A ship battery intelligent temperature control method based on service life attenuation characteristics

Abstract

The application belongs to the technical field of battery management, and particularly relates to a ship battery intelligent temperature control method based on life attenuation characteristics, which comprises the following steps: obtaining a battery health state value; obtaining an average temperature rise amplitude; calculating an impedance aging heat sensitivity factor; obtaining a dynamic load heat shock index; calculating a thermal runaway latent risk degree; performing numerical transformation on the thermal runaway latent risk degree to generate a cooling gain coefficient; determining a target cooling power; driving a cooling system to perform temperature adjustment; based on the size of the adjusted temperature, correcting model parameters to trigger parameter collection and calculation again. The application effectively solves the technical problem that the prior art ignores the influence of battery aging on thermal characteristics.

Description

Technical Field

[0001] This invention relates to the field of battery management technology. More specifically, this invention relates to a smart temperature control method for marine batteries based on their lifespan degradation characteristics. Background Technology

[0002] Driven by the rapid trend of ship electrification, marine power battery systems are facing increasingly complex challenges in marine operating conditions. Unlike automotive batteries, which operate in relatively stable environments, marine batteries are characterized by huge individual cell capacities, large-scale battery packs, and extremely long service lives. More importantly, during navigation, ships are affected by unpredictable winds, waves, ocean currents, and dynamic positioning requirements, often resulting in severe power oscillations in the load on their propulsion motors. This unsteady, high-frequency load fluctuation, imposed by environmental factors, poses a severe test to the electrochemical stability and thermal management capabilities of the battery system.

[0003] However, most existing marine battery temperature control systems rely on relatively primitive control logic, primarily using fixed temperature thresholds. For example, activating cooling when the monitored temperature exceeds 35°C is the sole trigger for thermal management. This static, passive response control method severely neglects the dynamic evolution of the battery's physical characteristics throughout its long lifespan. As is well known, with battery aging, irreversible degradation of its internal chemical components occurs, such as the loss of active lithium, electrolyte decomposition, and SEI film thickening. These microscopic changes lead to a significant increase in the battery's internal resistance.

[0004] This means that at the same charge / discharge rate, the ohmic heat generation rate of an aged battery will be much higher than that of a new battery, and its ability to withstand thermal stress will decrease significantly. If the temperature control system fails to detect this fundamental change in thermal characteristics caused by battery lifespan degradation and continues to cool the battery mechanically according to the standards for new batteries, serious consequences will occur: under heavy load conditions, aged batteries are prone to heat accumulation due to delayed heat dissipation, which may even lead to serious thermal runaway accidents; or unnecessary over-cooling may occur under light load conditions, resulting in a waste of the ship's valuable energy. Summary of the Invention

[0005] To address the technical problem of existing technologies neglecting the impact of battery aging on thermal characteristics, this invention provides a smart temperature control method for marine batteries based on lifespan degradation characteristics, comprising:

[0006] The system uses high-frequency synchronous sampling of the real-time voltage and current sequences of the battery module to retrieve the module's baseline parameters and obtain the battery health status value based on current usage conditions. It also calculates the average temperature rise based on the battery module's load intensity and internal heat distribution. Furthermore, it calculates the impedance aging thermistor based on the battery module's current aging state and its sensitivity to thermal effects, combined with the battery health status value. Finally, it obtains the dynamic load thermal shock index by combining current fluctuation characteristics and the impact of real-time temperature rise on the degree of aging. Based on the impact of uneven heat distribution on battery condition, and combined with thermal turbulence indicators, it calculates the latent risk of thermal runaway. The latent risk of thermal runaway is numerically transformed to generate a cooling gain coefficient. The target cooling power is determined based on the magnitude of the latent risk of thermal runaway. This target cooling power is converted into a low-level control signal, driving the cooling system to perform temperature regulation. Based on the adjusted temperature, the model parameters are corrected, triggering further parameter acquisition and calculation.

