Energy consumption management method and device of electric controller

Abstract

The application discloses an energy consumption management method and device of an electric controller, and comprises the following steps: in response to a power-on starting signal of the electric controller, acquiring vehicle running state data and battery state data, preprocessing the running state data and the battery state data to obtain processed running state data and battery state data; based on the processed running state data and the battery state data, constructing a vehicle energy consumption prediction model, the vehicle energy consumption prediction model being used for predicting vehicle energy consumption data at the next moment; based on the vehicle energy consumption data at the next moment, determining an energy consumption adjustment mode, and controlling the controller to execute a corresponding power output strategy according to the energy consumption adjustment mode, wherein the energy consumption adjustment mode comprises an economic mode and a power mode.

Description

Technical Field

[0001] This invention discloses an energy consumption management method and device for an electric controller, belonging to the field of electric controller technology. Background Technology

[0002] Currently, electric vehicle controllers generally employ energy management methods based on instantaneous power or battery state of charge. This means the controller collects signals such as vehicle speed, throttle opening, and battery voltage in real time, adjusting motor power output according to a preset fixed mapping relationship or a simple lookup table. This approach can only passively respond to current driving operations and cannot predict future energy consumption trends during driving. Furthermore, the controller always operates in a single mode and cannot proactively switch power output strategies based on predicted energy consumption. In addition, existing solutions do not effectively suppress and filter noise from raw sensor signals generated during vehicle operation, leading to significant fluctuations in energy consumption calculations. With the increasing complexity of electric vehicle applications, especially under conditions of frequent load changes and significant road condition variations, existing technologies struggle to achieve accurate and proactive management of controller power output. Summary of the Invention

[0003] The purpose of this application is to provide an energy consumption management method and apparatus for an electric controller, so as to at least solve the technical problem that existing electric controllers cannot actively adjust the power output mode according to predicted energy consumption.

[0004] According to one aspect of the present invention, in order to achieve the above-mentioned objective, an energy consumption management method for an electric controller is provided, comprising: In response to the power-on start signal of the electric controller, the vehicle operating status data and battery status data are acquired, and the operating status data and battery status data are preprocessed to obtain the processed operating status data and battery status data. Based on the processed operating status data and battery status data, a vehicle energy consumption prediction model is constructed. The vehicle energy consumption prediction model is used to predict the vehicle energy consumption data at the next moment. Based on the vehicle energy consumption data at the next moment, the energy consumption adjustment mode is determined, and the controller is controlled to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment modes include: economy mode and power mode.

[0005] Furthermore, the operating status data and battery status data are preprocessed to obtain processed operating status data and battery status data, including: Median filtering is performed on the operating status data and battery status data to obtain filtered operating status data and filtered battery status data; Based on the filtered operating status data and filtered battery status data, the Kalman filtering method is used to obtain the processed operating status data and processed battery status data.

[0006] Furthermore, based on the processed operating status data and battery status data, a vehicle energy consumption prediction model is constructed, including: Based on the processed operating status data and battery status data, determine the real-time power consumption data; Obtain several real-time power consumption data points within a historical time period to obtain power consumption time-series data; A vehicle energy consumption prediction model is constructed using a neural network model based on power consumption time-series data.

[0007] Furthermore, based on power consumption time-series data, a vehicle energy consumption prediction model is constructed using a neural network model, including: The power consumption time series data is divided into training and testing sets; Based on the characteristics of power consumption time series data, a radial basis function neural network is selected as the initial model; The radial basis function neural network is trained using the training set data to build a vehicle energy consumption prediction model; The vehicle energy consumption prediction model was evaluated using test set data to verify its prediction accuracy.

[0008] Furthermore, the controller executes the corresponding power output strategy according to the energy consumption adjustment mode, including: When the energy consumption adjustment mode is set to economy mode, the controller reduces the peak duty cycle of the PWM, limits the peak current of the motor, and adjusts the motor operating point to the high-efficiency range. When the energy consumption adjustment mode is in power mode, the controller maintains the current power output and dynamically allocates the output torque based on real-time load demand.

