Energy management method and device for engineering machinery and engineering machinery

Abstract

The invention relates to an energy management method and device for engineering machinery and the engineering machinery. The method comprises the steps that the current transportation working condition and vehicle state data are acquired; determining energy management parameters of the current transportation working condition according to the current transportation working condition and the vehicle state data; wherein the energy management parameters comprise at least one of a first energy management parameter and a second energy management parameter, the first energy management parameter comprises motor demand power and motor demand energy, and the second energy management parameter comprises brake energy recovery power and target recovery energy of a battery; and executing corresponding energy management operation according to the energy management parameters. According to the technical scheme, the motor demand power and the motor demand energy used for optimizing energy output and / or the braking energy recovery power used for optimizing energy recovery and the target recovery energy of the battery are determined based on the transportation working condition and the vehicle state data, the energy utilization rate of the engineering machinery can be effectively optimized, and the energy utilization rate of the engineering machinery is improved. And battery life and vehicle performance are both considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering machinery, and in particular to an energy management method and device for engineering machinery and engineering machinery. BACKGROUND

[0002] For engineering machinery such as electric wheel mining dump trucks, efficient management of energy is crucial to improving the performance of the drive system, prolonging the life of the battery, and improving the overall operating efficiency of the vehicle.

[0003] However, in the coordinated control of multiple power sources (such as the complex system of engine-generator-battery-electric wheel), the prior art is not perfect enough, and only simple rule-based energy management strategies are executed, such as controlling energy input and output only according to battery power threshold, vehicle speed threshold, parking brake, etc. The strategy flexibility is poor, it is difficult to adapt to complex and variable actual driving conditions, resulting in that the energy utilization rate cannot be optimized, and it is difficult to balance the battery life and vehicle performance. SUMMARY

[0004] To solve the above technical problems, the present application provides an energy management method and device for engineering machinery and engineering machinery, which determines the motor demand power and motor demand energy for optimizing energy output and / or brake energy recovery power and target recovery energy of the battery for optimizing energy recovery based on transportation conditions and vehicle state data, which can effectively optimize the energy utilization rate of engineering machinery and balance the battery life and vehicle performance.

[0005] To solve the above technical problems, the present application provides an energy management method for engineering machinery, which comprises the following steps: Obtain current transportation conditions and vehicle state data; Determine energy management parameters of the current transportation conditions according to the current transportation conditions and the vehicle state data; wherein the energy management parameters include at least one of first energy management parameters and second energy management parameters, the first energy management parameters include motor demand power and motor demand energy, and the second energy management parameters include brake energy recovery power and target recovery energy of the battery; Perform corresponding energy management operations according to the energy management parameters.

[0006] In some embodiments, obtaining the vehicle state data comprises: Obtaining acquisition data of different working systems; Processing the acquisition data based on a Kalman filtering algorithm to obtain the vehicle state data.

[0007] In some embodiments, the determining the energy management parameter of the current transportation working condition according to the current transportation working condition and the vehicle state data comprises: obtaining working condition supplementary information corresponding to the current transportation working condition; wherein the working condition supplementary information comprises predicted walking distance of the current transportation working condition and / or driver behavior data; determining the energy management parameter of the current transportation working condition according to the vehicle state data and the working condition supplementary information.

[0008] In some embodiments, the determining the energy management parameter of the current transportation working condition according to the vehicle state data and the working condition supplementary information comprises: when the current transportation working condition is heavy load uphill, obtaining uphill distance as the predicted walking distance, obtaining first driver behavior data as the driver behavior data, and determining the first energy management parameter according to the vehicle state data, the uphill distance and the first driver behavior data; and / or, when the current transportation working condition is light load downhill, obtaining downhill distance as the predicted walking distance, obtaining second driver behavior data as the driver behavior data, and determining the second energy management parameter according to the vehicle state data, the downhill distance and the second driver behavior data.

[0009] In some embodiments, when the current transportation working condition is heavy load uphill, the performing the corresponding energy management operation according to the energy management parameter comprises: comparing the size relationship between the current remaining energy of the battery and the motor demand energy; when the current remaining energy of the battery is greater than or equal to the motor demand energy, determining a driving mode based on the motor demand power and the battery rated output power to perform the corresponding energy management operation; wherein the driving mode comprises pure electric driving mode and hybrid driving mode; when the current remaining energy of the battery is less than the motor demand energy, entering the hybrid driving mode, and determining engine output power and engine output energy based on the motor demand power and the battery rated output power to perform the corresponding energy management operation.

