer model and Kalman filtering, including: The temperature vector at the current moment is expressed by the following formula: in, is the temperature vector at the current moment, is the temperature inside the battery at the current moment, , are the temperatures of the three surfaces of the battery at the current moment; The temperature vector at the next moment is expressed by the following formula: in, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature inside the battery at the next moment calculated initially, , The temperatures of the three surfaces of the battery at the next moment obtained by preliminary calculation; The temperature vector at the next moment after optimization is expressed by the following formula: in, is the temperature vector at the next moment after optimization, To optimize the temperature inside the battery at the next moment, The temperature of the three surfaces of the battery at the next moment after optimization; The temperature vector actually observed by the sensor is expressed by the following formula: in, is the temperature vector actually observed by the sensor, is the temperature inside the battery actually observed by the sensor at the next moment, is the temperature of the three surfaces of the battery actually observed by the sensor at the next moment; The control input vector is expressed by the following formula: in, is the control input vector, is the ambient temperature, To produce heat, is the specific heat capacity parameter of the battery, which is used to quantify the effect of heat on temperature change; The state prediction formula is obtained through the heat transfer model: in, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature vector at the current moment, is the state transfer matrix, that is, the heat transfer relationship between the internal temperature and the surface temperature of the battery, is the input control matrix, i.e., the influence of heat source input and ambient temperature on the system; The forecast error covariance matrix is ​​updated by the following formula to quantify the uncertainty of the forecast: in, is the predicted state error covariance matrix, is the error covariance matrix of the previous moment, is the process noise covariance matrix; The Kalman gain is calculated by the following formula: in, is the Kalman gain, is the observation matrix, is the observation noise covariance matrix; The predicted state of the preliminary calculation is corrected by the following formula to obtain the accurately optimized internal temperature and surface temperature: in, is the temperature vector at the next moment after optimization, is the temperature vector of the next moment obtained by preliminary calculation, is the temperature vector actually observed by the sensor, is the Kalman gain, is the observation matrix; The uncertainty of the corrected state is calculated by the following formula to update the error covariance matrix: in, is the corrected error covariance matrix, is the predicted state error covariance matrix, is the identity matrix, is the observation matrix, which describes the relationship between the actual observed temperature and the state variables. is the Kalman gain.

7. The energy-saving management method of lithium batteries according to claim 1, characterized in that: According to the optimized internal temperature and the optimized surface temperature, the internal temperature difference and the surface temperature difference are obtained by internal temperature difference calculation and surface temperature difference calculation respectively, including: The internal temperature difference is calculated by the following formula: in, is the internal temperature difference, is the internal temperature at the current moment, is the corrected internal temperature at the next moment; The surface temperature difference is calculated by the following formula: in, is the surface temperature difference, is the ambient temperature, is the corrected surface temperature at the next moment.

8. A lithium battery energy-saving management device, characterized in that: include: Data acquisition module, used to obtain real-time dynamic data and fixed parameters of lithium batteries; A battery power consumption calculation module, used to preliminarily calculate the battery power consumption at the current moment according to the real-time dynamic data and the battery fixed parameters through a temperature calculation model based on temperature correction, and dynamically correct the preliminarily calculated power consumption through a power consumption dynamic correction model to obtain optimized power consumption; The energy optimization module is used to calculate the energy consumption parameters and energy efficiency respectively through the energy consumption model and the energy efficiency model according to the real-time dynamic data, the battery fixed parameters and the optimized power consumption, and screen out the optimal energy consumption parameters and the optimal energy efficiency, and further calculate the optimal power consumption; The temperature control module is used to calculate the heat generation through the heat generation model according to the real-time dynamic data and the fixed parameters of the battery, optimize the internal temperature and the surface temperature by combining the heat transfer model and the Kalman filter, calculate the internal temperature difference and the surface temperature difference according to the optimized internal temperature and the surface temperature, generate the temperature adjustment instruction according to the internal temperature difference and the surface temperature difference, and regulate the temperature control module to execute the fast heating, fast heat dissipation or stable mode, and finally obtain the internal temperature and the surface temperature that meet the target temperature control requirements; The result output module is used to output the calculated target current, target voltage, target current, target power, and target temperature energy-saving control instructions, and adjust the operating state of the battery to meet the energy-saving target.

9. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the energy-saving management method for a lithium battery as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the energy-saving management method for lithium batteries according to any one of claims 1 to 7.

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