[0009] Furthermore, after determining the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, it also includes: Acquire real-time driving condition data for electric vehicles, which includes at least road slope information and road grade information; Based on real-time driving traffic data, the vehicle energy consumption data for the next moment is adjusted according to traffic conditions. The energy consumption adjustment mode is updated based on the corrected vehicle energy consumption data.

[0010] Furthermore, before determining the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, the following steps are also included: Construct a digital twin model of an electric vehicle, which includes at least a power output model of the controller, a driving dynamics model of the electric vehicle, and a discharge characteristic model of the power battery. The processed operating status data and battery status data are input into the digital twin model to simulate and extrapolate the energy consumption trend within a preset future period, thereby obtaining energy consumption trend prediction data. Based on energy consumption trend prediction data, a pre-adjustment control signal is generated before the vehicle energy consumption data in the next moment exceeds a preset threshold. The pre-adjustment control signal is a temporary power limiting signal. In response to the pre-adjustment control signal, the controller performs a temporary power limit, which is replaced by the power output corresponding to the energy consumption adjustment mode after the energy consumption adjustment mode is determined.

[0011] Furthermore, it also includes: At preset intervals, the real-time acquired operating status data, battery status data, and actual energy consumption data are stored in the storage unit. When the electric vehicle is in charging standby mode, the data in the storage unit is loaded to incrementally train the vehicle energy consumption prediction model and update the model parameters.

[0012] According to one embodiment of the present invention, an energy management device for an electric controller is also provided, comprising: The acquisition module is used to acquire vehicle operating status data and battery status data in response to the power-on start signal of the electric controller, and to preprocess the operating status data and battery status data to obtain processed operating status data and battery status data. The building module is used to build a vehicle energy consumption prediction model based on the processed operating status data and battery status data. The vehicle energy consumption prediction model is used to predict the vehicle energy consumption data at the next moment. The determination module is used to determine the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, and control the controller to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment modes include: economy mode and power mode.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0014] The energy consumption management method and device for an electric controller provided by this invention have the following advantages compared with the prior art: This application employs the following steps: by preprocessing the acquired vehicle operating status data and battery status data, noise interference in the original signal is eliminated, improving the accuracy of the input data for subsequent energy consumption prediction; by constructing a vehicle energy consumption prediction model to predict the vehicle energy consumption data at the next moment, a forward-looking basis is provided for mode switching; by determining the economic mode or power mode based on the predicted energy consumption and controlling the controller to output the corresponding power, the controller can switch to the economic mode in advance to perform power reduction operation before energy consumption increases, realizing the coordinated cooperation between predictive control and execution layer regulation, thereby solving the technical problem that existing electric controllers cannot actively adjust the power output mode according to predicted energy consumption. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of an energy consumption management method for an electric controller according to one embodiment of the present invention; Figure 2 This is a structural block diagram of an energy management device for an electric controller according to one embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application. It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] According to an embodiment of the present invention, an embodiment of an energy consumption management method for an electric controller is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0019] This method embodiment can be executed in an electronic device or similar computing device that includes memory and a processor. Taking operation on a vehicle terminal as an example, the vehicle terminal may include one or more processors (processors may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field-programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processors (TPUs), artificial intelligence (AI) type processors, etc.) and memory for storing data. Optionally, the vehicle terminal may also include transmission devices, input / output devices, and display devices for communication functions. Those skilled in the art will understand that the above structural description is illustrative and does not limit the structure of the vehicle terminal. For example, the vehicle terminal may include more or fewer components than described above, or have a different configuration than described above.