[0010] In some embodiments, the determining the driving mode based on the motor demand power and the battery rated output power to perform the corresponding energy management operation comprises: when the battery rated output power is greater than or equal to the motor demand power, entering the pure electric driving mode to perform the corresponding energy management operation; When the rated output power of the battery is less than the power required by the motor, the system enters a hybrid drive mode and performs corresponding energy management operations to compensate for the power required by the motor through the output power of the engine. And / or, The process of determining the engine output power and engine output energy based on the motor's required power and the battery's rated output power to perform corresponding energy management operations includes: When the rated output power of the battery is greater than or equal to the power required by the motor, the corresponding energy management operation is performed to compensate the energy required by the motor through the energy output of the engine, and to make the output power of the engine meet the power required by the motor, and the engine working time is less than the battery working time. When the rated output power of the battery is less than the required power of the motor, the corresponding energy management operation is performed to compensate the required energy of the motor by outputting energy from the engine, and to make the output power of the engine meet the required power of the motor, and the engine working time is equal to the battery working time.

[0011] In some embodiments, when the current transportation condition is a heavy-load uphill climb, the method further includes: When a braking signal is received, determine whether to perform regenerative braking based on the current vehicle speed, the current SOC value, and the target remaining SOC value. If so, then regenerate braking energy.

[0012] In some embodiments, when the current transportation condition is a lightly loaded downhill slope, the step of performing the corresponding energy management operation according to the energy management parameters includes: Compare the relationship between the regenerative braking power and the battery's rated regenerative braking power; When the rated recovery power of the battery is greater than or equal to the braking energy recovery power, an energy management operation is performed based on the braking energy recovery power and the target recovery energy, and energy recovery is stopped when the recovered energy is equal to the target recovery energy; When the battery's rated regenerative braking power is less than the regenerative braking power, energy management operations are performed based on the battery's rated regenerative braking power and the target regenerative braking energy. In some embodiments, energy recovery is stopped when the recovered energy equals the target recovered energy.

[0013] This application also provides an energy management device for engineering machinery, comprising: The memory is configured to store instructions; and The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the energy management method for engineering machinery as described above.

[0014] This application also provides an engineering machinery, including the energy management device for the engineering machinery as described above.

[0015] This application also provides an electronic device, including a storage medium and a controller, wherein a computer program is stored on the storage medium, and the computer program, when executed by the controller, implements the steps of the method described above.

[0016] This application also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described above.

[0017] This application discloses an energy management method, device, and construction machinery for engineering machinery. The method includes: acquiring current transportation conditions and vehicle status data; determining energy management parameters for the current transportation conditions based on the current transportation conditions and vehicle status data; wherein the energy management parameters include at least one of a first energy management parameter and a second energy management parameter, the first energy management parameter including motor power demand and motor energy demand, and the second energy management parameter including braking energy recovery power and target battery recovery energy; and performing corresponding energy management operations based on the energy management parameters. The technical solution of this application, by determining the motor power demand and motor energy demand for optimizing energy output, and / or the braking energy recovery power and target battery recovery energy for optimizing energy recovery, based on transportation conditions and vehicle status data, can effectively optimize the energy utilization rate of construction machinery while taking into account battery life and vehicle performance. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an energy management method for engineering machinery according to one embodiment.

[0019] Figure 2 This is a schematic diagram illustrating sensor data acquisition according to one embodiment.

[0020] Figure 3 This is a schematic diagram of the energy management process under heavy-load uphill conditions, according to one embodiment.

[0021] Figure 4 This is a schematic diagram of the energy management process under light load downhill conditions, according to one embodiment.

[0022] Figure 5 This is a schematic diagram of the structure of an energy management device for engineering machinery according to an embodiment. Detailed Implementation

[0023] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings. Preferred embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. In this application, "each" includes one or more items.

