Electro-hydraulic composite drive engineering machinery slewing system and control method thereof

Abstract

The application provides an electro-hydraulic composite driving engineering machinery rotating system and a control method thereof, and relates to the technical field of engineering machinery.The rotating system comprises a loading motor pump, an oil supplement motor pump, a safety overflow valve group, a first / second accumulator control valve, an accumulator, a four-quadrant pump / motor, a clutch, a series motor and a controller.The control method collects multiple source operation signals, identifies the whole machine working condition by using an LSTM-Transformer hybrid model, and distributes the series motor and the hydraulic driving torque according to the working condition, so as to control the action of each executing element.The application can realize the accurate capture and reuse of the rotating braking energy by using the electro-hydraulic deep coupling architecture and the intelligent control strategy, significantly improves the system energy efficiency and prolongs the endurance time.In addition, the application of deep learning improves the scientific nature of power distribution, and guarantees the stability, reliability and operation smoothness of the system operation under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of engineering machinery technology, and more specifically, to an electro-hydraulic hybrid drive engineering machinery rotation system and its control method. Background Technology

[0002] With the global energy transition and increasingly stringent environmental requirements, the electrification of construction machinery has become an inevitable trend in the industry. Currently, the electrification technology for construction machinery is still in its early stages. Existing solutions typically use electric motors to replace internal combustion engines to drive the original hydraulic transmission system. While this power source replacement achieves energy savings to some extent, the inherent low efficiency of hydraulic transmission systems and the difficulty in effectively recovering gravitational potential energy and inertial energy during rotational operation result in high overall power consumption. This not only necessitates larger capacity battery packs but also severely limits the overall operating time of the machine.

[0003] Although electro-hydraulic hybrid drive systems are widely recognized as an ideal solution for improving the energy efficiency of working devices in construction machinery and realizing energy recovery, there is still a lack of mature electro-hydraulic hybrid drive systems and their supporting control methods that can be widely applied to the complex working conditions of construction machinery, both domestically and internationally.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The present invention aims to provide an electro-hydraulic hybrid drive system for the rotation of engineering machinery and its control method, so as to solve the shortcomings of existing hydraulic transmission systems such as low efficiency and difficulty in energy recovery.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0007] An electro-hydraulic hybrid drive system for slewing engineering machinery, comprising:

[0008] Loading motor pump 1, replenishing oil motor pump 2, safety relief valve group 3, first accumulator control valve 4, second accumulator control valve 5, accumulator 6, four-quadrant pump / motor 7, clutch 8, series motor 9 and controller 10;

[0009] The upper oil port of the loaded motor pump 1 is connected to the upper oil port of the four-quadrant pump / motor 7, and the lower oil port of the loaded motor pump 1 is connected to the lower oil port of the four-quadrant pump / motor 7, forming a closed main drive circuit.

[0010] The oil inlet of the make-up motor pump 2 is connected to the system oil tank, and the oil outlet of the make-up motor pump 2 is connected to the high-pressure side and the low-pressure side of the main drive circuit respectively through the safety relief valve group 3.

[0011] The first accumulator control valve 4 is connected between the upper oil port of the four-quadrant pump / motor 7 and the accumulator 6, and the second accumulator control valve 5 is connected between the lower oil port of the four-quadrant pump / motor 7 and the accumulator 6.

[0012] The output shaft of the series motor 9 is mechanically connected to the power input end of the four-quadrant pump / motor 7 via the clutch 8;

[0013] The controller 10 is electrically connected to the series motor 9, the loading motor pump 1, the clutch 8, the first accumulator control valve 4, and the second accumulator control valve 5, respectively.

[0014] Preferably, the controller 10 has multiple analog and digital input interfaces for connecting to a sensor network including a pressure sensor, a speed sensor, a torque sensor, and a handle signal line;

[0015] The controller 10 has a multi-channel pulse width modulation output interface or a bus communication interface for driving the motor controller, proportional valve amplifier and clutch actuator.

[0016] A control method for an electro-hydraulic hybrid drive slewing system of engineering machinery, wherein the control method is executed by the controller 10, comprising:

[0017] S1, real-time acquisition of multi-source timing signals of the system through sensor network; wherein, the multi-source timing signals include: rotary system handle signal, pressure values ​​at the inlet and outlet of four-quadrant pump / motor 7, real-time torque and speed of series motor 9, real-time torque and speed of loading motor pump 1, pressure value at the outlet of accumulator 6, and pressure values ​​of the large and small chambers of the actuator cylinder of the engineering machinery.

[0018] S2, map the rotary system handle signal to the system demand target speed function, and after normalizing the multi-source time sequence signal, input it into the preset LSTM-Transformer hybrid recognition model for working condition recognition, and output the working condition recognition result;

[0019] S3, calculate the total required torque based on the working condition identification result, and decompose the total required torque into a series motor drive torque function and a hydraulic drive torque function;

[0020] S4. Based on the hydraulic drive torque function and the multi-source timing signal, the target control command is calculated and sent to each actuator to drive the rotary system to reach the target operating speed and perform energy recovery.

[0021] Preferably, the slewing system handle signal is mapped to a system-required target speed function, as shown in the formula:

[0022] ;

[0023] in, To set the target speed for the motor pump 1; This is the speed calibration coefficient for the rotary system; For handle signals; Indicates the current moment.

[0024] Preferably, the LSTM-Transformer hybrid recognition model adopts a cascaded structure of LSTM layer, Transformer layer and fully connected layer to perform local feature extraction, global correlation modeling and working condition classification on the normalized time series signal obtained by normalization processing;

[0025] The LSTM layer is used to extract local temporal dependency features of the standardized time-series signal; the Transformer layer is used to model global feature association based on self-attention mechanism and output global fusion features; the fully connected layer is used to pool and classify the global fusion features and output working condition recognition results.

