Electric forklift energy consumption optimization control method

Abstract

The invention provides an energy consumption optimization control method for an electric forklift. The method comprises the steps that multi-dimensional state parameters of a battery system, a driving system, a hydraulic system and a load system are collected in real time through a distributed sensing network; identifying a current operation stage based on the multi-mode sensing data, wherein the operation stage comprises no-load movement, load carrying, ramp driving, deceleration braking and fork lifting; and matching a control strategy from the optimization strategy library according to the characteristics of the operation stage, and dynamically adjusting execution parameters. Through fine management of the energy consumption of the electric forklift, the overall energy consumption is effectively reduced, the cruising ability and the operation efficiency of the forklift are improved, meanwhile, the adaptability of the forklift to different operation working conditions is improved, and it is ensured that the good energy consumption control effect and the good operation performance can be kept in various operation stages.

Description

Technical Field

[0001] The present invention belongs to the technical field of forklifts, and in particular relates to an energy consumption optimization control method for electric forklifts. Background Art

[0002] In the field of logistics and handling, electric forklifts are increasingly used. However, there are many problems with the energy consumption control of existing electric forklifts. On the one hand, the high energy consumption leads to increased operating costs and shortened driving time. Traditional control methods make it difficult to carry out refined energy consumption management according to different operation stages and working conditions. On the other hand, the energy recovery efficiency is low, especially in the stages of fork lowering, deceleration and braking, which fail to fully recover and utilize potential energy and kinetic energy. In addition, the existing technology is not adaptable enough to complex working conditions and cannot guarantee energy consumption optimization effects and operation safety in special circumstances such as cargo offset, slippery roads, and low temperature environments. These problems limit the performance and application scope of electric forklifts, and there is an urgent need for a more efficient and intelligent energy consumption optimization control method and system. Summary of the Invention

[0003] The object of the present invention is to provide an energy consumption optimization control method for an electric forklift to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a method for optimizing energy consumption control of an electric forklift, comprising: real-time acquisition of multi-dimensional state parameters of a battery system, a drive system, a hydraulic system, and a load system through a distributed sensor network; identifying the current operation phase based on multimodal sensing data, wherein the operation phase includes empty movement, load handling, ramp driving, deceleration and braking, and fork lifting; matching control strategies from an optimization strategy library according to the characteristics of the operation phase, and dynamically adjusting execution parameters.

[0005] Preferably, the potential energy recovery during the fork lifting and lowering phase includes: establishing a dual-parameter trigger condition of hydraulic pressure and speed, activating the recovery mode when the pressure threshold and the speed safety range are met at the same time; realizing the conversion of potential energy into electrical energy through the coordinated control of the electro-hydraulic proportional valve and the variable hydraulic motor; and correcting the maximum recovery power limit in real time based on the battery health status and charge state.

[0006] Preferably, it also includes: in the case of an emergency stop of the fork, starting a hydraulic shock suppression algorithm by detecting the pressure change rate; when cargo deviation is detected, switching to a mechanical energy dissipation mode and triggering a safety alarm signal.

[0007] Preferably, the control of the deceleration braking stage includes: establishing a three-level braking strategy based on the battery state of charge; smoothly transitioning the braking torque according to the rate of change of the brake pedal opening; the three-level braking strategy includes: a deep discharge area to maximize the regenerative braking intensity, a normal working area to linearly adjust the regenerative braking ratio according to the SOC, a full charge protection area, and enabling pure friction braking.

[0008] Preferably, it also includes: implementing a pulsed energy recovery preheating strategy based on the battery temperature in a low-temperature environment; and automatically entering a low-adhesion road braking mode when the wheel speed difference exceeds a threshold.

[0009] Preferably, it also includes: load identification, detecting load mutations through changes in hydraulic cylinder pressure gradient; using a multi-rate Kalman filter to fuse hydraulic static pressure data and motor dynamic response spectrum; and outputting a load confidence assessment report with a timestamp.

[0010] Preferably, the control of the no-load movement stage includes: retrieving a historical behavior feature database based on an operator ID; and generating an adaptive speed curve according to path planning and real-time positioning.

[0011] Preferably, the control of the slope driving stage includes: real-time calculation of the slope angle through a MEMS inclination sensor; implementation of acceleration limitation based on triple constraints of load, slope and battery status under uphill conditions; and dynamic optimization of the energy recovery intensity curve under downhill conditions.

[0012] Preferably, it also includes: building a digital twin energy consumption simulation platform, including: a virtual sensor model, a multi-physics field coupling algorithm and a hardware-in-the-loop interface module, and realizing offline optimization and online verification of control parameters through the digital twin system.

[0013] An energy consumption optimization control system for an electric forklift, comprising: a control module for implementing the method described above; a hydraulic energy recovery device comprising a reversible hydraulic power unit and an energy buffer module; and a distributed sensor network for real-time acquisition of the multidimensional state parameters described in claim 1.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] The present invention collects multi-dimensional state parameters in real time through a distributed sensor network, and accurately identifies the various operating stages of the forklift based on multimodal perception data, including empty movement, load handling, ramp driving and fork lifting, etc., and then matches the corresponding control strategy from the optimization strategy library according to the characteristics of different operating stages, and dynamically adjusts the execution parameters. Through the refined management of the energy consumption of electric forklifts, the overall energy consumption is effectively reduced, the endurance and operational efficiency of the forklift are improved, and the adaptability of the forklift to different operating conditions is improved, ensuring that good energy consumption control effects and operating performance can be maintained in various operating stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is the overall control flow chart of the present invention.

