Charging method of hybrid power gantry crane battery

Abstract

The invention relates to the field of hybrid power electrical systems, in particular to a charging method of a hybrid power gantry crane battery, which comprises the following steps: acquiring battery health data, environmental data and load data, dynamically adjusting a battery charging mode based on a real-time analysis result, and optimizing energy distribution between an internal combustion engine and the battery; the battery health analysis model can evaluate the health state of the battery and predict the residual life, and the charging path planning algorithm considers load requirements, environmental factors and the like, and dynamically optimizes the energy flow direction in the charging process; by adjusting the charging mode and energy distribution in real time, the charging efficiency can be effectively improved, the service life of the battery can be prolonged, overcharging and overdischarging can be avoided, and the stability and high efficiency of the system in a complex environment can be ensured.

Description

Technical Field

[0001] The invention relates to the field of hybrid power electrical systems, and in particular to a charging method for a hybrid power gantry crane battery. Background Art

[0002] Since gantry crane operations usually require high-load operations, the battery charging process directly affects its service life, charging efficiency, and the stability of the system's energy supply. The health of the battery has a profound impact on the energy flow and charging mode during the charging process. Therefore, optimizing the battery charging process and rationally allocating energy distribution between the internal combustion engine and the battery can significantly improve the efficiency of the overall system and extend the battery life. In addition, the load changes frequently in the gantry crane operating environment, and the charging process needs to be dynamically adjusted according to the actual load demand and environmental conditions to avoid damage or inefficiency caused by improper battery charging.

[0003] Existing hybrid gantry crane charging technologies usually rely on static charging modes and regulate the charging process through simple battery power monitoring and internal combustion engine power output control. Traditional technologies often lack real-time monitoring and regulation mechanisms for battery health status, and the energy distribution between the internal combustion engine and the battery is usually preset rather than dynamically adjusted based on real-time data. These traditional methods have the following shortcomings: Traditional methods are usually unable to evaluate the health status of the battery in real time, such as key indicators such as battery internal resistance and maximum charging capacity, which leads to over-discharge or over-charging of the battery during the charging process, thus affecting the battery life. When factors such as ambient temperature and load demand change, it is difficult for traditional methods to adaptively adjust the charging rate, resulting in low charging efficiency and possible damage to the battery. Existing technologies usually use a fixed energy distribution mode and are unable to dynamically adjust the energy flow according to load demand, charging status and battery health status, resulting in low system efficiency and inability to cope with drastic changes in load. Summary of the invention

[0004] In view of the many problems existing in the above-mentioned prior art, the present invention provides a charging method for a hybrid gantry crane battery. The present invention realizes the intelligent optimization of the battery charging process by introducing a battery health analysis model, a dynamic energy distribution algorithm and a charging path planning technology. The battery health analysis model evaluates the battery status in real time and predicts the remaining battery life; the energy distribution algorithm dynamically adjusts the energy flow between the internal combustion engine and the battery according to the battery power, load demand and environmental changes; the charging path planning technology optimizes the charging process according to real-time feedback data to improve charging efficiency and extend battery life. The present invention effectively improves the intelligence level of the charging process and avoids the problems of battery damage and inefficiency in the traditional charging mode.

[0005] A method for charging a hybrid gantry crane battery comprises the following steps: Collect environmental data, battery status data and load data; Based on the battery status data and the environmental data, evaluating the health status of the battery and generating a battery health analysis result; Determining whether to enable an internal combustion engine and generating internal combustion engine enabling decision data according to the battery health analysis result and the load data; Based on the internal combustion engine activation decision data and the battery health analysis result, planning a charging path and adjusting a charging mode to optimize charging efficiency; Charging is performed according to the charging path and the charging mode, and energy distribution between the internal combustion engine and the battery is adjusted in real time.

[0006] Preferably, the environmental data includes temperature, humidity, wind speed and air pressure information, and the load data includes load weight, operation mode, operation speed and lifting height information. The data are collected through real-time sensors and monitoring systems, connected to the battery management system, and fed back to the charging control system in real time.

[0007] Preferably, based on the battery status data and the environmental data, the health status of the battery is evaluated by the battery health analysis model, and the battery health analysis model generates the battery health analysis result and predicts the remaining life of the battery by calculating the battery's maximum discharge current, battery internal resistance, temperature distribution, charging history and discharge rate.

[0008] Preferably, the battery health analysis result includes: The battery health score is calculated using the following formula:

[0009] in, is the maximum charge capacity of the battery, in Ah; is the current available capacity of the battery, in Ah; is the current internal resistance of the battery, in mΩ; is the maximum internal resistance of the battery, in mΩ.

[0010] Preferably, according to the battery health analysis result and the load data, whether to enable the internal combustion engine is determined by the internal combustion engine enabling decision algorithm, and the internal combustion engine enabling decision algorithm calculates the enabling timing and power output by the following formula:

[0011] in, is the power output of the internal combustion engine in kW; is the remaining battery capacity in kWh; is the total electrical energy required by the gantry crane, in kWh; It is the maximum power of the internal combustion engine, measured in kW.

[0012] Preferably, the internal combustion engine activation decision data includes: The activation timing and power output of the internal combustion engine, the power output is calculated by the internal combustion engine activation decision algorithm, and the activation timing is based on the remaining battery power, load demand, charging status and battery health analysis results to determine in real time whether to activate the internal combustion engine.

[0013] Preferably, based on the internal combustion engine activation decision data and the battery health analysis results, the charging path planning algorithm is used to generate a charging path, and the charging path is dynamically optimized by considering load demand, charging point location, and environmental obstacle factors.

[0014] Preferably, the charging mode is adjusted according to the battery health status and environmental data, and the adjustment includes: When the battery health analysis result indicates that the battery health state is poor, the charging mode is automatically switched to the trickle charging mode to avoid overheating or overcharging of the battery; When the ambient temperature is low or the load demand is high, the charging mode can be adjusted to fast charging mode to restore the battery power as quickly as possible, but during this process, the charging rate and internal combustion engine power output will be adjusted by real-time feedback to keep the system stable.

[0015] Preferably, charging is performed according to the charging path and the charging mode, and the energy distribution between the internal combustion engine and the battery is adjusted in real time. The energy distribution is adjusted based on the current battery power, charging status, output power of the internal combustion engine and load demand. The energy flow between the internal combustion engine and the battery is dynamically adjusted through a closed-loop feedback system to optimize battery health and charging efficiency during the charging process.

