Automobile energy control method and device, vehicle and storage medium

Abstract

The invention relates to the field of vehicle control, and discloses an automobile energy control method and device, a vehicle and a storage medium, and the method comprises the steps: constructing an active-disturbance-rejection controller used for managing the state of charge of a battery in a new energy automobile, and an energy management function used for generating an equivalent fuel consumption minimization strategy; determining a fuel equivalent factor at the current moment by using an active-disturbance-rejection controller, calculating the energy management function based on the fuel equivalent factor, and determining the target generating capacity of the new energy vehicle; and in response to the target generating capacity, distributing the generated electric energy to complete energy control. The disturbance and uncertainty of the system are estimated and compensated in real time by introducing the active disturbance rejection controller, so that the adjustment of the state of charge of the battery is more accurate, the battery can still run stably even in the face of different driving working conditions and environment changes, and the reliability is improved; optimization is carried out through an equivalent consumption minimization strategy, effective balance is achieved between fuel consumption and batteries, and overall fuel consumption and unnecessary energy waste are reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to an automobile energy control method, device, vehicle, and storage medium. Background Art

[0002] With the continuous development of new energy vehicle technology, hybrid vehicles, as an important transitional solution, are experiencing a growing market demand. These vehicles utilize the synergy between electricity and fuel to achieve longer driving range and lower energy consumption.

[0003] In related technologies, hybrid vehicle energy management achieves efficient fuel consumption and electricity usage by coordinating the energy output of the generator (range extender) and battery to optimize vehicle range and performance. However, while rule-based approaches offer low computational requirements and are easy to implement, they require extensive parameter calibration and are limited in adaptability and optimality. Secondly, optimization-based control strategies can achieve near-global optimality, but their complex models and high computational complexity limit their real-time applicability. Thirdly, learning-based approaches rely on large amounts of data for training, and data quality directly impacts performance. Furthermore, model complexity complicates fault diagnosis. Therefore, there is an urgent need for robust, adaptable, and real-time energy management solutions with low computational complexity. Summary of the Invention

[0004] The embodiments of the present application provide an automobile energy control method, device, vehicle, and storage medium to solve the technical problems of poor robustness, adaptability, and real-time performance, as well as large computational complexity, in energy management solutions in related technologies.

[0005] An embodiment of the present application provides an automobile energy control method, comprising: constructing an active disturbance rejection controller for real-time management of the battery state of charge in a new energy vehicle, and an energy management function for generating an equivalent fuel consumption minimization strategy, wherein the new energy vehicle is a hybrid vehicle; using the active disturbance rejection controller to determine the fuel equivalent factor of the new energy vehicle at a current moment, calculating the energy management function based on the fuel equivalent factor, and determining a target power generation of the new energy vehicle; and distributing the generated electrical energy in response to the target power generation to complete energy control of the new energy vehicle.

[0006] In one embodiment of the present application, an active disturbance rejection controller for real-time management of the battery state of charge in a new energy vehicle is constructed, including: constructing a second-order tracking differentiator to output a tracking signal, wherein the tracking signal includes a target battery state of charge and a rate of change of the target battery state of charge; constructing an extended state observer based on the input and output of a target object to determine a real-time estimated state quantity and a total disturbance value of the target object, wherein the target object is a generator and battery model, and the real-time estimated state quantity is an estimated value of the tracking signal; constructing a nonlinear state error feedback for generating a control quantity of the target object based on a deviation between the tracking signal and the real-time estimated state quantity, a control quantity generated by nonlinear feedback, and the total disturbance value; and constructing an active disturbance rejection controller for managing the battery state of charge based on the tracking differentiator, the extended state observer, and the nonlinear state error feedback.

[0007] In one embodiment of the present application, the method further includes: determining an actual battery state of charge using the active disturbance rejection controller, and determining a fuel equivalence factor at a current moment according to a difference between the actual battery state of charge and the target battery state of charge.

[0008] In one embodiment of the present application, after determining the fuel equivalence factor at the current moment, the following steps are further included: Determining a road condition compensation factor and a temperature compensation factor of the new energy vehicle at a current moment, wherein the temperature compensation factor determines a corresponding fuel compensation coefficient according to a preset temperature range within which the temperature at the current moment is located, and the preset temperature ranges may be multiple; and the road condition compensation factor determines a corresponding fuel compensation factor according to the road condition type at the current moment; The fuel equivalence factor at the current moment is corrected according to the road condition compensation factor and the temperature compensation factor to determine a final fuel equivalence factor.

[0009] In one embodiment of the present application, determining the current traffic condition type includes: Determining a current motion state, a historical motion trend, and a state feature weight value of the new energy vehicle at a current moment, wherein the current motion state is associated with the speed and acceleration of the new energy vehicle, and the historical motion trend is characterized by the stability of the new energy vehicle as represented by the ratio of the acceleration standard deviation to the maximum acceleration fluctuation value within a preset time window; According to a first weighted product of the current motion state and the state feature weight value, and the state feature weight value.

[0010] In one embodiment of the present application, the method of constructing the energy management function includes: determining the instantaneous input power of the new energy vehicle, the instantaneous input power is determined by the product of the instantaneous fuel consumption of the engine serving as the generator and the lower calorific value of the fuel; determining the equivalent fuel consumption power of the new energy vehicle, the equivalent fuel consumption power is determined by the product of the fuel equivalence factor and the battery charging and discharging power; determining the smoothness penalty term of the new energy vehicle, the smoothness penalty term is determined by the product of the penalty coefficient and the penalty term, and the penalty term is determined by the square of the difference in power generation between the generator at the current moment and the previous moment; constructing the energy management function according to the sum of the instantaneous input power, the equivalent fuel consumption power and the smoothness penalty term.

[0011] In one embodiment of the present application, the penalty coefficient changes dynamically according to the absolute value of the difference between the target battery state of charge and the current battery state of charge.

[0012] In one embodiment of the present application, if the absolute value of the difference is in the first interval, the penalty coefficient is reduced; if the absolute value of the difference is in the second interval, the penalty coefficient is controlled to increase as the absolute value of the difference increases; if the absolute value of the difference is in the third interval, the penalty coefficient is increased; the first interval, the second interval and the third interval constitute the value range of the absolute value of the difference, and the interval ranges of the first interval, the second interval and the third interval decrease successively.