[0020] The memory can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the energy consumption management method of the electric controller in this embodiment of the invention. The processor executes various functional applications and data processing by running the computer program stored in the memory, thereby realizing the energy consumption management method of the electric controller described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0021] The transmission device is used to receive or send data via a network. Specific examples of the network mentioned above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0022] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0023] Example 1 Figure 1 This is a flowchart of an energy consumption management method for an electric controller according to one embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps: Step S110: In response to the power-on start signal of the electric controller, acquire vehicle operating status data and battery status data, preprocess the operating status data and battery status data to obtain processed operating status data and battery status data. The specific steps are as follows: In step S110, the electric controller first performs an initialization operation after power-on. This initialization operation includes at least register configuration, clock source calibration, peripheral initialization, and memory self-test. After the initialization operation is completed, the controller enters a standby state, waiting to receive an external start-up trigger signal. The power-on start signal can be a hard-wired level signal provided by the ignition switch, or a start command carried in a network management message sent by the vehicle's CAN bus. Upon detecting the power-on start signal, the controller enters the execution flow of the energy consumption management method.

[0024] In response to the aforementioned power-on start signal, the system begins acquiring vehicle operating status data. This data is acquired by reading data output from the onboard sensor network. Specifically, the system acquires the vehicle speed signal (output by the vehicle speed sensor), the motor speed signal (reported by the motor controller), the accelerator pedal opening signal (output by the accelerator pedal position sensor, which is proportional to the accelerator pedal opening), and the brake pedal signal (output by the brake pedal switch or brake pedal position sensor). All these signals are synchronously latched at the moment of acquisition, forming a time-aligned set of vehicle operating status data.

[0025] Simultaneously, in response to the aforementioned power-on start signal, the battery status data of the power battery is acquired synchronously. This acquisition is achieved by reading data output from the battery management system. A request message containing a request data type identifier is sent to the battery management system. Upon receiving the request message, the battery management system packages the power battery's terminal voltage signal, discharge current signal, battery temperature signal, and state of charge signal into a response message and sends it back. The system receives and parses the response message, extracting the battery status data from it. The acquisition of vehicle operating status data and battery status data is performed synchronously and in parallel, both falling within the same sampling period.

[0026] After acquiring the above data, the vehicle operating status data and battery status data are preprocessed.

[0027] The preprocessing includes median filtering of the operating status data and the battery status data. Median filtering is implemented as follows: a data buffer is allocated for each input signal, the length of which is denoted as L, where L is an odd number between 3 and 21; in this embodiment, L is 9. L consecutive sample values ​​are sequentially acquired for each signal and stored in the corresponding buffer. Then, the L sample values ​​in the buffer are sorted, and the median value after sorting is selected as the filtered output value of that signal at the current sampling time.

[0028] After median filtering, the Kalman filter is further applied to process the median-filtered data. The Kalman filter is executed as follows: State variables and error covariance matrices are maintained for each signal, and two recursive phases—time update and measurement update—are performed for each signal. In the time update phase, the posterior estimate from the previous sampling time is used as the prior estimate for the current time, and the prior error covariance is updated. In the measurement update phase, the current measurement value is obtained, the Kalman gain is calculated, and the prior estimate is corrected using the Kalman gain and the measurement value to obtain the posterior estimate for the current time. During the recursive process of the Kalman filter, pre-stored initial state estimates and initial error covariance matrices are read as the starting point for the recursion. The process noise covariance matrix and measurement noise covariance matrix are pre-configured according to the physical characteristics of each signal.

[0029] After the Kalman filter recursion is completed, the posterior estimates of each signal are output as processed operating state data and processed battery state data to the data buffer for use in subsequent model building steps.

[0030] Step S120: Based on the processed operating status data and battery status data, a vehicle energy consumption prediction model is constructed. This model is used to predict the vehicle energy consumption data at the next moment. The specific steps are as follows: In step S120, after preprocessing the operating status data and battery status data, the controller constructs a vehicle energy consumption prediction model based on the processed operating status data and battery status data. This vehicle energy consumption prediction model is used to predict the vehicle energy consumption data for the next moment.