[0016] Compared with the prior art, the advantages and beneficial effects of the present invention are: Through real-time analysis and feedback of the battery health status, the present invention can automatically adjust the charging mode according to the battery health status, load demand and environmental data, optimize the battery charging efficiency, avoid overcharging and over-discharging, and extend the battery life; The present invention dynamically optimizes the energy distribution between the internal combustion engine and the battery based on real-time data; especially during load fluctuations and charging, the internal combustion engine output power and battery charging rate can be adjusted in real time to ensure stable operation of the system; The present invention can adapt to changes in external conditions such as temperature changes and load fluctuations through a real-time feedback system, dynamically adjust the charging mode, and ensure the efficiency and safety of the charging process; The advantages of the present invention not only solve the deficiencies of battery health assessment and automatic adjustment of the charging process in the prior art, but also improve the overall efficiency of the system and the battery life by introducing intelligent path planning and dynamic feedback mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 A schematic diagram of the battery health analysis process in the present invention; Figure 3 A schematic diagram of the internal combustion engine activation decision and charging mode adjustment process in the present invention; Figure 4 This is a schematic diagram of charging path planning in the present invention; Figure 5 This is a schematic diagram of energy distribution adjustment in the present invention. DETAILED DESCRIPTION

[0018] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0019] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0020] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0021] like Figure 1 As shown, a method for charging a hybrid gantry crane battery comprises the following steps: Collect environmental data, battery status data and load data; Preferably, the environmental data includes temperature, humidity, wind speed and air pressure information, and the load data includes load weight, operation mode, operation speed and lifting height information. The data are collected through real-time sensors and monitoring systems, connected to the battery management system, and fed back to the charging control system in real time.

[0022] The collection of environmental data in the present invention mainly relies on integrated real-time sensors and monitoring systems, through which relevant information about the surrounding environment is obtained in real time. Environmental data includes temperature, humidity, wind speed and air pressure, etc. These factors directly affect the charging efficiency of the battery and the working state of the internal combustion engine. For example, too high or too low temperature may cause battery performance degradation, and changes in humidity and air pressure may affect the chemical reaction process of the battery or cause system instability. Therefore, real-time monitoring of environmental parameters is crucial to controlling the charging process.

[0023] In actual applications, environmental data provides a basis for dynamically adjusting the charging mode and charging path. If the temperature is too high, the charging mode will automatically switch to trickle charging to reduce the risk of battery overheating; in low temperature environments, a gentler charging method will be selected to avoid damage to battery performance during the charging process. Wind speed and air pressure data are used to assist in determining the potential impact of external climate on the charging path, ensuring that charging and energy distribution are not interfered with by the external environment under severe weather conditions, and that the charging process does not cause safety hazards.

[0024] Example: Assume that the gantry crane is in an environment with high temperature (45°C) and low humidity (30% RH). The sensor collects this information in real time and feeds it back to the battery management system. The battery management system automatically adjusts the charging mode to a lower current trickle charging mode based on this data, and reminds the operator to pay attention to the potential impact of the environment on the battery life. At this time, the changes in wind speed and air pressure will not directly affect the charging process, but still provide a reference for the subsequent charging path planning.

[0025] The collection of battery status data is completed by a dedicated battery management system (BMS). BMS can provide detailed information about the battery's health status and remaining power by monitoring key indicators such as battery voltage, current, temperature, and charge / discharge status in real time. These data are critical for evaluating battery performance. Battery status data is not only used for monitoring during the charging process, but also provides a basis for judging whether to enable the internal combustion engine. If the remaining battery power is low, the BMS will notify the control system to enable the internal combustion engine to ensure continuous power supply to the system.

[0026] By collecting battery status data in real time, the system can accurately evaluate the battery charging status. For example, when the battery power is lower than the set threshold, the system will automatically enable the internal combustion engine to replenish the battery power and avoid operation interruption caused by insufficient battery power. In addition, the BMS can also monitor the battery's temperature, internal resistance and other health parameters, and provide real-time feedback on the battery status to ensure that the battery operates within a safe operating range and avoid problems such as battery overheating or excessive discharge that affect system stability.

[0027] Example: During a certain operation, the BMS detects that the battery voltage drops below the set threshold (such as 19V). At this time, the system immediately sends a signal to the charging control system to decide to start the internal combustion engine. At the same time, the BMS continues to monitor the battery temperature and performs temperature control management through real-time feedback to ensure that the battery is charged within a safe temperature range.

[0028] The load data mainly includes load weight, operation mode, operation speed and lifting height. These data reflect the energy demand during the operation of the gantry crane, and directly affect the decision of charging mode and activation of the internal combustion engine. By collecting load data in real time, the system can accurately evaluate the energy required by the battery under the current operating conditions, and compare it with the health status of the battery to determine whether it is necessary to start the internal combustion engine for auxiliary charging. When the load is heavy and the operation speed is fast, the system will automatically adjust the charging mode and path planning according to the load data to ensure charging efficiency and smooth operation.

[0029] The collection of load data enables the charging control system to dynamically adjust according to the real-time operating status. For example, when the load weight increases, the system will calculate the higher energy demand and decide whether to enable the internal combustion engine or optimize the charging path to reduce the burden on the internal combustion engine and improve the battery charging efficiency. If the load is light or the operating speed is low, the system may choose to supply energy through the battery alone, reduce the activation frequency of the internal combustion engine, thereby reducing energy consumption and improving charging efficiency.

[0030] Example: Assume that in a certain operation, the load weight reaches 42 tons and the operation speed is 40m / min. After the system collects these data through real-time sensors, it combines the current battery power and status to calculate the optimal solution for the charging path and decides to enable the internal combustion engine to replenish the battery power. At this time, the internal combustion engine will provide auxiliary energy to ensure the normal operation. At the same time, the system adjusts the charging path according to the load data to ensure that the battery can be charged efficiently during the charging process and avoid performance degradation due to excessive battery discharge.

[0031] Based on the battery status data and the environmental data, evaluating the health status of the battery and generating a battery health analysis result; Preferably, Figure 2 As shown, based on the battery status data and the environmental data, the battery health status is evaluated by the battery health analysis model. The battery health analysis model generates the battery health analysis result and predicts the remaining life of the battery by calculating the battery's maximum discharge current, battery internal resistance, temperature distribution, charging history and discharge rate.