[0013] An embodiment of the present application also provides an automobile energy control device, including: a construction module, which constructs an active disturbance rejection controller for real-time management of the battery state of charge in a new energy vehicle, and an energy management function for generating an equivalent fuel consumption minimization strategy, wherein the new energy vehicle is a hybrid vehicle; a power generation determination module, which uses the active disturbance rejection controller to determine the fuel equivalent factor of the new energy vehicle at the current moment, calculates the energy management function based on the fuel equivalent factor, and determines the target power generation of the new energy vehicle; and a control response module, which is used to distribute the generated electrical energy in response to the target power generation to complete the energy control of the new energy vehicle.

[0014] An embodiment of the present application also provides a vehicle that adopts a method as described in any of the above embodiments.

[0015] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method of any of the above embodiments is implemented.

[0016] The solution implemented by the aforementioned automotive energy control method, device, vehicle, and storage medium utilizes an active disturbance rejection controller (ADRC) for real-time management of the battery state of charge (SOC) in new energy vehicles, along with a Hamiltonian function for generating an equivalent fuel consumption minimization strategy. The ADRC determines the fuel equivalence factor of the new energy vehicle at the current moment, and based on this fuel equivalence factor, the energy management function is calculated to determine the target power generation of the new energy vehicle. The ADRC, through the introduction of real-time estimation and compensation for system disturbances and uncertainties, enables more precise regulation of the battery state of charge (SOC), ensuring stable operation even under varying driving conditions and environmental changes, reducing performance fluctuations caused by external disturbances, and improving reliability. Furthermore, through optimization of the energy management function using the equivalent consumption minimization strategy, an effective balance is achieved between fuel consumption and battery power, ensuring that the generator operates in a high-efficiency range (i.e., maximizing fuel efficiency), reducing overall fuel consumption and unnecessary energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 An exemplary system architecture diagram of an automobile energy control device applicable to an embodiment of the present application; Figure 2 A schematic flow chart of the automobile energy control method provided in an embodiment of the present application; Figure 3 A schematic diagram of a framework flow of the automobile energy control method provided in an embodiment of the present application; Figure 4 A schematic diagram of adjusting the fuel equivalent factor in the automobile energy control method provided in an embodiment of the present application; Figure 5 A schematic structural diagram of the automobile energy control device provided in an embodiment of the present application; Figure 6 A schematic structural diagram of an electronic device in one embodiment of the present application; Figure 7 Another structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] In order to enable those skilled in the art to better understand the improvements of the technical solution provided by the present disclosure, the present disclosure briefly introduces the implementation scenarios and related information of the automobile energy control method in the related art.

[0021] Figure 1 1 is a functional block diagram of a vehicle 100 provided in an embodiment of the present application. Vehicle 100 is a hybrid electric vehicle, including but not limited to a range-extended electric vehicle and a plug-in hybrid electric vehicle. Vehicle 100 may include a perception system, a display device, and a computing platform. The perception system may include several sensors for sensing information about the environment surrounding vehicle 100. For example, the perception system may include a positioning system, which may be a global positioning system (GPS), a Beidou system or other positioning system, an inertial measurement unit (IMU), a lidar, a millimeter-wave radar, an ultrasonic radar, and a camera.

[0022] Some or all functions of the vehicle 100 may be controlled by a computing platform. The computing platform may include multiple processors. A processor is a circuit with signal processing capabilities. In one implementation, the processor may be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit. The logical relationship of the hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. Furthermore, it may also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. Furthermore, the computing platform may also include a memory for storing instructions, and some or all of the multiple processors may call the instructions in the memory to implement the corresponding functions of the vehicle energy control device 101.

[0023] Currently, energy management for extended-range vehicles (ERVs) achieves efficient fuel consumption and electricity usage by coordinating the energy output of the range extender and battery to optimize vehicle range and performance. However, rule-based approaches, while computationally efficient and easy to implement, require extensive parameter calibration and are limited in adaptability and optimality. Alternatively, optimization-based control strategies can achieve near-global optimality, but their complex models and high computational complexity limit their real-time application. Alternatively, learning-based approaches rely on large amounts of data for training, with data quality directly impacting performance. Furthermore, model complexity complicates fault diagnosis.

[0024] In view of this, in order to solve the above problems of insufficient SOC management accuracy, poor real-time performance of fuel consumption optimization, and weak robustness of energy management strategy, the embodiments of the present application provide an automobile energy control method, device, vehicle and storage medium, wherein, please refer to Figure 2 , Figure 2 A flow chart of a vehicle energy control method provided in an embodiment of the present application is provided, and the method includes the following steps: Step S201, constructing an active disturbance rejection controller for real-time management of the state of charge of a battery in a new energy vehicle, and an energy management function for generating an equivalent fuel consumption minimization strategy, wherein the new energy vehicle is a hybrid vehicle; Step S202: determining the fuel equivalence factor of the new energy vehicle at the current moment using an active disturbance rejection controller, calculating an energy management function based on the fuel equivalence factor, and determining a target power generation of the new energy vehicle; Step S203 , in response to the target power generation, the generated electric energy is distributed to complete the energy control of the new energy vehicle.

[0025] Active Disturbance Rejection Control (ADRC) is an advanced control strategy based on error feedback and disturbance estimation. It achieves efficient control of complex systems by observing and compensating for disturbances, without relying on precise mathematical models. ADRC includes a tracking differentiator (TD), an extended state observer (ESO), and a nonlinear state error feedback (NLSEF). The Equivalent Consumption Minimization Strategy (ECMS) is one of the core optimization strategies in hybrid electric vehicle energy management. It minimizes fuel consumption throughout the vehicle's lifecycle by rationally allocating the output power of fuel and electrical energy sources.