[0031] First, real-time power consumption data is determined based on the processed operating status data and battery status data. Specifically, the processed terminal voltage signal is multiplied by the processed discharge current signal to obtain the instantaneous discharge power of the power battery, and this instantaneous discharge power is used as the real-time power consumption data at the current moment. The vehicle speed signal, motor speed signal, accelerator pedal opening signal, and brake pedal signal in the processed operating status data are used as related variables for power consumption and are stored synchronously with the real-time power consumption data.

[0032] Then, several real-time power consumption data points within the historical time period are acquired to obtain power consumption time-series data. Specifically, the controller maintains a circular data queue in memory. The length of this data queue is fixed. Each time real-time power consumption data is acquired at a sampling time, it is stored at the tail of the data queue. When the data queue is full, newly stored data overwrites the old data at the head of the queue. The several real-time power consumption data points stored in this data queue are arranged in chronological order of sampling time to form the power consumption time-series data. The historical time period refers to a preset duration preceding the current sampling time, which is determined by the length of the data queue and the sampling period.

[0033] Next, a vehicle energy consumption prediction model is constructed based on power consumption time-series data. In this embodiment, a neural network model is used as the basic architecture of the vehicle energy consumption prediction model; specifically, a radial basis function neural network is selected as the initial model.

[0034] The radial basis function neural network comprises an input layer, hidden layers, and an output layer. The number of nodes in the input layer is the same as the dimension of the input vector, which includes at least the current power consumption data and several historical power consumption data points from the power consumption time-series data. The number of nodes in the hidden layer is pre-set, and each hidden layer node corresponds to a radial basis function, which is a Gaussian function. The output layer has one node, and its output value is the predicted vehicle energy consumption for the next time step.

[0035] Before constructing the radial basis function neural network, the power consumption time series data is first divided into a training set and a test set. The power consumption time series data is divided into two parts according to a preset ratio, with one part serving as the training set and the other as the test set.

[0036] After selecting a radial basis function neural network (RBFNN) as the initial model based on the characteristics of the power consumption time series data, the RBFNN is trained using the training set data. The training process includes determining the data centers of each node in the hidden layer, determining the spread constants corresponding to each data center, and determining the connection weights between each node in the hidden layer and the output layer.

[0037] The data centers for each node in the hidden layer are determined using the K-means clustering algorithm. Specifically, the number of clusters is set to the number of hidden layer nodes, and the same number of samples as the number of clusters are randomly selected from the training set as initial cluster centers. The distance between each sample in the training set and each cluster center is calculated, and each sample is assigned to the category corresponding to the nearest cluster center. After each sample classification, the mean of each category is recalculated as the new cluster center. The above classification and mean update steps are repeated until the cluster centers no longer change or the change is less than a preset threshold. The final cluster centers are used as the data centers for each node in the hidden layer.

[0038] The expansion constant for each data center is determined as follows: calculate the Euclidean distance between each pair of data centers, take the minimum distance value as the baseline expansion constant, and take the expansion constant of each data center as a preset multiple of the baseline expansion constant.

[0039] The connection weights between each node in the hidden layer and the output layer are determined using the least squares method. Specifically, each sample in the training set is input into the radial basis function neural network, and the output values ​​of each node in the hidden layer are calculated to form a regression matrix. Based on the known energy consumption data corresponding to each sample in the training set as the expected output, the optimal solution for the connection weights is calculated using the least squares formula.

[0040] After training, the vehicle energy consumption prediction model is evaluated using test set data. Each sample in the test set is input into the trained radial basis function neural network to obtain the corresponding energy consumption prediction value. The predicted energy consumption value is compared with the corresponding known energy consumption data in the test set, and the prediction error is calculated. If the prediction error is less than a preset accuracy threshold, the vehicle energy consumption prediction model is considered validated; if the prediction error is greater than or equal to the preset accuracy threshold, the number of hidden layer nodes or the expansion constant is adjusted, and the training and validation steps are repeated.

