Control method and system for hydrogen power battery forklift

Abstract

The invention provides a control method and system for a hydrogen power battery forklift. The method comprises the steps that the driving road condition and the current acceleration of the hydrogen power battery forklift are obtained; adjusting the output power of the motor according to the driving road of the hydrogen power battery forklift and the current acceleration; an output power prediction model is constructed according to the driving data of the hydrogen power battery forklift truck under different road conditions, and the hydrogen power battery forklift truck adjusts the working state of a battery stack of the hydrogen power battery forklift truck according to the output power prediction model; and adjusting the working state of the cell stack, and performing early warning according to the predicted output power and the actual output power. Through the method and the corresponding system, energy waste is reduced, and the overall energy utilization efficiency is improved.

Description

Technical Field

[0001] The invention provides a control method and system for a hydrogen-powered battery forklift, and relates to the technical field of vehicle engineering. Background Art

[0002] Hydrogen-powered batteries usually refer to fuel cells that use hydrogen as the main energy source. This type of battery generates electricity through the electrochemical reaction of hydrogen and oxygen, while only releasing water and a small amount of heat, which is very environmentally friendly. Fuel cells have higher energy efficiency than traditional batteries and internal combustion engines, making them an attractive energy solution for logistics, warehousing and other heavy industries. Forklifts are indispensable equipment in logistics, warehousing, manufacturing and freight industries. Traditional forklifts mainly rely on internal combustion engines or batteries (such as lead-acid batteries or lithium batteries) for driving. However, the emissions generated by internal combustion engine forklifts are harmful to the environment and have low energy efficiency; although electric forklifts are environmentally friendly, their long battery charging time, service life and operation continuity are the main limiting factors. The advantages of hydrogen-powered battery forklifts over traditional electric forklifts are faster refueling time and longer operating endurance. The high power density of hydrogen batteries allows forklifts to operate for a long time without frequent shutdowns for charging, greatly improving work efficiency. In addition, due to its environmentally friendly characteristics, hydrogen-powered forklifts are particularly suitable for modern enterprises pursuing carbon neutrality.

[0003] However, existing hydrogen-powered battery forklifts do not take road conditions into consideration. When going downhill, the output power is the same and is not adjusted according to road conditions. Sometimes, brakes are even required to reduce speed, which greatly increases energy consumption and forklift tire wear. Summary of the invention

[0004] The present invention provides a control method for a hydrogen powered battery forklift to solve the above-mentioned problems:

[0005] The present invention provides a control method for a hydrogen powered battery forklift, the method comprising:

[0006] Obtain the road conditions and current acceleration of the hydrogen-powered battery forklift;

[0007] Adjust the motor output power according to the road the hydrogen-powered battery forklift is traveling on and the current acceleration;

[0008] An output power prediction model is constructed based on the driving data of the hydrogen-powered battery forklift under different road conditions, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model;

[0009] After adjusting the working status of the battery stack, an early warning is issued based on the predicted output power and the actual output power.

[0010] Furthermore, the road conditions and current acceleration of the hydrogen-powered battery forklift are obtained, including:

[0011] The inclination angle sensor and acceleration sensor installed on the hydrogen-powered battery forklift detect the inclination angle and acceleration of the driving road of the hydrogen-powered battery forklift;

[0012] The downhill speed of the hydrogen-powered battery forklift is obtained through the sampling time interval of the acceleration and sensor;

[0013] The driving road inclination angle and downhill speed data are sent to the intelligent control system of the hydrogen-powered battery forklift for processing.

[0014] Furthermore, the output power of the motor is adjusted according to the driving road and current acceleration of the hydrogen-powered battery forklift, including:

[0015] Obtain the friction coefficient of the hydrogen-powered battery forklift according to the road conditions on which the hydrogen-powered battery forklift travels;

[0016] The intelligent control system adjusts the output power of the motor according to the friction coefficient and the current acceleration through a built-in output power adjustment model. Specifically, the output power adjustment model is:

[0017]

[0018] Among them, P motor is the adjusted output power, η is the efficiency coefficient, which takes into account the efficiency of the motor and transmission system, m is the mass of the forklift, g is the acceleration of gravity, θ is the inclination angle of the road on which the hydrogen-powered battery forklift is traveling, ρ is the air density, and C d is the air resistance coefficient, A is the projection area of ​​the forklift in the direction of travel, and V is the downhill speed of the hydrogen-powered battery forklift.

[0019] Furthermore, an output power prediction model is constructed according to the driving road conditions and the output power, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model, including:

[0020] Collect driving data of hydrogen-powered battery forklifts under different road conditions, including road slope, road friction coefficient, forklift driving speed, acceleration and corresponding output power;

[0021] Through deep learning technology, a model is built to predict future output power based on historical data of road conditions and output power. The input of the model is road slope, road friction coefficient, forklift speed and acceleration, and the output is the predicted output power.

[0022] Use the collected historical data to train the model and minimize the prediction error by adjusting the model's parameters;

[0023] During the driving process of the forklift, the current driving road conditions and acceleration data are obtained in real time, and these data are input into the trained prediction model to obtain the predicted output power;

[0024] According to the predicted output power, the working state of the battery stack of the hydrogen-powered battery forklift is dynamically adjusted. When the predicted output power is greater than the current output power, the hydrogen supply of the battery stack is increased. When the predicted output power is less than the current output power, the hydrogen supply of the battery stack is reduced.