[0026] For example, ADRC uses an extended state observer to treat the nonlinear, strongly coupled characteristics of the battery SOC and external disturbances as a total disturbance, and uses NLSEF to compensate in real time, achieving high-precision SOC tracking without model dependence. At the same time, the energy management function, based on optimal control theory, transforms the dynamic energy management problem into an instantaneous optimization problem. The fuel equivalent factor coordinates the energy distribution between the range extender and the battery to minimize global equivalent fuel consumption. Because the fuel equivalent factor is a dynamic coordination parameter connecting SOC management and fuel optimization, it adjusts in real time with SOC deviations. By updating the energy management function in real time, the global optimization goal is transformed into an instantaneous optimal decision, avoiding the short-sightedness problem caused by rolling optimization in traditional MPC. Negative feedback control ensures that the actual generated power tracks the target value, avoiding power deviations caused by range extender efficiency fluctuations, transmission losses, etc., and ensuring the effective implementation of the energy allocation strategy.

[0027] In some embodiments, alternatives to ADRC include using a linear active disturbance rejection controller to simplify parameter tuning, or replacing the ESO with a sliding mode observer to speed up disturbance estimation. The energy management function can introduce a penalty term, or use dynamic programming to pre-generate an offline optimization trajectory, combined with real-time correction factor adjustments, to balance computational complexity and optimization accuracy. Fuzzy control rules or neural networks can also be used to fit the fuel equivalence factor. Furthermore, for extended-range hybrid vehicles, the power generation strategy can be adjusted based on the grid's charging status, such as prioritizing charging during off-peak hours to reduce the range extender's operating time.

[0028] This approach reduces SOC fluctuations under complex operating conditions, preventing overcharging or over-discharging, and extending battery life. Complex operating conditions include, but are not limited to, rapid acceleration, deceleration, and hill climbing. Furthermore, ensuring the SOC remains within the target range provides a stable foundation for energy allocation, preventing power interruptions or efficiency degradation caused by SOC anomalies. By adjusting the fuel equivalence factor in real time, the energy management strategy dynamically adapts to the SOC state, avoiding the one-size-fits-all approach of fixed factors. The energy management function, influenced by the dynamic factor, balances instantaneous optimization with global fuel minimization, ensuring that the target power generation consistently matches current energy demand and reduces overall vehicle fuel consumption. Furthermore, ADRC's real-time SOC control offsets system disturbances, such as changes in battery internal resistance and fluctuations in power generation efficiency, ensuring the accuracy and reliability of the fuel equivalence factor. The energy management function, based on the dynamic factor, calculates the target power generation quickly in response to changing operating conditions, avoiding performance losses caused by energy allocation lags. Accurate vehicle energy control is achieved by coordinating the operating modes of the engine and motor with the target power generation for power allocation.

[0029] In related technologies, in order to ensure the accuracy of real-time SOC tracking and solve the problem that linear feedback control cannot adapt to a wide range of operating conditions, such as sudden changes in generator power during rapid acceleration and surges in battery internal resistance at low temperatures, which can easily cause overshoot or oscillation.

[0030] To solve the above problems, in some embodiments, an active disturbance rejection controller for real-time management of the battery state of charge in new energy vehicles is constructed, including: A second-order tracking differentiator is constructed to output a tracking signal, wherein the tracking signal includes a target battery state of charge and a rate of change of the target battery state of charge; An extended state observer is constructed based on the input and output of the target object to determine the real-time estimated state quantity and total disturbance value of the target object. The target object is the generator and battery model, and the real-time estimated state quantity is the estimated value of the tracking signal. According to the deviation between the tracking signal and the real-time estimated state quantity, the control quantity generated by the nonlinear feedback and the total disturbance value, a nonlinear state error feedback for generating the control quantity of the target object is constructed; An active disturbance rejection controller for managing battery state of charge is constructed based on a tracking differentiator, an extended state observer and nonlinear state error feedback.

[0031] For example, the tracking differentiator uses nonlinear filtering and dynamic tracking mechanisms to address sudden changes in target SOC commands, such as the shock caused by a rapid increase in target SOC during sudden acceleration. Through state expansion and nonlinear state error feedback, the total disturbance is incorporated into the system state space, eliminating the need for a precise model. The NLSEF uses nonlinear gain scheduling to achieve high-precision regulation with small errors and robust control with large errors. TD addresses command smoothness, ESO addresses state and disturbance estimation, and NLSEF addresses nonlinear control and disturbance compensation. These three elements work together to ensure system stability in scenarios with model uncertainty and complex disturbances, without relying on a precise mathematical model of the generator (i.e., range extender) and battery, significantly reducing engineering implementation complexity.

[0032] Through the above method, by introducing the active disturbance rejection controller, the system disturbance and uncertainty can be estimated and compensated in real time, making the adjustment of the battery state of charge more precise, ensuring that the SOC can accurately approach the target value, and quickly respond to changes in driving conditions, improving accuracy and real-time performance; through the robust design of ADRC, the system can still maintain stable operation when facing different driving conditions and environmental changes, improving the reliability of the system, reducing performance fluctuations caused by external disturbances, and enhancing robustness and reliability.

[0033] In some embodiments, the fuel equivalence factor (FEF) of new energy vehicles is significantly affected by ambient temperature and road conditions. Conventional models suffer from two major drawbacks: 1) Low temperatures increase battery internal resistance, while high temperatures increase cooling energy consumption, both of which cause actual energy consumption to deviate from the calibrated value; and 2) road conditions such as congestion and slopes can alter the load on the drive system, and existing models do not dynamically adjust the FEF. To accurately determine the FEF at the current moment, the following details are provided. In one embodiment, after determining the fuel equivalence factor at the current moment, the method further includes: Determine the road condition compensation factor and temperature compensation factor of the new energy vehicle at the current moment. The temperature compensation factor determines the corresponding fuel compensation coefficient according to the preset temperature range of the current temperature. There are multiple preset temperature ranges. The road condition compensation factor determines the corresponding fuel compensation factor according to the current road condition type. The current fuel equivalence factor is corrected based on the road condition compensation factor and the temperature compensation factor to determine the final fuel equivalence factor.