[0026] Preferably, when extracting the local temporal dependency features of the standardized time-series signal through the LSTM layer, the LSTM gate state is calculated, and the hidden layer state sequence containing the local action logic is output, i.e., the local temporal dependency features, specifically:

[0027] Based on the features of the current input and the hidden state at the previous time step, the forget gate of the LSTM layer discards the portion of historical information whose correlation with the current task is lower than a set threshold. The formula is as follows:

[0028] ;

[0029] in, Output for the forget gate; Use the Sigmoid activation function; Here is the forget gate weight matrix; This represents the hidden state of the LSTM layer at the previous time step. Input signal for the current moment; This is the forget gate bias vector;

[0030] The newly added features at the current time step are updated into the cell state through the input gate of the LSTM layer, as shown in the formula:

[0031] ;

[0032] ;

[0033] in, For input gate output; The input gate weight matrix; The input gate bias vector; For candidate cell states, record the latest information at the current moment; This is the candidate cell state weight matrix; This is the candidate cell state bias vector; It is the hyperbolic tangent activation function;

[0034] Next, the outputs of the forget gate and the input gate are combined to update the cell state, using the following formula:

[0035] ;

[0036] in, , These represent the cell states at the current and previous moments, respectively. For element-wise multiplication;

[0037] By controlling the degree to which cell states are output to the hidden layer through the output gate, local temporal dependency features are obtained, as shown in the formula:

[0038] ;

[0039] ;

[0040] ;

[0041] in, Output gate output; , These are the weight matrix and bias vector of the output gate, respectively; This represents the current hidden state of the LSTM layer. The final output features of the LSTM layer; For a moment The hidden state of the LSTM layer; The length of the sampling sequence.

[0042] Preferably, the process of outputting global fusion features through the self-attention mechanism of the Transformer layer is as follows:

[0043] First, the final output features of the LSTM layer Mapped to query vector Key vector AND value vector ;

[0044] Then, global attention weights between signals at different time steps are calculated using multi-head self-attention to capture multi-scale global features in parallel. The formula is as follows:

[0045] ;

[0046] ;

[0047] in, For the first Single-head attention output; It is a self-attention function; , , The first When calculating single-head attention , , The weights;

[0048] For multi-head attention output; Indicates feature concatenation operation; For the number of attention heads; For multi-head output projection weight matrix;

[0049] Multi-head attention output and final output features of LSTM layer By applying residual connections and layer normalization, the global fusion feature is output, as shown in the formula:

[0050] ;

[0051] ;

[0052] in, Output for residual connection and layer normalization processing; For layer normalization operation; It is a feedforward neural network; This is a global fusion feature.

[0053] Preferably, the process of pooling and classifying the global fused features through the fully connected layer to output the working condition identification result is as follows:

[0054] The globally fused features are fed into a fully connected layer, and the probability distribution of each preset working condition category is output through a normalized exponential function, as shown in the formula:

[0055] ;

[0056] in, For the first The probability of a particular type of working condition; This represents the number of operating condition categories for the electro-hydraulic system. For the first The fully connected layer output corresponding to the type of working condition; It is an exponential function;

[0057] The working condition corresponding to the maximum probability in the probability distribution is selected as the working condition identification result of the current whole machine operation.

[0058] Preferably, the expression for the total required torque is:

[0059] ;

[0060] in, for Total torque required for rotation at any given moment; for External load torque at any given moment; It is the equivalent moment of inertia; Target angular acceleration; Indicates to Find the differential; The target angular velocity; This is the equivalent damping coefficient;

[0061] Based on the operating condition identification results, the total required torque is divided into series motor drive torque and hydraulic drive torque to achieve optimal allocation, as expressed in the following expression:

[0062] ;

[0063] ;

[0064] in, for The target driving torque of the series motor at any given moment; This is the motor drive torque distribution coefficient; for The hydraulic drive torque at any given moment; The hydraulic drive torque distribution coefficient is negative when the working condition identification result is an energy recovery working condition.

[0065] Preferably, the target control command includes: the target displacement of the four-quadrant pump / motor 7, the liquid flow rate and valve opening of the first accumulator control valve 4 or the second accumulator control valve 5, and the target rotational speed of the loading motor pump 1.

[0066] The formula for calculating the target displacement of the four-quadrant pump / motor 7 is as follows:

[0067] ;

[0068] ;

[0069] in, for The displacement command that needs to be adjusted at all times, i.e. the target displacement of the four-quadrant pump / motor; for The hydraulic drive torque at any given moment; for Constantly monitored four-quadrant pump / motor differential pressure; For the four-quadrant pump / motor drive efficiency; The inlet pressure of the four-quadrant pump / motor; The outlet pressure of the four-quadrant pump / motor;

[0070] The formula for calculating the liquid flow rate of the first accumulator control valve 4 or the second accumulator control valve 5 is as follows:

[0071] ;

[0072] in, for The liquid flow rate of the accumulator control valve at all times; For flow coefficient; for The flow area of ​​the valve is controlled at all times, i.e., the valve opening. for The pressure difference across the valve at any given time is the difference between the accumulator pressure and the circuit pressure. The density of the oil;

[0073] The formula for calculating the valve opening degree of the first accumulator control valve 4 or the second accumulator control valve 5 is as follows:

[0074] ;

[0075] in, This is the area-opening ratio coefficient, determined by the valve port structure; This is the valve opening control signal, with a value of 0 or 1, where 0 means fully closed and 1 means fully open;

[0076] The formula for calculating the target speed of the motor pump 1 is as follows:

[0077] ;

[0078] ;

[0079] in, for The target rotational speed of motor pump 1 is constantly being loaded; for Target flow rate on the pump side at all times; For hydraulic volumetric efficiency; for Real-time four-quadrant pump / motor displacement; for The set target speed of motor pump 1 is constantly applied.

[0080] This invention also provides an electro-hydraulic hybrid drive engineering machinery, including a chassis, a slewing platform mounted on the chassis, and the aforementioned electro-hydraulic hybrid drive engineering machinery slewing system that drives the slewing platform to rotate relative to the chassis. By integrating the above system, this engineering machinery achieves efficient energy management under various typical working conditions such as excavation, loading, and leveling.

[0081] Preferably, the controller has a built-in central processing unit and a storage unit, and the storage unit executes computer program instructions that implement the control method of the electro-hydraulic hybrid drive engineering machinery rotary system as described above.

[0082] The present invention also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor of the device on which the computer-readable storage medium is located, implement a control method for an electro-hydraulic hybrid drive engineering machinery rotation system as described above.