[0017] Figure 2 This is a flow chart of potential energy recovery for fork lifting of the present invention.

[0018] Figure 3 This is a flow chart of the fork emergency stop and offset processing of the present invention.

[0019] Figure 4 It is a deceleration braking control flow chart of the present invention.

[0020] Figure 5 It is a flow chart of the environmental adaptability processing of the present invention.

[0021] Figure 6 It is a load identification flow chart of the present invention.

[0022] Figure 7 It is the no-load movement optimization flow chart of the present invention.

[0023] Figure 8 It is a flow chart of the slope driving control of the present invention.

[0024] Figure 9 It is the digital twin optimization flow chart of the present invention.

[0025] Figure 10 It is a flow chart of the system architecture of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] Example 1:

[0028] A method for optimizing energy consumption control for electric forklifts includes: real-time acquisition of multi-dimensional state parameters of the battery system, drive system, hydraulic system, and load system through a distributed sensor network; identifying the current operation phase based on multimodal sensing data, including unloaded movement, loaded handling, ramp travel, deceleration and braking, and fork lifting; matching control strategies from an optimization strategy library based on the characteristics of the operation phase, and dynamically adjusting execution parameters. Potential energy recovery during the fork lifting phase includes: establishing dual-parameter trigger conditions for hydraulic pressure and speed, activating the recovery mode when both the pressure threshold and the speed safety range are met; achieving potential energy-to-electricity conversion through coordinated control of an electro-hydraulic proportional valve and a variable hydraulic motor; and real-time correction of the maximum recovery power limit based on the battery health and state of charge. Furthermore, the method includes: activating a hydraulic shock suppression algorithm through pressure change rate detection during an emergency fork stop; and switching to mechanical energy dissipation mode and triggering a safety alarm signal when cargo displacement is detected. Control during the deceleration braking phase includes establishing a three-level braking strategy based on the battery state of charge; smoothly transitioning braking torque based on the rate of change in brake pedal opening; the three-level braking strategy includes a deep discharge zone that maximizes regenerative braking intensity; a normal operating zone that linearly adjusts the regenerative braking ratio based on the state of charge; and a full charge protection zone that activates pure friction braking. Furthermore, in low-temperature environments, a pulsed energy regeneration warm-up strategy is implemented based on battery temperature; and low-adhesion braking mode is automatically entered when the wheel speed difference exceeds a threshold. Furthermore, load identification is implemented by detecting sudden load changes through hydraulic cylinder pressure gradient changes; a multi-rate Kalman filter is used to fuse hydraulic and static pressure data with the motor dynamic response spectrum; and a timestamped load confidence assessment report is output. Control during the unloaded movement phase includes accessing a historical behavior feature database based on operator ID; generating an adaptive speed profile based on path planning and real-time positioning. Control during the hill driving phase includes real-time slope angle calculation using a MEMS inclination sensor; acceleration limits based on the triple constraints of load, slope, and battery state during uphill driving; and dynamic optimization of the energy regeneration intensity profile during downhill driving. It also includes: building a digital twin energy consumption simulation platform, including: virtual sensor model, multi-physics field coupling algorithm and hardware-in-the-loop interface module, and realizing offline optimization and online verification of control parameters through the digital twin system.

[0029] An energy consumption optimization control system for an electric forklift, comprising: a control module for an energy consumption optimization control method for an electric forklift; a hydraulic energy recovery device comprising a reversible hydraulic power unit and an energy buffer module; and a distributed sensor network for real-time acquisition of the multidimensional state parameters described in claim 1.

[0030] Through the above technical solution, the present invention collects multi-dimensional state parameters in real time through a distributed sensor network, and accurately identifies various operating stages of the forklift based on multimodal perception data, including empty movement, load handling, ramp driving and fork lifting, etc., and then matches the corresponding control strategy from the optimization strategy library according to the characteristics of different operating stages, and dynamically adjusts the execution parameters. Through the refined management of the energy consumption of electric forklifts, the overall energy consumption is effectively reduced, the endurance and operational efficiency of the forklift are improved, and the adaptability of the forklift to different operating conditions is improved, ensuring that good energy consumption control effects and operating performance can be maintained in various operating stages.

[0031] Example 2:

[0032] The specific implementation process of this embodiment is as follows: First, a distributed sensor network is deployed across key forklift systems, including voltage and current sensors in the battery management system, speed and torque sensors on the drive motor, pressure and flow sensors in the hydraulic system, and weight distribution sensors on the fork carriage. These sensors collect real-time status parameters such as the battery's charge and discharge status, the motor's operating conditions, pressure changes in the hydraulic system, and the load's weight distribution at a millisecond sampling rate, generating a multi-dimensional real-time monitoring data stream.

[0033] When a forklift begins operation, the central controller receives multimodal sensor data streams from various sensors and identifies the current operation phase using a feature extraction algorithm. During the unloaded movement phase, the system identifies the operation by analyzing the drive motor's speed fluctuations and the battery discharge curve. During the loaded handling phase, the system uses fork weight sensor data and hydraulic system pressure changes for identification. The ramp driving phase is detected using inclination sensor data and sudden increases in motor load. The deceleration braking phase is identified by sudden drops in motor speed and regenerative braking current. The fork lifting phase is determined based on the hydraulic system pressure curve and height sensor data. A corresponding feature vector model is established for each operation phase, enabling accurate classification.