[0034] For example, an equivalent relationship between electric energy and the calorific value of fuel is established; since the temperature-battery internal resistance / cooling power consumption has a nonlinear relationship, partition compensation is more realistic; similarly, the driving power requirements under different road conditions vary significantly, and the equivalent factor is corrected through the synergistic use of two factors.

[0035] For example, according to the real-time state of the controlled object and the control target as the input of the next stage, there is a certain lag. In order to improve the real-time performance of the control model, a mechanism to identify various working conditions is designed to adjust the compensation. Traffic condition prediction needs to consider both vehicle speed and congestion. In theory, the faster the vehicle speed, the higher the power consumption. However, at low speeds, the motor frequently starts and stops, which also consumes a lot of power. The traffic condition prediction formula is as follows:

[0036] in, Indicates the current motion state. is the maximum vehicle speed, is the maximum acceleration, k is the characteristic term that ensures that the velocity and acceleration are in the state Contribution balance, generally ranging from 10 to 15; is the vehicle speed corresponding to the current time t, is the acceleration corresponding to the current moment t; W is the state feature weight value, which is 0.6.

[0037] Indicates the stability of historical movement trends, Preset time window The standard deviation of acceleration, is the maximum acceleration fluctuation value, where T is the control period of the ADRC-ECMS (active disturbance rejection control-equivalent fuel consumption minimum strategy) model.

[0038] According to the road condition prediction formula, the road condition A relationship table with corresponding road conditions, where road condition types are divided into urban congestion, urban ordinary, expressway, and highway; The road condition classification mapping results are shown in the following table:

[0039] Among them, the fuel equivalence factor can be determined according to different road conditions Road condition compensation factor , which can be obtained from the following table:

[0040] In one embodiment, determining the current traffic condition type includes: Determine the current motion state, historical motion trend, and state feature weights of the new energy vehicle at the current moment. The current motion state is associated with the speed and acceleration of the new energy vehicle. The historical motion trend is characterized by the stability of the new energy vehicle by the ratio of the acceleration standard deviation to the maximum acceleration fluctuation value within a preset time window. According to the first weighted product of the current motion state and the state feature weight value, and the state feature weight value.

[0041] For example, based on the above embodiment, considering the impact of different temperatures on vehicle energy consumption, since EVR vehicles can use the waste heat of the range extender to heat the battery and provide heating at low temperatures, the energy consumption is relatively lower than that of pure electric vehicles, but it still requires additional power consumption. At the same time, this feature varies in different low temperature ranges. The additional power consumption is larger in extremely low temperature environments, and the additional power consumption is lower in low temperature environments. In normal temperature environments, EVR vehicles generally have no additional power consumption. In high temperature environments, a small amount of power is used for cooling. Taking the above factors into consideration, the temperature compensation factor It can be obtained from the following table:

[0042] The comprehensive road condition compensation factor and temperature compensation factor can be used to correct the fuel equivalent factor control parameters input to the ECMS by the ADRC control model:

[0043] Where S is the final fuel equivalence factor, is the fuel equivalence factor calculated before compensation, is the road condition compensation factor, is the temperature compensation factor.

[0044] Through the above method, the battery internal resistance mutation point can be matched through multi-interval preset coefficients, thereby improving the temperature compensation effect; by adopting different road condition compensation factors for different road conditions, the road condition compensation effect is improved. In addition, the nonlinear superposition law of temperature and road conditions on energy consumption is revealed, which is conducive to the accurate determination of the fuel equivalent factor.

[0045] In one embodiment, the core pain point of coordinated control of battery state of charge and fuel equivalence factor in hybrid electric vehicle energy management is addressed. Traditional SOC estimation methods are susceptible to current sensor errors and battery aging, resulting in large deviations between actual SOC and true value, affecting the accuracy of energy allocation strategies. At the same time, SOC measurement and fuel equivalence factor calculation are independent of each other and lack a closed-loop coordination mechanism, resulting in delayed strategy response.

[0046] To solve the above technical problems, the present application uses an active disturbance rejection controller to determine the actual battery state of charge, and determines the fuel equivalence factor at the current moment based on the difference between the actual battery state of charge and the target battery state of charge.

[0047] For example, an extended state observer (ESO) treats the battery's nonlinear characteristics and external disturbances as a total disturbance and estimates them in real time. Combined with nonlinear state error feedback, this method achieves accurate SOC measurement without model dependency. Dynamic correction of the SOC by the ESO overcomes the cumulative error of the ampere-hour integration method, allowing the SOC to approach its true value. For example, if the SOC is low (i.e., below a first threshold), the equivalent fuel cost of battery charging is increased, prioritizing the generator. If the SOC is high (i.e., above a second threshold), the charging cost is reduced, prioritizing battery power. If the SOC is in the intermediate range (i.e., between the first and second thresholds), a linear transition is implemented to ensure smooth factor changes and avoid frequent generator starts and stops. The first threshold is lower than the second threshold.

[0048] Through this approach, ADRC forms a closed loop between SOC estimation and dynamic factor adjustment, reducing the overall error of energy management strategies. It also reduces equivalent fuel consumption under NEDC conditions, with significant advantages in urban congestion. It also eliminates the need for precise battery models and complex calibration, shortening parameter adjustment cycles and enabling direct integration into existing vehicle controllers. Through the deep coupling of ADRC's high-precision SOC measurement and dynamic factor adjustment, it solves the problems of inaccurate measurement, lagging strategies, and large errors in hybrid vehicle energy management.

[0049] In related technologies, traditional energy management strategies only focus on the instantaneous fuel consumption of the engine, and fail to incorporate the equivalent fuel consumption of battery charging and discharging into a unified optimization framework, resulting in low overall vehicle energy efficiency and insufficient coordinated optimization of fuel consumption and battery energy consumption; frequent sudden changes in power generation, such as a sudden increase in power during rapid acceleration, will aggravate engine wear, increase noise and vibration, and reduce energy conversion efficiency, causing excessive fluctuations in generator power; in addition, fixed-weight energy management strategies cannot adapt to complex road conditions, and it is difficult to achieve a balance between fuel economy and power smoothness, resulting in a poor match between actual operating conditions and targets.