[0083] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0084] First, the system's energy efficiency is significantly improved, and its range is greatly enhanced. This invention leverages the dual advantages of the high dynamic response characteristics of a series motor direct drive and the high power density of a hydraulic system by constructing an electro-hydraulic composite drive architecture. The introduction of a four-quadrant pump / motor and accumulator allows the system to convert inertial energy into hydraulic energy stored in the accumulator during the slewing braking phase, instead of consuming energy through the relief valve. During subsequent start-up or acceleration phases, the stored energy is released through the accumulator control valve to assist the motor in driving the slewing, thereby reducing the power output of the power battery. Experimental data shows that this energy recovery mechanism can reduce the energy consumption of the slewing system per cycle in loading operations with frequent slewing, directly translating into an extension of the overall machine's range.

[0085] Secondly, the invention features high accuracy in identifying operating conditions and scientific power allocation. It introduces an LSTM-Transformer hybrid deep learning model, overcoming the limitations of traditional logic gating methods in handling complex and random operating environments. This model can simultaneously extract local transient features and global cyclic features during the operation, achieving extremely high accuracy in identifying operating conditions such as excavation, leveling, loading, and dumping. Based on accurate operating condition identification, the controller can match the optimal motor and hydraulic torque allocation ratio for each type of action, ensuring the power system always operates within a high-efficiency range and avoiding unnecessary losses caused by power redundancy.

[0086] Third, the system's operational stability and reliability are guaranteed. This invention solves the cavitation and overpressure problems that may occur in closed-loop systems when frequently switching between drive and recovery modes by setting up an independent replenishing oil pump and safety relief valve assembly. The replenishing system can automatically compensate for oil pressure fluctuations in the main circuit, maintaining the stability of the system pressure reference. Simultaneously, the presence of the clutch provides a physical decoupling mechanism, allowing emergency actions to be performed via the other power source even in the event of a partial failure on either the hydraulic or electrical system, thus enhancing the survivability of the construction machinery in extreme operating environments.

[0087] Fourth, operational smoothness and control precision are significantly improved. This invention achieves deep coupling control of the speed loop and torque loop through synchronous and coordinated control of the load motor pump speed, four-quadrant pump / motor displacement, and series motor torque. During the slewing start-up phase, the motor provides rapid torque response; during the constant speed phase, the hydraulic system provides stable power support; and during the braking phase, smooth deceleration is achieved by adjusting the accumulator control valve opening. This multi-source power coordinated control method eliminates the reversing shock common in general hydraulic systems, making the slewing action smoother and more precise, and reducing operator fatigue.

[0088] In summary, this invention, through the deep integration of mechanical structural innovation and intelligent control algorithms, solves the core pain points of low efficiency and difficult energy recovery in the electrification process of construction machinery, providing key technical support for achieving green, efficient, and long-endurance operation of construction machinery. The implementation of this invention not only helps reduce equipment operating costs but also aligns with the global construction machinery industry's technological roadmap towards low-carbon and intelligent transformation. Attached Figure Description

[0089] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0090] Figure 1 This is a schematic diagram of the principle structure of the electro-hydraulic hybrid drive engineering machinery rotary system provided in Example 1.

[0091] Figure 2 This is a flowchart illustrating a rotary control method for electro-hydraulic hybrid drive engineering machinery, as provided in Example 1.

[0092] Reference numerals: 1. Loading motor pump; 2. Make-up oil motor pump; 3. Safety relief valve assembly; 4. First accumulator control valve; 5. Second accumulator control valve; 6. Accumulator; 7. Four-quadrant pump / motor; 8. Clutch; 9. Series motor; 10. Controller.

[0093] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0095] Example 1

[0096] Embodiment 1 of the present invention provides an electro-hydraulic hybrid drive engineering machinery slewing system and its control method, which can be implemented by an electro-hydraulic hybrid drive engineering machinery slewing system and its controller (hereinafter referred to as control equipment), and in particular, executed by one or more processors within the control equipment.

[0097] In this embodiment, the control device may be an electronic device equipped with a processor. The processor carries a computer program for the electro-hydraulic hybrid drive engineering machinery rotation system and its control method, and the computer program can be executed. Examples include computers, smartphones, smart tablets, workstations, etc., which are not limited here.

[0098] In existing electrification technology pathways, electric motors are typically used to simply replace internal combustion engines to drive traditional hydraulic systems. While this eliminates direct emissions, it doesn't fundamentally solve the inherent low efficiency problem of hydraulic transmission systems. Especially under frequent slewing and braking conditions in construction machinery, the braking kinetic energy contained in the huge rotational inertia and the gravitational potential energy during operations on specific slopes are often dissipated as heat through the relief valve, making energy recovery difficult. This ineffective energy loss directly leads to high overall power consumption, forcing the equipment to be equipped with large-capacity and heavy battery packs, significantly shortening the operating time after a single charge. Furthermore, traditional systems lack a sensitive power distribution mechanism when facing complex and changing operating conditions, failing to adjust the operating points of the motor and hydraulic components in real time according to load changes, causing the power system to operate in uneconomical zones for extended periods.

[0099] This invention constructs a composite drive architecture with deep electro-hydraulic coupling and combines it with a time-series feature recognition strategy based on deep learning. The aim is to achieve precise decoupling and composite of the slewing power source, improve the efficiency of braking energy capture and reuse, and reduce the overall energy consumption of the machine. This significantly extends the effective operating time of the construction machinery without increasing the battery capacity.

[0100] like Figure 1 As shown, an electro-hydraulic hybrid drive system for engineering machinery rotation is constructed on the basis of deep coupling of electrical and hydraulic energy, aiming to achieve high efficiency in power output and maximize energy recovery. The system specifically includes a loading motor pump 1, a replenishing oil motor pump 2, a safety relief valve group 3, a first accumulator control valve 4, a second accumulator control valve 5, an accumulator 6, a four-quadrant pump / motor 7, a clutch 8, a series motor 9, and a controller 10 as the core of logic operations.

[0101] Regarding the hardware connection of the hydraulic circuit, the loading motor pump 1, as the hydraulic conversion element for the main drive power, has two main oil ports, defined as the upper oil port and the lower oil port, respectively. These two oil ports are fully enclosedly connected to the corresponding upper and lower oil ports of the four-quadrant pump / motor 7 through high-pressure shock-resistant pipelines, thus forming a closed hydraulic power circulation circuit. In this closed circuit, the oil flows back and forth between the loading motor pump 1 and the four-quadrant pump / motor 7, reducing the throttling losses caused by the reversing valve. The oil inlet of the replenishing motor pump 2 extends below the system oil tank level, and its outlet is connected to the input end of the safety relief valve assembly 3. The safety relief valve assembly 3 integrates a check valve and a relief valve, and its output ends are connected to the high-pressure side and low-pressure side of the main closed circuit, respectively. When the system operates and the circuit pressure drops due to leakage, the replenishing oil pump 2 injects oil from the oil tank into the circuit through the safety relief valve group 3 to maintain the system's basic pressure. When the circuit pressure exceeds the preset safety limit, the safety relief valve group 3 performs a pressure relief action to protect the hydraulic components from damage.

