Energy management method, device and vehicle for hybrid vehicle

Abstract

The application relates to the technical field of vehicles, in particular to an energy management method and device of a hybrid vehicle and the vehicle. The method comprises the following steps: determining an energy demand of a first target path section; determining a target discrete energy value sequence based on the energy demand; determining a target optimization value corresponding to engine output mechanical energy of the first target path section based on the target discrete energy value sequence, a state of charge of a power battery of the hybrid vehicle at a starting point of the first target path section and a target function; and performing power generation control on the engine on the first target path section by using the target optimization value; wherein the target optimization value is one value in the target discrete energy value sequence; the target function is used for minimizing a first fuel consumption and minimizing a second fuel consumption; the first fuel consumption is a cumulative value of respective fuel consumptions of second target path sections; and the fuel consumption of the second target path section is determined based on an optimization value of engine output mechanical energy of the second target path section.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more particularly to the field of hybrid vehicle technology, specifically to an energy management method, device, and vehicle for a hybrid vehicle. Background Technology

[0002] Hybrid vehicles integrate both an engine and an electric motor, leveraging the high energy density of fuel while utilizing the flexible adjustment capabilities and energy recovery features of the electric motor, demonstrating significant advantages in energy conservation and emission reduction. Optimizing the energy management strategies of hybrid vehicles to further improve fuel economy has become an important research direction in the field of vehicle technology.

[0003] In existing technologies, some energy management methods employ global optimization schemes based on navigation information. These methods typically first acquire the vehicle's navigation information, perform route planning based on the origin and destination, and then perform energy planning based on the route planning results to obtain the engine start-stop control sequence. However, in these methods, the decision variables for energy planning are limited to the binary start-stop state of the engine; that is, they only determine which road segments the engine should be on and off, without specifying how much mechanical energy the engine should output after starting. After receiving the start-stop command, the underlying controller still needs to determine the power allocation between the engine and the electric motor, which carries the risk of execution deviation. Furthermore, some existing methods use dynamic programming algorithms for global energy optimization, but dynamic programming requires traversing the entire state space, and the computational load increases exponentially with the number of road segments, making it difficult to deploy in real-time on the vehicle controller. Summary of the Invention

[0004] This application provides an energy management method, device, and vehicle for hybrid vehicles, to at least solve the problems of execution deviation caused by only planning the engine start-stop state without specifying the specific value of the engine's output mechanical energy in the prior art, and the problem that the global optimization algorithm has a large computational load and is difficult to deploy in real time. The technical solution adopted in this application is as follows: In a first aspect, this application provides an energy management method for a hybrid vehicle, comprising: The energy demand of the first target path segment is determined; wherein the first target path segment is one of multiple path segments divided into the planned path of the hybrid vehicle; based on the energy demand, a target discrete energy value sequence is determined; based on the target discrete energy value sequence, the state of charge of the hybrid vehicle's power battery at the starting point of the first target path segment, and the objective function, the target optimization value corresponding to the engine output mechanical energy of the first target path segment is determined; using the target optimization value, the engine is controlled to generate electricity on the first target path segment; wherein the target optimization value is a value in the target discrete energy value sequence; the objective function is used to minimize the first fuel consumption and Minimize the second fuel consumption; the first fuel consumption is the cumulative value of the fuel consumption of each of the second target path segments; the fuel consumption of the second target path segment is determined based on the optimized value of the engine output mechanical energy of the second target path segment; the second target path segment is the path segment that precedes the first target path segment among multiple path segments; the second fuel consumption is the fuel consumption corresponding to the minimum allowable value of the engine output mechanical energy of the first target path segment; the minimum allowable value is determined based on the cumulative energy demand from the starting point of the first target path segment to the end point of the planned path, and the difference in state of charge between the state of charge and the target state of charge.