[0050] In some embodiments, to solve the above problems, the energy management function is constructed in a manner including: Determine the instantaneous input power of the new energy vehicle, which is determined by multiplying the instantaneous fuel consumption of the engine as a generator by the lower calorific value of the fuel; Determine the equivalent fuel consumption power of new energy vehicles, which is determined by multiplying the fuel equivalence factor by the battery charge and discharge power; Determine the smoothness penalty term for new energy vehicles. The smoothness penalty term is determined by the product of the penalty coefficient and the penalty term. The penalty term is determined by the square of the difference between the generator's current and previous power generation times. The energy management function is constructed based on the sum of instantaneous input power, equivalent fuel consumption power and smoothness penalty term.

[0051] For example, instantaneous input power represents the energy consumed per unit time by the engine. This dimensionality is unified by converting fuel consumption into power units (kW). The fuel equivalence factor (FEF) converts battery power consumption into equivalent fuel power, achieving a unified measure of fuel and electrical energy consumption. The FEF's dynamic characteristics adjust with SOC deviation. When the SOC is low, it increases, increasing the equivalent cost of charging and prioritizing engine power generation. When the SOC is high, it decreases, reducing the equivalent cost of discharging and prioritizing battery energy. A squared penalty is used to impose a cost on drastic changes in generator power, introducing a dynamic constraint into the optimization objective. The squared power difference ensures that the penalty increases nonlinearly with the amplitude of the fluctuation; greater fluctuations result in heavier penalties, forcing the optimization strategy to favor gentle power changes and reduce frequent engine starts and stops or sudden load changes.

[0052] Among them, the energy management function integrates the three major goals of fuel economy, battery energy consumption, and power smoothness into a single optimization indicator, and achieves multi-objective collaborative optimization through minimization; through the dynamic balance of each sub-item, for example, the instantaneous input power represents the direct energy consumption of the engine, the equivalent fuel consumption power represents the indirect energy consumption of the battery, and the smoothness penalty item represents the stability cost of the power system. The three together constitute an energy cost function that reflects the comprehensive performance of the vehicle.

[0053] Through the above method, by optimizing the equivalent consumption minimization strategy, an effective balance is achieved between fuel consumption and battery usage, ensuring that the range extender operates in the high-efficiency area, thereby maximizing fuel efficiency, reducing overall fuel consumption and unnecessary energy waste, optimizing fuel efficiency and saving energy; at the same time, by modifying the energy management function of the equivalent consumption minimization strategy and adding constraints, drastic changes in the output power of the range extender are effectively avoided, improving the system stability and driving experience, and reducing output power fluctuations.

[0054] In related technologies, large variations in range extender output power over time can lead to reduced fuel efficiency, increased mechanical wear, reduced ride comfort, increased battery management challenges, and exacerbated emissions. Such frequent variations can make it difficult for the range extender to operate within its optimal efficiency range, increase stress and thermal cycling on mechanical components, and thus accelerate wear and failure risk. Furthermore, power fluctuations put pressure on the battery management system, potentially accelerating battery aging, and can cause increased noise and vibration, impacting passenger comfort.

[0055] Therefore, an optimization strategy is used in the energy management system to smooth the power output of the range extender to ensure the efficiency and stability of the system. In order to minimize the output power fluctuation of the range extender, a penalty term for the range extender output power is added to the original energy management function. For example, the expression of the energy management function is: Formula (1) In formula (1), is the first target power generation of the generator in period t, is the second target power generation of the generator in period t-1, is the penalty coefficient and its value range is 0.1-0.9, is the instantaneous fuel consumption of the engine as a generator, is the fuel equivalence factor, is the battery charging and discharging power, It is the low calorific value of fuel.

[0056] In this embodiment, by optimizing the equivalent consumption minimization strategy, an effective balance is achieved between fuel consumption and battery usage, ensuring that the range extender operates in a high-efficiency area, thereby maximizing fuel efficiency, reducing overall fuel consumption and unnecessary energy waste, and optimizing fuel efficiency and saving energy; at the same time, by modifying the energy management function of the equivalent consumption minimization strategy and adding constraints, drastic changes in the output power of the range extender are effectively avoided, improving the stability of the system and the driving experience, and reducing output power fluctuations.

[0057] This application aims to solve the problem of how to dynamically adjust the penalty coefficient based on the difference between the target battery state of charge and the current battery state of charge to better manage the battery charging and discharging process, ensure that the battery operates within a reasonable state of charge range, and improve the battery's safety, stability and service life.

[0058] In related technologies, battery state-of-charge management solutions that dynamically adjust penalty coefficients based solely on the absolute value of the difference suffer from insufficient adjustment accuracy and uneven response efficiency. When the absolute value of the difference falls within different ranges, a unified linear adjustment logic struggles to strike a balance between rapid correction and precise control.

[0059] Exemplarily, the penalty coefficient changes dynamically according to the absolute value of the difference between the target battery state of charge and the current battery state of charge. The target battery state of charge and the current battery state of charge range from 0.2 to 0.8, and the absolute value of the difference ranges from 0 to 0.6.

[0060] For example, based on the hierarchical response logic, the continuous absolute values ​​of the differences are divided into three intervals, so that the penalty coefficient adjustment is upgraded from a single strategy to multi-mode switching; in the first interval, a weak intervention strategy is adopted, and a low penalty coefficient is fixed to reduce the sensitive response to small fluctuations; in the second interval, a proportional control strategy is adopted, and a linear function is used to achieve precise matching of the coefficient and the difference, which conforms to the classic logic in feedback control that the greater the deviation, the stronger the adjustment; in the third interval, a strong intervention strategy is adopted, and a high penalty coefficient of 0.9 is fixed to quickly bring the SOC back to a reasonable range by maximizing the adjustment force to avoid continued expansion of the deviation. The third interval has the smallest range and triggers strong intervention only when the SOC deviates seriously, avoiding system shocks caused by frequent activation of high penalty coefficients; the second interval has a medium range, covering the main fluctuation range of the absolute value of the difference, ensuring the continuity of routine adjustments; the first interval has the largest range, providing a looser fault tolerance space for minor deviations and reducing the frequency of adjustments; the three form a distribution that is tight at both ends and wide in the middle, avoiding the normal state of SOC fluctuating in a small range near the target value.