[0032] In the present invention, the Battery Health Analysis Model (BHAM) evaluates the health status of the battery in real time through comprehensive analysis of battery status data and environmental data. This process is crucial for the charging optimization of hybrid gantry cranes, especially during dynamic charging, where the evaluation of the battery health status helps the system determine the timing of starting the internal combustion engine, the planning of the charging path, and the adjustment of the charging mode, thereby achieving more efficient charging and extending the battery life.

[0033] The core principle of the battery health analysis model is to evaluate the health of the battery, predict its remaining life, and provide decision support for the charging control system by integrating various battery status information and external environmental data. Specifically, the battery health analysis model analyzes the battery health status through the following key factors: The maximum discharge current is the maximum current value that the battery can safely provide without damage. This parameter is closely related to the capacity of the battery and the chemical characteristics of the battery. As the battery ages, the maximum discharge current gradually decreases and the battery cannot provide enough current to meet high load requirements. Therefore, the maximum discharge current is an important evaluation indicator in battery health analysis.

[0034] Battery internal resistance is the resistance to the flow of current inside the battery, and its size directly affects the charging and discharging efficiency of the battery. As the battery is used for a longer time, the internal resistance gradually increases. The increase in battery internal resistance not only affects the charging efficiency, but also causes the battery to generate excessive heat when it is under high load or fast charging, which may cause the battery to overheat or performance degradation. Therefore, monitoring and analysis of internal resistance helps to evaluate the health status of the battery.

[0035] The temperature distribution of the battery affects its chemical reaction rate and internal resistance change. Too high or too low temperature will affect the battery's charge and discharge performance and even shorten the battery life. Monitoring the temperature distribution can help determine whether the battery is within the safe operating temperature range and adjust the charging mode according to temperature changes to prevent overcharging or battery damage.

[0036] The battery's charge history and discharge rate reflect the battery's usage pattern and load conditions. Frequent deep charge and discharge (such as discharging the battery to a very low level and then quickly charging it) will accelerate battery aging and affect its life. By analyzing the battery's charge history and discharge rate, the health analysis model can predict the battery's future performance and determine whether maintenance measures or battery replacement are needed.

[0037] Based on the above parameters, the battery health analysis model can predict the remaining service life of the battery. This prediction is usually based on historical data and model algorithms to determine the performance trend of the battery in the future. By combining the battery health score and the prediction of the remaining service life, the system can provide a reliable basis for subsequent charging scheduling and internal combustion engine activation decisions.

[0038] By real-time monitoring and analysis of the battery health status, the system can automatically adjust the charging mode to suit the current condition of the battery. For example, when the battery temperature is too high, the system will automatically reduce the charging rate or switch to trickle charging mode to prevent the battery from overheating; when the battery health status is poor, the charging mode will be adjusted to a more gentle method to extend battery life and improve safety.

[0039] The battery health analysis model can accurately evaluate the remaining battery power and health status, thereby helping the system optimize the timing of activating the internal combustion engine. When the battery power is low and the battery health status is poor, the system will promptly activate the internal combustion engine to provide additional energy to ensure the stable operation of the gantry crane. When the battery health status is good and the remaining power is sufficient, the system may choose to use the battery alone to reduce the activation frequency of the internal combustion engine, thereby saving fuel and improving overall energy efficiency.

[0040] Through long-term monitoring and analysis of battery health analysis results, the system can predict possible battery failures or performance degradation in the future. For example, an abnormal increase in internal resistance may indicate the aging process of the battery. Identifying these signs in advance allows maintenance personnel to take measures before the battery fails and avoid interruptions to operations due to battery performance degradation. In this way, the system can reduce the failure rate and replace or repair the battery before it fails, thereby ensuring stable operation of the equipment.

[0041] The battery health analysis model helps to slow down the aging process of the battery by comprehensively evaluating the working status of the battery. For example, by controlling the battery to operate within a safe temperature range and avoiding over-discharge and over-charge, the battery life can be effectively extended. In addition, the system can also dynamically adjust the charging strategy according to the changes in the battery health status, such as avoiding the use of high current for fast charging and reducing the internal pressure of the battery, thereby improving the long-term reliability of the battery.

[0042] Example: Assume that during an operation, the maximum discharge current value of the battery is close to the maximum safety limit of the battery. At the same time, the BMS detects that the internal resistance of the battery has increased significantly and the temperature has begun to rise. At this time, the battery health analysis model will evaluate that the battery health score is low and predict that the remaining service life of the battery may drop significantly in the next 100 hours. Based on this information, the system will automatically adjust the charging mode, switch to a lower charging current and delay the charging process to ensure that the battery does not continue to charge in an unstable state. At the same time, the system will recommend enabling the internal combustion engine to provide additional energy to avoid excessive discharge of the battery, thereby extending the battery life.

[0043] In actual operation, the system will automatically adjust these factors to ensure that the battery charging process always remains in the best state under different load conditions and environmental influences, optimizing energy management and reducing battery loss.

[0044] Preferably, the battery health analysis result includes: The battery health score is calculated using the following formula:

[0045] in, is the maximum charge capacity of the battery, in Ah; is the current available capacity of the battery, in Ah; is the current internal resistance of the battery, in mΩ; is the maximum internal resistance of the battery, in mΩ.

[0046] Capacity Ratio: The First Part of the Formula The ratio of the current battery capacity to the maximum capacity is calculated, reflecting the remaining capacity of the battery. As the battery usage time increases, the battery capacity gradually decays, so this ratio will be less than 1, which means that the battery health status gradually decreases over time.

[0047] Internal resistance ratio: the second part of the formula Indicates the ratio of the battery's current internal resistance to the maximum internal resistance. Increased internal resistance is a sign of battery aging because it directly affects the battery's charging and discharging efficiency, resulting in energy loss and temperature rise. This section further characterizes the battery's health status by reflecting the increase in internal resistance. Higher internal resistance means that the battery's performance is degraded, and the system needs to take measures to delay battery aging.

[0048] Comprehensive health score: The health score of the battery is calculated by combining the capacity ratio and the internal resistance ratio. This score is a dynamic value that can reflect the health of the battery in real time. When the capacity and internal resistance of the battery are close to the limit value, the health score will drop significantly, prompting the system to adjust the charging strategy or enable the internal combustion engine in time.

[0049] The health score provides an intuitive assessment of the battery's health status. By continuously monitoring the battery's health score, the system can predict the remaining life of the battery and intervene in advance to avoid operational interruptions due to battery performance degradation.