[0007] This invention effectively solves the problems of existing technologies that neglect the impact of battery state changes on thermal characteristics during use and rely solely on passive control based on fixed temperature thresholds. By collecting various data during battery operation and combining them with the battery's service life and health status, it comprehensively assesses the battery's heat generation and safety risks, and then formulates targeted temperature regulation strategies. This avoids potential safety issues caused by insufficient heat dissipation after battery aging and reduces energy waste caused by excessive cooling under light loads. Simultaneously, by real-time correction of model parameters, it improves the accuracy and adaptability of temperature control, ensuring that temperature regulation continuously matches the actual operating state of the battery and meets the temperature control requirements of marine batteries in complex operating environments.

[0008] Preferably, obtaining the battery health status value includes:

[0009] The system synchronously acquires real-time voltage and current sequences of the battery module using a high-frequency sampling frequency; it obtains multi-point surface temperature sequences distributed at different locations on the module using temperature sensors, and also acquires the ambient temperature; it indexes and reads the initial internal resistance value, nominal cycle life, reference thermal distribution deviation constant, and the current cumulative cycle count of the battery module from the database; it uses the least squares method to perform linear regression fitting on the data points of the real-time voltage and current sequences within a short time window to obtain the dynamic internal resistance value sequence; it calculates the ratio of the current cumulative cycle count to the nominal cycle life, and defines the difference between 1 and this ratio as the battery health status value.

[0010] Preferably, obtaining the average temperature rise includes:

[0011] The variance of the real-time current sequence is calculated to obtain the current fluctuation variance; the standard deviation of the multi-point surface temperature sequence is calculated to obtain the thermal turbulence index; the arithmetic mean of the multi-point surface temperature sequence is subtracted from the ambient temperature to obtain the average temperature rise.

[0012] Preferably, the resistance aging thermosensitive factor includes:

[0013] ;

[0014] In the formula, Indicates the resistance to aging thermistor; This is the dynamic internal resistance value; This is the initial internal resistance value; This represents the battery health status value. It is the natural logarithm function; It is a natural exponential function; It is a natural constant; It is the first smallest positive number, and the denominator is guaranteed to be non-zero.

[0015] This invention combines the changes in the state and health of the battery after use to calculate a value that reflects the battery's sensitivity to heat. This value reflects the changes in the battery's response to heat as the usage time increases, allowing the system to make appropriate temperature control adjustments at different stages of battery use. This avoids using a uniform standard to treat batteries with different degrees of aging and reduces safety hazards caused by battery aging.

[0016] Preferably, the dynamic load thermal shock index satisfies the following expression:

[0017] ;

[0018] In the formula, Indicates the dynamic load thermal shock index; It is an impedance aging thermosensitive factor; For current fluctuation variance; These are the pre-obtained electrothermal conversion weighting coefficients; This represents the average temperature rise. The reference temperature rise constant is obtained in advance; If it is the second smallest positive number, the denominator must not be 0; This represents the hyperbolic tangent function.

[0019] This invention comprehensively considers the effects of current changes, heat generation, and battery aging to derive a value that reflects the battery's ability to withstand thermal shock. This value can accurately reflect the thermal pressure faced by the battery under different operating conditions, allowing the system to clearly understand the battery's thermal load status in different scenarios. This avoids improper control caused by relying on a single factor, enabling temperature regulation to better fit actual operating conditions and improving the pertinence and effectiveness of temperature control.

[0020] Preferably, the latent risk of thermal runaway satisfies the following expression:

[0021] ;

[0022] In the formula, Indicates the degree of potential risk of thermal runaway; The dynamic load thermal shock index; Indicators of thermal turbulence; This is the baseline thermal distribution deviation constant, which is not zero; It is the natural logarithm function; It is a natural exponential function.

[0023] This invention combines external thermal shock conditions and internal temperature distribution uniformity of the battery to calculate a value that reflects battery safety risks. This value can predict potential safety issues in advance, allowing the system to detect potential risks in a timely manner and avoid safety accidents caused by ignoring local temperature anomalies or uneven heat distribution. It also provides a clear direction for subsequent cooling adjustments, thereby improving the safety of battery use.

[0024] Preferably, generating the cooling gain coefficient includes:

[0025] The thermal runaway potential risk is mapped to the interval [0,1] using the maximum-minimum normalization method. The mapped value is then used as an index to find the corresponding cooling gain value in the preset gain coefficient table to obtain the cooling gain coefficient.