[0041] After the model is validated, the power consumption data and power consumption time series data acquired at the current moment are input into the vehicle energy consumption prediction model. After forward calculation, the model outputs the vehicle energy consumption prediction value for the next moment, which can be called in the subsequent energy consumption adjustment mode determination steps.

[0042] Step S140: Based on the vehicle energy consumption data at the next moment, determine the energy consumption adjustment mode, and control the controller to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment modes include: economy mode and power mode. The specific steps are as follows: In step 140, after obtaining the vehicle energy consumption data for the next time moment, an energy consumption adjustment mode is determined based on this data. The energy consumption adjustment mode includes an economy mode and a power mode.

[0043] The specific method for determining the energy consumption adjustment mode is as follows: A preset energy consumption threshold is read, and the vehicle's energy consumption data for the next moment is compared with this threshold. If the vehicle's energy consumption data for the next moment is greater than the preset threshold, it is determined that the controller needs to operate at a lower energy consumption level in the next moment, and the energy consumption adjustment mode is determined to be the economy mode. If the vehicle's energy consumption data for the next moment is less than or equal to the preset threshold, it is determined that the controller does not need to limit energy consumption in the next moment, and the energy consumption adjustment mode is determined to be the power mode.

[0044] Once the energy consumption adjustment mode is determined, the controller will execute the corresponding power output according to the determined energy consumption adjustment mode.

[0045] When the energy consumption adjustment mode is set to economy mode, the controller executes the power output corresponding to economy mode. Specifically, the controller reduces the peak duty cycle of the PWM, shortening the conduction time of the power switching transistors, thereby reducing the effective value of the terminal voltage of the motor windings. The controller limits the peak current of the motor, restricting the peak value of the motor phase current below a preset current threshold through current closed-loop control. Simultaneously, the controller adjusts the motor's operating point to the high-efficiency range. The high-efficiency range refers to the speed-torque operating region where the motor efficiency is greater than a preset efficiency threshold. Adjusting the motor operating point is achieved by regulating the inverter's output voltage vector and voltage frequency.

[0046] When the energy consumption adjustment mode is set to power mode, the controller executes the power output corresponding to power mode. Specifically, the controller maintains the current PWM peak duty cycle unchanged, keeping the current power output level constant. Simultaneously, the controller acquires the real-time load demand, which is determined by the accelerator pedal opening signal and the current vehicle speed signal. The controller dynamically allocates output torque based on this real-time load demand, adjusting the inverter's output voltage vector and voltage frequency to ensure the motor output torque follows the changes in load demand in real time.

[0047] During the switching between economic mode and power mode, the controller records the current mode state and re-executes the energy consumption adjustment mode determination steps when the next sampling period arrives, so as to realize the dynamic update of the mode according to the predicted energy consumption.

[0048] Based on steps S110 to S140 above, in this embodiment of the invention, by preprocessing the acquired vehicle operating status data and battery status data, noise interference in the original signal is eliminated, improving the accuracy of the input data for subsequent energy consumption prediction; by constructing a vehicle energy consumption prediction model to predict the vehicle energy consumption data at the next moment, a forward-looking basis is provided for mode switching; by determining the economic mode or power mode based on the predicted energy consumption and controlling the controller to output the corresponding power, the controller can switch to the economic mode in advance to perform power reduction operation before energy consumption increases, realizing the coordinated cooperation between predictive control and execution layer regulation, thereby solving the technical problem that existing electric controllers cannot actively adjust the power output mode according to the predicted energy consumption.

[0049] Furthermore, the operating status data and battery status data are preprocessed to obtain processed operating status data and battery status data. This includes: performing median filtering on the operating status data and battery status data to obtain filtered operating status data and filtered battery status data; and applying Kalman filtering based on the filtered operating status data and filtered battery status data to obtain processed operating status data and processed battery status data. This embodiment performs dual preprocessing—median filtering and Kalman filtering—on the operating status data and battery status data sequentially. First, median filtering removes impulse noise and abnormal abrupt changes from the original signal; then, Kalman filtering dynamically corrects and smooths the time-series data, thereby preserving the signal's trend while filtering out noise.