[0050] According to the battery health score, the charging control system can dynamically adjust the charging mode. If the battery health score is low, it means that the available capacity of the battery is reduced and the internal resistance is high. At this time, the system may choose a lower current charging mode to avoid heat accumulation and over-discharge caused by high current charging, thereby extending the battery life.

[0051] The battery health score directly affects whether to enable the internal combustion engine. When the battery health score is below a certain threshold, the system can decide to enable the internal combustion engine to replenish the battery energy according to the load demand, ensuring that the operation of the gantry crane is not affected by insufficient battery power. In addition, the timing of enabling the internal combustion engine can also be optimized based on the health score to reduce unnecessary energy consumption.

[0052] The battery health score provides a reliable early warning mechanism for operators. When the health score continues to drop, the system will prompt the operator to perform battery maintenance or replacement operations to avoid sudden battery failure, which may cause operation stagnation or safety problems. Through this mechanism, operators can prepare in advance and reduce the system failure rate.

[0053] Example: Assume that in one operation, the maximum charge capacity of the battery The available capacity of the battery is 100 Ah. For 60 Ah, the current internal resistance of the battery is 25 mΩ, maximum internal resistance is 50 mΩ. According to the above formula, the battery health score is:

[0054] This score indicates that the battery is in a very unhealthy state, has lost a lot of capacity, and has a serious increase in internal resistance. At this time, the system will activate the corresponding early warning mechanism based on the health score, and recommend immediately enabling the internal combustion engine for auxiliary charging and adjusting the charging mode to low power mode to prevent further battery degradation.

[0055] If the battery health score is high (such as 0.8 or higher), the system may choose to rely solely on battery power and reduce the frequency of activation of the internal combustion engine, thereby improving the energy efficiency of the system.

[0056] By integrating the battery's maximum charging capacity, current capacity, internal resistance and environmental data, the battery health analysis model can evaluate the battery's health status in real time and generate a battery health score. This score not only helps evaluate the remaining life of the battery, but also guides the system's optimization decisions during the charging process, such as whether to enable the internal combustion engine, adjust the charging mode, and plan the route. This intelligent health assessment provides a reliable charging management mechanism for the hybrid gantry crane, which can effectively extend the battery's service life and ensure the stable operation of the equipment.

[0057] Determining whether to enable an internal combustion engine and generating internal combustion engine enabling decision data according to the battery health analysis result and the load data; Preferably, Figure 3 As shown, according to the battery health analysis result and the load data, the internal combustion engine activation decision algorithm is used to determine whether to activate the internal combustion engine. The internal combustion engine activation decision algorithm calculates the activation timing and power output through the following formula:

[0058] in, is the power output of the internal combustion engine in kW; is the remaining battery capacity in kWh; is the total electrical energy required by the gantry crane, in kWh; It is the maximum power of the internal combustion engine, measured in kW.

[0059] In the charging management solution for hybrid gantry cranes, the core role of the internal combustion engine activation decision algorithm is to decide whether to enable the internal combustion engine based on the battery health analysis results and load data, and calculate the power output of the internal combustion engine. This decision is crucial to ensure that the gantry crane can still operate efficiently during peak loads and to optimize battery life. Through this algorithm, the system can calculate the power that the internal combustion engine should output based on the changes in battery power and the total energy required, and then reasonably dispatch the energy distribution between the battery and the internal combustion engine.

[0060] When the battery remaining Less than the total power required That is, when the battery power is not enough to drive the gantry crane alone, the system will determine whether to enable the internal combustion engine by calculating the required power output ratio.

[0061] According to the ratio of the remaining battery power to the total electrical energy, the power output of the internal combustion engine The calculation results show that the internal combustion engine needs to provide support, and the internal combustion engine will output the corresponding power according to the calculation results until the battery is sufficient or the work demand decreases.

[0062] When the remaining battery power is high, Close to , the power output of the internal combustion engine is At low or even zero fuel consumption, the battery will drive the gantry crane alone. In this way, the system is able to maximize battery life and reduce the operating time of the internal combustion engine, thereby improving overall energy efficiency.

[0063] If the remaining battery charge is significantly lower than the total required power, The value of will be very small, and the calculation result will indicate that the internal combustion engine needs to provide a larger power output to supplement the insufficient power of the battery.

[0064] Power output of internal combustion engines It is inversely proportional to the remaining power of the battery, which means that the more sufficient the battery power is, the smaller the power output of the internal combustion engine is. Conversely, when the battery power is insufficient, the internal combustion engine will automatically provide more power support according to the ratio of battery power to total electrical energy.

[0065] This algorithm ensures that the gantry crane can adjust the energy source according to the real-time load demand and battery status, thereby improving work efficiency and avoiding excessive discharge of the battery, while reducing the ineffective work of the internal combustion engine and improving fuel utilization efficiency.

[0066] The system calculates and dynamically adjusts the power output of the internal combustion engine so that it is only enabled when the battery is insufficient to support demand. In this way, the battery's power can be maximized, the frequency of use of the internal combustion engine can be reduced, and energy consumption and fuel costs can be reduced.

[0067] Since the battery is solely responsible for driving the gantry crane most of the time, the use of the internal combustion engine is limited to when the battery is low on power, which reduces the battery's charge and discharge cycles, slows the battery's aging process and extends the battery's service life.

[0068] When the battery is fully charged, the battery can supply power alone, and the power output of the internal combustion engine is reduced to a minimum, reducing the burden on the internal combustion engine; when the battery is low on power, the internal combustion engine will automatically provide sufficient power support based on load demand and battery power, ensuring that the gantry crane can still operate efficiently under high load, preventing shutdown or inefficiency due to insufficient battery power.

[0069] By calculating the power output of the internal combustion engine in real time, the system can accurately adjust the energy supply according to the actual load demand, avoiding excessive work of the internal combustion engine and excessive discharge of the battery, thereby improving the overall energy efficiency and operating efficiency of the equipment.

[0070] When the battery is fully charged, the internal combustion engine hardly participates in work, which not only saves fuel but also reduces the wear and tear of the internal combustion engine, thereby reducing the maintenance cost and downtime of the equipment.

[0071] Example: Assuming the total power demand of the gantry crane is 100 kWh, and the remaining capacity of the battery The maximum power of the internal combustion engine is 30 kWh. According to the internal combustion engine activation decision algorithm, the internal combustion engine power output The calculation is:

[0072] In this case, the battery power cannot meet the entire load demand, so the internal combustion engine will output 45 kW of power to supplement the battery and ensure that the gantry crane continues to work stably.