[0005] Based on the aforementioned technical means, this application discretizes the engine output mechanical energy of each path segment into multiple candidate energy values, and selects the target optimization value from these based on an objective function for power generation control. This achieves precise planning of engine output energy, solving the execution deviation problem caused by the prior art's inability to specify the specific output energy due to only controlling the engine's start-stop state. Simultaneously, the objective function minimizes the amount of fuel consumed and the lower limit of remaining fuel consumption determined based on remaining energy demand and state-of-charge deviation, ensuring that energy allocation decisions for each path segment consider the global optimum, effectively reducing overall vehicle fuel consumption.

[0006] In one possible implementation, the process of determining the target discrete energy value sequence includes: determining a first energy value based on the product of energy demand and a first adjustment coefficient; determining a second energy value based on the product of the engine's rated power and the expected maximum driving time of the first target path segment; determining the larger of the first and second energy values ​​as the maximum permissible output mechanical energy of the engine for the first target path segment; and discretizing the energy closed interval between 0 and the maximum permissible output mechanical energy of the engine according to a preset energy step size to obtain the target discrete energy value sequence.

[0007] Based on the aforementioned technical methods, the energy upper limit is determined by multiplying the energy demand by an adjustment coefficient, and the physical upper limit is determined by combining the engine's rated power and the expected maximum driving time. The larger of the two values ​​is taken as the engine's maximum permissible mechanical energy output. This ensures that the coverage of the discrete energy value sequence meets the actual driving needs of the path segment without exceeding the engine's physical output capacity. On this basis, discretization is performed according to a preset energy step size to construct a finite and reasonable candidate energy value sequence, ensuring that the subsequent optimization process is both feasible and engineering-practical.

[0008] In one possible implementation, the process of determining the minimum allowable value includes: when the state of charge difference is greater than or equal to 0, determining the first mechanical energy corresponding to the first electrical energy based on the state of charge difference, the total capacity of the power battery, and the charging and discharging efficiency of the power battery; wherein, the first electrical energy is the energy that the power battery needs to release to adjust the state of charge to the target state of charge; and determining the energy difference between the cumulative energy demand and the first mechanical energy as the minimum allowable value.

[0009] Based on the aforementioned technical methods, when the current state of charge (SOC) is higher than the target SOC, the excess electrical energy is converted into replaceable mechanical energy through the motor's charging and discharging efficiency. This excess energy is then deducted from the cumulative energy demand to determine the minimum mechanical energy required by the engine. This method fully utilizes the battery's remaining electrical energy, avoiding fuel waste caused by redundant engine output. Simultaneously, the introduction of charging and discharging efficiency ensures the physical accuracy of the energy conversion. While ensuring the battery ultimately reaches the target SOC, it minimizes the mechanical energy required by the engine, thereby effectively reducing overall vehicle fuel consumption.

[0010] In one possible implementation, the process of determining the minimum allowable value includes: when the state of charge difference is less than 0, determining the second mechanical energy corresponding to the second electrical energy based on the state of charge difference, the total capacity of the power battery, and the charging and discharging efficiency of the power battery; wherein, the second electrical energy is the energy that the power battery needs to replenish to adjust the state of charge to the target state of charge; and determining the sum of the cumulative energy demand and the second mechanical energy as the minimum allowable value.

[0011] Based on the aforementioned technical methods, when the current state of charge (SOC) is lower than the target SOC, the minimum mechanical energy that the engine needs to provide is determined by converting the required replenishment of electricity into additional mechanical energy output by the engine through charging efficiency calculation, and then summing this value with the cumulative energy demand for the remaining road segment. This method ensures that the battery can be replenished to the target SOC before the destination while meeting the energy demand for the remaining road segment. It avoids a decrease in vehicle power performance or excessive battery discharge due to insufficient charge, thus balancing fuel economy and vehicle reliability, and achieving coordinated optimization of energy consumption and charge maintenance.

[0012] In one possible implementation, the process of determining the fuel consumption of the second target path segment includes: determining the fuel consumption of the second target path segment based on the optimization value of the engine output mechanical energy of the second target path segment, the engine's power generation efficiency, and the lower heating value of the fuel.