[0034] The system's built-in optimization strategy library stores pre-optimized control strategies for different operating phases. When the system detects the current no-load movement phase, it automatically matches the light-load cruising strategy, appropriately reducing the motor's base speed and optimizing the PWM modulation frequency. During the load-carrying phase, the system activates the dynamic torque distribution strategy, automatically adjusting the motor's output characteristic curve based on the real-time load weight. During the slope driving phase, the slope compensation strategy is triggered, introducing feedforward compensation into the motor control algorithm to offset the effects of the slope. During the deceleration braking phase, the maximum energy recovery strategy is activated, maximizing regenerative braking energy by adjusting the inverter switching timing. During the fork lifting phase, the hydraulic system energy-saving strategy is adopted, intelligently adjusting the pump station output pressure according to the lifting direction. These strategies have been verified through offline simulation and actual vehicle testing to ensure optimal energy consumption control while maintaining operational efficiency.

[0035] During strategy execution, the system continuously monitors the response status of each system and dynamically adjusts control parameters through a closed-loop feedback mechanism. For example, during load handling, if the center of gravity shift is detected, the system will adjust the motor torque distribution ratio in real time. During slope travel, the compensation coefficient is dynamically adjusted based on the actual climbing resistance. During fork lowering, the hydraulic valve opening is fine-tuned based on the actual descent speed to optimize energy recovery efficiency. This dynamic adjustment mechanism ensures that the control strategy can adapt to actual operating conditions and maintain optimal energy efficiency.

[0036] The entire control process forms a complete perception-decision-execution closed loop. Through precise identification of operating phases and intelligent strategy matching, the system optimizes energy consumption throughout the entire operating cycle of the electric forklift. The system places special emphasis on smoothing transitions between operating conditions, employing a gradual parameter adjustment algorithm when switching between operating phases to avoid energy fluctuations or mechanical shock caused by sudden changes in control instructions. An abnormal operating condition detection mechanism is also established, automatically switching to safe mode when sensor data indicates anomalies, maintaining basic energy control functions while ensuring operational safety.

[0037] Example 3:

[0038] During the electric forklift's fork lifting and lowering phase, the system first establishes a dual-parameter trigger condition: hydraulic pressure and speed. When the forks begin to descend, the hydraulic system's pressure sensor monitors the hydraulic cylinder pressure in real time, while the speed sensor measures the fork's descent speed. The control system has preset pressure thresholds and speed safety ranges. When the hydraulic pressure exceeds the set thresholds and the fork's descent speed is within the safe range, the system automatically activates the potential energy recovery mode. This dual-parameter trigger mechanism ensures the safety and reliability of the potential energy recovery process, preventing false triggering or energy recovery under hazardous conditions.

[0039] When potential energy recovery mode is activated, the system achieves energy conversion through the coordinated control of an electro-hydraulic proportional valve and a variable displacement hydraulic motor. The valve dynamically adjusts its opening based on the current hydraulic pressure to control the hydraulic oil flow. Simultaneously, the variable displacement hydraulic motor automatically adjusts its displacement based on system requirements, converting hydraulic energy into mechanical energy. The hydraulic motor drives a generator, converting mechanical energy into electrical energy and feeding it back to the battery system. During this process, the control system monitors hydraulic system parameters in real time and uses a closed-loop control algorithm to maximize energy conversion efficiency while maintaining smooth hydraulic system operation.

[0040] During the energy recovery process, the system continuously monitors the battery's health and state of charge. The battery management system provides health parameters such as battery temperature, internal resistance, cycle count, and current state of charge percentage. Based on these parameters, the control system calculates and adjusts the maximum recovery power limit in real time. When the battery is nearing full charge or the temperature is too high, the system automatically reduces the recovery power. When the battery is in good condition and the state of charge is low, the recovery power is appropriately increased. This dynamic adjustment mechanism ensures energy recovery efficiency while effectively protecting the battery system and extending battery life.

[0041] The entire potential energy recovery process utilizes a layered control architecture. The bottom execution layer is responsible for precise control of the electro-hydraulic proportional valve and variable hydraulic motor; the middle control layer optimizes the energy conversion process; and the upper decision layer adjusts the recovery strategy based on system status. Data exchange between these layers is achieved through a high-speed communication network, ensuring the real-time and accurate execution of control commands. The system also incorporates a safety protection mechanism that immediately exits recovery mode upon detecting an abnormal operating condition, ensuring operational safety.

[0042] In practice, the system continuously optimizes control parameters through historical data learning and adaptive algorithms. It records the efficiency of each potential energy recovery event, analyzes the optimal control parameters under different operating conditions, and gradually refines its control strategy library. Furthermore, the system features self-diagnosis capabilities, capable of identifying anomalies such as hydraulic system leaks and sensor failures and initiating appropriate protective measures.

[0043] This embodiment effectively recovers potential energy during the lowering phase of an electric forklift's forks by combining multi-parameter triggering, coordinated control, and dynamic adjustment. Compared to traditional methods, this significantly improves energy recovery efficiency while ensuring safe and reliable operation under various operating conditions. The entire control process is fully automated, requiring no human intervention, significantly improving the energy efficiency and operational economy of the electric forklift.