[0025] Figure 1 This is a flowchart illustrating an energy management method for construction machinery according to one embodiment. For example... Figure 1 As shown, an energy management method for engineering machinery according to this application includes the following steps: S1, obtain current transportation conditions and vehicle status data; S2, determine the energy management parameters for the current transportation conditions based on the current transportation conditions and vehicle status data; wherein, the energy management parameters include at least one of the first energy management parameters and the second energy management parameters, the first energy management parameters include the motor demand power and the motor demand energy, and the second energy management parameters include the braking energy recovery power and the target recovery energy of the battery; S3 executes the corresponding energy management operation based on the energy management parameters.

[0026] The current transportation conditions reflect the load and road conditions of the construction machinery during transportation, including heavy-load uphill and unloaded downhill. Vehicle status data reflects the vehicle's condition. This data can be raw collected data, filtered raw data, or data extracted from the raw data using model algorithms. For example, raw collected data can include current, voltage, remaining battery power, temperature, and torque. Filtered raw data removes noise, reduces errors, and improves accuracy. Data extracted from the raw data using model algorithms can more directly reflect the construction machinery's condition, such as vehicle load, energy consumption rate, and road conditions. Determining energy management parameters based on the current transportation conditions and vehicle status data allows for a better match between energy management parameters and the current transportation conditions and vehicle status. Compared to controlling energy output and recovery solely based on battery power thresholds, vehicle speed thresholds, and parking brakes, this approach offers greater flexibility and adaptability to complex and changing real-world conditions.

[0027] Energy management parameters include the motor's required power and energy for optimizing energy output (i.e., the first energy management parameter), and / or, the braking energy recovery power and the target energy recovery from the battery for optimizing energy recovery (i.e., the second energy management parameter). By selecting these energy management parameters in conjunction with current transportation conditions and vehicle status data during energy output management, it is possible to ensure that the motor outputs sufficient torque to overcome gravity and friction, while optimizing energy output based on the battery's remaining charge and health status to avoid over-discharge. During energy recovery management, the energy recovery system can be precisely controlled, and the energy recovery intensity can be adjusted in a timely manner, efficiently converting the kinetic energy of the wheels into electrical energy stored back in the battery while avoiding any impact on vehicle braking performance and stability. Therefore, the energy utilization rate of construction machinery can be effectively optimized, while also considering battery life and vehicle performance.

[0028] In some embodiments, the current transportation condition can be determined by data collected from on-board sensors. For example, the vehicle's load mass information can be collected by a weighing system, the road slope can be determined by an inclination sensor, and the rolling resistance can be calculated by an acceleration sensor. When the vehicle's load mass reaches a preset weight threshold, the road slope detected by the inclination sensor is greater than a preset slope threshold, and the rolling resistance is greater than a preset resistance threshold, it can be determined that the vehicle is in a heavy-load uphill condition. Similarly, it can also be determined whether the vehicle is in a light-load downhill condition or other conditions that require energy management optimization, such as heavy-load downhill or light-load uphill, based on the above-mentioned collected data.

[0029] In some embodiments, step S1, acquiring vehicle status data, includes: Acquire data collected from different working systems; The collected data is processed using the Kalman filter algorithm to obtain vehicle status data.

[0030] Please refer to Figure 2 In some embodiments, data such as motor current, voltage, speed, and wheel position are collected in real time, for example, through electric wheel current sensors, voltage sensors, speed sensors, and position sensors; data such as speed, torque, temperature, and fuel are obtained through the engine ECU; relevant data such as generator voltage, current, and temperature are obtained through the traction controller; relevant data such as battery SOC, voltage, current, maximum allowable charging and discharging current, and battery pack temperature are obtained through the battery management controller; the vehicle's loaded weight information is collected through the weighing system; the road gradient is determined through the tilt sensor; and the rolling resistance is calculated through the acceleration sensor.

[0031] By utilizing various sensors and related data acquisition and processing in engineering machinery, a state-space model of the engineering machinery is established based on multi-dimensional data. Based on the established state-space model, the Kalman filter algorithm is initialized, and the initial state estimate, covariance matrix, and system noise covariance matrix are set.

[0032] Kalman filtering performs state estimation through two steps: prediction and update. In the prediction step, the current state is predicted based on the system model and the state estimate from the previous time step. In the update step, the predicted state is corrected using the current observation data and the predicted state, and the optimal state estimate for the current time step is obtained by updating the covariance matrix. By continuously repeating the prediction and update steps, noise and interference from sensor measurements are effectively filtered out, resulting in more accurate vehicle load conditions, energy consumption rates, and road conditions. This data can also provide a foundation for subsequent state estimation and energy management.