[0013] Based on the aforementioned technical methods, the fuel consumption of the second target path segment is expressed as a function of the optimal value of engine output mechanical energy, power generation efficiency, and the lower heating value of fuel, thus achieving precise quantification of fuel consumption. Since each candidate energy value corresponds to a specific fuel consumption, the first fuel consumption in the objective function can be accurately calculated, providing a precise cost input for global optimization and ensuring the accuracy and reliability of energy management decisions. Simultaneously, this calculation method is directly related to the actual energy demand of the path segment, avoiding deviations caused by empirical estimations.

[0014] In one possible implementation, based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment, and the objective function, the target optimization value corresponding to the engine output mechanical energy of the first target path segment is determined, including: determining the constraint conditions of the objective function; and determining the target optimization value corresponding to the engine output mechanical energy of the first target path segment based on the target discrete energy value sequence, the state of charge, the objective function, and the constraint conditions.

[0015] Based on the aforementioned technical methods, by introducing constraints into the objective function, the optimization process for engine output mechanical energy is conducted within the feasible region that satisfies the total fuel limit and the battery state of charge safety boundary. This avoids infeasible solutions such as exceeding the remaining fuel or overcharging / over-discharging the battery, ensuring the feasibility and safety of the optimization results in actual driving. The optimization method, determined jointly by the constraints and the discretized candidate energy values, balances global fuel economy optimization with vehicle operation safety.

[0016] In one possible implementation, the constraints include at least the following: the total fuel consumption corresponding to the total engine output mechanical energy is less than the remaining fuel of the hybrid vehicle at the starting point of the planned path; the total engine output mechanical energy is obtained by summing the target optimization values ​​corresponding to the engine output mechanical energy of multiple path segments; the state of charge is between the undervoltage state of charge and the overvoltage state of charge; wherein, the undervoltage state of charge is the state of charge corresponding to the undervoltage protection threshold voltage of the power battery; and the overvoltage state of charge is the state of charge corresponding to the overvoltage protection threshold voltage of the power battery.

[0017] Based on the aforementioned technical methods, a total fuel consumption constraint ensures that the total fuel consumption throughout the journey does not exceed the vehicle's remaining fuel, avoiding infeasible solutions where the vehicle runs out of fuel and cannot complete the trip. Simultaneously, a state-of-charge boundary constraint ensures that the power battery remains within a safe range between undervoltage and overvoltage thresholds throughout the driving process, preventing performance degradation and safety hazards caused by over-discharging or over-charging. These two constraints together constitute the feasible region boundary of the optimization problem, enabling the final determined target optimization value to achieve global fuel economy optimization while ensuring safety and feasibility.

[0018] In one possible implementation, the process of determining the state of charge (SOC) includes: determining the target SOC of the power battery at the starting point of the third target path segment; wherein the third target path segment is the path segment preceding the first target path segment; the target energy difference between the energy demand of the third target path segment and the target optimization value corresponding to the engine output mechanical energy of the third target path segment; determining the change in SOC of the third target path segment based on the target energy difference and the charging and discharging efficiency of the power battery; if the target energy difference is greater than 0, determining the difference between the target SOC and the change in SOC as the SOC; if the target energy difference is not greater than 0, determining the sum between the target SOC and the change in SOC as the SOC.

[0019] Based on the aforementioned technical methods, the change in state of charge (SOC) is accurately calculated using the energy difference and charging / discharging efficiency of the preceding path segment. The direction of SOC increase or decrease is determined by the sign of the energy difference, thus achieving accurate recursion of the SOC at the starting point of each path segment. This method makes the SOC a natural calculation result of energy allocation, ensuring continuous change in the battery state between paths. It provides accurate starting point data for optimization decisions in subsequent path segments, guaranteeing the physical consistency and executability of the global energy management strategy.