[0026] This invention obtains a correlation coefficient that can guide the cooling intensity by reasonably converting risk values. This coefficient can transform complex risk assessment results into a control basis that the system can directly use, making the cooling adjustment intensity more in line with actual needs, avoiding insufficient or excessive cooling, ensuring that the battery can operate at the appropriate temperature, reducing energy waste, and improving the operating efficiency of the cooling system.

[0027] Preferably, determining the target cooling power includes:

[0028] The system has three preset power levels: energy-saving mode, standard mode, and high-power suppression mode. Based on the cooling gain coefficient, the corresponding power level is directly selected as the target cooling power.

[0029] Preferably, the driving cooling system performs temperature regulation, including:

[0030] The target cooling power is converted into a low-level control signal and drives the cooling system to perform temperature regulation. The difference between the Joule heat power generated by the current sequence and dynamic internal resistance value and the target cooling power is calculated and divided by the equivalent heat capacity of the battery module, thereby deriving the expected temperature change rate per unit time.

[0031] Preferably, triggering the re-collection and calculation of parameters includes:

[0032] Calculate the measured temperature change rate after temperature adjustment. If the measured change rate exceeds the expected temperature change rate, update the correction coefficient in the dynamic internal resistance calculation model using the current operating data and trigger a new round of parameter acquisition and calculation process.

[0033] The beneficial effects of this invention are as follows: This invention provides a more reasonable and reliable solution for temperature control of marine batteries. Through scientific data analysis and evaluation, it achieves precise control of battery temperature, ensuring safe and stable operation of the battery during long-term use, extending its service life, and rationally utilizing energy to reduce ship operating costs. This method meets the complex operating conditions of ship navigation, requires minimal manual intervention, and can automatically adjust cooling strategies according to battery status and usage scenarios, improving the stability and practicality of the ship battery system and providing strong support for the development of ship electrification. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating an intelligent temperature control method for ship batteries based on lifespan decay characteristics according to the present invention.

[0035] Figure 2 This is a schematic diagram illustrating the closed-loop cooling control and temperature response recording based on adaptive gain in this invention;

[0036] Figure 3 This diagram schematically illustrates the monitoring and feedback correction criterion for the rate of change after temperature adjustment in this invention. Detailed Implementation

[0037] This invention discloses an intelligent temperature control method for ship batteries based on lifespan degradation characteristics, referring to... Figure 1 This includes steps S1-S4:

[0038] S1: High-frequency synchronous sampling of the real-time voltage and current sequences of the battery module, retrieving the reference parameters of the battery module, and obtaining the battery health status value based on the current usage conditions.

[0039] It is important to note that in the complex sea conditions of ship navigation, the operating conditions of battery systems exhibit significant randomness and dynamism. Furthermore, marine batteries have extremely long service lives. Traditional static parameters, such as the factory-specified internal resistance, cannot accurately reflect the aging state of the battery after years of charge-discharge cycles. With increasing service time, the loss of active lithium and the decomposition of the electrolyte lead to a gradual increase in internal resistance, resulting in a significant increase in Joule heat generated by the same current surge. Moreover, batteries from different batches or with different aging stages exhibit drastically different voltage drops and heat generation under the same operating conditions. Relying solely on static data will prevent the temperature control system from detecting this potential risk of heat accumulation. Therefore, it is essential to move away from reliance on static nominal values ​​and calculate dynamic internal resistance and health status in real time. This provides accurate indicators reflecting the current physical state for subsequent thermal management, ensuring more timely cooling intervention during battery aging and preventing thermal runaway caused by parameter drift.

[0040] Specifically, the real-time voltage and current sequences of the battery module are sampled at high frequency, the reference parameters of the battery module are retrieved, and the battery health status value is obtained based on the current usage conditions, including:

[0041] The system synchronously acquires real-time voltage and current sequences of the battery module using a high-frequency sampling frequency; it obtains multi-point surface temperature sequences distributed at different locations on the module using temperature sensors, and also acquires the ambient temperature; it indexes and reads the initial internal resistance value, nominal cycle life, reference thermal distribution deviation constant, and the current cumulative cycle count of the battery module from the database; it uses the least squares method to perform linear regression fitting on the data points of the real-time voltage and current sequences within a short time window to obtain the dynamic internal resistance value sequence; it calculates the ratio of the current cumulative cycle count to the nominal cycle life, and defines the difference between 1 and this ratio as the battery health status value.