[0025] Furthermore, after adjusting the working state of the battery stack, an early warning is issued according to the predicted output power and the actual output power, including:

[0026] Obtaining the actual output power data of the battery stack after adjustment and the output power data predicted by the model, and performing time synchronization between the actual output power data and the predicted output power data;

[0027] Compare the predicted output power with the actual output power and calculate the difference between the two;

[0028] When the difference between the actual output power and the predicted output power exceeds the set warning threshold, a warning signal is triggered.

[0029] The present invention proposes a control system for a hydrogen-powered battery forklift, the system comprising:

[0030] The module for obtaining the driving condition of the forklift is used to obtain the driving road condition and current acceleration of the hydrogen-powered battery forklift;

[0031] An output power adjustment module is used to adjust the output power of the motor according to the driving road and current acceleration of the hydrogen-powered battery forklift;

[0032] A prediction adjustment module, used to construct an output power prediction model based on the driving data of the hydrogen-powered battery forklift under different road conditions, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model;

[0033] The early warning module is used to adjust the working state of the battery stack and issue an early warning based on the predicted output power and the actual output power.

[0034] Furthermore, the module for obtaining the driving condition of the forklift includes:

[0035] The acceleration and tilt angle detection module is used to detect the tilt angle and acceleration of the road on which the hydrogen-powered battery forklift is traveling by using a tilt angle sensor and an acceleration sensor installed on the hydrogen-powered battery forklift;

[0036] A speed acquisition module is used to acquire the downhill speed of the hydrogen-powered battery forklift through the acceleration and the sampling time interval of the sensor;

[0037] The data sending module is used to send the driving road inclination angle and downhill speed data to the intelligent control system of the hydrogen-powered battery forklift for processing.

[0038] Furthermore, it is characterized in that the output power adjustment module includes:

[0039] A friction coefficient acquisition module is used to acquire the friction coefficient of the hydrogen-powered battery forklift according to the road conditions on which the hydrogen-powered battery forklift is traveling;

[0040] The output power calculation module is used for the intelligent control system to adjust the output power of the motor according to the friction coefficient and the current acceleration through the built-in output power adjustment model. Specifically, the output power adjustment model is:

[0041]

[0042] Among them, P motor is the adjusted output power, η is the efficiency coefficient, which takes into account the efficiency of the motor and transmission system, m is the mass of the forklift, g is the acceleration of gravity, θ is the inclination angle of the road on which the hydrogen-powered battery forklift is traveling, ρ is the air density, and C d is the air resistance coefficient, A is the projection area of ​​the forklift in the direction of travel, and V is the downhill speed of the hydrogen-powered battery forklift.

[0043] Furthermore, the prediction adjustment module includes:

[0044] A raw data collection module is used to collect driving data of hydrogen-powered battery forklifts under different road conditions, wherein the driving data includes road slope, road friction coefficient, forklift driving speed, acceleration and corresponding output power;

[0045] A model building module is used to build a model based on the historical data of driving road conditions and output power through deep learning technology to predict future output power. The input of the model is the road slope, road friction coefficient, forklift driving speed and acceleration, and the output is the predicted output power;

[0046] The training and adjustment module is used to train the model using the collected historical data and minimize the prediction error by adjusting the parameters of the model;

[0047] The prediction module is used to obtain the current driving road conditions and acceleration data in real time during the driving process of the forklift, and input these data into the trained prediction model to obtain the predicted output power;

[0048] The battery stack adjustment module is used to dynamically adjust the battery stack working state of the hydrogen-powered battery forklift according to the predicted output power. When the predicted output power is greater than the current output power, the hydrogen supply of the battery stack is increased; when the predicted output power is less than the current output power, the hydrogen supply of the battery stack is reduced.

[0049] Furthermore, the early warning module includes:

[0050] A time synchronization module is used to obtain the actual output power data of the battery stack after adjustment and the output power data predicted by the model, and to synchronize the actual output power data with the predicted output power data;

[0051] A comparison module is used to compare the predicted output power with the actual output power and calculate the difference between the two;

[0052] The trigger warning module is used to trigger a warning signal when the difference between the actual output power and the predicted output power exceeds a set warning threshold.

[0053] The beneficial effects of the present invention are as follows: optimization of energy efficiency, by precisely adjusting the output power of the motor to adapt to specific driving conditions, reducing energy waste and improving overall energy utilization efficiency; improving performance and reliability, maintaining the stability and power performance of the forklift under various road surfaces and loading conditions, increasing operating results and reducing failure rates; extending battery life, by optimizing the charging and discharging process of the battery stack, avoiding overcharging or over-discharging of the battery, and extending the battery life; improving safety and maintenance, by timely discovering and reporting performance deviations through an early warning system, preventing potential safety accidents, and facilitating timely maintenance to prevent larger mechanical failures. Through this technical solution that integrates sensor data, real-time monitoring and intelligent prediction, the operating efficiency and safety of hydrogen-powered battery forklifts can be significantly improved, while reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of a control method for a hydrogen-powered battery forklift according to the present invention. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0058] One embodiment of the present invention provides a control method for a hydrogen powered battery forklift, the method comprising:

[0059] Obtain the road conditions and current acceleration of the hydrogen-powered battery forklift;

[0060] Adjust the motor output power according to the road the hydrogen-powered battery forklift is traveling on and the current acceleration;

[0061] An output power prediction model is constructed based on the driving data of the hydrogen-powered battery forklift under different road conditions, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model;

[0062] After adjusting the working status of the battery stack, an early warning is issued based on the predicted output power and the actual output power.