[0020] In one possible implementation, the engine is controlled to generate electricity on a first target path segment using a target optimization value, including: determining the estimated travel time of the first target path segment when the starting point is about to be reached; determining the engine control command corresponding to the first target path segment based on the target optimization value and the estimated travel time; wherein the engine control command indicates the control speed and control torque of the engine; and generating electricity on the engine on the first target path segment based on the engine control command.

[0021] Based on the aforementioned technical methods, the average power is obtained by combining the target optimization value with the estimated travel time. Then, based on the optimal energy consumption curve, this power is transformed into specific speed and torque commands. This achieves a precise mapping from energy optimization results to the actual engine control signals, enabling the global optimization results to be directly applied to the vehicle execution layer and ensuring the executability of the optimization scheme. Simultaneously, determining the speed and torque based on the optimal energy consumption curve ensures that the engine always operates within its efficient operating range during this path segment, further reducing fuel consumption.

[0022] In one possible implementation, the process of dividing the planned route into segments includes: dividing the planned route into multiple segments based on changes in traffic congestion index, road gradient, and / or travel time per unit mileage along the planned route.

[0023] Based on the above technical means, by dividing the path into segments at nodes where traffic congestion index, road gradient, or travel time changes significantly, the driving conditions within each path segment are made relatively uniform, avoiding the deviation in demand estimation caused by drastic changes in conditions. This improves the prediction accuracy of energy demand for each road segment, provides more accurate input data for subsequent global optimization, and effectively controls the number of path segments to balance computational complexity and optimization effect.

[0024] Secondly, this application provides an energy management device for a hybrid vehicle, comprising: a first determining module, a second determining module, a third determining module, and a control module.

[0025] The first determining module is used to determine the energy demand of the first target path segment; wherein, the first target path segment is one of the multiple path segments divided into the planned path of the hybrid vehicle; The second determining module is used to determine the target discrete energy value sequence based on energy demand; The third determining module is used to determine the target optimization value corresponding to the engine output mechanical energy of the first target path segment based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment and the objective function. The control module is used to control the engine's power generation on the first target path segment using the target optimization value; The target optimization value is a value in the target discrete energy value sequence; The objective function is used to minimize the first fuel consumption and minimize the second fuel consumption; The first fuel consumption is the cumulative value of the fuel consumption of each of the second target path segments; the fuel consumption of the second target path segment is determined based on the optimization value of the engine output mechanical energy of the second target path segment; the second target path segment is the path segment that precedes the first target path segment among multiple path segments. The second fuel consumption is the fuel consumption corresponding to the minimum allowable value of the engine output mechanical energy for the first target path segment. The minimum allowable value is determined based on the cumulative energy demand from the starting point of the first target path segment to the end point of the planned path, as well as the difference in state of charge between the state of charge and the target state of charge.

[0026] The aforementioned second determining module is also used to determine the first energy value based on the product of the energy demand and the first adjustment coefficient; The second energy value is determined by multiplying the engine's rated power by the estimated maximum travel time of the first target path segment. The larger of the first energy value and the second energy value is determined as the maximum permissible mechanical energy output of the engine for the first target path segment; The energy closed interval between 0 and the engine's maximum permissible output mechanical energy is discretized according to a preset energy step size to obtain the target discrete energy value sequence.

[0027] The control module is also used to determine the first mechanical energy corresponding to the first electrical energy based on the state of charge difference, the total capacity of the power battery, and the charging and discharging efficiency of the power battery when the state of charge difference is greater than or equal to 0; wherein, the first electrical energy is the energy that the power battery needs to release to adjust the state of charge to the target state of charge. The energy difference between the cumulative energy demand and the first mechanical energy is determined as the minimum allowable value.

[0028] The control module is also used to determine the second mechanical energy corresponding to the second electrical energy based on the state of charge difference, the total capacity of the power battery, and the charging and discharging efficiency of the power battery when the state of charge difference is less than 0; wherein, the second electrical energy is the energy that the power battery needs to replenish to adjust the state of charge to the target state of charge. The sum of the cumulative energy demand and the second mechanical energy is determined as the minimum allowable value.