[0033] Taking the system data of the battery management controller for an electric wheel mining dump truck as an example, its discrete state-space model is as follows:

[0034] It is the state vector at time k, containing the battery voltage U, current I, temperature T, and remaining battery charge (SOC), etc. .

[0035] It is a state transition matrix that describes how the state changes over time, such as the battery's electrical characteristics, charging and discharging process, and thermal balance characteristics.

[0036] It is a control input matrix. It controls the input vector, such as the SOC value.

[0037] It is process noise, with a mean of 0 and a covariance of . The Gaussian distribution represents unpredictable disturbances in the system.

[0038] It is the observation vector at time k, obtained by sensor measurement, such as current measured by a current sensor, voltage measured by a voltage sensor, etc.

[0039] It is the observation matrix, which maps the state vector to the observation space.

[0040] It is observation noise, with a mean of 0 and a covariance of . The Gaussian distribution represents the sensor measurement error.

[0041] Prediction steps: State prediction: Based on the state estimate from the previous time step. Predict the current state: ; Covariance prediction: The covariance matrix of the predicted state: ; Update steps: Kalman gain calculation: Calculate the Kalman gain Used to weigh predicted and observed values: ; Status Update: Covariance Update: Updates the covariance matrix of the state estimates. .

[0042] Using Kalman gain, and combining the predicted state with the observed values, the state estimate is updated: This means obtaining more accurate data parameters.

[0043] In electric wheel energy management control strategies, the advantages of Kalman filtering over other algorithms are: 1. Real-time and efficient: The recursive algorithm can update the state estimate in real time, quickly respond to vehicle dynamic changes, and adapt to real-time control requirements; 2. Multi-source fusion: Effectively integrates information from multiple sensors, leveraging the strengths of each sensor to improve the accuracy of state estimation; 3. Strong noise resistance: Through a noise statistical model, system and measurement noise are processed to ensure stable and reliable estimation results; 4. Model-driven: Based on the state-space model, it accurately describes the dynamic characteristics of the system, providing strong support for optimizing energy allocation.

[0044] It is understandable that the process of acquiring vehicle status data is not limited to using Kalman filtering; for example, second-order filtering can also be used.

[0045] In some embodiments, step S2, determining the energy management parameters for the current transportation condition based on the current transportation conditions and vehicle status data, includes: Obtain supplementary operating condition information corresponding to the current transportation operating condition; wherein, the supplementary operating condition information includes the estimated walking distance and / or driver behavior data for the current transportation operating condition; Based on vehicle status data and supplementary operating condition information, determine the energy management parameters for the current transportation operating condition.

[0046] Among them, the supplementary operating condition information is used to supplement the current transportation operating condition, so as to correct the current transportation operating condition and make it more in line with the actual use of construction machinery, thereby improving the accuracy of the corresponding energy management parameters.

[0047] The estimated travel distance for the current transportation condition can be determined based on historical data or set by the user. For example, when transporting repeatedly on a certain slope, the slope length can be calculated based on the travel time and speed during the first transport, and the previously calculated slope length can be used during the second transport. By combining the estimated travel distance, the energy management parameters for the current transportation condition can be determined more accurately, such as more accurately estimating the energy required by the motor.

[0048] Driver behavior data refers to the driver's driving habits under the current transportation conditions. The driver behavior data will vary depending on the current transportation conditions. When transporting repeatedly on a certain slope, the driver behavior data, such as acceleration changes and average speed, can be determined based on the collected data during the first transport. The driver behavior data determined during the first transport can then be used during the second transport. Alternatively, driver behavior data can be the driver's driving behavior data under the same conditions over a period of time. Since different drivers have different driving habits, the energy management parameters of the current transportation conditions can be determined more accurately based on driver behavior data. For example, the energy and power required by the motor can be estimated more accurately.

[0049] In some embodiments, energy management parameters for the current transportation condition are determined based on vehicle status data and supplementary operating condition information, including: When the current transportation condition is heavy-load uphill, the uphill distance is obtained as the estimated travel distance, the first driver's behavior data is obtained as the driver's behavior data, and the first energy management parameter is determined based on the vehicle status data, uphill distance, and first driver's behavior data.