[0063] The working principle and effect of the above technical solution are as follows: the hydrogen-powered battery forklift is equipped with sensors for real-time monitoring of the conditions of the driving road (such as road surface flatness, slope, etc.) and the current acceleration of the forklift. According to the road conditions and acceleration data, the forklift control system automatically adjusts the output power of the motor. For example, on an uphill road, the control system may increase the output power to maintain the stability and efficiency of the forklift. When driving at a steady speed on a flat road, the output power may be reduced to optimize energy consumption and extend battery life. Using the collected driving data (data for different types of roads and under different acceleration conditions), an output power prediction model is established through machine learning methods; this model can predict the optimal motor power output required under specific driving conditions to achieve optimal energy efficiency and power performance; according to the output of the prediction model, the forklift's battery management system (Battery Management The forklift monitors the difference between the actual motor output power and the power output of the prediction model in real time. If the actual output power is significantly different from the predicted value, the system will issue an early warning to prompt the operator or maintenance system to check for possible problems, such as motor failure, battery performance degradation, or control system errors. Energy efficiency optimization, by accurately adjusting the motor output power to adapt to specific driving conditions, reduce energy waste and improve overall energy efficiency; improve performance and reliability, maintain the stability and power performance of the forklift under various road and loading conditions, increase operation results and reduce failure rates; extend battery life, by optimizing the charging and discharging process of the battery stack, avoid overcharging or overdischarging of the battery, and extend the battery life; improve safety and maintenance, timely detect and notify performance deviations through the early warning system, prevent potential safety accidents, and facilitate timely maintenance to prevent larger mechanical failures. Through this technical solution integrating sensor data, real-time monitoring and intelligent prediction, the operating efficiency and safety of hydrogen-powered battery forklifts can be significantly improved, while reducing operating costs.

[0064] One embodiment of the present invention is a control method for a hydrogen-powered battery forklift, which obtains the driving road conditions and current acceleration of the hydrogen-powered battery forklift, including:

[0065] The inclination angle sensor and acceleration sensor installed on the hydrogen-powered battery forklift detect the inclination angle and acceleration of the driving road of the hydrogen-powered battery forklift;

[0066] The downhill speed of the hydrogen-powered battery forklift is obtained through the sampling time interval of the acceleration and sensor;

[0067] The driving road inclination angle and downhill speed data are sent to the intelligent control system of the hydrogen-powered battery forklift for processing.

[0068] The working principle and effect of the above technical solution are as follows: a tilt angle sensor and an acceleration sensor are set on the hydrogen-powered battery forklift to monitor and detect the tilt angle and acceleration of the forklift under different road conditions in real time. The tilt angle sensor can help determine the safe operation state of the forklift at the same time, and the acceleration sensor provides dynamic data of the movement state of the forklift. The speed of the forklift when going downhill can be calculated by the acceleration data measured by the acceleration sensor and the corresponding sampling time interval. Usually, this speed can be obtained by integrating the acceleration within the time interval, or more simply, by calculating the speed change Δv=a*Δt (where a is the acceleration and Δt is the sampling interval), and the collected tilt angle data and downhill speed data are sent to the intelligent control system of the forklift. The system analyzes the driving state of the forklift based on these real-time data, and can adjust the power output, intervene in the brake system, etc. to adapt to the current driving conditions; improve driving safety, by real-time monitoring of the tilt angle and acceleration of the forklift, the intelligent control system can timely adjust the output power during the tilting process of the forklift to optimize the driving performance. When driving downhill, the output power is accurately Control can optimize the power consumption of the power system to maintain a suitable driving speed and avoid potential risks and power consumption caused by rapid descent; energy efficiency and maintenance cost optimization, through automatic adjustment to adapt to changes in inclination and acceleration, the intelligent control system can help optimize fuel use and reduce unnecessary energy consumption. At the same time, due to smoother operation, it can also reduce mechanical wear and maintenance costs. The collected inclination angle and speed data can be used for long-term performance analysis and predictive maintenance, helping companies to develop more effective operating strategies and maintenance plans. Through this technical integration, hydrogen-powered battery forklifts not only have their driving safety enhanced, but also their overall operational efficiency and performance are improved.