[0044] Example 4:

[0045] When an electric forklift performs an emergency stop, the hydraulic system experiences dramatic pressure fluctuations. High-frequency pressure sensors installed in the hydraulic circuit monitor the rate of pressure change in real time. When the rate of pressure change exceeds a preset threshold, the system automatically activates a hydraulic shock suppression algorithm. This algorithm adjusts the opening of the electro-hydraulic proportional valve and the displacement of the variable hydraulic motor to gradually reduce the amplitude of pressure fluctuations in the hydraulic system in a step-by-step manner, effectively suppressing damage caused by hydraulic shock. The system also records the pressure variation characteristics of each emergency stop condition to optimize subsequent suppression parameter settings.

[0046] During an emergency fork stop, the system monitors the cargo's posture in real time using inclination and displacement sensors mounted on the fork carriage. If the horizontal displacement of the cargo exceeds a safety threshold, the system immediately switches to mechanical energy dissipation mode. In this mode, the hydraulic system stops energy recovery and instead dissipates mechanical energy as heat via the hydraulic throttle valve. This also triggers an audible and visual alarm. This alarm signal is then transmitted simultaneously via the CAN bus to the onboard display terminal and remote monitoring platform, prompting operators to promptly check the cargo's status.

[0047] The hydraulic shock suppression algorithm operates in three phases: first, a rapid response phase, in which the system completes initial valve opening adjustment within 50 milliseconds of detecting a sudden pressure change; second, a precise adjustment phase, in which closed-loop control is performed based on real-time pressure feedback to gradually stabilize system pressure; and finally, a recovery phase, in which the system automatically switches back to normal control mode once pressure stabilizes. The entire suppression process utilizes a fuzzy PID control strategy to ensure a balance between response speed and control accuracy.

[0048] The switching logic for mechanical energy dissipation mode is based on multi-sensor data fusion technology. The system comprehensively evaluates factors such as cargo deflection, deflection speed, and the current fork height, and calculates a deflection risk factor using a weighted algorithm. When the risk factor exceeds a critical value, in addition to activating energy dissipation mode, it automatically reduces the fork lift speed and limits the drive motor's output torque, forming a multi-layered safety protection mechanism. The system also records relevant parameters of the deflection event for subsequent operational safety analysis.

[0049] Safety alarm signals are triggered using a tiered warning mechanism. Three levels are assigned based on the severity of cargo displacement: Level 1 triggers only audible and visual alerts; Level 2 restricts some operational functions; and Level 3 forces the forklift into a safety protection state. Alarm signals include data such as timestamps, location information, and device status, facilitating subsequent tracing and analysis. The system also supports real-time push notifications to managers' mobile devices via wireless networks.

[0050] The dual protection mechanisms of hydraulic shock suppression and mechanical energy dissipation effectively address energy optimization and safety issues during forklift emergency stops. The system intelligently identifies hazardous conditions and automatically takes appropriate action, significantly improving equipment safety and reliability while maintaining operational efficiency. The entire control process requires no human intervention, achieving fully automated energy optimization and safety protection.

[0051] Embodiment 5:

[0052] In this energy-optimizing control method for electric forklifts during the deceleration and braking phases, a distributed sensor network first collects the battery system's state of charge (SOC) parameters in real time. When the forklift detects that it has entered the deceleration and braking phase, the control system automatically applies a three-level braking strategy based on the current battery SOC. This strategy divides the battery state into three operating ranges, each employing a differentiated braking control method.

[0053] In the deep discharge region—that is, when the battery's state of charge falls below a preset first threshold—the control system activates maximum regenerative braking mode. At this point, the electric forklift's drive motor switches to generator mode, converting the maximum kinetic energy generated during braking into electrical energy that is fed back to the battery system. In this mode, the friction braking system serves only as an auxiliary braking device, intervening during emergency braking or when regenerative braking capacity is insufficient. This control method maximizes energy recovery when the battery charge is low, extending the forklift's operating time.

[0054] In the normal operating range, when the battery state of charge (SOC) is between the first and second thresholds, the control system employs a linearly regulated regenerative braking strategy. This strategy calculates the optimal regenerative braking ratio in real time based on the current battery SOC, reducing the regenerative braking torque linearly as the battery SOC increases. This is achieved by mapping the battery SOC to a regenerative braking ratio coefficient ranging from 0-100% using a lookup table or real-time calculation. This progressive control approach ensures energy recovery efficiency while preventing damage to the battery due to overcharging.

[0055] In the full-charge protection zone—that is, when the battery state of charge (SOC) exceeds a preset second threshold—the control system automatically switches to pure friction braking mode. Regenerative braking is completely disabled, and all braking demands are handled by the friction braking system. This control method effectively prevents battery overcharging, protects battery health, and extends battery life. The system also continuously monitors the battery state of charge (SOC) and automatically resumes regenerative braking when the SOC returns to the normal operating range.

[0056] For brake torque control, the system uses a high-precision sensor to monitor the brake pedal position and its rate of change in real time. When brake pedal actuation is detected, the control system calculates the target braking torque gradient based on the pedal position change rate, achieving a smooth transition in braking torque. This process involves first establishing a mapping between brake pedal position and target braking torque. The target torque command is then filtered using a first-order inertia factor. The filtering time constant is dynamically adjusted based on the pedal position change rate. When the pedal is pressed quickly, a smaller time constant is used to ensure a quick response; when the pedal is pressed slowly, a larger time constant is used to ensure a smooth torque transition.