[0073] The internal combustion engine activation decision algorithm in the present invention dynamically calculates the ratio of the remaining battery power to the total power demand to reasonably adjust the activation timing and power output of the internal combustion engine. In this way, the continuous operation of the gantry crane can be ensured, and the service life of the battery can be effectively extended, the energy distribution can be optimized, and the overall work efficiency can be improved. Through precise energy scheduling, the system can achieve optimal fuel and battery management, ensuring that the hybrid gantry crane works efficiently and economically.

[0074] Preferably, the internal combustion engine activation decision data includes: The activation timing and power output of the internal combustion engine, the power output is calculated by the internal combustion engine activation decision algorithm, and the activation timing is based on the remaining battery power, load demand, charging status and battery health analysis results to determine in real time whether to activate the internal combustion engine.

[0075] The core function of the internal combustion engine activation decision data is to collect and analyze the following four key data in real time: battery remaining power, load demand, charging status and battery health analysis results. These data provide the necessary information for calculating the timing of internal combustion engine activation and power output.

[0076] The remaining power of the battery directly determines whether the battery can meet the working requirements of the gantry crane alone. When the battery power is low, the system needs to determine whether to start the internal combustion engine to make up for the lack of battery power.

[0077] In the present invention, the battery power is the primary reference data for activating the internal combustion engine. If the remaining battery power is lower than a set threshold, the system will consider activating the internal combustion engine to provide additional power support.

[0078] Load demand is the total amount of electrical energy required by the gantry crane to perform a specific operation. This demand depends on many factors, such as the operation mode, load weight, travel speed, etc. Changes in load demand affect the rate at which the battery power is consumed.

[0079] If the load demand is large and the battery power is insufficient to support the operation, the system will meet the load demand by enabling the internal combustion engine to ensure that the work is not affected.

[0080] The charging status reflects the current charging process of the battery, for example, whether the battery is being charged, whether the charging process is complete, whether the charging rate is efficient, etc.

[0081] When the battery is in the process of charging, the system may delay the activation of the internal combustion engine to reduce energy loss and reduce unnecessary energy conversion. If the charging state is good and the battery power increases rapidly, the activation of the internal combustion engine may be delayed.

[0082] Battery health analysis evaluates the battery's maximum charging capacity, internal resistance, temperature and other indicators to obtain a health score to reflect the battery's current health status. The battery health status directly affects the battery's charging and discharging efficiency. The higher the internal resistance, the lower the charging efficiency, which may cause the battery to consume too quickly.

[0083] If the battery is in poor health and may not be able to meet long-term high-load operation, the internal combustion engine will be activated in advance to reduce the burden on the battery and extend the battery life.

[0084] The internal combustion engine activation decision data not only determines whether to activate the internal combustion engine, but also calculates the power output of the internal combustion engine. The power output is calculated based on the proportional relationship between the battery power and the load demand, ensuring that the internal combustion engine provides just the right amount of power support without over-consuming fuel or causing excessive battery discharge.

[0085] The activation time and power output are calculated and decided through the following steps: Determine when to enable the engine: By calculating the ratio of the remaining battery power to the total power demand in real time, the system evaluates whether the battery can support the current load alone. If the battery power is insufficient, the system will decide to enable the internal combustion engine.

[0086] Power output calculation: Based on the remaining battery power, total power demand and the maximum power of the internal combustion engine, the required power output of the internal combustion engine is calculated to ensure that the gantry crane can meet the operating requirements.

[0087] The system can accurately control the activation time and power output of the internal combustion engine, and realize the coordinated work between the internal combustion engine and the battery. When the battery is fully charged, the battery can drive the gantry crane alone; when the battery is insufficient, the internal combustion engine will automatically provide the necessary power support to ensure that the gantry crane can operate normally under high load or long-term operation.

[0088] This intelligent energy scheduling reduces the ineffective work of the internal combustion engine, thereby improving overall energy efficiency and operating economy.

[0089] By properly enabling the internal combustion engine based on the battery health analysis results, the over-discharge of the battery can be reduced, thereby extending the battery life. Especially when the battery health score is low, the timely activation of the internal combustion engine can effectively slow down battery aging and improve the overall sustainability of the system.

[0090] The system ensures that the battery is not over-discharged while avoiding over-reliance on the internal combustion engine, thereby improving charging efficiency and energy efficiency. At the same time, the system can monitor and adjust the activation strategy of the internal combustion engine in real time to avoid unnecessary power output of the internal combustion engine, save fuel, and reduce operating costs.

[0091] When load demand is high or battery health is poor, the system can effectively balance the workload of the battery and internal combustion engine, ensuring stable operation of the gantry crane and reducing the risk of equipment failure or downtime.

[0092] The entire internal combustion engine activation decision process is automated. Based on real-time battery data, load demand, and health analysis results, the system can independently make a decision on whether to activate the internal combustion engine without human intervention. This not only improves the intelligence level of the system, but also improves operating efficiency and system response speed.

[0093] Example: Assume that during the operation of the gantry crane, the remaining battery power is The total electrical energy required is 50 kWh. The maximum power of the internal combustion engine is 100 kWh. is 150 kW.

[0094] According to the internal combustion engine activation decision data, the power output of the internal combustion engine is:

[0095] At this point, the system will decide to enable the internal combustion engine and let it output 75 kW of power to supplement the insufficient battery power and ensure that the gantry crane can continue to operate.

[0096] The internal combustion engine activation decision data intelligently determines whether to activate the internal combustion engine and calculates its power output by integrating the remaining battery power, load demand, charging status and battery health analysis results. This mechanism can ensure efficient energy management of the hybrid gantry crane under different working conditions, optimize battery use, extend equipment life, and ensure the continuity and stability of operations. Through intelligent scheduling and decision-making, the system can reduce fuel consumption, reduce operating costs, and improve energy efficiency and safety.

[0097] Based on the internal combustion engine activation decision data and the battery health analysis result, planning a charging path and adjusting a charging mode to optimize charging efficiency; Preferably, Figure 4As shown, based on the internal combustion engine activation decision data and the battery health analysis results, the charging path planning algorithm is used to generate a charging path, and the charging path is dynamically optimized by considering load demand, charging point location, and environmental obstacle factors.

[0098] The core function of the charging path planning algorithm is to generate an optimal charging path by real-time collection and analysis of battery status, internal combustion engine activation decision and environmental data. During the charging process, the gantry crane not only needs to be charged, but also needs to balance the load demand, charging time and the timing of internal combustion engine activation to ensure that the entire charging process does not affect the operating efficiency.