[0102] The connection structure of the energy storage unit is crucial for the recovery of gravitational potential energy and kinetic energy. The inlet of the first accumulator control valve 4 is connected to the upper oil port pipeline of the four-quadrant pump / motor 7, and its outlet connects to the inlet of the accumulator 6. Similarly, the inlet of the second accumulator control valve 5 is connected to the lower oil port pipeline of the four-quadrant pump / motor 7, and its outlet also points to the accumulator 6. This symmetrically distributed control valve structure ensures that the system has a technical path to introduce high-pressure oil into the accumulator 6 for physical storage, regardless of whether the slewing mechanism is in forward or reverse braking mode. The accumulator 6 adopts a bladder-type or piston-type structure to absorb the pressure energy generated during braking.

[0103] The mechanical power coupling is flexibly switched via clutch 8. The power shaft of the four-quadrant pump / motor 7 is connected to one end of clutch 8, while the other end of clutch 8 is connected to the output shaft of the series motor 9. The series motor 9, as an electric drive unit, directly participates in the torque output of the rotary mechanism. The controller 10 drives the clutch 8 to perform engagement or disengagement actions by outputting pulse signals or bus commands.

[0104] When the clutch 8 is engaged, the series motor 9 and the four-quadrant pump / motor 7 rotate coaxially, and the rotary mechanism obtains a combined torque of electric drive and hydraulic drive. When the clutch 8 is disengaged, the series motor 9 and the hydraulic system are completely decoupled at the mechanical level, and can work independently with a single power source according to the operation requirements.

[0105] The power output end of the four-quadrant pump / motor 7 is connected to the rotary platform of the engineering machinery through a rotary reducer.

[0106] The controller 10 also features multiple analog and digital input interfaces for connecting to a sensor network including pressure sensors, speed sensors, torque sensors, and handle signal lines. The controller 10 also features multiple pulse-width modulation output interfaces or bus communication interfaces for driving motor controllers, proportional valve amplifiers, and clutch actuators.

[0107] like Figure 2 As shown, this embodiment also provides an electro-hydraulic hybrid drive engineering machinery rotation system and its control method, which includes steps S1 to S4.

[0108] S1, real-time acquisition of multi-source timing signals of the system through sensor network; wherein, the multi-source timing signals include: rotary system handle signal, pressure values ​​at the inlet and outlet of four-quadrant pump / motor 7, real-time torque and speed of series motor 9, real-time torque and speed of loading motor pump 1, pressure value at the outlet of accumulator 6, and pressure values ​​of the large and small chambers of the actuator cylinder of the engineering machinery.

[0109] During the operation of the construction machinery, the controller 10 acquires the slewing system handle signal through the analog input interface. This signal represents the operator's expectation of rotation speed and direction. Simultaneously, the controller 10 reads pressure sensor data installed at the inlet and outlet of the four-quadrant pump / motor 7 in real time. and This is used to calculate the instantaneous working state of the hydraulic side. In addition, the controller 10 obtains the torque of the series motor 9 via the CAN bus or a dedicated encoder interface. With rotational speed And the torque of the loaded motor pump 1 With rotational speed The pressure sensor at the outlet of accumulator 6 feeds back the real-time pressure value to controller 10 to assess the energy reserve status. In order to accurately identify the overall machine operating condition, controller 10 also simultaneously collects the pressure of the large and small chambers of the boom cylinder, stick cylinder, and bucket cylinder of the construction machinery. These pressure change characteristics imply whether the current operation is in the digging, lifting, or unloading stage.

[0110] S2, the rotary system handle signal is mapped to the system's target rotational speed function, and the multi-source time-series signal is normalized and then input into a preset LSTM-Transformer hybrid recognition model for working condition recognition, and the working condition recognition result is output.

[0111] Specifically, the rotary system handle signal is mapped to a system-required target speed function, as shown in the formula:

[0112] ;

[0113] in, To set the target speed for the motor pump 1; This is the speed calibration coefficient for the rotary system; For handle signals; Indicates the current moment.

[0114] To achieve intelligent power distribution, the controller 10 preprocesses the acquired multi-source time-series signals. These signals constitute an input sequence with dimension d and sampling sequence length T. The controller 10 executes a normalization algorithm, using the engineering extrema of each signal to map the original data to a standard range, eliminating dimensional differences between different physical quantities such as voltage, pressure, and torque, and generating a standardized time-series signal.

[0115] Then, the standardized time-series signal is input into the pre-built LSTM-Transformer hybrid recognition model.

[0116] The LSTM-Transformer hybrid recognition model adopts a cascaded structure of LSTM layer, Transformer layer and fully connected layer to perform local feature extraction, global correlation modeling and working condition classification on the normalized time series signal obtained by normalization processing.

[0117] The LSTM layer is used to extract local temporal dependency features of the standardized time-series signal; the Transformer layer is used to model global feature association based on self-attention mechanism and output global fusion features; the fully connected layer is used to pool and classify the global fusion features and output working condition recognition results.