[0061] Specifically, the current battery state of charge (SOC) is used as a feedback signal and compared with the target SOC. The penalty coefficient is adjusted based on the difference between the two, and the battery's charge and discharge strategy is then adjusted. Through continuous feedback and adjustment, the battery SOC approaches the target value and remains within a relatively stable and reasonable range. The penalty coefficient is similar to a penalty mechanism. When the battery SOC deviates from the target value, the current charge and discharge behavior is penalized by increasing the penalty coefficient. For example, the charge and discharge power is reduced to slow the trend of further deviation of the battery state and encourage it to approach the target state. In this way, the battery is prevented from operating for a long time at an unfavorable SOC, reducing damage to battery performance and life.

[0062] By limiting the battery state of charge (SOC) range to 0.2-0.8, overcharging and over-discharging are avoided, reducing safety risks such as thermal runaway, fire, and explosion caused by overcharging and over-discharging, thereby improving battery safety. Furthermore, dynamically adjusting the penalty coefficient allows the battery state of charge to more steadily approach the target value, reducing significant fluctuations in the battery state of charge. For example, during charging, if the current SOC increases too rapidly and approaches the target value, the penalty coefficient is increased, reducing the charging current, allowing the SOC to steadily approach the target value. This avoids problems such as battery heating and voltage instability caused by excessive charging current, and enhances battery operational stability. A reasonable SOC range and dynamically adjusted penalty coefficient can reduce irreversible damage caused by internal chemical reactions in the battery. When the battery SOC deviates from the reasonable range, the penalty mechanism adjusts the charge and discharge strategy to prevent the battery from continuing to operate under adverse conditions, thereby effectively extending the battery's cycle life. For example, this prevents lithium plating caused by prolonged overcharging and structural damage to electrode materials caused by over-discharge, thereby extending the battery's service life. In electric vehicles, a stable battery SOC can ensure the stability of vehicle power output, improve the accuracy of range estimation, and enhance user experience.

[0063] Exemplarily, if the absolute value of the difference is in the first interval, the penalty coefficient is reduced and set to 0.1; if the absolute value of the difference is in the second interval, the penalty coefficient is controlled to increase with the increase of the absolute value of the difference; if the absolute value of the difference is in the third interval, the penalty coefficient is increased and set to 0.9; the first interval, the second interval and the third interval constitute the value range of the absolute value of the difference, and the interval ranges of the first interval, the second interval and the third interval decrease successively, wherein the interval range of the penalty coefficient is 0.1~0.9.

[0064] Through the above method, a low coefficient is fixed in the first interval to avoid oscillations in the charge and discharge strategy caused by small deviations, so that the SOC fluctuates stably around the target value; linear adjustment in the second interval ensures precise control under medium deviations. For example, when the absolute value of the difference increases from 0.2 to 0.5, the coefficient increases smoothly from 0.4 to 1.0 to avoid over- or under-adjustment; a high coefficient is fixed in the third interval. When the SOC deviates seriously (such as the absolute value of the difference = 0.6), the correction time can be shortened and the battery's residence time in a high-risk state can be reduced. The interval division simplifies the calculation logic, reduces the amount of calculation compared to the full-range dynamic function, and adapts to low-power battery management systems; through refined control, the battery's residence time in the high-risk SOC interval is reduced, extending the cycle life; at the same time, the frequent adjustment of the charge and discharge strategy is reduced, and the growth rate of the battery's internal impedance is reduced.

[0065] In some embodiments, see Figure 3, is a schematic diagram of a framework flow of the vehicle energy control method provided in an embodiment of the present application; wherein, the energy management framework of the extended-range vehicle is as follows: An extended-range vehicle (ERV) is a vehicle architecture that combines pure electric and traditional internal combustion engine technologies, extending the range of an electric vehicle by generating electricity through a range extender. The energy management method in this application is applied to the battery maintenance mode of an ERV. In battery maintenance mode, the ERV charges the battery through fuel-generated electricity, and the battery discharges electricity to the motor to drive the vehicle. The general energy management function for the ERV's equivalent fuel consumption minimization strategy is: Formula (2) Among them, formula (1) is obtained by transforming formula (2). In formula (2), is the first target power generation of the range extender in period t, is the instantaneous fuel consumption of the engine, is the equivalence factor, is the charge and discharge power, The fuel has a low calorific value. Stay on target Nearby, and the vehicle's operating state is relatively complex, the fuel equivalence factor needs to be dynamically adjusted To maintain the battery This application uses the ADRC algorithm to adjust the fuel equivalence factor in real time. Then, the target power generation of the range extender, i.e., the output power of the range extender, is obtained according to the energy management function of the ECMS. The above steps are calculated cyclically within the driving cycle.

[0066] In some embodiments, see Figure 4 , which is a schematic diagram of adjusting the fuel equivalent factor in the automobile energy control method provided in an embodiment of the present application, wherein the adaptive equivalent factor solution based on ADRC is described in detail as follows: During vehicle operation, system dynamics fluctuate significantly. ADRC, with its superior disturbance compensation capabilities, adaptability, and robustness, offers superior performance compared to traditional PID (proportional-integral-derivative) control. ADRC uses an extended state observer (ESO) to estimate and compensate for system disturbances and uncertainties in real time. This, combined with nonlinear state error feedback and a tracking differentiator, enables precise control of complex systems. The tracking differentiator (TD) generates a smooth desired signal and its derivative. The ESO estimates the system state and total disturbance in real time, improving system robustness. Nonlinear state error feedback (SEF) regulates the control input through nonlinear feedback, achieving precise control.

[0067] Tracking Differentiator Design In SOC control, TD is used for smooth tracking , TD is designed as a simple second-order system to generate the reference trajectory and its derivatives. Its basic form can be described as: Formula (3) Among them, in formula (3) is smooth value (i.e., target battery state of charge), yes The rate of change, It is an adjustment parameter to control the response speed of TD. is the sampling period, is the fastest control synthesis function.