[0099] Internal combustion engine activation decision data This is the decision data for determining when to activate the internal combustion engine to replenish the battery power. It contains the calculation results of the timing and power output of the internal combustion engine activation based on the current battery remaining power, load demand and battery health status.

[0100] These data help the system determine whether the charging process needs to be supported by the internal combustion engine. If the battery is fully charged, the internal combustion engine will not be enabled temporarily.

[0101] The battery health score is a key indicator for evaluating the battery status. The battery health analysis results affect the selection of the charging path, especially when the battery health is poor. The system will give priority to the path that can reduce the battery burden to extend the battery life.

[0102] For example, if the battery's health score is low, the charging path may choose a shorter path to avoid subjecting the battery to long, high-intensity charging loads.

[0103] Load demand refers to the total electrical energy required for gantry crane operation at a certain moment. The planning of the charging path must take into account whether the load demand exceeds the power output capacity of the battery and internal combustion engine during the charging process. Depending on the load demand, the system may need to enable the internal combustion engine during the charging process or accelerate the charging process.

[0104] Charging points are preset stations where the gantry crane can be charged. The system needs to take into account the distribution of charging points and the availability of charging stations when planning the route, ensuring that the charging process is not interrupted and that the nearest charging point is found for charging before the battery is exhausted.

[0105] Environmental obstacle factors include obstacles on the charging path (such as stacked goods, other equipment, etc.), and other external environmental factors that affect driving, such as wind speed, temperature, etc. These factors will affect the choice of charging path, and the system needs to dynamically adjust the charging path to avoid obstacles and select the best route for charging.

[0106] In harsh environments (such as high wind speeds, high temperatures, etc.), the system will prioritize paths that reduce exposure to adverse environments and reduce energy loss during charging.

[0107] The charging path planning algorithm can adjust the charging path in real time based on environmental data, internal combustion engine activation decisions and battery health analysis results. The system automatically selects the path based on current load demand, available charging point locations and battery health status, which can minimize charging time and unnecessary energy consumption while ensuring efficient operation of the gantry crane.

[0108] For example, under high load conditions, if the battery is running low, the system provides power support by enabling the internal combustion engine and selecting the shortest charging path to avoid overloading the battery.

[0109] By properly planning the charging path, a balance can be found between the workload of the battery and the internal combustion engine. For example, when the battery is in poor health, the system will avoid long-term high-power charging and try to use the internal combustion engine to provide support to avoid excessive discharge of the battery, thereby extending the battery life.

[0110] The system dynamically optimizes the path and charging mode to ensure that the battery is charged at the most appropriate time, thereby improving charging efficiency and the energy efficiency of the entire system.

[0111] Through real-time path optimization, the gantry crane can continue to operate during the charging process without affecting efficiency, avoiding downtime caused by low battery power and other problems. The combination of intelligent activation of the internal combustion engine and battery management ensures efficient and continuous operation of the gantry crane.

[0112] For example, when the battery power is close to being exhausted, the system rationally arranges the coordinated work of the internal combustion engine and the battery according to the load demand and the location of the charging point, thus avoiding operation interruptions and improving operation continuity.

[0113] By incorporating environmental obstacles into path planning, the system improves its ability to adapt to complex environments. In severe weather (such as high wind speed, high temperature or low temperature), the system can avoid unsuitable areas or long-term high-load charging to avoid equipment failure or premature battery aging.

[0114] The system monitors the location and status of charging points in real time to ensure that the charging points in the selected charging path are not over-occupied, thereby reducing the waiting time during the charging process and ensuring the smooth completion of the charging task.

[0115] Example: Assume that during a charging process, the remaining battery power of the gantry crane is 60 kWh, the load demand is 100 kWh, and the maximum power of the internal combustion engine is 150 kW. The system calculates the decision data for enabling the internal combustion engine and decides to enable the internal combustion engine to provide 40 kW of power to make up for the lack of battery power. Then, the charging path planning algorithm generates a path based on the following factors: The remaining battery capacity is 60 kWh, which means that 40 kWh needs to be replenished to meet the load demand.

[0116] Charging point location: The nearest charging point is 10 km away from the current work location.

[0117] Environmental obstacles: There are several obstacles in the working area. The system avoids these areas and chooses a relatively flat and smooth route.

[0118] Internal combustion engine activation: Based on the low battery condition, the system starts the internal combustion engine to ensure that the operation is not affected by low battery during the charging process.

[0119] Finally, the charging path planning algorithm determined the best charging route of 10 km, with the internal combustion engine providing 40 kW of power support and the battery continuing to charge to replenish the remaining power. Through this optimized path, the gantry crane can smoothly complete the charging process without affecting operating efficiency.

[0120] The charging path planning algorithm achieves dynamic optimization of the charging path by real-time evaluation of battery health, load demand, charging point location, and environmental obstacles. This algorithm not only ensures the efficiency of the charging process and the extension of battery life, but also maintains efficient operation under complex environmental conditions. By reasonably scheduling the workload of the internal combustion engine and the battery, the system can maximize energy utilization while ensuring the operating efficiency of the gantry crane, reduce operation and maintenance costs, and improve the operating safety and reliability of the overall system.

[0121] Preferably, the charging mode is adjusted according to the battery health status and environmental data, and the adjustment includes: When the battery health analysis result indicates that the battery health state is poor, the charging mode is automatically switched to the trickle charging mode to avoid overheating or overcharging of the battery; When the ambient temperature is low or the load demand is high, the charging mode can be adjusted to fast charging mode to restore the battery power as quickly as possible, but during this process, the charging rate and internal combustion engine power output will be adjusted by real-time feedback to keep the system stable.

[0122] The internal combustion engine activation decision data is calculated based on the remaining battery power, load demand, charging status, and battery health analysis results (such as health score, battery internal resistance, maximum discharge current, etc.). This data helps the system determine when to activate the internal combustion engine to supplement the insufficient energy of the battery and ensure stability and efficiency during the charging process.

[0123] Battery health analysis results provide key inputs for charging path planning, especially the battery health score and its remaining life prediction. A battery with poor health may limit the charging rate and charging mode, so the path planning algorithm must take these variables into account and properly allocate the use of the internal combustion engine and the charging rate to avoid overheating or excessive discharge of the battery.