[0118] When extracting the local temporal dependency features of the normalized time-series signal through the LSTM layer, the LSTM gate state is calculated, and the hidden layer state sequence containing local action logic is output, i.e., the local temporal dependency features, specifically:

[0119] Based on the features of the current input and the hidden state at the previous time step, the forget gate of the LSTM layer determines which parts of the historical information have a low correlation with the current task, and discards them using the following formula:

[0120] ;

[0121] in, Output for the forget gate; Use the Sigmoid activation function; Here is the forget gate weight matrix; This represents the hidden state of the LSTM layer at the previous time step. Input signal for the current moment; This is the forget gate bias vector;

[0122] The newly added features at the current time step are updated into the cell state through the input gate of the LSTM layer, as shown in the formula:

[0123] ;

[0124] ;

[0125] in, For input gate output; The input gate weight matrix; The input gate bias vector; For candidate cell states, record the latest information at the current moment; This is the candidate cell state weight matrix; This is the candidate cell state bias vector; It is the hyperbolic tangent activation function;

[0126] Next, the outputs of the forget gate and the input gate are combined to update the cell state, using the following formula:

[0127] ;

[0128] in, , These represent the cell states at the current and previous moments, respectively. For element-wise multiplication;

[0129] By controlling the degree to which cell states are output to the hidden layer through the output gate, local temporal dependency features are obtained, as shown in the formula:

[0130] ;

[0131] ;

[0132] ;

[0133] in, Output gate output; , These are the weight matrix and bias vector of the output gate, respectively; This represents the current hidden state of the LSTM layer. The final output features of the LSTM layer; For a moment The hidden state of the LSTM layer; The length of the sampling sequence.

[0134] The process of outputting global fused features through the self-attention mechanism of the Transformer layer is as follows:

[0135] First, the final output features of the LSTM layer Mapped to query vector Key vector AND value vector The scaling dot product operation is used to identify the action correlation across time steps, such as identifying whether the current pressure pulse belongs to the hard soil impact during the excavation process or the normal slewing start.

[0136] Then, global attention weights between signals at different time steps are calculated using multi-head self-attention to capture multi-scale global features in parallel. The formula is as follows:

[0137] ;

[0138] ;

[0139] in, For the first Single-head attention output; , , The first When calculating single-head attention , , The weights; For self-attention function, ,in, It is the transpose symbol; for The feature dimension of a vector (positive integer);

[0140] For multi-head attention output; Indicates feature concatenation operation; For the number of attention heads; For multi-head output projection weight matrix;

[0141] Multi-head attention output and final output features of LSTM layer By applying residual connections and layer normalization, the global fusion feature is output, as shown in the formula:

[0142] ;

[0143] ;

[0144] in, Output for residual connection and layer normalization processing; For layer normalization operation; It is a feedforward neural network, using a two-layer fully connected network with ReLU activation; This is a global fusion feature.

[0145] The process of pooling and classifying the globally fused features through the fully connected layer to output the working condition identification result is as follows:

[0146] The globally fused features are fed into a fully connected layer, and the probability distribution of each preset working condition category is output through a normalized exponential function, as shown in the formula:

[0147] ;

[0148] in, For the first The probability of a particular type of working condition; This represents the number of operating condition categories for the electro-hydraulic system. For the first The fully connected layer output corresponding to the type of working condition; It is an exponential function;

[0149] The working condition corresponding to the maximum probability in the probability distribution is selected as the working condition identification result of the current whole machine operation.

[0150] S3, calculate the total required torque based on the working condition identification result, and decompose the total required torque into a series motor drive torque function and a hydraulic drive torque function.

[0151] The expression for the total required torque is:

[0152] ;

[0153] in, for Total torque required for rotation at any given moment; for The external load torque at any given time is detected by sensors or estimated by models. The equivalent moment of inertia is determined by the overall machine structure. Target angular acceleration; Indicates to Find the differential; For the target angular velocity, ; The equivalent damping coefficient is determined experimentally.

[0154] Based on the operating condition identification results, the total required torque is divided into series motor drive torque and hydraulic drive torque to achieve optimal allocation, as expressed in the following expression:

[0155] ;

[0156] ;

[0157] in, for The target driving torque of the series motor at any given moment; The motor drive torque distribution coefficient is determined by the operating condition identification result; for The hydraulic drive torque at any given moment; The hydraulic drive torque distribution coefficient is negative when the working condition identification result is an energy recovery working condition.

[0158] In smooth working conditions requiring high-frequency start-stop, the controller 10 is enlarged. To leverage the rapid response advantage of the motor; in digging rotation conditions requiring high load holding pressure, the controller 10 adds... The proportion.

[0159] For example, if the working condition identification result is "heavy-load excavation", then set It is 0.7. The value is set to 0.3 to leverage the high power density advantage of hydraulic systems; if it is "no-load rotation", then it is set to... It is 0.2. It is 0.8.

[0160] S4. Based on the hydraulic drive torque function and the multi-source timing signal, the target control command is calculated and sent to each actuator to drive the rotary system to reach the target operating speed and perform energy recovery.

[0161] The target control commands include: the target displacement of the four-quadrant pump / motor 7, the liquid flow rate and valve opening of the first accumulator control valve 4 or the second accumulator control valve 5, and the target rotational speed of the loading motor pump 1.

[0162] Specifically, when the four-quadrant pump / motor functions as a pump (driving rotation) or a motor (regenerative braking), the torque output satisfies a unified mechanical model:

[0163] ;

[0164] ;

[0165] in, for The pump / motor output torque is displayed in four quadrants at all times. The pump operating condition is the drive torque, and the motor operating condition is the braking torque. for Constantly monitored four-quadrant pump / motor differential pressure; for The pump / motor displacement is displayed at all times. The pump operating condition is the actual output displacement, and the motor operating condition is the absorption displacement. The inlet pressure of the four-quadrant pump / motor; This refers to the outlet pressure of the four-quadrant pump / motor.

[0166] When a four-quadrant pump / motor is used as a pump, the flow output satisfies a geometric relationship with the speed and displacement:

[0167] ;

[0168] in, for Target flow rate on the pump side at all times; for Real-time four-quadrant pump / motor displacement; for The set target speed of motor pump 1 is constantly applied.

[0169] When the four-quadrant pump / motor is used as the motor (in regenerative braking condition), the torque is determined by the pressure difference, displacement, and efficiency:

[0170] ;

[0171] in, for The braking torque of the motor under constant operating conditions is the braking component that recovers the rotational energy. for Constantly monitored four-quadrant pump / motor differential pressure; for The displacement of the motor under constant operating conditions, and the displacement absorbed during energy recovery; The efficiency of the four-quadrant pump / motor drive.

[0172] Therefore, the formula for calculating the target displacement of the four-quadrant pump / motor 7 can be derived as follows:

[0173] ;

[0174] in, for The displacement command that needs to be adjusted at all times, i.e. the target displacement of the four-quadrant pump / motor; for The hydraulic drive torque at any given moment; for Constantly monitored four-quadrant pump / motor differential pressure; The efficiency of the four-quadrant pump / motor drive.