[0042] It should be noted that the battery health status value ranges from 0 to 1.

[0043] At this point, the battery health status value has been obtained.

[0044] S2: Based on the load intensity and internal heat distribution of the battery module, the average temperature rise is obtained; based on the sensitivity of the battery module's current aging state to thermal effects, combined with the battery health status value, the impedance aging thermal sensitivity factor is calculated; combined with the influence of current fluctuation characteristics and real-time temperature rise on the degree of aging, the dynamic load thermal shock index is obtained.

[0045] It is important to note that when facing severe sea conditions, the motor load of a ship's propulsion system often exhibits high-frequency, violent oscillations. These unsteady current surges are the primary cause of transient temperature rises within the battery. Simple average current calculations smooth out these peak fluctuations, thus underestimating the actual thermal load. Furthermore, as a complex system composed of numerous cells connected in series and parallel, the internal thermal field of a battery module is often uneven. Individual cells may become hotspots due to poor heat dissipation or high internal resistance. If only average temperature is monitored, these potentially dangerous localized hotspots are easily masked. From a thermodynamic perspective, the higher the degree of thermal non-uniformity, the worse the system's thermal stability and the greater the probability of localized thermal runaway. Therefore, it is necessary to introduce current fluctuation variance to assess the intensity of external load oscillations and to introduce thermal non-uniformity to characterize the dispersion of the internal thermal field. This will allow for the construction of a comprehensive indicator system that can reflect the battery's dual thermal state under pressure from both internal and external factors, providing comprehensive data support for subsequent risk assessment.

[0046] Specifically, based on the intensity of the load on the battery module and its internal heat distribution, the average temperature rise is obtained, including:

[0047] The variance of the real-time current sequence is calculated to obtain the current fluctuation variance; the standard deviation of the multi-point surface temperature sequence is calculated to obtain the thermal turbulence index; the arithmetic mean of the multi-point surface temperature sequence is subtracted from the ambient temperature to obtain the average temperature rise.

[0048] Thus, the average temperature rise was obtained.

[0049] It should be noted that the aging process of a battery is not linear, but rather has a certain inflection point. When the battery's health is at a high level, its internal resistance changes relatively slowly; however, when the battery's health declines to a certain stage, the rate of increase in internal resistance and the tendency to generate heat often deteriorate exponentially, a phenomenon commonly referred to as a "sudden drop." At this point, simple linear compensation is insufficient to cover the additional thermal risks caused by the increased internal resistance. This invention introduces a coupled model of the natural logarithm and exponential function, which can maintain the stability of the control strategy when the battery's health is high, avoiding excessive system sensitivity; while when the battery's health drops to the critical region, it can rapidly amplify the thermal sensitivity factor. This satisfies the nonlinear characteristic that batteries become increasingly sensitive to the thermal environment after aging, ensuring that the temperature control system can provide sufficient vigilance at the end of the battery's life cycle, preventing the aged battery from unexpectedly overheating under normal operating conditions, and effectively solving the safety hazards of using new standards for old batteries.

[0050] Preferably, based on the sensitivity of the battery module to thermal effects in its current aging state, and in conjunction with the battery health status value, an impedance aging thermal sensitivity factor is calculated, including:

[0051] The impedance aging thermistor satisfies the following expression:

[0052] ;

[0053] In the formula, Indicates the resistance to aging thermistor; This is the dynamic internal resistance value; This is the initial internal resistance value; This represents the battery health status value. It is the natural logarithm function; It is a natural exponential function; It is a natural constant; It is the first smallest positive number, and the denominator is guaranteed to be non-zero.

[0054] In the formula, The logarithmic gain, representing the degradation of internal resistance, reflects the improvement in basic heat generation capacity due to aging. A nonlinear amplifier highly sensitive to health status was constructed when When the value is high, this value is small and stable. After falling to a certain threshold, this value increases exponentially. The sensitivity of the battery's current aging state to thermal effects was calculated; that is, the more severe the aging, the larger the factor, and the more stringent the system's heat management should be.