[0069] One embodiment of the present invention is a control method for a hydrogen-powered battery forklift, which adjusts the output power of a motor according to the driving road and current acceleration of the hydrogen-powered battery forklift, including:

[0070] Obtain the friction coefficient of the hydrogen-powered battery forklift according to the road conditions on which the hydrogen-powered battery forklift travels;

[0071] The intelligent control system adjusts the output power of the motor according to the friction coefficient and the current acceleration through a built-in output power adjustment model. Specifically, the output power adjustment model is:

[0072]

[0073] Among them, P motoris the adjusted output power, η is the efficiency coefficient, which takes into account the efficiency of the motor and transmission system, m is the mass of the forklift, g is the acceleration of gravity, θ is the inclination angle of the road on which the hydrogen-powered battery forklift is traveling, ρ is the air density, and C d is the air resistance coefficient, A is the projection area of ​​the forklift in the direction of travel, and V is the downhill speed of the hydrogen-powered battery forklift.

[0074] Considering the downhill speed and acceleration, the formula uses the data measured by the acceleration sensor to calculate the downhill speed. This is because the speed of the forklift will increase due to gravity when going downhill. By measuring the acceleration in real time and combining it with the initial speed, the current downhill speed of the forklift can be accurately estimated. The influence of gravity and slope. The m*g*sin(θ)*V_ part in the formula takes into account the gravity component of the forklift during the downhill process. The slope angle θ is measured by the tilt angle sensor and is used to calculate the gravity component along the slope, which is crucial for determining the power that the motor needs to provide. Considering air resistance, the 0.5*ρ*C_d*A*V^3 part in the formula represents the effect of air resistance on the travel of the forklift. This part takes into account the air density ρ, the air resistance coefficient C_d, the projection area A of the forklift in the travel direction, and the downhill speed V. As the speed increases, the effect of air resistance on the travel of the forklift will also increase. The efficiency coefficient η is introduced into the formula to take into account the actual efficiency of the motor and transmission system. This helps to more accurately estimate the actual power that the motor needs to output. The formula comprehensively considers a variety of factors that affect the power required for a forklift to go downhill, including gravity, slope, air resistance, and motor efficiency, so that the actual required motor output power can be calculated more accurately; safety, by real-time monitoring and calculation of the required power, the motor output power can be adjusted in time to ensure that the forklift maintains stability and safety during the downhill process, avoiding safety problems caused by insufficient or excessive power; energy efficiency, accurate power calculation helps the forklift to use energy more effectively and avoid unnecessary energy waste. By adjusting the motor output power to match the actual needs of the forklift, better energy utilization efficiency can be achieved; adaptability, the parameters in the formula can be adjusted according to the specific specifications and working environment of the forklift, making the formula widely adaptable. Forklifts of different models and qualities and different working environments can get the accurate required power value by adjusting the parameters in the formula; in summary, this formula is designed to ensure the stability, safety and energy efficiency of the forklift during the downhill process. By comprehensively considering multiple factors and adjusting the motor output power in real time, the formula can help the forklift achieve a more optimized operating state.

[0075] One embodiment of the present invention is a control method for a hydrogen-powered battery forklift, which constructs an output power prediction model according to the driving road conditions and the output power, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model, including:

[0076] Collect driving data of hydrogen-powered battery forklifts under different road conditions, including road slope, road friction coefficient, forklift driving speed, acceleration and corresponding output power;

[0077] Through deep learning technology, a model is built to predict future output power based on historical data of road conditions and output power. The input of the model is road slope, road friction coefficient, forklift speed and acceleration, and the output is the predicted output power.

[0078] Use the collected historical data to train the model and minimize the prediction error by adjusting the model's parameters;

[0079] During the driving process of the forklift, the current driving road conditions and acceleration data are obtained in real time, and these data are input into the trained prediction model to obtain the predicted output power;

[0080] According to the predicted output power, the working state of the battery stack of the hydrogen-powered battery forklift is dynamically adjusted. When the predicted output power is greater than the current output power, the hydrogen supply of the battery stack is increased. When the predicted output power is less than the current output power, the hydrogen supply of the battery stack is reduced.

[0081] The working principle and effect of the above technical solution are as follows: during the operation of the hydrogen-powered battery forklift, data about the operating environment and the forklift itself are collected through a variety of sensors, such as road slope, road friction coefficient, driving speed, acceleration and motor output power, and a deep learning model is constructed. A prediction model is constructed using deep learning technology (such as convolutional neural network, recurrent neural network or other suitable machine learning model). The input of this model includes road slope, road friction coefficient, forklift driving speed and acceleration, and the output is the predicted motor output power; the collected historical data is used to train the deep learning model, and the difference between the actual output power and the model predicted power is minimized by adjusting the network parameters and using an appropriate loss function (such as mean square error), real-time prediction and dynamic adjustment, and the current driving data is collected in real time during the operation of the forklift, and input into the trained model to obtain the predicted output power; according to the predicted output power, the working state of the battery stack is dynamically adjusted, for example, the hydrogen supply is increased or decreased through the hydrogen supply system to match the required output power. Optimize energy consumption. Accurate power output prediction makes battery energy utilization more efficient and avoids waste of electrical energy. Improve operating efficiency. Timely adjust the hydrogen supply to ensure the performance of the forklift under various driving conditions, without overloading or overload. Extend equipment life and avoid excessive wear of batteries and motors caused by frequent operation at inappropriate power, thereby extending the maintenance cycle and overall life of the forklift. Improve operational safety. By keeping the forklift output power matching actual needs, the potential risk of accidents caused by unstable or mismatched power output is reduced. Through these technologies, hydrogen-powered battery forklifts can not only maintain efficient operation under various driving conditions, but also maximize resource utilization and optimize operations through intelligent data analysis and dynamic adjustment.