[0175] During the calculation process, the controller 10 monitors the inlet and outlet pressure difference of the four-quadrant pump / motor 7 in real time. And based on its transmission efficiency Nonlinear compensation is performed. When the four-quadrant pump / motor 7 is used as a motor to drive the rotary platform, the displacement calculation follows the proportional relationship between torque, pressure, and displacement; when it is used as a pump to perform energy recovery, the displacement calculation shifts to the principle of maximizing energy capture efficiency.

[0176] The formula for calculating the liquid flow rate of the first accumulator control valve 4 or the second accumulator control valve 5 is as follows:

[0177] ;

[0178] in, for The liquid flow rate of the accumulator control valve is monitored at all times; positive values ​​indicate liquid discharge and negative values ​​indicate liquid filling. For flow coefficient; for The flow area of ​​the valve is controlled at all times, i.e., the valve opening. for The pressure difference across the valve at any given time is the difference between the accumulator pressure and the circuit pressure. The density of the oil;

[0179] The formula for calculating the valve opening degree of the first accumulator control valve 4 or the second accumulator control valve 5 is as follows:

[0180] ;

[0181] in, This is the area-opening ratio coefficient, determined by the valve port structure; This is the valve opening control signal, with a value of 0 or 1, where 0 means fully closed and 1 means fully open.

[0182] To ensure the flow balance of the closed loop, the controller 10 synchronously calculates the output speed function of the loading motor pump 1 to ensure that the flow provided by the loading motor pump 1 is precisely matched with the required flow of the four-quadrant pump / motor 7 under the target displacement.

[0183] The formula for calculating the target speed of the motor pump 1 is as follows:

[0184] ;

[0185] ;

[0186] in, for The target rotational speed of motor pump 1 is constantly being loaded; for Target flow rate on the pump side at all times; For hydraulic volumetric efficiency; for Real-time four-quadrant pump / motor displacement; for The set target speed of motor pump 1 is constantly applied.

[0187] The controller 10 uses the target rotation speed as the main command and synchronously adjusts the output speed of the series motor 9 and the oil supply flow of the loading motor pump 1 through the PID algorithm to ensure that the speed vectors of the two are consistent at the mechanical coupling point.

[0188] The controller 10 sends electrical signals to each actuator via the control bus. The load motor pump 1 operates at the set speed, establishing a closed-loop cycle. The displacement adjustment mechanism of the four-quadrant pump / motor 7 adjusts according to... Adjust the swashplate angle. The series motor 9 receives torque commands and outputs mechanical work. The clutch 8 remains engaged according to operating conditions, achieving power synthesis. The first accumulator control valve 4 and the second accumulator control valve 5... Adjust the opening degree to guide the oil to flow orderly between the main circuit and the accumulator 6.

[0189] Through the coordinated action of the aforementioned multi-physical domain components, the rotary system eventually reaches the target operating speed and dynamically performs energy recovery or auxiliary drive tasks according to the operating conditions.

[0190] To more clearly illustrate the operating principle of this invention in practical applications, a typical excavator slewing cycle is used as an example for detailed description below. This cycle includes four stages: starting acceleration, constant speed slewing, braking deceleration, and stopping.

[0191] During the acceleration phase, the controller 10 detects a step change in the handle signal and identifies it as an acceleration condition in step S1. At this time, the controller 10 commands the clutch 8 to engage. To overcome the enormous static friction and inertia of the rotary platform, the controller 10 sets a high motor distribution coefficient and hydraulic distribution coefficient in step S2. The series motor 9 outputs the maximum starting torque, while the motor pump 1 quickly builds up pressure, and the four-quadrant pump / motor 7 operates at its maximum displacement. If the accumulator 6 contains energy recovered from the previous cycle, the controller 10 will open the corresponding accumulator control valve, allowing the high-pressure oil in the accumulator 6 to enter the inlet of the four-quadrant pump / motor 7, thus providing pressure energy to assist the starting process and reducing the energy consumption of the power battery.

[0192] During the uniform rotation phase, the rotary platform has reached the target speed. Controller 10 identifies this as a steady-state condition in step S1. At this point, the required torque decreases significantly, requiring only the overcoming of frictional resistance. In step S2, controller 10 lowers the power distribution ratio, the series motor 9 enters the high-efficiency operating range, the displacement of the four-quadrant pump / motor 7 is reduced, and the speed of the loading motor pump 1 also decreases accordingly to reduce system overflow and heat loss. The replenishing oil motor pump 2 continues to operate, ensuring that the closed loop is always filled with oil and maintains a slight positive pressure to avoid cavitation.

[0193] During the braking and deceleration phase, the operator resets the handle, and the controller 10 recognizes this as an energy recovery operation. At this time, the enormous inertia of the slewing platform is transmitted to the four-quadrant pump / motor 7 via the clutch 8. The controller 10 instructs the four-quadrant pump / motor 7 to switch to pump mode, and its discharge port pressure rapidly increases. Based on the direction of rotation of the four-quadrant pump / motor 7, the controller 10 precisely opens either the first accumulator control valve 4 or the second accumulator control valve 5, pumping the high-pressure oil generated during braking into the accumulator 6. During this process, the series motor 9 can switch to generator mode to perform energy recovery, or remain in a zero-torque output state, depending on the battery status. By adjusting the displacement of the four-quadrant pump / motor 7 and the opening of the accumulator control valve, the controller 10 achieves a smooth braking effect, avoiding the severe vibration and heat waste caused by braking via the relief valve in traditional hydraulic systems.

[0194] During the stopping phase, the slewing platform speed drops to zero. Controller 10 instructs the accumulator control valve to fully close, locking up the pressure energy within the accumulator 6. Clutch 8 remains engaged or disengaged according to the expected subsequent action. Load motor pump 1 stops outputting power, and the system enters standby power-saving mode. Through this complete cycle, the present invention achieves dynamic matching between power output and load demand, and effectively converts previously wasted braking kinetic energy into hydraulic energy storage.