[0050] In the current transportation condition of heavy-load uphill travel, the uphill distance is used as the estimated travel distance. The uphill distance can be the length of the entire slope or the planned travel length for this trip. The first driver behavior data is the driver behavior data under heavy-load uphill conditions. Based on the vehicle status data, uphill distance, and first driver behavior data, the first energy management parameter is determined, which can make the first energy management parameter more suitable for energy output optimization under heavy-load uphill travel.

[0051] In some embodiments, when the current transportation condition is heavy-load uphill, step S3 involves performing corresponding energy management operations based on energy management parameters, including: Compare the current remaining energy of the battery with the energy demanded by the motor; When the battery's current remaining energy is greater than or equal to the motor's energy demand, the drive mode is determined based on the motor's power demand and the battery's rated output power to perform corresponding energy management operations; the drive mode includes pure electric drive mode and hybrid drive mode. When the battery's current remaining energy is less than the motor's required energy, the system enters hybrid drive mode and determines the engine's output power and energy based on the motor's required power and the battery's rated output power to perform corresponding energy management operations.

[0052] Please refer to Figure 3 Get the current SOC and the target remaining SOC value. 剩 Combined with the total battery energy E 电总 Calculate the current remaining energy E of the battery. 电 =E 电总 *(SOC-SOC) 剩 Based on vehicle state data estimated by Kalman filtering, such as gradient, rolling resistance, load, and speed, and combined with uphill distance and first driver behavior data, the required motor power P is calculated. 需 And the energy required by the motor E 需 The target remaining SOC value refers to the minimum acceptable SOC value to avoid over-discharge of the battery.

[0053] Next, the relationship between the battery's current remaining energy and the motor's energy demand is compared, and corresponding capacity management operations are performed based on the comparison results.

[0054] Among them, when the battery's current remaining energy E 电 Greater than or equal to the motor's energy requirement E 需 At that time, based on the motor's required power P 需 With the battery's rated output power P 电 Determine the driving mode to perform corresponding energy management operations; the driving mode includes pure electric driving mode and hybrid driving mode.

[0055] When the battery currently has remaining energy E 电 Less than the energy required by the motor E 需 At that time, it enters hybrid drive mode and adjusts the power output based on the motor's power demand P. 需 With the battery's rated output power P 电 Determine the engine output power P 发 With engine output energy E 发 In order to perform the corresponding energy management operations.

[0056] In some embodiments, a drive mode is determined based on the motor's power demand and the battery's rated output power to perform corresponding energy management operations, including: When the rated output power of the battery is greater than or equal to the power required by the motor, it enters the pure electric drive mode to perform the corresponding energy management operation. When the rated output power of the battery is less than the power required by the motor, the system enters hybrid drive mode and performs corresponding energy management operations to compensate for the power required by the motor through the output power of the engine.

[0057] Please refer to the following: Figure 3 , when E 电 ≥E 需 When, if P 电 ≥P 需 Then it enters pure electric drive mode. If P 电 <P 需 Then it enters hybrid drive mode. In hybrid drive mode, the engine output power P 发 Compensation motor power demand P 需 Rated output power P of medium battery 电 The portion that cannot be met, during the entire uphill process, is the battery operating time t. 电 and engine operating time t 发 They are equal and participate together in energy management.

[0058] In some embodiments, the engine output power and engine output energy are determined based on the motor's required power and the battery's rated output power to perform corresponding energy management operations, including: When the rated output power of the battery is greater than or equal to the power required by the motor, the corresponding energy management operation is performed to compensate the energy required by the motor through the output energy of the engine, and to make the output power of the engine meet the power required by the motor, and the engine working time is less than the battery working time. When the rated output power of the battery is less than the power required by the motor, the corresponding energy management operation is performed to compensate for the energy required by the motor by outputting energy from the engine, and to ensure that the output power of the engine meets the power required by the motor. The engine working time is equal to the battery working time.