[0055] For example, if , ,but ;like , ,but . , Round to two decimal places.

[0056] Thus, the resistance aging thermosensitive factor was obtained.

[0057] It should be noted that when a ship encounters wind and waves, performs dynamic positioning, or berths / unberths, the propulsion motor needs to frequently adjust its power, causing drastic fluctuations in the load current flowing through the battery. These high-frequency, high-amplitude current surges are the direct driving force for heat generation within the battery. However, the same current surge can be drastically dangerous depending on the battery's baseline temperature rise. If the battery is already at a high temperature, such drastic current fluctuations can exacerbate the high temperature, easily inducing thermal runaway. Therefore, current or temperature cannot be viewed in isolation; the variance of the current fluctuation must be combined with the current temperature rise. Furthermore, utilizing the saturation characteristics of the hyperbolic tangent function can prevent the calculated exponent from diverging infinitely under extreme conditions, confining it within a reasonable physical range. This allows for the calculation of the actual thermal shock intensity exerted on the battery under the current transient condition, avoiding frequent false alarms due to a single parameter exceeding its limit.

[0058] Preferably, the dynamic load thermal shock index is obtained by combining the influence of current fluctuation characteristics and real-time temperature rise on the degree of aging, including:

[0059] Obtain from the database the electrothermal conversion weighting coefficients used to convert the current fluctuation variance into equivalent temperature, and the reference temperature rise constant characterizing the rise of the battery module temperature from the initial state to the stable thermal equilibrium state.

[0060] The dynamic load thermal shock index satisfies the following expression:

[0061] ;

[0062] In the formula, Indicates the dynamic load thermal shock index; It is an impedance aging thermosensitive factor; For current fluctuation variance; These are the pre-obtained electrothermal conversion weighting coefficients; This represents the average temperature rise. The reference temperature rise constant is obtained in advance; If it is the second smallest positive number, the denominator must not be 0; This represents the hyperbolic tangent function.

[0063] In the formula, By combining the dramatic fluctuations in current with the current temperature rise, the intensity of the current physical heat load can be characterized. middle As a leading factor, converting physical thermal load into an impact assessment of aging degree means that for aged batteries, the same load fluctuation is considered a greater impact. It plays a normalization and regulation role when the temperature rises When the temperature is low, this inhibition index increases; when the temperature rise approaches or exceeds... When the value approaches 1, the index reflects the interaction between load and aging.

[0064] Thus, the dynamic load thermal shock index was obtained.

[0065] S3: Based on the degree of impact of uneven heat distribution on battery condition, and combined with thermal turbulence index, calculate the latent risk of thermal runaway; perform numerical transformation on the latent risk of thermal runaway to generate cooling gain coefficient.

[0066] It should be noted that large ships are often composed of hundreds or even thousands of individual battery modules. Due to limitations in physical space and heat dissipation design, uneven temperature distribution often exists within the battery pack. In actual operation, such localized hotspots pose a significant safety hazard. According to the "weakest link" principle, the safety of the system depends on the hottest cell. Focusing only on the average temperature may mask the fact that individual cells are about to experience thermal runaway. Thermal turbulence indicators are crucial for capturing this risk. When significant uneven heat distribution occurs, it means that the internal thermal balance of the system has been disrupted. At this point, even if the external load impact is moderate, the potential cascading reaction risk is extremely high. Therefore, this invention designs an inverse variant of the Sigmoid function as the denominator, using the thermal turbulence indicator as a lever to amplify risk. As thermal unevenness intensifies, the denominator decreases rapidly, thereby drastically increasing the overall risk rating. This forces the system to shift its focus from average safety to the safety of its weakest link, enabling early prediction of thermal runaway.

[0067] Specifically, based on the impact of uneven heat distribution on battery condition, and in conjunction with thermal turbulence indicators, the potential risk of thermal runaway is calculated, including:

[0068] The potential risk of thermal runaway satisfies the following expression:

[0069] ;

[0070] In the formula, Indicates the degree of potential risk of thermal runaway; The dynamic load thermal shock index; Indicators of thermal turbulence; This is the baseline thermal distribution deviation constant, which is not zero; It is the natural logarithm function; It is a natural exponential function.