[0082] One embodiment of the present invention is a control method for a hydrogen-powered battery forklift, characterized in that after adjusting the working state of the battery stack, an early warning is given according to the predicted output power and the actual output power, including:

[0083] Obtaining the actual output power data of the battery stack after adjustment and the output power data predicted by the model, and performing time synchronization between the actual output power data and the predicted output power data;

[0084] Compare the predicted output power with the actual output power and calculate the difference between the two;

[0085] When the difference between the actual output power and the predicted output power exceeds the set warning threshold, a warning signal is triggered.

[0086] The working principle and effect of the above technical solution are: real-time monitoring and recording of the actual output power data of the battery stack after adjustment and the output power data predicted by the deep learning model. To ensure the accuracy of the comparison, the two types of data need to be synchronized in time to ensure that the data at the corresponding time points match, so as to accurately evaluate the deviation between the actual output and the predicted output. Use mathematical methods (such as calculating the absolute difference or percentage difference between the two) to evaluate the difference between the actual output power and the predicted output power. This calculation helps identify the deviation between the actual performance and the expected performance of the battery stack under specific operating conditions. Set an early warning threshold that represents the maximum acceptable power deviation range. When the deviation between the actual output power and the predicted output power exceeds this threshold, the system will automatically trigger a warning signal. This early warning signal can be a visual or sound warning, or it can be a notification automatically sent to the monitoring system to prompt the operation and maintenance personnel to check or intervene. Enhanced monitoring and response capabilities. By real-time monitoring and comparing power output, the system can promptly detect abnormal conditions of the equipment, allowing operators or automatic systems to respond quickly, preventing equipment failures from developing into more serious problems and improving the reliability of equipment operation. The early warning system can help avoid damage caused by equipment overload or reduced production efficiency due to the failure of the equipment to reach the required power, thereby improving the operational reliability of the entire equipment and optimizing maintenance and operating costs. Early warning system identification of problems can reduce the possibility of sudden failures, help to carry out maintenance in a more planned manner, reduce the cost of emergency repairs and production losses that may be caused by sudden equipment cessation, data-driven optimization decisions, long-term collection and comparison of actual output and predicted output data can reveal potential patterns and trends, and provide a scientific basis for future operation optimization and equipment adjustment. Through this series of technical measures, hydrogen-powered battery forklifts can achieve smarter energy management, higher operational safety and more reliable equipment performance, ultimately improving overall work efficiency and reducing operating costs.

[0087] One embodiment of the present invention provides a control system for a hydrogen powered battery forklift, the system comprising:

[0088] The module for obtaining the driving condition of the forklift is used to obtain the driving road condition and current acceleration of the hydrogen-powered battery forklift;

[0089] An output power adjustment module is used to adjust the output power of the motor according to the driving road and current acceleration of the hydrogen-powered battery forklift;

[0090] A prediction adjustment module, used to construct an output power prediction model based on the driving data of the hydrogen-powered battery forklift under different road conditions, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model;

[0091] The early warning module is used to adjust the working state of the battery stack and issue an early warning based on the predicted output power and the actual output power.

[0092] The working principle and effect of the above technical solution are as follows: the hydrogen-powered battery forklift is equipped with sensors for real-time monitoring of the conditions of the driving road (such as road surface flatness, slope, etc.) and the current acceleration of the forklift. According to the road conditions and acceleration data, the forklift control system automatically adjusts the output power of the motor. For example, on an uphill road, the control system may increase the output power to maintain the stability and efficiency of the forklift. When driving at a steady speed on a flat road, the output power may be reduced to optimize energy consumption and extend battery life. Using the collected driving data (data for different types of roads and under different acceleration conditions), an output power prediction model is established through machine learning methods; this model can predict the optimal motor power output required under specific driving conditions to achieve optimal energy efficiency and power performance; according to the output of the prediction model, the forklift's battery management system (Battery Management The forklift monitors the difference between the actual motor output power and the power output of the prediction model in real time. If the actual output power is significantly different from the predicted value, the system will issue an early warning to prompt the operator or maintenance system to check for possible problems, such as motor failure, battery performance degradation, or control system errors. Energy efficiency optimization, by accurately adjusting the motor output power to adapt to specific driving conditions, reduce energy waste and improve overall energy efficiency; improve performance and reliability, maintain the stability and power performance of the forklift under various road and loading conditions, increase operation results and reduce failure rates; extend battery life, by optimizing the charging and discharging process of the battery stack, avoid overcharging or overdischarging of the battery, and extend the battery life; improve safety and maintenance, timely detect and notify performance deviations through the early warning system, prevent potential safety accidents, and facilitate timely maintenance to prevent larger mechanical failures. Through this technical solution integrating sensor data, real-time monitoring and intelligent prediction, the operating efficiency and safety of hydrogen-powered battery forklifts can be significantly improved, while reducing operating costs.