[0057] This embodiment also incorporates a safety protection mechanism. When an abnormal operating condition is detected, such as excessive battery temperature or a brake system malfunction, the system automatically switches to safe mode, prioritizing braking performance. Simultaneously, the control system records energy consumption data at each stage and continuously optimizes control parameters using a machine learning algorithm, continuously improving energy efficiency. The entire control process is completed in microseconds, ensuring real-time performance and reliability.

[0058] Example 6:

[0059] During operation, when the ambient temperature sensor detects a low temperature, the control system automatically activates a pulsed energy recovery preheating strategy. This strategy periodically adjusts the regenerative braking intensity to preheat the battery while ensuring braking performance. In practice, the system first monitors the battery temperature. When the temperature falls below a preset threshold, pulsed energy recovery mode is activated. In this mode, the control system alternates between high- and low-intensity regenerative braking at a fixed frequency. The periodic changes in current generate heat within the battery, thereby increasing the battery temperature. As the battery temperature rises, the system gradually reduces the pulse frequency until it exits this mode, ensuring that the battery always operates within the optimal temperature range.

[0060] During braking, the control system continuously monitors the wheel speed signals of each drive wheel. If the speed difference between any two drive wheels exceeds a preset safety threshold, the system automatically determines that low-adhesion road conditions exist and immediately switches to low-adhesion braking mode. In this mode, the control system first reduces the proportion of regenerative braking and increases the participation of friction braking, while dynamically adjusting the braking force distribution between wheels based on the wheel speed difference. Using an electronic brake force distribution algorithm, the system ensures that each drive wheel maintains a similar slip ratio in slippery road conditions, preventing single-wheel locking or skidding. Furthermore, the system appropriately extends the brake response time to maintain vehicle stability through a smooth braking force buildup process.

[0061] When implementing the pulsed energy recovery and preheating strategy, the control system comprehensively considers multiple factors, including battery temperature, state of charge, and braking demand. When the battery temperature is too low but the state of charge is close to full, the system appropriately reduces the pulse intensity to avoid overcharging risks. In emergency braking situations, the system prioritizes braking performance and temporarily suspends the preheating function. The system also coordinates with other vehicle control systems via the CAN bus to ensure that the energy recovery and preheating processes do not affect the normal operation of the forklift.

[0062] During low-adhesion braking mode, the control system evaluates changes in the road's adhesion coefficient in real time. When the wheel speed differential continues to decrease and falls below the recovery threshold, the system gradually increases the regenerative braking ratio, smoothly transitioning to conventional braking mode. This transition utilizes a slope control algorithm to ensure that changes in braking force do not cause vehicle jerk or shock. The system also features a false trigger prevention mechanism that delays mode switching when brief fluctuations in wheel speed differential are detected, preventing frequent mode changes from impacting driving comfort.

[0063] The entire control process is implemented through a distributed controller network, with data exchanged between subsystems via a high-speed bus. The main controller is responsible for strategic decision-making and coordinated control, while the motor controller and brake controller respectively execute specific torque and braking force adjustment commands. The system features a comprehensive safety monitoring mechanism. Upon detecting any abnormality, appropriate protective measures are immediately implemented to ensure safe operation of the forklift under various operating conditions.

[0064] Embodiment seven:

[0065] During electric forklift operation, the load identification module monitors hydraulic system pressure changes in real time via the hydraulic cylinder pressure sensor. As the forklift handles cargo, the hydraulic cylinder pressure exhibits a gradient characteristic that changes with load. The system sets a pressure change threshold. When the pressure change per unit time exceeds the preset threshold, it identifies a sudden load change event. This pressure gradient-based detection method rapidly responds to load changes, avoiding the identification lag associated with sampling delays found in traditional methods.

[0066] The load identification system uses a multi-rate Kalman filter to fuse the hydraulic static pressure data and the motor dynamic response spectrum. Hydraulic system pressure data is collected at a lower sampling frequency, while the motor dynamic response spectrum is acquired at a higher sampling frequency. The multi-rate Kalman filter establishes a state-space model to time-align and weightedly fuse sensor data from different sampling frequencies. The filter first downsamples the high-frequency motor spectrum data and then synchronously fuses it with the hydraulic pressure data. During the fusion process, the system dynamically adjusts the weighting coefficients of each data source based on sensor accuracy and operating conditions. This ensures that the faster-responding motor spectrum data is prioritized in the event of sudden load changes, while prioritizing the accuracy of the hydraulic static pressure data under stable operating conditions.

[0067] After processing with a multi-rate Kalman filter, the system generates a timestamped load confidence assessment report. This report includes three key parameters: the time of the load mutation event, the magnitude of the load change, and the confidence score. The timestamp is accurate to the millisecond level and synchronized with the forklift control system's master clock. The confidence score is calculated based on multiple dimensions, including sensor data consistency, environmental interference, and historical data matching, and is expressed as a continuous value between 0 and 1. The system then grades the load identification results based on the confidence score. High-confidence results are directly used for energy optimization control, medium-confidence results trigger a secondary verification process, and low-confidence results initiate a fault diagnosis process.

[0068] The load confidence assessment report is transmitted in real time via the CAN bus to the energy optimization control module. Based on the load information in the report and the characteristics of the current operation phase, the control module dynamically adjusts the motor output power and hydraulic system pressure setpoints. In the early stages of a sudden load change, the system uses a predictive control algorithm to preemptively adjust power parameters, avoiding the response delay associated with traditional feedback control. When a stable load is detected, the system switches to optimal efficiency control mode, ensuring the motor operates within its optimal efficiency range. This dynamic control strategy, based on precise load identification, effectively addresses the energy waste associated with inaccurate load identification in traditional methods.