[0071] In the formula, the denominator It's a reverse. Function variant, as the thermal turbulence index increases, the exponential term... Decrease, the denominator decreases, thus amplifying the numerator. The value indicates that when the internal temperature of the battery module is extremely uneven, even a moderate load impact will drastically amplify the risk. As a basic risk item, it ensures that the thermal turbulence index itself constitutes an independent source of risk.

[0072] For example, if , Scenario 1: Uniform temperature. Then the denominator , Scenario 2: Uneven temperature. Then the denominator , . , Round to three decimal places; , Round to two decimal places.

[0073] At this point, the potential risk level of thermal runaway was obtained.

[0074] It should be noted that the calculated thermal runaway potential risk is a complex comprehensive indicator with intricate physical meaning, and its numerical range may fluctuate significantly depending on the extreme nature of the operating conditions. To ensure that the thermal runaway potential risk can be smoothly integrated with the control logic of the actuators, it must be normalized. This process is akin to translating a complex dialect into a standard language that a machine can understand. Through normalization, the risk is mapped to a dimensionless gain coefficient, typically within a specific range of 0 to 1 or greater than 1, allowing the control system to intuitively determine how many times the required cooling capacity is relative to the base power.

[0075] Preferably, the thermal runaway potential risk is numerically transformed to generate a cooling gain coefficient, including:

[0076] The thermal runaway potential risk is mapped to the interval [0,1] using the maximum-minimum normalization method. The mapped value is then used as an index to find the corresponding cooling gain value in the preset gain coefficient table to obtain the cooling gain coefficient.

[0077] Thus, the cooling gain coefficient was obtained.

[0078] S4: Determine the target cooling power based on the magnitude of the thermal runaway potential risk; convert the target cooling power into a low-level control signal and drive the cooling system to perform temperature regulation; based on the regulated temperature, correct the model parameters and trigger parameter acquisition and calculation again.

[0079] It should be noted that a ship's energy system is a typical isolated system, carrying a limited and extremely valuable total amount of energy. Traditional temperature control strategies often employ a one-size-fits-all approach, activating the cooling system at full power once a threshold is reached. This not only results in significant energy waste but can also lead to excessive battery temperature fluctuations due to overcooling, negatively impacting battery life. This invention calculates a comprehensive risk level encompassing aging, load impact, and heat distribution information, mapping it to a cooling gain coefficient. This allows for precise, on-demand control. Under low-risk conditions, such as light loads, new batteries, and uniform temperature, the cooling power is appropriately reduced to maintain the battery within its optimal operating temperature range and maximize energy savings. Under high-risk conditions, such as heavy loads, older batteries, and uneven temperature, the cooling resources are over-allocated through the gain coefficient to suppress these risks.

[0080] Specifically, the target cooling power is determined based on the magnitude of the potential risk of thermal runaway, including:

[0081] The system has three preset power levels: energy-saving mode, standard mode, and high-power suppression mode. Based on the cooling gain coefficient, the corresponding power level is directly selected as the target cooling power.

[0082] It should be noted that the system pre-sets a first gain threshold and a second gain threshold to distinguish cooling needs, where the second gain threshold is greater than the first gain threshold, dividing the numerical range of the cooling gain coefficient into low-gain, medium-gain, and high-gain regions. First, the cooling gain coefficient calculated at the current moment is compared with the two thresholds: if the cooling gain coefficient is less than the first gain threshold, it indicates that only basic low-power heat dissipation is needed, and the system classifies the cooling gain coefficient as falling into the low-gain region, directly selecting the power value corresponding to the energy-saving mode as the target cooling power; if the cooling gain coefficient is between the first and second gain thresholds, it indicates that the current heat dissipation demand is within the rated range, and the system classifies the cooling gain coefficient as falling into the medium-gain region, selecting the power value corresponding to the standard mode as the target cooling power; if the cooling gain coefficient is greater than the second gain threshold, it indicates that strong cooling is urgently needed for heat suppression, and the system classifies the cooling gain coefficient as falling into the high-gain region, directly selecting the maximum power value corresponding to the strong suppression mode as the target cooling power.