[0050] Furthermore, based on the processed operating status data and battery status data, a vehicle energy consumption prediction model is constructed, including: determining real-time power consumption data based on the processed operating status data and battery status data; acquiring several real-time power consumption data points within a historical time period to obtain power consumption time-series data; and constructing a vehicle energy consumption prediction model using a neural network model based on the power consumption time-series data. This embodiment determines real-time power consumption data based on processed operating status data and battery status data, then acquires several real-time power consumption data points within a historical time period to form time-series data, and subsequently uses a neural network to construct a prediction model, enabling the vehicle energy consumption prediction model to capture the dynamic changes in power consumption over time.

[0051] Furthermore, based on power consumption time-series data, a vehicle energy consumption prediction model is constructed using a neural network model, including: dividing the power consumption time-series data into a training set and a test set; selecting a radial basis function neural network as the initial model according to the characteristics of the power consumption time-series data; training the radial basis function neural network using the data in the training set to construct the vehicle energy consumption prediction model; and evaluating the vehicle energy consumption prediction model using the data in the test set to verify the prediction accuracy of the vehicle energy consumption prediction model.

[0052] This embodiment trains and validates a radial basis function neural network by dividing power consumption time-series data into training and testing sets. It leverages the characteristics of the radial basis function neural network, such as its simple structure, strong nonlinear approximation ability, and fast training speed, to achieve the construction of a vehicle energy consumption prediction model and the verification of its prediction accuracy with relatively low computational cost.

[0053] Furthermore, the controller executes corresponding power output strategies based on the energy consumption adjustment mode, including: when the energy consumption adjustment mode is the economy mode, the controller reduces the PWM peak duty cycle, limits the motor peak current, and adjusts the motor operating point to the high-efficiency range; when the energy consumption adjustment mode is the power mode, the controller maintains the current power output and dynamically allocates output torque based on real-time load demand. This embodiment achieves differentiated control by reducing the duty cycle, limiting current, and adjusting the operating point in the economy mode, and maintaining power output and dynamically allocating torque in the power mode, thus realizing on-demand adaptation between energy saving and power response.

[0054] Furthermore, after determining the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, the process also includes: acquiring real-time driving road condition data for electric vehicles, which includes at least road slope information and road grade information; performing road condition compensation correction on the vehicle energy consumption data at the next moment based on the real-time driving road condition data; and updating the energy consumption adjustment mode based on the corrected vehicle energy consumption data. This embodiment, by introducing real-time driving road condition data to compensate and correct the predicted energy consumption, enables the energy consumption adjustment mode to be dynamically updated according to changes in the actual driving environment, enhancing the environmental adaptability of the mode determination.

[0055] Furthermore, before determining the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, the process includes: constructing a digital twin model of the electric vehicle, which at least includes a power output model of the controller, a driving dynamics model of the electric vehicle, and a discharge characteristic model of the power battery; inputting the processed operating status data and battery status data into the digital twin model to simulate and extrapolate the energy consumption change trend within a preset future period to obtain energy consumption trend prediction data; based on the energy consumption trend prediction data, generating a pre-adjustment control signal before the vehicle energy consumption data at the next moment exceeds a preset threshold, which is a temporary power limitation signal; responding to the pre-adjustment control signal, controlling the controller to execute the temporary power limitation, wherein the temporary power limitation is replaced by the power output corresponding to the energy consumption adjustment mode after the energy consumption adjustment mode is determined.

[0056] This embodiment uses a digital twin model to implement temporary power limits before energy consumption exceedance events occur, achieving predictive early intervention. Moreover, this temporary limit is replaced by the formal power output after the energy consumption adjustment mode is determined, thus taking into account the timing coordination between emergency pre-adjustment and steady-state mode control.