[0069] The system also establishes a historical load identification database, storing detailed parameters and corresponding control effects for each sudden load change event. This data is used to offline optimize the parameters of the multi-rate Kalman filter and train the load forecasting model. Over time, the system's load identification accuracy and response speed will gradually improve, forming a virtuous cycle of self-optimization. Furthermore, the database provides maintenance personnel with load trend analysis, helping to identify potential faults in advance.

[0070] Embodiment 8:

[0071] To optimize energy consumption during the unloaded movement phase of an electric forklift, the system first obtains the operator's identification information through a distributed sensor network. When the operator starts the forklift, the onboard control system automatically reads the operator ID and retrieves historical behavioral characteristic data stored in the cloud or local database. This historical behavioral characteristic includes personalized parameters such as the operator's usual acceleration patterns, steering habits, braking frequency, and typical driving speed. The system analyzes this historical data using a machine learning algorithm to build a model of the operator's operating characteristics.

[0072] For route planning, the system combines warehouse map data with real-time positioning information to calculate the optimal path from the current location to the target location. The path planning algorithm considers factors such as path length, number of turns, and obstacle distribution to generate multiple possible routes. Simultaneously, the system uses onboard sensors to monitor the surrounding environment in real time, including the location of other mobile devices and pedestrian activity areas, to dynamically adjust the pre-set path.

[0073] Based on the operator's characteristic model and real-time path information, the system generates an adaptive speed profile. This speed profile not only considers the geometric characteristics of the path but also incorporates the operator's personal habits. For example, for an operator accustomed to steady operation, the system generates a relatively gentle acceleration and deceleration profile; for an experienced operator, the system allows for a faster response. During the speed profile generation process, the system calculates the optimal energy consumption solution in real time, minimizing energy consumption while ensuring operational efficiency.

[0074] During unloaded movements, the system continuously monitors the forklift's actual operating status, including speed, acceleration, steering angle, and other parameters, and compares them with a pre-set adaptive speed curve. If deviations are detected, the system dynamically adjusts control parameters or prompts the operator to correct their behavior. The system also records the actual performance of each operation and updates its database of historical operator behavior characteristics, enabling continuous optimization of control strategies.

[0075] This embodiment combines operator-specific characteristics with real-time path information to achieve precise energy consumption control during the empty movement phase. The system not only adjusts control strategies based on individual operators but also adapts to various warehouse environments and path conditions, significantly reducing energy consumption during the empty movement phase while maintaining operational efficiency. This personalized control approach offers a better balance between operator comfort and energy efficiency than traditional unified control methods.

[0076] Embodiment 9:

[0077] To optimize energy consumption during incline driving, an electric forklift first uses a frame-mounted MEMS tilt sensor to measure the forklift's angle to the horizontal in real time, calculating the slope of the road. This sensor utilizes a triaxial accelerometer and gyroscope, and a Kalman filter algorithm eliminates vibration interference to ensure accurate slope angle measurement. The control system updates slope data at a sampling rate of 100Hz, providing real-time input for subsequent control strategies.

[0078] When an uphill slope is detected, the control system activates a triple constraint mechanism. The first constraint is based on the load weight. The fork load is measured via a pressure sensor and the required driving torque is calculated based on the slope angle. The second constraint considers the current slope and limits the maximum allowable acceleration based on the slope angle. The third constraint monitors the battery status, including parameters such as SOC, temperature, and internal resistance, to prevent overcurrent discharge. The control system integrates these three constraints using a fuzzy logic algorithm to dynamically calculate the optimal acceleration limit, ensuring climbing capability while avoiding battery overload.

[0079] On downhill slopes, the system calculates the theoretically recoverable energy based on the slope angle and load weight, and establishes a dynamic energy recovery intensity curve. This curve utilizes piecewise linearization, initially setting a low recovery intensity to avoid sudden braking and gradually increasing it as vehicle speed increases. When a steeper slope is detected, the system automatically increases the energy recovery intensity while maintaining a stable speed through PID control. The recovered energy is efficiently stored in the power battery via a bidirectional DC-DC converter.

[0080] In slope transition zones, the system incorporates transition control logic. When the slope angle exceeds a set threshold, the controller smoothly switches between uphill and downhill modes within 0.5 seconds, preventing sudden power output changes. Simultaneously, the system communicates with the vehicle control system via the CAN bus to coordinate motor torque distribution and hydraulic system pressure to ensure driving stability.

[0081] For complex slope conditions, the system features a memory function that records the slope change trend over the last 10 seconds, predicting upcoming slope changes and adjusting control parameters in advance. This function significantly reduces control delays and improves energy recovery efficiency when encountering continuously undulating roads.

[0082] In special circumstances, such as when a slippery road surface or low temperature is detected, the system automatically reduces energy recovery intensity and increases mechanical braking participation to prevent tire slippage. At the same time, the driver is reminded through the HMI interface to pay attention to slope driving safety.

[0083] The entire hill-climbing control process utilizes a Model Predictive Control (MPC) framework, with a two-second prediction horizon and rolling optimization of control parameters. The system recalculates the optimal control variables every 50 milliseconds, ensuring optimal energy efficiency under all hill conditions. All control parameters and operating data are recorded in onboard memory for subsequent analysis and strategy optimization.