[0083] At this point, the target cooling power has been obtained.

[0084] It's important to note that the target cooling power is only a theoretical value. Converting it into pump speed, fan duty cycle, or valve opening requires underlying drive conversion. Furthermore, the safety monitoring of marine battery systems demands traceability. Storing the adjusted temperature data in a database is not only for verifying the current cooling effect but also for accumulating long-term operational data. This data is a valuable asset for subsequent optimization of battery life models, analysis of accident causes, and improvement of temperature control algorithms. Within the framework of the Industrial Internet, this step achieves a complete closed loop from computational decision-making to physical execution and data assetization, ensuring the system's controllability and evolvability.

[0085] Specifically, the target cooling power is converted into a low-level control signal, which drives the cooling system to perform temperature regulation, including:

[0086] The target cooling power is converted into a low-level control signal and drives the cooling system to perform temperature regulation. The difference between the Joule heat power generated by the current sequence and dynamic internal resistance value and the target cooling power is calculated and divided by the equivalent heat capacity of the battery module, thereby deriving the expected temperature change rate per unit time.

[0087] It should be noted that, Figure 2 The diagram illustrates the closed-loop cooling control and temperature response recording based on adaptive gain. It shows the target cooling power curve, the equivalent curve of the actuator control command, and the adjusted battery temperature curve. Throughout the entire ship operation cycle, the diagram presents the linkage response characteristics of cooling control and temperature changes: the target cooling power is dynamically adjusted according to the operating conditions, the actuator control command follows synchronously, and the adjusted battery temperature is always maintained within a reasonable range. At the same time, the system stores the temperature data in the historical database in real time, which not only restores the closed-loop control logic of demand change-command response-temperature stability, but also realizes the traceability of operating data, which meets the actual application requirements of unattended ship batteries.

[0088] It's important to note that any theoretical model may contain deviations in practical applications. The battery's internal resistance characteristics can be affected by factors such as environmental humidity and mechanical vibration, leading to discrepancies between the calculated dynamic internal resistance value and the actual value. This deviation propagates through subsequent steps, ultimately manifesting as substandard temperature regulation—for example, the temperature may rise instead of fall after cooling, or fall too slowly. Therefore, the system must possess self-diagnosis and self-correction capabilities. By monitoring the rate of temperature change, the accuracy of the pre-set parameters can be inferred. If the effect does not conform to physical laws, it indicates that the model parameters have drifted, at which point an online correction mechanism must be triggered. This endows the system with adaptive evolution capabilities, allowing it to continuously fine-tune its sensing model as the battery ages and the environment changes, always maintaining optimal control.

[0089] Preferably, based on the adjusted temperature, the model parameters are corrected, triggering another round of parameter acquisition and calculation, including:

[0090] Calculate the measured temperature change rate after temperature adjustment. If the measured change rate exceeds the expected temperature change rate, update the correction coefficient in the dynamic internal resistance calculation model using the current operating data and trigger a new round of parameter acquisition and calculation process.

[0091] It should be noted that, Figure 3 This is a chart illustrating the monitoring and feedback correction criteria for the rate of temperature change after temperature regulation. It includes the measured temperature change rate curve, the expected maximum temperature rise threshold, the expected strong cooling rate threshold, and the model validity verification interval. Throughout the entire ship operation cycle, the measured temperature change rate curve consistently remained within the model validity verification interval, not exceeding the expected threshold range, indicating that the cooling regulation effect met expectations. This high degree of agreement between the measured and expected values ​​clearly demonstrates the reliability of the temperature control model of this invention, enabling stable operation without additional parameter corrections, and providing a clear basis for subsequent possible model optimization.

[0092] This completes the intelligent temperature control of the ship's batteries.