[0059] Please refer to Figure 3 , when E电 <E 需 In this case, hybrid driving is required, if P 电 ≥P 需 Then the engine output energy E 发 Compensation motor energy demand E 需 The current remaining energy E of the battery 电 The unmet needs, and at the same time, the engine output power P 发 Only P needs to be satisfied 需 Therefore, the engine operating time t during the entire uphill process is... 发、 Satisfy E 发 The requirement is met; the battery participates in the entire working process. If P 电 <P 需 Engine output energy E 发 Compensation motor energy demand E 需 The current remaining energy E of the battery 电 The unmet needs, and at the same time, the engine output power P 发 Only the motor power requirement P needs to be met. 需 In short, the engine and battery participate in the entire working process.

[0060] Through the energy management operations described above under heavy-load uphill conditions, it is possible to ensure that the motor outputs sufficient torque to overcome gravity and friction. Simultaneously, based on the battery's remaining charge and health status, energy output is optimized to prevent over-discharge and maintain battery life. It should be noted that when energy management parameters are determined without supplementary operating condition information, the energy management process can also be similar to... Figure 3 The processing flow is the same, the only difference being the basis for determining the energy management parameters.

[0061] In some embodiments, when the current transportation condition is a heavy-load uphill climb, the method further includes: When a braking signal is received, determine whether to perform regenerative braking based on the current vehicle speed, the current SOC value, and the target remaining SOC value. If so, then regenerate braking energy.

[0062] Please continue to refer to this. Figure 3 During the entire energy management process uphill, if braking is required, the system determines whether to perform regenerative braking based on the current vehicle speed, current SOC value, and target remaining SOC value. For example, if the current vehicle speed is >5 km / h and the target remaining SOC value is SOC... 剩 If the current SOC is less than 0.9, regenerative braking will be initiated, and the current SOC value will be updated. Simultaneously, the energy management strategy will continue to be dynamically adjusted based on driver data. Conversely, if the current vehicle speed is greater than 5 km / h and the target remaining SOC value is not met, regenerative braking will be initiated. 剩If the current SOC is less than 0.9, then electric braking or mechanical braking will be applied, and regenerative braking will not be performed.

[0063] In some embodiments, energy management parameters for the current transportation condition are determined based on vehicle status data and supplementary operating condition information, including: When the current transportation condition is a light-load downhill, the downhill distance is obtained as the estimated travel distance, the second driver behavior data is obtained as the driver behavior data, and the second energy management parameters are determined based on the vehicle status data, downhill distance, and second driver behavior data.

[0064] In the current transportation condition of light-load downhill, the downhill distance is used as the estimated travel distance. The downhill distance can be the length of the entire slope or the planned travel length for this trip. The second driver behavior data is the driver behavior data under the light-load downhill condition. Based on the vehicle status data, downhill distance, and second driver behavior data, a second energy management parameter is determined, which can make the second energy management parameter more suitable for energy recovery optimization under light-load downhill conditions.

[0065] In some embodiments, when the current transportation condition is a lightly loaded downhill slope, step S3 involves performing corresponding energy management operations based on energy management parameters, including: Compare the relationship between the regenerative braking power and the rated regenerative braking power of the battery; When the battery's rated recovery power is greater than or equal to the braking energy recovery power, energy management operations are performed based on the braking energy recovery power and the target recovery energy, and energy recovery is stopped when the recovered energy is equal to the target recovery energy; When the battery's rated regenerative braking power is less than the braking energy regenerative braking power, energy management operations are performed based on the battery's rated regenerative braking power and the target regenerative energy.

[0066] Please refer to Figure 4 Based on the current SOC value and the target SOCmax value, combined with the total battery energy E 电总 Calculate the target recoverable energy E of the battery. 电回 =E 电总 *(SOCmax-SOC), the regenerative braking power P is calculated based on the vehicle state parameters estimated by Kalman filtering, combined with the downhill distance and second driver behavior data. 制 The target value SOCmax refers to the maximum acceptable SOC value to avoid overcharging the battery.

[0067] Next, the regenerative braking power P is compared. 制 With the battery's rated recycling power P 电回 Based on the size relationship, perform corresponding energy management operations according to the comparison results.

[0068] Where, if P电回 ≥P 制 Then, the energy generated by the motor braking can be completely recovered by the battery. At this time, the current recovery power is calculated according to P. 制 The recovered energy E is determined by the recovery time t and the recovery power P. 制 The decision is that E = P 制 *t.