[0093] In one embodiment of the present invention, a control system for a hydrogen-powered battery forklift is provided, wherein the module for obtaining the driving condition of the forklift comprises:

[0094] The acceleration and tilt angle detection module is used to detect the tilt angle and acceleration of the road on which the hydrogen-powered battery forklift is traveling by using a tilt angle sensor and an acceleration sensor installed on the hydrogen-powered battery forklift;

[0095] A speed acquisition module is used to acquire the downhill speed of the hydrogen-powered battery forklift through the acceleration and the sampling time interval of the sensor;

[0096] The data sending module is used to send the driving road inclination angle and downhill speed data to the intelligent control system of the hydrogen-powered battery forklift for processing.

[0097] The working principle and effect of the above technical solution are as follows: a tilt angle sensor and an acceleration sensor are set on the hydrogen-powered battery forklift to monitor and detect the tilt angle and acceleration of the forklift under different road conditions in real time. The tilt angle sensor can also help determine the safe operating status of the forklift, while the acceleration sensor provides dynamic data of the forklift's motion status. The acceleration data measured by the acceleration sensor and the corresponding sampling time interval can be used to calculate the speed of the forklift when going downhill. Usually, this speed can be obtained by integrating the acceleration within a time interval, or more simply, by calculating the speed change Δv=a*Δt (where a is the acceleration and Δt is the sampling interval). The collected tilt angle data and downhill speed data are sent to the forklift's intelligent control system, which analyzes the forklift's driving status based on these real-time data, and can adjust the power output, intervene in the braking system, etc. to adapt to the current driving conditions; improve driving safety. By monitoring the forklift's tilt angle and acceleration in real time, the intelligent control system can adjust the output power in time during the forklift's tilting process and optimize driving performance. When driving downhill, the power consumption of the power system can be optimized by accurately controlling the output power to maintain a suitable driving speed and avoid potential risks and power consumption caused by rapid descents; energy efficiency and maintenance cost optimization. By automatically adjusting to adapt to changes in tilt and acceleration, the intelligent control system can help optimize fuel use and reduce unnecessary energy consumption. At the same time, due to smoother operation, it can also reduce mechanical wear and maintenance costs.

[0098] The collected tilt angle and speed data can be used for long-term performance analysis and predictive maintenance, helping companies develop more effective operating strategies and maintenance plans. Through this technology integration, hydrogen-powered battery forklifts not only have their driving safety enhanced, but also their overall operational efficiency and performance are improved.

[0099] In one embodiment of the present invention, a control system for a hydrogen-powered battery forklift, the output power adjustment module comprises:

[0100] A friction coefficient acquisition module is used to acquire the friction coefficient of the hydrogen-powered battery forklift according to the road conditions on which the hydrogen-powered battery forklift is traveling;

[0101] The output power calculation module is used for the intelligent control system to adjust the output power of the motor according to the friction coefficient and the current acceleration through the built-in output power adjustment model. Specifically, the output power adjustment model is:

[0102]

[0103] Among them, P motor is the adjusted output power, η is the efficiency coefficient, which takes into account the efficiency of the motor and transmission system, m is the mass of the forklift, g is the acceleration of gravity, θ is the inclination angle of the road on which the hydrogen-powered battery forklift is traveling, ρ is the air density, and C d is the air resistance coefficient, A is the projection area of ​​the forklift in the direction of travel, and V is the downhill speed of the hydrogen-powered battery forklift.

[0104] In one embodiment of the present invention, a control system for a hydrogen-powered battery forklift, the prediction and adjustment module comprises:

[0105] A raw data collection module is used to collect driving data of hydrogen-powered battery forklifts under different road conditions, wherein the driving data includes road slope, road friction coefficient, forklift driving speed, acceleration and corresponding output power;

[0106] A model building module is used to build a model based on the historical data of driving road conditions and output power through deep learning technology to predict future output power. The input of the model is the road slope, road friction coefficient, forklift driving speed and acceleration, and the output is the predicted output power;

[0107] The training and adjustment module is used to train the model using the collected historical data and minimize the prediction error by adjusting the parameters of the model;

[0108] The prediction module is used to obtain the current driving road conditions and acceleration data in real time during the driving process of the forklift, and input these data into the trained prediction model to obtain the predicted output power;

[0109] The battery stack adjustment module is used to dynamically adjust the battery stack working state of the hydrogen-powered battery forklift according to the predicted output power. When the predicted output power is greater than the current output power, the hydrogen supply of the battery stack is increased; when the predicted output power is less than the current output power, the hydrogen supply of the battery stack is reduced.

[0110] The working principle and effect of the above technical solution are as follows: during the operation of the hydrogen-powered battery forklift, data about the operating environment and the forklift itself are collected through a variety of sensors, such as road slope, road friction coefficient, driving speed, acceleration and motor output power, and a deep learning model is constructed. A prediction model is constructed using deep learning technology (such as convolutional neural network, recurrent neural network or other suitable machine learning model). The input of this model includes road slope, road friction coefficient, forklift driving speed and acceleration, and the output is the predicted motor output power; the collected historical data is used to train the deep learning model, and the difference between the actual output power and the model predicted power is minimized by adjusting the network parameters and using an appropriate loss function (such as mean square error), real-time prediction and dynamic adjustment, and the current driving data is collected in real time during the operation of the forklift, and input into the trained model to obtain the predicted output power; according to the predicted output power, the working state of the battery stack is dynamically adjusted, for example, the hydrogen supply is increased or decreased through the hydrogen supply system to match the required output power. Optimize energy consumption. Accurate power output prediction makes battery energy utilization more efficient and avoids waste of electrical energy. Improve operating efficiency. Timely adjust the hydrogen supply to ensure the performance of the forklift under various driving conditions, without overloading or overload. Extend equipment life and avoid excessive wear of batteries and motors caused by frequent operation at inappropriate power, thereby extending the maintenance cycle and overall life of the forklift. Improve operational safety. By keeping the forklift output power matching actual needs, the potential risk of accidents caused by unstable or mismatched power output is reduced. Through these technologies, hydrogen-powered battery forklifts can not only maintain efficient operation under various driving conditions, but also maximize resource utilization and optimize operations through intelligent data analysis and dynamic adjustment.