[0093] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A smart temperature control method for ship batteries based on lifespan degradation characteristics, characterized in that, include: High-frequency synchronous sampling of the real-time voltage and current sequences of the battery module; Retrieve the battery module's baseline parameters and obtain the battery health status value based on the current usage conditions; The average temperature rise is obtained based on the load intensity and internal heat distribution of the battery module; the impedance aging thermistor is calculated based on the sensitivity of the battery module to thermal effects in its current aging state, combined with the battery health status value. In the formula, Indicates the resistance to aging thermistor; This is the dynamic internal resistance value; This is the initial internal resistance value; This represents the battery health status value. It is the natural logarithm function; It is a natural exponential function; It is a natural constant; The first minimum positive number is used to ensure that the denominator is not zero; combining the characteristics of current fluctuations and the influence of real-time temperature rise on the degree of aging, the dynamic load thermal shock index is obtained. In the formula, Indicates the dynamic load thermal shock index; For current fluctuation variance; These are the pre-obtained electrothermal conversion weighting coefficients; This represents the average temperature rise. The reference temperature rise constant is obtained in advance; If it is the second smallest positive number, the denominator must not be 0; Represents the hyperbolic tangent function; Based on the impact of uneven heat distribution on battery performance, and combined with thermal turbulence indicators, the potential risk of thermal runaway is calculated. In the formula, Indicates the degree of potential risk of thermal runaway; Indicators of thermal disturbance; The baseline thermal distribution deviation constant is not zero; a numerical transformation is performed on the latent risk of thermal runaway to generate a cooling gain coefficient. Acquiring thermal disturbance index: Use temperature sensors to acquire multi-point surface temperature sequences distributed at different locations on the module, and acquire the ambient temperature; calculate the standard deviation of the multi-point surface temperature sequences to obtain the thermal disturbance index; The target cooling power is determined based on the magnitude of the potential risk of thermal runaway; The target cooling power is converted into a low-level control signal, which drives the cooling system to perform temperature regulation. Based on the regulated temperature, the model parameters are corrected, triggering another parameter acquisition and calculation.

2. The intelligent temperature control method for ship batteries based on lifespan degradation characteristics according to claim 1, characterized in that, The process of obtaining the battery health status value includes: The system synchronously acquires the real-time voltage and current sequences of the battery module using a high-frequency sampling frequency; it indexes and reads the initial internal resistance value, nominal cycle life, reference thermal distribution deviation constant, and the current cumulative cycle count of the battery module from the database; it uses the least squares method to perform linear regression fitting on the data points of the real-time voltage and current sequences within a short time window to obtain the dynamic internal resistance value sequence; it calculates the ratio of the current cumulative cycle count to the nominal cycle life, and defines the difference between 1 and this ratio as the battery health status value.

3. The intelligent temperature control method for ship batteries based on lifespan degradation characteristics according to claim 1, characterized in that, The obtained average temperature rise includes: The variance of the real-time current sequence is calculated to obtain the current fluctuation variance; the average temperature rise is obtained by subtracting the ambient temperature from the arithmetic mean of the multi-point surface temperature sequence.

4. The intelligent temperature control method for ship batteries based on lifespan degradation characteristics according to claim 1, characterized in that, The generation of the cooling gain coefficient includes: The thermal runaway potential risk is mapped to the interval [0,1] using the maximum-minimum normalization method. The mapped value is then used as an index to find the corresponding cooling gain value in the preset gain coefficient table to obtain the cooling gain coefficient.

5. The intelligent temperature control method for ship batteries based on lifespan degradation characteristics according to claim 1, characterized in that, Determining the target cooling power includes: The system has three preset power levels: energy-saving mode, standard mode, and high-power suppression mode. Based on the cooling gain coefficient, the corresponding power level is directly selected as the target cooling power.

6. The intelligent temperature control method for ship batteries based on lifespan degradation characteristics according to claim 1, characterized in that, The drive cooling system performs temperature regulation, including: The target cooling power is converted into a low-level control signal and drives the cooling system to perform temperature regulation. The difference between the Joule heat power generated by the current sequence and dynamic internal resistance value and the target cooling power is calculated and divided by the equivalent heat capacity of the battery module, thereby deriving the expected temperature change rate per unit time.

7. The intelligent temperature control method for ship batteries based on lifespan degradation characteristics according to claim 1, characterized in that, The triggering of re-collection and calculation of parameters includes: Calculate the measured temperature change rate after temperature adjustment. If the measured change rate exceeds the expected temperature change rate, update the correction coefficient in the dynamic internal resistance calculation model using the current operating data and trigger a new round of parameter acquisition and calculation process.

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