[0084] Embodiment 10:

[0085] The implementation method of this embodiment in constructing a digital twin energy consumption simulation platform is as follows: the platform consists of three parts: a virtual sensor model, a multi-physics field coupling algorithm, and a hardware-in-the-loop interface module. By establishing a high-fidelity digital twin system, the offline optimization and online verification functions of the control parameters are realized.

[0086] The virtual sensor model accurately replicates the characteristics of various physical sensors on an actual forklift through mathematical modeling, including current sensors, voltage sensors, temperature sensors, pressure sensors, and accelerometers. This model simulates the output characteristics of real sensors under different operating conditions, including signal noise, sampling frequency, and measurement error. Within the digital twin environment, the virtual sensor model provides energy consumption simulations with multi-dimensional state parameter inputs consistent with those of the actual vehicle, ensuring comparability between simulation data and actual operating data.

[0087] The multi-physics coupling algorithm is the core computing engine of the digital twin energy consumption simulation platform. Using finite element analysis, the algorithm establishes a multi-physics coupling model for each system of the electric forklift. This model includes an electrochemical-thermal coupling model for the battery system, an electromechanical coupling model for the drive system, a fluid-mechanical coupling model for the hydraulic system, and a dynamic model for the load system. By solving the simultaneous equations of these coupled models, the algorithm accurately simulates the energy flow and conversion process of the forklift in various operating phases. In particular, the algorithm accounts for energy loss mechanisms unique to different operating phases, such as rolling resistance during unloaded movement, potential energy changes during load handling, and the influence of gravity when traveling on slopes.

[0088] The hardware-in-the-loop interface module enables bidirectional data exchange between the digital twin system and the actual forklift control system. This module, which includes a real-time communication protocol stack, a data format converter, and signal conditioning circuitry, converts virtual signals in the simulation environment into electrical signals recognizable by the actual controller and feeds the controller's output instructions back to the digital twin system. Through this module, the digital twin system can receive commands from the actual controller in real time and transmit simulation results back to the controller, forming a complete hardware-in-the-loop test loop.

[0089] During the offline optimization phase, the digital twin system evaluates the energy consumption performance of different control strategies through batch simulation testing. The system automatically generates test scenarios encompassing a variety of typical operating conditions and boundary conditions, such as varying loads, slopes, and speed combinations. For each test scenario, the system executes multiple candidate control strategies in parallel, recording their respective energy consumption data and operating state parameters. Based on this simulation data, the system employs a multi-objective optimization algorithm to identify the control parameter combination with the lowest energy consumption while meeting operational performance requirements. The optimized results are then stored in the optimization strategy library.

[0090] During the online verification phase, the digital twin system operates synchronously with the actual forklift control system. The system receives real-time operating data from the actual forklift and creates a fully corresponding digital twin in the virtual environment. By comparing actual operating data with simulated predictions, the system continuously evaluates the effectiveness of the current control strategy. If actual energy consumption deviates from expected values, the system automatically initiates a parameter adjustment process, reoptimizing control parameters based on the latest operating data and deploying the updated parameters to the actual controller via the hardware-in-the-loop interface.

[0091] The implementation of this digital twin energy consumption simulation platform enables continuous optimization and adjustment of electric forklift energy consumption control strategies based on actual operating conditions. The platform ensures the authenticity of simulation data through virtual sensor models, ensures the accuracy of energy consumption calculations through multi-physics coupling algorithms, and enables seamless switching of control strategies through hardware-in-the-loop interfaces. This closed-loop optimization mechanism significantly improves the system's adaptability and control performance, enabling the forklift to maintain optimal energy consumption performance under various operating conditions.

[0092] Example 11:

[0093] The system of the present invention primarily consists of three components: a control module, a hydraulic energy recovery device, and a distributed sensor network. The control module, serving as the system's core processing unit, is responsible for executing the control methods of Examples 1 through 10. This module utilizes a high-performance embedded processor with a built-in multi-threaded real-time operating system, capable of parallel processing of multidimensional status data from the sensor network and rapidly generating optimal control instructions. The control module communicates with the forklift's various actuators via the CAN bus, enabling precise energy consumption control.

[0094] The hydraulic energy recovery device is a key energy management component of the system, consisting of a reversible hydraulic power unit and an energy buffer module. The reversible hydraulic power unit utilizes a bidirectional variable pump / motor structure, capable of switching to generator mode when the forks are lowered or the vehicle is braked, converting mechanical energy into hydraulic energy. The energy buffer module, comprising a high-pressure accumulator and an intelligent regulating valve group, stores recovered energy and releases it when needed. Working in conjunction with the control module, the device dynamically adjusts energy recovery and release strategies based on the operation phase, significantly improving energy utilization efficiency.

[0095] A distributed sensor network is deployed at key locations throughout the forklift, including the drive system, hydraulic system, steering system, and fork mechanism. This network, comprised of multiple high-precision sensor nodes, collects real-time data on multiple parameters, including motor speed, hydraulic pressure, fork height, vehicle acceleration, and steering angle. The sensor nodes utilize wireless transmission technology to aggregate data to the control module via a self-organizing network. The network features adaptive sampling frequency adjustment, dynamically adjusting data collection density based on changing operating conditions to ensure comprehensive and accurate operational status information.