[0195] The working condition recognition algorithm in this embodiment is highly advanced. The LSTM layer extracts local features from the time-series signal using its internal forget gate structure. When the excavator is traveling or operating on bumpy roads, the sensors generate high-frequency noise. The LSTM's forget gate automatically filters out these interference signals that do not conform to the physical logic of the swing by calculating the correlation weight between the historical state and the current input. Meanwhile, the Transformer layer's multi-head self-attention mechanism examines the data globally. For example, in "swinging" operations, the swing action is usually accompanied by the rapid extension of the boom. The Transformer layer can identify the logical coupling between the changing trend of the boom pressure sensor and the swing handle signal, thereby accurately determining that the current swinging condition is in place and adjusting the torque distribution coefficient in advance to reserve sufficient power.

[0196] Furthermore, this invention fully considers the nonlinear characteristics of hydraulic components in the control of the four-quadrant pump / motor 7. In the execution parameter calculation of S3, the controller 10 incorporates an efficiency mapping table for the four-quadrant pump / motor 7 at different speeds and pressures. When the system operates in a low-temperature environment causing changes in oil viscosity, the controller 10 automatically corrects the displacement control function by sensing the transient response of the pressure, ensuring that the speed control accuracy of the rotary platform is unaffected by environmental factors. This strategy, combining model-based predictive control with deep learning recognition, significantly improves the system's control quality.

[0197] The coordinated operation of the replenishing oil pump 2 and the safety relief valve assembly 3 ensures the long-term stable operation of the system. In a closed circuit, due to unavoidable internal leakage of hydraulic components, the amount of oil in the circuit gradually decreases. The replenishing oil pump 2, through the replenishing oil check valve in the safety relief valve assembly 3, continuously replenishes filtered cold oil to the low-pressure side of the circuit. This not only maintains pressure stability but also achieves circulating cooling of the oil in the circuit, preventing aging of seals due to localized overheating and extending the service life of the entire machine.

[0198] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0199] The electro-hydraulic hybrid drive slewing system and its control method for construction machinery provided in this embodiment achieve deep integration of electric and hydraulic drives through ingenious mechanical structure design, and achieve precise control over complex operating conditions by relying on advanced deep learning algorithms. The system not only solves the bottleneck problem of low energy recovery efficiency during the electrification of construction machinery, but also significantly improves the smoothness of operation and the overall operating efficiency of the machine through decoupling and composite control of the power source. This technical solution provides a reliable path to achieving green, intelligent, and efficient construction machinery, and has significant engineering application value and market prospects.

[0200] Example 2

[0201] The second embodiment of the present invention also provides an electro-hydraulic hybrid drive engineering machine, comprising:

[0202] The above-described electro-hydraulic hybrid drive slewing system for engineering machinery includes a chassis, a slewing platform mounted on the chassis, and a slewing platform that drives the slewing platform to rotate relative to the chassis.

[0203] By integrating the above systems, this construction machinery achieves efficient energy management under various typical working conditions such as excavation, loading, and leveling.

[0204] Example 3

[0205] The third embodiment of the present invention also provides a control device for an electro-hydraulic hybrid drive engineering machinery slewing system, which includes a memory and a processor. The memory stores a computer program, which can be executed by the processor to realize the control method of the electro-hydraulic hybrid drive engineering machinery slewing system as described above.

[0206] Example 4

[0207] The fourth embodiment of the present invention also provides a computer-readable storage medium storing computer-readable instructions. When the computer-readable instructions are executed by the processor of the device where the computer-readable storage medium is located, they realize the electro-hydraulic hybrid drive engineering machinery slewing system and its control method as described above.

[0208] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An electro-hydraulic hybrid drive system for engineering machinery rotation, characterized in that, include: Loading motor pump (1), replenishing oil motor pump (2), safety relief valve group (3), first accumulator control valve (4), second accumulator control valve (5), accumulator (6), four-quadrant pump / motor (7), clutch (8), series motor (9) and controller (10); The upper oil port of the loading motor pump (1) is connected to the upper oil port of the four-quadrant pump / motor (7), and the lower oil port of the loading motor pump (1) is connected to the lower oil port of the four-quadrant pump / motor (7), forming a closed main drive circuit. The oil inlet of the supplementary oil pump (2) is connected to the system oil tank, and the oil outlet of the supplementary oil pump (2) is connected to the high-pressure side and low-pressure side of the main drive circuit respectively through the safety relief valve group (3); The first accumulator control valve (4) is connected between the upper oil port of the four-quadrant pump / motor (7) and the accumulator (6), and the second accumulator control valve (5) is connected between the lower oil port of the four-quadrant pump / motor (7) and the accumulator (6). The output shaft of the series motor (9) is mechanically connected to the power input end of the four-quadrant pump / motor (7) via a clutch (8); The controller (10) is electrically connected to the series motor (9), the loading motor pump (1), the clutch (8), the first accumulator control valve (4), and the second accumulator control valve (5), respectively.

2. The electro-hydraulic hybrid drive engineering machinery rotary system according to claim 1, characterized in that, The controller (10) has multiple analog and digital input interfaces for connecting to a sensor network including a pressure sensor, a speed sensor, a torque sensor and a handle signal line. The controller (10) has a multi-channel pulse width modulation output interface or a bus communication interface for driving the motor controller, proportional valve amplifier and clutch actuator.

3. A control method for a rotary system of engineering machinery based on any one of claims 1-2, characterized in that, The control method is executed by the controller (10) and includes the following steps: S1, real-time acquisition of multi-source timing signals of the system through sensor network; wherein, the multi-source timing signals include: rotary system handle signal, pressure values ​​at the inlet and outlet of the four-quadrant pump / motor (7), real-time torque and speed of the series motor (9), real-time torque and speed of the loading motor pump (1), pressure value at the outlet of the accumulator (6) and pressure values ​​of the large and small chambers of the actuator cylinder of the engineering machinery. S2, map the rotary system handle signal to the system demand target speed function, and after normalizing the multi-source time sequence signal, input it into the preset LSTM-Transformer hybrid recognition model for working condition recognition, and output the working condition recognition result; S3, calculate the total required torque based on the working condition identification result, and decompose the total required torque into a series motor drive torque function and a hydraulic drive torque function; S4. Based on the hydraulic drive torque function and the multi-source timing signal, the target control command is calculated and sent to each actuator to drive the rotary system to reach the target operating speed and perform energy recovery.

4. The control method for the electro-hydraulic hybrid drive engineering machinery rotation system according to claim 3, characterized in that, The rotary system handle signal is mapped to a system target speed function, as shown in the formula: ； in, The target speed for loading the motor pump (1) is set; This is the speed calibration coefficient for the rotary system; For handle signals; Indicates the current moment.