[0068] Extended State Observer Design By estimating the system state and total disturbance in real time, ESO provides the controller with more comprehensive information, enabling the control strategy to dynamically adapt to system changes. Compared to traditional PID control, ESO not only enhances system robustness and effectively compensates for unmodeled dynamics and external disturbances, but also reduces reliance on precise system models. This capability enables ADRC to excel in complex and uncertain environments, ensuring that controlled objects such as the battery state of charge (SOC) accurately track their target values, improving overall system stability and performance. The design of ESO is as follows: Formula (4) Among them, in formula (4) is the actual SOC value, 、 and is the observer gain, is the input of the range extender-battery model, 、 and is the state quantity of ESO, e is the error, b0 is the compensation coefficient of the total disturbance of the system, and the estimation range of the total disturbance and the size of the compensation component of the extended state observer (ESO) are determined. First, b0 is set to a small value to observe the control effect during system operation, and then the b0 value is gradually increased until the control effect is relatively stable, that is, the output signal has no overshoot and / or the response speed meets the preset requirements, then the control effect is determined to be relatively stable. On the contrary, if the system vibrates or is unstable during operation, the b0 value can be appropriately reduced or other parameters can be adjusted.

[0069] Linear state error feedback (SEF) utilizes state and disturbance estimates provided by an extended state observer (ESO) to design a nonlinear control law to generate control inputs. SEF is used to reduce the error between the system state and the target state, thereby achieving precise control of the system. SEF enhances the system's robustness to disturbances and uncertainties, improving its dynamic response performance. The SEF design form is shown below: Formula (5) Among them, in formula (5) is the virtual control quantity of the controlled object (range extender-battery model), 、 They are the target battery state of charge, the target battery state of charge change rate, is the initial equivalent factor, and is the controller coefficient, 、 The deviation between different tracking signals and real-time estimated state quantities, at the current moment As the next moment Loop iteration.

[0070] In this implementation, the advantages of ADRC and ECMS are combined to achieve precise target tracking of SOC. By adjusting the power output of the range extender and battery in real time, the SOC is ensured to always remain within a safe range, improving the efficiency and life of the battery. Under dynamic driving conditions, the power output of the engine and electric motor can be effectively distributed, optimizing overall energy use. Through real-time energy distribution strategies, the vehicle can maintain optimal performance under various operating conditions. By optimizing the energy management function of the ECMS and adding constraints, drastic changes in the output power of the range extender are effectively avoided. This ensures that the range extender operates in the high-efficiency area, maximizes fuel efficiency, and improves system stability.

[0071] Through the above-mentioned method, the automobile energy control method of the present application has the following technical effects: First, through the introduction of the ADRC, this method can estimate and compensate for system disturbances and uncertainties in real time, making the adjustment of the battery state of charge more precise, ensuring that the SOC can accurately approach the target value, and quickly respond to changes in driving conditions, thereby improving accuracy and real-time performance.

[0072] Second, the robust design of the ADRC enables the system to maintain stable operation in the face of different driving conditions and environmental changes, reducing performance fluctuations caused by external disturbances and enhancing robustness and reliability.

[0073] Third, by optimizing the equivalent consumption minimization strategy, an effective balance is achieved between fuel consumption and battery usage, ensuring that the range extender operates in the high-efficiency area, thereby maximizing fuel efficiency, reducing overall fuel consumption and unnecessary energy waste, and optimizing fuel efficiency and saving energy.

[0074] Fourth, by modifying the energy management function of the equivalent consumption minimization strategy and adding constraints, the drastic changes in the range extender's output power are effectively avoided, the stability and driving experience are improved, and the output power fluctuations are reduced.

[0075] In one embodiment, an automobile energy control device is provided, which is used to execute the automobile energy control method provided in any of the above embodiments. Figure 5 , Figure 5 A schematic diagram of the structure of the automobile energy control device provided in the embodiment of the present application is shown as follows: Figure 5 As shown, the automobile energy control device includes a construction module 501, a power generation determination module 502 and a control response module 503, wherein: Building module 501, building an active disturbance rejection controller for real-time management of the battery state of charge in the new energy vehicle and an energy management function for generating an equivalent fuel consumption minimization strategy, where the new energy vehicle is a hybrid vehicle; The power generation determination module 502 uses an active disturbance rejection controller to determine the fuel equivalence factor of the new energy vehicle at the current moment, calculates the energy management function based on the fuel equivalence factor, and determines the target power generation of the new energy vehicle; The control response module 503 is used to distribute the generated electric energy in response to the target power generation to complete the energy control of the new energy vehicle.

[0076] The specific definitions of the vehicle energy control device can be found in the definitions of the vehicle energy control method above and will not be further elaborated here. Each module in the aforementioned vehicle energy control device may be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules may be embedded in or independent of a processor within an electronic device in hardware form, or may be stored in memory within the electronic device in software form, allowing the processor to call and execute the corresponding operations of each module.

[0077] In this embodiment, the automobile energy control device is essentially provided with multiple modules for executing the automobile energy control method in any of the above embodiments. The specific functions and technical effects can be referred to the above embodiments and will not be repeated here.

[0078] In one embodiment, a vehicle is provided, comprising the automobile energy control device provided by any one of the above embodiments.

[0079] The specific definitions of the vehicle can be found in the above-mentioned definitions of the vehicle energy control method and will not be further elaborated here. Each module in the aforementioned vehicle may be implemented in whole or in part through software, hardware, or a combination thereof. Each of the aforementioned modules may be embedded in or independent of a processor within an electronic device in hardware form, or may be stored in a memory within the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0080] In one embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure may be as shown in FIG. Figure 6 As shown. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external client via a network connection. The computer program is executed by the processor to implement the functions or steps on the server side of the above method.

[0081] In one embodiment, an electronic device is provided. The electronic device may be a client, and its internal structure diagram may be as follows: Figure 7 As shown. The electronic device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external server via a network connection. The computer program is executed by the processor to implement the functions or steps of the client side of the above method.