[0069] If P 制 <P 电回 Then the battery can only recover power P. 电回 During the recovery process, the recovered energy E is converted from the battery's rated recovery power P. 电回 The recovery time t determines the value, i.e., E = P. 电回 *t.

[0070] During the recycling process, the current SOC value is continuously updated, and the recovered energy E is monitored against the target recovered energy E of the battery. 电回 The relationship when E≥E 电回 When that time comes, energy recovery will cease.

[0071] Through the energy management operations described above under light-load downhill conditions, the energy recovery system can be precisely controlled. The energy recovery intensity can be adjusted based on vehicle speed and battery status, efficiently converting the kinetic energy of the wheels into electrical energy stored back in the battery, while simultaneously avoiding any impact on vehicle braking performance and stability during the energy recovery process. It should be noted that when energy management parameters are not determined in conjunction with supplementary operating condition information, the energy management process can also be... Figure 4 The processing flow is the same, the only difference being the basis for determining the energy management parameters.

[0072] The energy management strategy of this application has adaptive capabilities, and can automatically adjust energy management parameters according to real-time changes in operating conditions, vehicle operating status and dynamic changes in battery performance, so as to achieve continuous optimization of the energy management strategy.

[0073] The energy management method for construction machinery disclosed in this application includes: acquiring current transportation conditions and vehicle status data; determining energy management parameters for the current transportation conditions based on the current transportation conditions and vehicle status data; wherein the energy management parameters include at least one of a first energy management parameter and a second energy management parameter, the first energy management parameter including motor power demand and motor energy demand, and the second energy management parameter including braking energy recovery power and target battery recovery energy; and performing corresponding energy management operations based on the energy management parameters. The technical solution of this application, by determining the motor power demand and motor energy demand for optimizing energy output, and / or the braking energy recovery power and target battery recovery energy for optimizing energy recovery, based on transportation conditions and vehicle status data, can effectively optimize the energy utilization rate of construction machinery while taking into account battery life and vehicle performance.

[0074] Based on the same inventive concept as the foregoing embodiments, this invention provides an energy management device for engineering machinery, such as... Figure 5 As shown, the energy management device for construction machinery includes: a processor 310 and a memory 311 storing stored instructions; wherein, Figure 5 The processor 310 shown in the diagram does not indicate that there is only one processor 310, but only indicates the positional relationship of the processor 310 relative to other devices. In practical applications, there can be one or more processors 310; similarly, Figure 5 The memory 311 shown in the diagram has the same meaning, that is, it is only used to indicate the positional relationship of memory 311 relative to other devices. In practical applications, there can be one or more memories 311. When the processor 310 runs the computer program, the energy management method described above is implemented.

[0075] The energy management device of the construction machinery may further include at least one network interface 312. The various components in the energy management device of the construction machinery are coupled together via a bus system 313. It is understood that the bus system 313 is used to realize communication between these components. In addition to a data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 5 The general designated all buses as Bus System 313.

[0076] The memory 311 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 311 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0077] The memory 311 in this embodiment of the invention is used to store various types of data to support the operation of the energy management device of the construction machinery. Examples of this data include: any computer programs used to operate on the energy management device of the construction machinery, such as operating systems and applications; contact data; phone book data; messages; pictures; videos, etc. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications, such as media players, browsers, etc., used to implement various application services. Here, the program implementing the method of this embodiment of the invention can be included in the application.

[0078] This application also provides an engineering machinery, including the energy management device for the engineering machinery as described above.

[0079] Based on the same inventive concept as the foregoing embodiments, this embodiment also provides a computer-readable storage medium storing a computer program. The computer-readable storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc. When the computer program stored in the computer-readable storage medium is executed by a processor, it implements the energy management method described above. For the specific steps implemented when the computer program is executed by the processor, please refer to [link to relevant documentation]. Figure 1 The description of the illustrated embodiments will not be repeated here.

[0080] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An energy management method for engineering machinery, characterized in that, The method includes the following steps: Obtain current transportation conditions and vehicle status data; Based on the current transportation conditions and the vehicle status data, energy management parameters for the current transportation conditions are determined; wherein, the energy management parameters include at least one of a first energy management parameter and a second energy management parameter, the first energy management parameter including motor power demand and motor energy demand, and the second energy management parameter including braking energy recovery power and target battery recovery energy; Perform the corresponding energy management operation based on the energy management parameters.