[0057] Furthermore, it also includes: storing real-time acquired operating status data, battery status data, and actual energy consumption data into the storage unit at preset time intervals; when the electric vehicle is in charging standby mode, loading the data in the storage unit to incrementally train the vehicle energy consumption prediction model and update the model parameters. This embodiment utilizes the non-driving period of electric vehicle charging standby to incrementally train the prediction model, enabling the model to continuously iterate and update based on actual operating data without affecting the normal driving of the vehicle.

[0058] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0059] This invention also provides an energy management device for an electric controller, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0060] Example 2 Figure 2 An energy management device for an electric controller according to one embodiment of the present invention includes: The acquisition module 201 is used to acquire vehicle operating status data and battery status data in response to the power-on start signal of the electric controller, and to preprocess the operating status data and battery status data to obtain processed operating status data and battery status data. Module 202 is used to build a vehicle energy consumption prediction model based on the processed operating status data and battery status data. The vehicle energy consumption prediction model is used to predict the vehicle energy consumption data at the next moment. The determination module 203 is used to determine the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, and control the controller to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment modes include: economy mode and power mode.

[0061] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0062] Example 3 According to one embodiment of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the energy consumption management method of the electric controller described above when it runs.

[0063] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program: Step S1: In response to the power-on start signal of the electric controller, acquire vehicle operating status data and battery status data, preprocess the operating status data and battery status data to obtain processed operating status data and battery status data. Step S2: Based on the processed operating status data and battery status data, construct a vehicle energy consumption prediction model. The vehicle energy consumption prediction model is used to predict the vehicle energy consumption data at the next moment. Step S3: Based on the vehicle energy consumption data at the next moment, determine the energy consumption adjustment mode, and control the controller to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment modes include: economy mode and power mode.

[0064] Example 4 According to one embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the storage medium is located to execute the above-described energy management method of the electric controller.

[0065] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps: Step S1: In response to the power-on start signal of the electric controller, acquire vehicle operating status data and battery status data, preprocess the operating status data and battery status data to obtain processed operating status data and battery status data. Step S2: Based on the processed operating status data and battery status data, construct a vehicle energy consumption prediction model. The vehicle energy consumption prediction model is used to predict the vehicle energy consumption data at the next moment. Step S3: Based on the vehicle energy consumption data at the next moment, determine the energy consumption adjustment mode, and control the controller to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment modes include: economy mode and power mode.

[0066] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0067] According to one embodiment of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the above-described energy management method for an electric controller.

[0068] Example 5 Optionally, in this embodiment, the above-mentioned computer program product can be configured as a computer program that performs the following steps: Step S1: In response to the power-on start signal of the electric controller, acquire vehicle operating status data and battery status data, preprocess the operating status data and battery status data to obtain processed operating status data and battery status data. Step S2: Based on the processed operating status data and battery status data, construct a vehicle energy consumption prediction model. The vehicle energy consumption prediction model is used to predict the vehicle energy consumption data at the next moment. Step S3: Based on the vehicle energy consumption data at the next moment, determine the energy consumption adjustment mode, and control the controller to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment modes include: economy mode and power mode.

[0069] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0070] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0072] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0075] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An energy consumption management method for an electric controller, characterized in that, include: In response to the power-on start signal of the electric controller, vehicle operating status data and battery status data are acquired, and the operating status data and battery status data are preprocessed to obtain processed operating status data and battery status data. Based on the processed operating status data and battery status data, a vehicle energy consumption prediction model is constructed, which is used to predict the vehicle energy consumption data at the next moment. Based on the vehicle energy consumption data at the next moment, an energy consumption adjustment mode is determined, and the controller is controlled to execute the corresponding power output strategy according to the energy consumption adjustment mode. The energy consumption adjustment mode includes an economy mode and a power mode.

2. The energy consumption management method for an electric controller according to claim 1, characterized in that, The operating status data and the battery status data are preprocessed to obtain processed operating status data and battery status data, including: The operating status data and the battery status data are subjected to median filtering to obtain filtered operating status data and filtered battery status data. Based on the filtered operating status data and the filtered battery status data, the Kalman filtering method is used to obtain the processed operating status data and the processed battery status data.