[0082] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed: In step S201, an active disturbance rejection controller is constructed for real-time management of the state of charge of the battery in the new energy vehicle, and an energy management function is constructed for generating an equivalent fuel consumption minimization strategy. In step S202, the fuel equivalent factor of the new energy vehicle at the current moment is determined using the active disturbance rejection controller, and the energy management function is calculated based on the fuel equivalent factor to determine the target power generation of the new energy vehicle. In step S203, the generated electrical energy is distributed in response to the target power generation to complete the energy control of the new energy vehicle.

[0083] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: In step S201, an active disturbance rejection controller is constructed for real-time management of the state of charge of the battery in the new energy vehicle, and an energy management function is constructed for generating an equivalent fuel consumption minimization strategy. In step S202, the fuel equivalent factor of the new energy vehicle at the current moment is determined using the active disturbance rejection controller, and the energy management function is calculated based on the fuel equivalent factor to determine the target power generation of the new energy vehicle. In step S203, the generated electrical energy is distributed in response to the target power generation to complete the energy control of the new energy vehicle.

[0084] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or electronic device can be referred to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0085] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct memory bus RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0086] Those skilled in the art will clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device or system can be divided into different functional units or modules to complete all or part of the functions described above.

[0087] The embodiments provided above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for controlling automobile energy, characterized in that: The method comprises: Constructing an active disturbance rejection controller for real-time management of the battery state of charge in a hybrid electric vehicle and an energy management function for generating an equivalent fuel consumption minimization strategy; Determining a fuel equivalence factor of the new energy vehicle at a current moment using the active disturbance rejection controller, calculating the energy management function based on the fuel equivalence factor, and determining a target power generation of the new energy vehicle; In response to the target power generation, the generated electric energy is distributed to complete the energy control of the new energy vehicle.

2. The automobile energy control method according to claim 1, wherein: Build an active disturbance rejection controller for real-time management of battery state of charge in new energy vehicles, including: constructing a second-order tracking differentiator to output a tracking signal, the tracking signal including a target battery state of charge and a rate of change of the target battery state of charge; An extended state observer is constructed according to the input and output of the target object to determine a real-time estimated state quantity and a total disturbance value of the target object, wherein the target object is a generator and a battery model, and the real-time estimated state quantity is an estimated value of the tracking signal; Constructing a nonlinear state error feedback to generate the control quantity of the target object according to the deviation between the tracking signal and the real-time estimated state quantity, the control quantity generated by nonlinear feedback, and the total disturbance value; According to the tracking differentiator, the extended state observer and the nonlinear state error feedback, an active disturbance rejection controller for managing the battery state of charge is constructed.

3. The automobile energy control method according to claim 2, characterized in that: The method further includes: determining an actual battery state of charge using the active disturbance rejection controller, and determining a fuel equivalence factor at a current moment according to a difference between the actual battery state of charge and the target battery state of charge.

4. The automobile energy control method according to claim 3, characterized in that: After determining the fuel equivalence factor at the current moment, it also includes: Determining a road condition compensation factor and a temperature compensation factor of the new energy vehicle at a current moment, wherein the temperature compensation factor determines a corresponding fuel compensation coefficient according to a preset temperature range within which the temperature at the current moment is located, and the preset temperature ranges may be multiple; and the road condition compensation factor determines a corresponding fuel compensation factor according to the road condition type at the current moment; The fuel equivalence factor at the current moment is corrected according to the road condition compensation factor and the temperature compensation factor to determine a final fuel equivalence factor.

5. The automobile energy control method according to claim 4, characterized in that: Determine the current traffic conditions, including: Determining a current motion state, a historical motion trend, and a state feature weight value of the new energy vehicle at a current moment, wherein the current motion state is associated with the speed and acceleration of the new energy vehicle, and the historical motion trend is characterized by the stability of the new energy vehicle as represented by the ratio of the acceleration standard deviation to the maximum acceleration fluctuation value within a preset time window; The road condition type at the current moment is determined based on a first weighted product of the current motion state and the state feature weight value, and a second weighted product of the complement of the state feature weight value and the historical motion trend.

6. The automobile energy control method according to claim 1, wherein: The energy management function is constructed in the following manner: Determining an instantaneous input power of the new energy vehicle, where the instantaneous input power is determined by multiplying the instantaneous fuel consumption of the engine serving as the generator by the lower calorific value of the fuel; Determining the equivalent fuel consumption power of the new energy vehicle, where the equivalent fuel consumption power is determined by multiplying the fuel equivalence factor by the battery charge and discharge power; Determining a smoothness penalty term for the new energy vehicle, where the smoothness penalty term is determined by multiplying a penalty coefficient by a penalty term, and the penalty term is determined by the square of a difference between a current moment and a previous moment in power generation of the generator; A Hamiltonian function is constructed according to the sum of the instantaneous input power, the equivalent fuel consumption power, and the smoothness penalty term.

7. The automobile energy control method according to claim 6, characterized in that: The penalty coefficient changes dynamically according to the absolute value of the difference between the target battery state of charge and the current battery state of charge.

8. The automobile energy control method according to claim 7, characterized in that: If the absolute value of the difference is in the first interval, the penalty coefficient is reduced; if the absolute value of the difference is in the second interval, the penalty coefficient is controlled to increase as the absolute value of the difference increases; if the absolute value of the difference is in the third interval, the penalty coefficient is increased; the first interval, the second interval and the third interval constitute the value range of the absolute value of the difference, and the interval ranges of the first interval, the second interval and the third interval decrease successively.

9. An automobile energy control device, characterized in that: include: Building a module to build an active disturbance rejection controller for real-time management of the state of charge of a battery in a new energy vehicle, and an energy management function for generating an equivalent fuel consumption minimization strategy, wherein the new energy vehicle is a hybrid electric vehicle; a power generation determination module, which uses the active disturbance rejection controller to determine the fuel equivalence factor of the new energy vehicle at a current moment, calculates the energy management function based on the fuel equivalence factor, and determines the target power generation of the new energy vehicle; The control response module is used to distribute the generated electric energy in response to the target power generation to complete the energy control of the new energy vehicle.

10. A vehicle, characterized in that: The vehicle adopts the method according to any one of claims 1 to 8.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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