[0111] In one embodiment of the present invention, a control system for a hydrogen-powered battery forklift, the early warning module comprises:

[0112] A time synchronization module is used to obtain the actual output power data of the battery stack after adjustment and the output power data predicted by the model, and to synchronize the actual output power data with the predicted output power data;

[0113] A comparison module is used to compare the predicted output power with the actual output power and calculate the difference between the two;

[0114] The trigger warning module is used to trigger a warning signal when the difference between the actual output power and the predicted output power exceeds a set warning threshold.

[0115] The working principle and effect of the above technical solution are: real-time monitoring and recording of the actual output power data of the battery stack after adjustment and the output power data predicted by the deep learning model. To ensure the accuracy of the comparison, the two types of data need to be synchronized in time to ensure that the data at the corresponding time points match, so as to accurately evaluate the deviation between the actual output and the predicted output. Use mathematical methods (such as calculating the absolute difference or percentage difference between the two) to evaluate the difference between the actual output power and the predicted output power. This calculation helps identify the deviation between the actual performance and the expected performance of the battery stack under specific operating conditions. Set an early warning threshold that represents the maximum acceptable power deviation range. When the deviation between the actual output power and the predicted output power exceeds this threshold, the system will automatically trigger a warning signal. This early warning signal can be a visual or sound warning, or it can be a notification automatically sent to the monitoring system to prompt the operation and maintenance personnel to check or intervene. Enhanced monitoring and response capabilities. By real-time monitoring and comparing power output, the system can promptly detect abnormal conditions of the equipment, allowing operators or automatic systems to respond quickly, preventing equipment failures from developing into more serious problems and improving the reliability of equipment operation. The early warning system can help avoid damage caused by equipment overload or reduced production efficiency due to the failure of the equipment to reach the required power, thereby improving the operational reliability of the entire equipment and optimizing maintenance and operating costs. Early warning system identification of problems can reduce the possibility of sudden failures, help to carry out maintenance in a more planned manner, reduce the cost of emergency repairs and production losses that may be caused by sudden equipment cessation, data-driven optimization decisions, long-term collection and comparison of actual output and predicted output data can reveal potential patterns and trends, and provide a scientific basis for future operation optimization and equipment adjustment. Through this series of technical measures, hydrogen-powered battery forklifts can achieve smarter energy management, higher operational safety and more reliable equipment performance, ultimately improving overall work efficiency and reducing operating costs.

[0116] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A control method for a hydrogen powered battery forklift, characterized in that: The method comprises: Obtain the road conditions and current acceleration of the hydrogen-powered battery forklift; Adjust the motor output power according to the road the hydrogen-powered battery forklift is traveling on and the current acceleration; An output power prediction model is constructed based on the driving data of the hydrogen-powered battery forklift under different road conditions, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model; After adjusting the working status of the battery stack, an early warning is issued based on the predicted output power and the actual output power.

2. A control method for a hydrogen powered battery forklift according to claim 1, characterized in that: Obtain the road conditions and current acceleration of the hydrogen-powered battery forklift, including: The inclination angle sensor and acceleration sensor installed on the hydrogen-powered battery forklift detect the inclination angle and acceleration of the driving road of the hydrogen-powered battery forklift; The downhill speed of the hydrogen-powered battery forklift is obtained through the sampling time interval of the acceleration and sensor; The driving road inclination angle and downhill speed data are sent to the intelligent control system of the hydrogen-powered battery forklift for processing.

3. The control method of a hydrogen powered battery forklift according to claim 1, characterized in that: Adjust the motor output power according to the road and current acceleration of the hydrogen-powered battery forklift, including: Obtain the friction coefficient of the hydrogen-powered battery forklift according to the road conditions on which the hydrogen-powered battery forklift travels; The intelligent control system adjusts the output power of the motor according to the friction coefficient and the current acceleration through a built-in output power adjustment model. Specifically, the output power adjustment model is: Among them, P motor is the adjusted output power, η is the efficiency coefficient, which takes into account the efficiency of the motor and transmission system, m is the mass of the forklift, g is the acceleration of gravity, θ is the inclination angle of the road on which the hydrogen-powered battery forklift is traveling, ρ is the air density, and C d is the air resistance coefficient, A is the projection area of ​​the forklift in the direction of travel, and V is the downhill speed of the hydrogen-powered battery forklift.