[0096] During system startup, the control module first completes initialization, including hardware self-tests, parameter loading, and communication link establishment. After initialization, the hydraulic energy recovery device enters standby mode, the accumulator is precharged to operating pressure, and the hydraulic valves are calibrated. The distributed sensor network then begins operating, with each sensor node collecting data at a preset sampling frequency and transmitting it to the control module via a network protocol.

[0097] After receiving real-time data, the control module first performs data fusion processing to eliminate sensor noise and interference and extract valid state features. It then identifies the current operation phase based on multi-dimensional parameters, including unloaded movement, loaded handling, ramp travel, or fork lift. For each operation phase, the module selects the optimal control solution from a built-in optimization strategy library, including motor drive parameters, hydraulic system pressure, and energy recovery intensity. During strategy execution, the system continuously monitors state changes and dynamically adjusts control parameters to ensure optimal energy consumption performance under various operating conditions.

[0098] During the fork's descent phase, the system activates energy recovery mode. The control module precisely adjusts the operating mode of the reversible hydraulic power unit based on the fork's load and descent speed, converting gravitational potential energy into hydraulic energy storage. When a braking signal is detected, the system coordinates motor regenerative braking and hydraulic energy recovery to maximize kinetic energy recovery efficiency. When driving on slopes, the system comprehensively analyzes factors such as slope, load, and speed to optimize motor output torque, while also utilizing energy stored in the accumulator to assist the drive and reduce battery consumption.

[0099] The system also has the ability to handle abnormal operating conditions. When sensors detect unusual conditions such as cargo displacement or slippery road conditions, the control module automatically switches to safety-first mode, optimizing energy consumption while ensuring operational safety. During system operation, all key parameters and control decisions are recorded in non-volatile memory, providing data support for subsequent performance analysis and strategy optimization. Through this intelligent closed-loop control, the system significantly improves the electric forklift's energy efficiency and extends battery life, while ensuring operational safety and stability under various conditions.

[0100] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0101] The above description is only used to illustrate the technical solution of the present invention and is not intended to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing energy consumption of an electric forklift, characterized in that: include: Real-time acquisition of multi-dimensional status parameters of the battery system, drive system, hydraulic system and load system through a distributed sensor network; Identify the current operation phase based on multimodal sensing data, including unloaded movement, loaded handling, ramp travel, deceleration and braking, and fork lifting; Match control strategies from the optimization strategy library according to the characteristics of the operation stage and dynamically adjust execution parameters.

2. The method for optimizing energy consumption of an electric forklift according to claim 1, characterized in that: The potential energy recovery during the fork lifting and lowering phase includes: establishing a dual-parameter trigger condition of hydraulic pressure and speed, activating the recovery mode when the pressure threshold and speed safety range are simultaneously met; realizing the conversion of potential energy into electrical energy through the coordinated control of the electro-hydraulic proportional valve and the variable hydraulic motor; and correcting the maximum recovery power limit in real time based on the battery health status and charge state.

3. The method for optimizing energy consumption of an electric forklift according to claim 2, wherein: Also includes: In the case of an emergency stop of the fork, the hydraulic shock suppression algorithm is activated by detecting the pressure change rate; When cargo deviation is detected, it switches to mechanical energy dissipation mode and triggers a safety alarm signal.

4. The method for optimizing energy consumption of an electric forklift according to claim 1, characterized in that: The control of the deceleration braking stage includes: establishing a three-level braking strategy based on the battery state of charge; smoothly transitioning the braking torque according to the rate of change of the brake pedal opening; The three-level braking strategy includes: a deep discharge zone to maximize the regenerative braking intensity, a normal working zone to linearly adjust the regenerative braking ratio according to the SOC, and a full charge protection zone to enable pure friction braking.

5. The method for optimizing energy consumption of an electric forklift according to claim 4, characterized in that: Also includes: In low-temperature environments, a pulsed energy recovery preheating strategy is implemented based on the battery temperature; When the wheel speed difference exceeds the threshold, the vehicle automatically enters low-adhesion road braking mode.

6. The method for optimizing energy consumption of an electric forklift according to claim 1, characterized in that: Also includes: Load identification, detecting sudden load changes through changes in hydraulic cylinder pressure gradients; A multi-rate Kalman filter is used to fuse the hydrostatic pressure data and the motor dynamic response spectrum; Outputs a load confidence assessment report with a timestamp.

7. The method for optimizing energy consumption of an electric forklift according to claim 1, characterized in that: The control of the no-load movement stage includes: retrieving a historical behavior feature database based on an operator ID; and generating an adaptive speed curve based on path planning and real-time positioning.

8. The method for optimizing energy consumption of an electric forklift according to claim 1, characterized in that: The control of the slope driving stage includes: real-time calculation of the slope angle through a MEMS inclination sensor; implementation of acceleration limits based on the triple constraints of load, slope, and battery status under uphill conditions; and dynamic optimization of the energy recovery intensity curve under downhill conditions.

9. The method for optimizing energy consumption of an electric forklift according to claim 1, characterized in that: Also includes: Build a digital twin energy consumption simulation platform, including: virtual sensor model, multi-physics field coupling algorithm and hardware-in-the-loop interface module, and realize offline optimization and online verification of control parameters through the digital twin system.

10. An energy consumption optimization control system for an electric forklift, characterized in that: include: A control module, configured to implement the method according to any one of claims 1 to 9; A hydraulic energy recovery device comprising a reversible hydraulic power unit and an energy buffer module; A distributed sensor network is used to collect the multi-dimensional state parameters described in claim 1 in real time.

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