3. The energy consumption management method for an electric controller according to claim 1, characterized in that, Based on the processed operating status data and battery status data, a vehicle energy consumption prediction model is constructed, including: Based on the processed operating status data and battery status data, determine the real-time power consumption data; Obtain several real-time power consumption data points within a historical time period to obtain power consumption time-series data; Based on the power consumption time-series data, a neural network model is used to construct the vehicle energy consumption prediction model.

4. The energy consumption management method for the electric controller according to claim 3, characterized in that, Based on the power consumption time-series data, a vehicle energy consumption prediction model is constructed using a neural network model, including: The power consumption time-series data is divided into a training set and a test set; Based on the characteristics of the power consumption time series data, a radial basis function neural network is selected as the initial model; The radial basis function neural network is trained using the data from the training set to construct the vehicle energy consumption prediction model; The vehicle energy consumption prediction model is evaluated using the data from the test set to verify its prediction accuracy.

5. The energy consumption management method for an electric controller according to claim 1, characterized in that, According to the energy consumption adjustment mode, the controller is controlled to execute the corresponding power output strategy, including: When the energy consumption adjustment mode is the economic mode, the controller is controlled to reduce the PWM peak duty cycle, limit the motor peak current, and adjust the motor operating point to the high-efficiency range; When the energy consumption adjustment mode is the power mode, the controller is controlled to maintain the current power output and dynamically allocate the output torque based on the real-time load demand.

6. The energy consumption management method for an electric controller according to claim 1, characterized in that, After determining the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, the process also includes: The real-time driving road condition data of the electric vehicle is obtained, and the real-time driving road condition data includes at least road slope information and road grade information; Based on the real-time driving traffic data, the vehicle energy consumption data for the next moment is adjusted for traffic conditions. The energy consumption adjustment mode is updated based on the corrected vehicle energy consumption data.

7. The energy consumption management method for an electric controller according to claim 1, characterized in that, Before determining the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, the process also includes: Construct a digital twin model of the electric vehicle, the digital twin model including at least the power output model of the controller, the driving dynamics model of the electric vehicle, and the discharge characteristic model of the power battery; The processed operating status data and battery status data are input into the digital twin model to simulate and extrapolate the energy consumption change trend within a preset future period, thereby obtaining energy consumption trend prediction data. Based on the energy consumption trend prediction data, a pre-adjustment control signal is generated before the vehicle energy consumption data at the next moment exceeds a preset threshold. The pre-adjustment control signal is a temporary power limiting signal. In response to the pre-adjustment control signal, the controller is controlled to perform a temporary power limit, wherein the temporary power limit is replaced by the power output corresponding to the energy consumption adjustment mode after the energy consumption adjustment mode is determined.

8. The energy consumption management method for an electric controller according to any one of claims 1-7, characterized in that, Also includes: The real-time acquired operating status data, battery status data, and actual energy consumption data are stored in the storage unit at preset intervals. When the electric vehicle is in charging standby mode, the data in the storage unit is loaded to incrementally train the vehicle energy consumption prediction model and update the model parameters.

9. An energy consumption management device for an electric controller, characterized in that, include: The acquisition module is used to acquire vehicle operating status data and battery status data in response to the power-on start signal of the electric controller, and to preprocess the operating status data and battery status data to obtain processed operating status data and battery status data. The module is used to build a vehicle energy consumption prediction model based on the processed operating status data and battery status data. The vehicle energy consumption prediction model is used to predict the vehicle energy consumption data at the next moment. The determination module is used to determine the energy consumption adjustment mode based on the vehicle energy consumption data at the next moment, and control the controller to execute the corresponding power output strategy according to the energy consumption adjustment mode, wherein the energy consumption adjustment mode includes: economy mode and power mode.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the energy management method of the electric controller according to any one of claims 1 to 8.

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