4. The control method of a hydrogen powered battery forklift according to claim 1, characterized in that: An output power prediction model is constructed according to the driving road conditions and the output power, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model, including: Collect driving data of hydrogen-powered battery forklifts under different road conditions, including road slope, road friction coefficient, forklift driving speed, acceleration and corresponding output power; Through deep learning technology, a model is built to predict future output power based on historical data of road conditions and output power. The input of the model is road slope, road friction coefficient, forklift speed and acceleration, and the output is the predicted output power. Use the collected historical data to train the model and minimize the prediction error by adjusting the model's parameters; During the driving process of the forklift, the current driving road conditions and acceleration data are obtained in real time, and these data are input into the trained prediction model to obtain the predicted output power; According to the predicted output power, the working state of the battery stack of the hydrogen-powered battery forklift is dynamically adjusted. When the predicted output power is greater than the current output power, the hydrogen supply of the battery stack is increased. When the predicted output power is less than the current output power, the hydrogen supply of the battery stack is reduced.

5. The control method of a hydrogen powered battery forklift according to claim 1, characterized in that: After adjusting the working status of the battery stack, an early warning is issued based on the predicted output power and the actual output power, including: Obtaining the actual output power data of the battery stack after adjustment and the output power data predicted by the model, and performing time synchronization between the actual output power data and the predicted output power data; Compare the predicted output power with the actual output power and calculate the difference between the two; When the difference between the actual output power and the predicted output power exceeds the set warning threshold, a warning signal is triggered.

6. A control system for a hydrogen powered battery forklift, characterized in that: The system comprises: The module for obtaining the driving condition of the forklift is used to obtain the driving road condition and current acceleration of the hydrogen-powered battery forklift; An output power adjustment module is used to adjust the output power of the motor according to the driving road and current acceleration of the hydrogen-powered battery forklift; A prediction adjustment module, used to construct an output power prediction model based on the driving data of the hydrogen-powered battery forklift under different road conditions, and the hydrogen-powered battery forklift adjusts the working state of the battery stack of the hydrogen-powered battery forklift according to the output power prediction model; The early warning module is used to adjust the working state of the battery stack and issue an early warning based on the predicted output power and the actual output power.

7. A control system for a hydrogen powered battery forklift according to claim 6, characterized in that: The module for obtaining the driving condition of the forklift includes: The acceleration and tilt angle detection module is used to detect the tilt angle and acceleration of the road on which the hydrogen-powered battery forklift is traveling by using a tilt angle sensor and an acceleration sensor installed on the hydrogen-powered battery forklift; A speed acquisition module is used to acquire the downhill speed of the hydrogen-powered battery forklift through the acceleration and the sampling time interval of the sensor; The data sending module is used to send the driving road inclination angle and downhill speed data to the intelligent control system of the hydrogen-powered battery forklift for processing.

8. The control system of a hydrogen powered battery forklift according to claim 6, characterized in that: The output power adjustment module comprises: A friction coefficient acquisition module is used to acquire the friction coefficient of the hydrogen-powered battery forklift according to the road conditions on which the hydrogen-powered battery forklift is traveling; The output power calculation module is used for the intelligent control system to adjust the output power of the motor according to the friction coefficient and the current acceleration through the built-in output power adjustment model. Specifically, the output power adjustment model is: Among them, P motor is the adjusted output power, η is the efficiency coefficient, which takes into account the efficiency of the motor and transmission system, m is the mass of the forklift, g is the acceleration of gravity, θ is the inclination angle of the road on which the hydrogen-powered battery forklift is traveling, ρ is the air density, and C d is the air resistance coefficient, A is the projection area of ​​the forklift in the direction of travel, and V is the downhill speed of the hydrogen-powered battery forklift.

9. The control system of a hydrogen powered battery forklift according to claim 6, characterized in that: The prediction adjustment module includes: A raw data collection module is used to collect driving data of hydrogen-powered battery forklifts under different road conditions, wherein the driving data includes road slope, road friction coefficient, forklift driving speed, acceleration and corresponding output power; A model building module is used to build a model based on the historical data of driving road conditions and output power through deep learning technology to predict future output power. The input of the model is the road slope, road friction coefficient, forklift driving speed and acceleration, and the output is the predicted output power; The training and adjustment module is used to train the model using the collected historical data and minimize the prediction error by adjusting the parameters of the model; The prediction module is used to obtain the current driving road conditions and acceleration data in real time during the driving process of the forklift, and input these data into the trained prediction model to obtain the predicted output power; The battery stack adjustment module is used to dynamically adjust the battery stack working state of the hydrogen-powered battery forklift according to the predicted output power. When the predicted output power is greater than the current output power, the hydrogen supply of the battery stack is increased; when the predicted output power is less than the current output power, the hydrogen supply of the battery stack is reduced.

10. A control system for a hydrogen powered battery forklift according to claim 6, characterized in that: The early warning module comprises: A time synchronization module is used to obtain the actual output power data of the battery stack after adjustment and the output power data predicted by the model, and to synchronize the actual output power data with the predicted output power data; A comparison module is used to compare the predicted output power with the actual output power and calculate the difference between the two; The trigger warning module is used to trigger a warning signal when the difference between the actual output power and the predicted output power exceeds a set warning threshold.

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