[0124] In the present invention, the optimization of the charging path is not limited to calculating the charging speed and charging time, but also takes into account factors such as load demand, the spatial location of the charging point, and possible environmental obstacles. By introducing these data, the charging path planning algorithm can be adjusted in real time to ensure that the charging process meets the current operating conditions.

[0125] Load demand is one of the key factors in charging path planning. During the operation of the gantry crane, changes in load directly affect the power demand of the system. When the load is high, the charging rate needs to be accelerated so that the battery can recover enough power in a shorter time; when the load is low, the charging process can be slowed down to reduce the system load. Therefore, the path planning algorithm must take into account the impact of the current load on the battery charging demand and intelligently adjust each step of the charging path to always maintain efficient charging when the load fluctuates.

[0126] The location of the charging point is another key factor. In actual applications, the charging piles and charging facilities of the gantry crane may not be located in the center of the working area, but distributed in various locations. The charging path planning algorithm needs to calculate the optimal path from each charging point in real time to achieve the charging target in the shortest time and with the lowest energy consumption.

[0127] In addition, environmental obstacles (such as buildings, equipment obstacles, etc.) are also important factors affecting the selection of charging paths. The charging path planning algorithm combines environmental data to identify possible obstacles in real time and adjusts the path planning accordingly to avoid obstacles encountered during the charging process and ensure that the charging system can operate stably under various environmental conditions.

[0128] By dynamically optimizing load demand, charging point location, and environmental obstacles, the charging path planning algorithm can maximize charging efficiency and reduce charging time while ensuring battery charging safety. For example, under high load conditions, the charging path will automatically adjust to a fast charging path to ensure rapid battery recovery; while under low load, the system will select a slower charging path to optimize energy consumption during charging.

[0129] By combining the battery health analysis results with the internal combustion engine activation decision data, the system can make reasonable adjustments based on the battery health status and real-time environmental conditions to avoid overcharging or over-discharging the battery and extend the battery life. In addition, the environmental obstacle recognition function in path planning can ensure that the charging process is not interfered with by external factors, avoid system failures, and ensure the continuous operation of the gantry crane.

[0130] Since the gantry crane may work in harsh environments, such as high temperature, low temperature, high humidity or places with obstacles, the charging path planning algorithm can respond to these changes in real time to ensure that the charging process is efficient and stable. For example, in a low temperature environment, the system will adjust the charging rate and plan a reasonable path to ensure that the battery charging process is not affected by temperature changes, while also avoiding interference from external obstacles to the system.

[0131] The charging path planning algorithm also considers the relationship between the power output of the internal combustion engine and the battery charging requirements. During the charging path planning process, the activation and power output of the internal combustion engine are dynamically optimized based on the battery health status, load requirements and charging path, ensuring that the collaboration between the battery and the internal combustion engine can optimize the energy distribution of the entire system, avoid energy waste and improve operating efficiency.

[0132] Example: Assuming that in certain working environments, the load demand is large and the charging point is far away, and the battery health has declined, the charging path planning algorithm will automatically select the best path, taking into account the battery health and the timing of the internal combustion engine, quickly reach the charging point, and provide additional support by adjusting the power output of the internal combustion engine to reduce charging time. In addition, the system will also consider possible obstacles in the environment, such as buildings or other equipment, and adjust the path in real time to ensure a smooth charging process.

[0133] The charging path planning algorithm in the present invention performs dynamic optimization by comprehensively considering the battery health analysis results, internal combustion engine activation decision data, load demand, charging point location and environmental obstacle factors. This algorithm not only improves charging efficiency and reduces charging time, but also protects the health of the battery and ensures the stable operation of the system. Through this intelligent optimization system, the gantry crane can adapt to charging needs under different environmental conditions and achieve an efficient, safe and stable charging process.

[0134] like Figure 5 As shown, charging is performed according to the charging path and the charging mode, and the energy distribution between the internal combustion engine and the battery is adjusted in real time.

[0135] Preferably, charging is performed according to the charging path and the charging mode, and the energy distribution between the internal combustion engine and the battery is adjusted in real time. The energy distribution is adjusted based on the current battery power, charging status, output power of the internal combustion engine and load demand. The energy flow between the internal combustion engine and the battery is dynamically adjusted through a closed-loop feedback system to optimize battery health and charging efficiency during the charging process.

[0136] Real-time adjustment of energy distribution is one of the core functions of the present invention. Through intelligent adjustment between the output power of the internal combustion engine and the battery charging demand, it is ensured that the battery charging process can be optimized to the maximum extent under any circumstances. This adjustment mechanism is realized through a closed-loop feedback control system, which can dynamically adjust the energy flow between the battery and the internal combustion engine according to various parameters monitored in real time, thereby maintaining the stability of the system and the charging efficiency.

[0137] The health status of the battery directly affects its performance during the charging process. During the charging process, when the battery health status is poor, it may cause the charging rate to decrease or the internal combustion engine output power to be unstable. Therefore, the present invention monitors the battery status in real time and adjusts the charging mode according to the battery health analysis results. For example, when the battery is over-discharged or has a high temperature, the charging system will automatically adjust the charging rate and switch to a charging mode that is more suitable for the current state of the battery (such as trickle charging mode) to avoid battery damage.

[0138] The remaining power of the battery is a key factor in determining the charging mode and whether the internal combustion engine is enabled. When the remaining power of the battery is insufficient, the charging system will give priority to using the power of the internal combustion engine to provide additional energy support to ensure the continuous charging process. When the battery power is sufficient, the system will gradually reduce the power output of the internal combustion engine, give priority to using the battery for charging, and reduce the use of the internal combustion engine to improve the overall efficiency of the system.

[0139] Changes in charging status and load demand will affect the adjustment of energy distribution. For example, when the load demand is high, the system will increase the output power of the internal combustion engine to supplement the battery's power and ensure that the load demand is met; conversely, when the load demand is low, the system will reduce the use of the internal combustion engine to allow the battery to complete more charging tasks as much as possible. Through this intelligent adjustment, the system can ensure that the battery charging process is efficient and safe while meeting the load demand.

[0140] The closed-loop feedback control system in the present invention is the core of dynamic adjustment. The system receives data from multiple sensors such as batteries, internal combustion engines and loads in real time, and inputs these data into the energy distribution algorithm. According to the feedback data, the system will dynamically adjust the power distribution between the internal combustion engine and the battery to ensure that the system is always in the optimal operating state. This process is real-time and automatic, without manual intervention, which greatly improves the operating efficiency and reliability of the system.