5. The control method for the electro-hydraulic hybrid drive engineering machinery rotation system according to claim 3, characterized in that, The LSTM-Transformer hybrid recognition model adopts a cascaded structure of LSTM layer, Transformer layer and fully connected layer to perform local feature extraction, global correlation modeling and working condition classification on the normalized time series signal obtained by normalization processing. The LSTM layer is used to extract local temporal dependency features of the standardized time-series signal; the Transformer layer is used to model global feature association based on self-attention mechanism and output global fusion features; the fully connected layer is used to pool and classify the global fusion features and output working condition recognition results.

6. The control method for the electro-hydraulic hybrid drive engineering machinery rotation system according to claim 5, characterized in that, When extracting the local temporal dependency features of the normalized time-series signal through the LSTM layer, the LSTM gate state is calculated, and the hidden layer state sequence containing local action logic is output, i.e., the local temporal dependency features, specifically: Based on the features of the current input and the hidden state at the previous time step, the forget gate of the LSTM layer discards the portion of historical information whose correlation with the current task is lower than a set threshold. The formula is as follows: ； in, Output for the forget gate; Use the Sigmoid activation function; This is the forget gate weight matrix; This represents the hidden state of the LSTM layer at the previous time step. Input signal for the current moment; This is the forget gate bias vector; The newly added features at the current time step are updated into the cell state through the input gate of the LSTM layer, as shown in the formula: ； ； in, For input gate output; The input gate weight matrix; The input gate bias vector; For candidate cell states, record the latest information at the current moment; This is the candidate cell state weight matrix; This is the candidate cell state bias vector; It is the hyperbolic tangent activation function; Next, the outputs of the forget gate and the input gate are combined to update the cell state, using the following formula: ； in, , These represent the cell states at the current and previous moments, respectively. For element-wise product; By controlling the degree to which cell states are output to the hidden layer through the output gate, local temporal dependency features are obtained, as shown in the formula: ； ； ； in, Output gate output; , These are the weight matrix and bias vector of the output gate, respectively; This represents the current hidden state of the LSTM layer. The final output features of the LSTM layer; For a moment The hidden state of the LSTM layer; The length of the sampling sequence.

7. The control method for the electro-hydraulic hybrid drive engineering machinery rotation system according to claim 6, characterized in that, The process of outputting global fused features through the self-attention mechanism of the Transformer layer is as follows: First, the final output features of the LSTM layer Mapped to query vector Key vector AND value vector ; Then, global attention weights between signals at different time steps are calculated using multi-head self-attention to capture multi-scale global features in parallel. The formula is as follows: ； ； in, For the first Single-head attention output; It is a self-attention function; , , The first When calculating single-head attention , , The weights; For multi-head attention output; Indicates feature concatenation operation; For the number of attention heads; For multi-head output projection weight matrix; Multi-head attention output and final output features of LSTM layer By applying residual connections and layer normalization, the global fusion feature is output, as shown in the formula: ； ； in, Output for residual connection and layer normalization processing; For layer normalization operation; It is a feedforward neural network; This is a global fusion feature.

8. The control method for the electro-hydraulic hybrid drive engineering machinery rotation system according to claim 7, characterized in that, The process of pooling and classifying the globally fused features through the fully connected layer to output the working condition identification result is as follows: The globally fused features are fed into a fully connected layer, and the probability distribution of each preset working condition category is output through a normalized exponential function, as shown in the formula: ； in, For the first The probability of a particular type of working condition; This represents the number of operating condition categories for the electro-hydraulic system. For the first The fully connected layer output corresponding to the type of working condition; It is an exponential function; The working condition corresponding to the maximum probability in the probability distribution is selected as the working condition identification result of the current whole machine operation.

9. The control method for the electro-hydraulic hybrid drive engineering machinery rotation system according to claim 3, characterized in that, The expression for the total required torque is: ； in, for Total torque required for rotation at any given moment; for External load torque at any given moment; It is the equivalent moment of inertia; Target angular acceleration; Indicates to Find the differential; The target angular velocity; This is the equivalent damping coefficient; Based on the operating condition identification results, the total required torque is divided into series motor drive torque and hydraulic drive torque to achieve optimal allocation, as expressed in the following expression: ； ； in, for The target driving torque of the series motor at any given moment; This is the motor drive torque distribution coefficient; for The hydraulic drive torque at any given moment; The hydraulic drive torque distribution coefficient is negative when the working condition identification result is an energy recovery working condition.

10. The control method for the electro-hydraulic hybrid drive engineering machinery rotation system according to claim 3, characterized in that, The target control commands include: the target displacement of the four-quadrant pump / motor (7), the liquid flow rate and valve opening of the first accumulator control valve (4) or the second accumulator control valve (5), and the target rotational speed of the loading motor pump (1). The formula for calculating the target displacement of the four-quadrant pump / motor (7) is as follows: ； ； in, for The displacement command that needs to be adjusted at all times, i.e. the target displacement of the four-quadrant pump / motor; for The hydraulic drive torque at any given moment; for Constantly monitored four-quadrant pump / motor differential pressure; For the four-quadrant pump / motor drive efficiency; The inlet pressure of the four-quadrant pump / motor; The outlet pressure of the four-quadrant pump / motor; The formula for calculating the liquid flow rate of the first accumulator control valve (4) or the second accumulator control valve (5) is as follows: ； in, for The liquid flow rate of the accumulator control valve at all times; For flow coefficient; for The flow area of ​​the valve is controlled at all times, i.e., the valve opening. for The pressure difference across the valve at any given time is the difference between the accumulator pressure and the circuit pressure. The density of the oil; The formula for calculating the valve opening of the first accumulator control valve (4) or the second accumulator control valve (5) is as follows: ； in, This is the area-opening ratio coefficient, determined by the valve port structure; This is the valve opening control signal, with a value of 0 or 1, where 0 means fully closed and 1 means fully open; The formula for calculating the target speed of the motor pump (1) is as follows: ； ； in, for The target rotational speed of the motor pump (1) is constantly being applied; for Target flow rate on the pump side at all times; For hydraulic volumetric efficiency; for Real-time four-quadrant pump / motor displacement; for The set target speed of the motor pump (1) is constantly loaded.

Images