2. The method according to claim 1, characterized in that, Obtaining the vehicle status data includes: Acquire data collected from different working systems; The collected data is processed using the Kalman filter algorithm to obtain the vehicle status data.

3. The method according to claim 1 or 2, characterized in that, The step of determining the energy management parameters for the current transportation condition based on the current transportation condition and the vehicle status data includes: Obtain supplementary operating condition information corresponding to the current transportation operating condition; wherein, the supplementary operating condition information includes the estimated walking distance and / or driver behavior data for the current transportation operating condition; Based on the vehicle status data and the supplementary operating condition information, the energy management parameters for the current transportation operating condition are determined.

4. The method according to claim 3, characterized in that, Based on the vehicle status data and the supplementary operating condition information, the energy management parameters for the current transportation operating condition are determined, including: When the current transportation condition is heavy-load uphill, the uphill distance is obtained as the expected travel distance, the first driver behavior data is obtained as the driver behavior data, and the first energy management parameter is determined based on the vehicle status data, the uphill distance, and the first driver behavior data. And / or, When the current transportation condition is a light-load downhill, the downhill distance is obtained as the expected travel distance, the second driver behavior data is obtained as the driver behavior data, and the second energy management parameter is determined based on the vehicle status data, the downhill distance, and the second driver behavior data.

5. The method according to claim 4, characterized in that, When the current transportation condition is heavy-load uphill, the execution of corresponding energy management operations based on the energy management parameters includes: Compare the current remaining energy of the battery with the energy required by the motor; When the current remaining energy of the battery is greater than or equal to the energy required by the motor, the driving mode is determined based on the motor's required power and the battery's rated output power to perform corresponding energy management operations; wherein, the driving mode includes pure electric driving mode and hybrid driving mode; When the remaining energy of the battery is less than the energy required by the motor, the system enters a hybrid drive mode and determines the engine output power and engine output energy based on the motor's required power and the battery's rated output power to perform corresponding energy management operations.

6. The method according to claim 5, characterized in that, The process of determining the drive mode based on the motor's required power and the battery's rated output power to perform corresponding energy management operations includes: When the rated output power of the battery is greater than or equal to the power required by the motor, it enters the pure electric drive mode to perform the corresponding energy management operation. When the rated output power of the battery is less than the power required by the motor, the system enters a hybrid drive mode and performs corresponding energy management operations to compensate for the power required by the motor through the output power of the engine. And / or, The process of determining the engine output power and engine output energy based on the motor's required power and the battery's rated output power to perform corresponding energy management operations includes: When the rated output power of the battery is greater than or equal to the power required by the motor, the corresponding energy management operation is performed to compensate the energy required by the motor through the energy output of the engine, and to make the output power of the engine meet the power required by the motor, and the engine working time is less than the battery working time. When the rated output power of the battery is less than the required power of the motor, the corresponding energy management operation is performed to compensate the required energy of the motor by outputting energy from the engine, and to make the output power of the engine meet the required power of the motor, and the engine working time is equal to the battery working time.

7. The method according to claim 4, characterized in that, When the current transportation condition is a heavy-load uphill climb, the method further includes: When a braking signal is received, determine whether to perform regenerative braking based on the current vehicle speed, the current SOC value, and the target remaining SOC value. If so, then regenerate braking energy.

8. The method according to claim 4, characterized in that, When the current transportation condition is a light-load downhill slope, the execution of corresponding energy management operations based on the energy management parameters includes: Compare the relationship between the regenerative braking power and the battery's rated regenerative braking power; When the rated recovery power of the battery is greater than or equal to the braking energy recovery power, an energy management operation is performed based on the braking energy recovery power and the target recovery energy. When the battery's rated recovery power is less than the braking energy recovery power, an energy management operation is performed based on the battery's rated recovery power and the target recovery energy.

9. An energy management device for engineering machinery, characterized in that, include: The memory is configured to store instructions; as well as A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the energy management method for engineering machinery according to any one of claims 1 to 8.

10. An engineering machinery, characterized in that, The energy management device for engineering machinery as described in claim 9.

11. A storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 8.

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