[0141] By dynamically adjusting the energy distribution between the internal combustion engine and the battery, the present invention can effectively reduce the risk of overcharging or discharging, protect the battery from damage, and improve charging efficiency. For example, when the battery power is low and the load demand is high, the output power of the internal combustion engine will be increased, thereby ensuring that the energy flow during the charging process always meets the demand and avoiding damage to the health of the battery due to insufficient charging; and when the battery health is poor or close to full, the charging system will automatically slow down the charging speed and enter the trickle charging mode to maximize the battery life.

[0142] The mechanism of adjusting energy distribution through real-time feedback enables the system to maintain stable operation at all times. When the external load changes or the environmental conditions fluctuate drastically, the system can respond quickly and automatically adjust the power output during the charging process. By precisely controlling the energy flow between the battery and the internal combustion engine, the system can ensure that sufficient power support is always provided under different working conditions to avoid insufficient battery power or overload of the internal combustion engine.

[0143] The present invention avoids energy conflicts between the internal combustion engine and the battery by intelligently optimizing energy distribution, thus maximizing the charging efficiency. At the same time, the output power of the internal combustion engine is not constant, but is dynamically adjusted according to charging demand, load changes, and battery health status. This flexible control method effectively reduces energy waste and improves the energy-saving effect of the overall system.

[0144] During the operation of the gantry crane, the charging environment may be affected by many factors, such as temperature fluctuations, load changes, etc. The present invention can respond quickly based on real-time monitoring data, and dynamically adjust the energy flow between the internal combustion engine and the battery to ensure the efficiency and stability of the battery charging process under complex working conditions. For example, under high load or low temperature conditions, the system will increase the power output of the internal combustion engine to ensure that the charging process is not disturbed by the external environment.

[0145] Example: When the load demand of the gantry crane suddenly increases, the power output of the internal combustion engine will automatically increase to compensate for the situation where the battery cannot provide enough energy. At this time, the system will monitor the health status and remaining power of the battery. If the battery power is low, the system will give priority to using the power of the internal combustion engine to supplement the battery power. When the battery power is sufficient, the system will reduce the participation of the internal combustion engine and rely more on the battery to provide energy. At the same time, the system adjusts the charging rate and charging mode in real time according to the changes in load demand and battery status, thereby ensuring stability and efficiency throughout the charging process.

[0146] Through a closed-loop feedback control system, the present invention can intelligently adjust the energy flow between the internal combustion engine and the battery to ensure the efficiency and safety of the charging process and the long-term health of the battery. Based on dynamic optimization of battery health status, charging requirements, load conditions and environmental changes, the charging process can self-adjust according to real-time feedback to achieve the effect of optimizing battery charging efficiency, reducing energy waste and extending battery life.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0148] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for charging a hybrid gantry crane battery, characterized in that: The following steps are involved: Collect environmental data, battery status data and load data; Based on the battery status data and the environmental data, evaluating the health status of the battery and generating a battery health analysis result; Determining whether to enable an internal combustion engine and generating internal combustion engine enabling decision data according to the battery health analysis result and the load data; Based on the internal combustion engine activation decision data and the battery health analysis result, planning a charging path and adjusting a charging mode to optimize charging efficiency; Charging is performed according to the charging path and the charging mode, and energy distribution between the internal combustion engine and the battery is adjusted in real time.

2. The method for charging a hybrid gantry crane battery according to claim 1, characterized in that: The environmental data includes temperature, humidity, wind speed and air pressure information, and the load data includes load weight, operation mode, operation speed and lifting height information. The data is collected through real-time sensors and monitoring systems, connected to the battery management system, and fed back to the charging control system in real time.

3. The method for charging a hybrid gantry crane battery according to claim 1, characterized in that: Based on the battery status data and the environmental data, the battery health status is evaluated by the battery health analysis model. The battery health analysis model generates the battery health analysis result and predicts the remaining life of the battery by calculating the battery's maximum discharge current, battery internal resistance, temperature distribution, charging history and discharge rate.

4. The method for charging a hybrid gantry crane battery according to claim 3, characterized in that: The battery health analysis results include: The battery health score is calculated using the following formula: in, is the maximum charge capacity of the battery, in Ah; is the current available capacity of the battery, in Ah; is the current internal resistance of the battery, in mΩ; is the maximum internal resistance of the battery, in mΩ.

5. The method for charging a hybrid gantry crane battery according to claim 1, characterized in that: According to the battery health analysis result and the load data, whether to enable the internal combustion engine is determined by the internal combustion engine enabling decision algorithm, and the internal combustion engine enabling decision algorithm calculates the enabling timing and power output by the following formula: in, is the power output of the internal combustion engine in kW; is the remaining battery capacity in kWh; is the total electrical energy required by the gantry crane, in kWh; It is the maximum power of the internal combustion engine, measured in kW.

6. The method for charging a hybrid gantry crane battery according to claim 5, characterized in that: The internal combustion engine activation decision data includes: The activation timing and power output of the internal combustion engine, the power output is calculated by the internal combustion engine activation decision algorithm, and the activation timing is based on the remaining battery power, load demand, charging status and battery health analysis results to determine in real time whether to activate the internal combustion engine.

7. The method for charging a hybrid gantry crane battery according to claim 1, characterized in that: Based on the internal combustion engine activation decision data and the battery health analysis result, the charging path planning algorithm is used to generate a charging path, and the charging path is dynamically optimized by considering load demand, charging point location, and environmental obstacle factors.

8. The method for charging a hybrid gantry crane battery according to claim 7, characterized in that: The charging mode is adjusted according to the battery health status and environmental data, and the adjustment includes: When the battery health analysis result indicates that the battery health state is poor, the charging mode is automatically switched to the trickle charging mode to avoid overheating or overcharging of the battery; When the ambient temperature is low or the load demand is high, the charging mode can be adjusted to fast charging mode to restore the battery power as quickly as possible, but during this process, the charging rate and internal combustion engine power output will be adjusted by real-time feedback to keep the system stable.

9. The method for charging a hybrid gantry crane battery according to claim 1, characterized in that: Charging is performed according to the charging path and the charging mode, and the energy distribution between the internal combustion engine and the battery is adjusted in real time. The energy distribution is adjusted based on the current battery power, charging status, output power of the internal combustion engine and load demand. The energy flow between the internal combustion engine and the battery is dynamically adjusted through a closed-loop feedback system to optimize battery health and charging efficiency during the charging process.

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