[0029] The control module is also used to determine the fuel consumption of the second target path segment based on the optimization value of the engine output mechanical energy, the engine power generation efficiency, and the lower heating value of the fuel.

[0030] The second determining module mentioned above is also used to determine the constraints of the objective function; Based on the target discrete energy value sequence, state of charge, objective function, and constraints, the target optimization value corresponding to the engine output mechanical energy of the first target path segment is determined.

[0031] The above constraints include at least the following: The total fuel consumption corresponding to the total engine output mechanical energy is less than the remaining fuel of the hybrid vehicle at the starting point of the planned path; the total engine output mechanical energy is obtained by summing the target optimization values ​​corresponding to the engine output mechanical energy of multiple path segments; The state of charge is between the undervoltage state of charge and the overvoltage state of charge; the undervoltage state of charge is the state of charge corresponding to the undervoltage protection threshold voltage of the power battery; the overvoltage state of charge is the state of charge corresponding to the overvoltage protection threshold voltage of the power battery. The second determining module is further used to determine the target state of charge (SOC) of the power battery at the starting point of the third target path segment; wherein the third target path segment is the path segment preceding the first target path segment; the target energy difference between the energy demand of the third target path segment and the target optimization value corresponding to the engine output mechanical energy of the third target path segment; based on the target energy difference and the charging and discharging efficiency of the power battery, the change in SOC of the third target path segment is determined; if the target energy difference is greater than 0, the difference between the target SOC and the change in SOC is determined as the SOC; if the target energy difference is not greater than 0, the sum between the target SOC and the change in SOC is determined as the SOC.

[0032] The aforementioned control module is also used to determine the estimated travel time of the first target path segment when the starting point is about to be reached; Based on the target optimization value and the estimated travel time, the engine control command corresponding to the first target path segment is determined; wherein, the engine control command indicates the control speed and control torque of the engine; based on the engine control command, the engine performs power generation control on the first target path segment.

[0033] The aforementioned first determining module is also used to divide the planned route into multiple route segments based on the traffic congestion index change points, road slope change points, and / or travel time per unit mileage change points on the planned route.

[0034] Thirdly, this application provides a vehicle including a solid-state battery, which is designed using the energy management method for hybrid vehicles described in the first aspect.

[0035] Fourthly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the method described in the first aspect and any possible implementation thereof.

[0036] Fifthly, this application provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0037] In a sixth aspect, this application provides a computer program product comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any of its possible implementations.

[0038] It should be noted that the technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0041] Figure 1 This is a schematic diagram of the structure of a vehicle shown in an embodiment of this application; Figure 2 This is a flowchart illustrating an energy management method for a hybrid vehicle according to an embodiment of this application; Figure 3 This is a flowchart illustrating another energy management method for a hybrid vehicle according to an embodiment of this application; Figure 4 This is a flowchart illustrating another energy management method for a hybrid vehicle according to an embodiment of this application; Figure 5 This is a flowchart illustrating another energy management method for a hybrid vehicle according to an embodiment of this application; Figure 6 This is a block diagram illustrating an energy management device for a hybrid vehicle according to an embodiment of this application; Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Detailed Implementation

[0042] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0043] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application 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 this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0044] In the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0045] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0046] The energy management device for hybrid vehicles provided in this application embodiment is used to execute the energy management method for hybrid vehicles described in the first aspect or any possible implementation, so as to achieve precise planning and control of the mechanical energy output by the engine in each path segment during the driving process of the hybrid vehicle, thereby achieving the technical effect of minimizing the overall fuel consumption.

[0047] Figure 1 This is a schematic diagram of the structure of a vehicle as shown in an embodiment of this application. Figure 1 As shown, the vehicle 100 includes a control device 101, a data acquisition device 102, and an execution device 103.

[0048] The control device 101 is communicatively connected to the data acquisition device 102 and the execution device 103. This communication connection can be achieved via a controller local area network (CLAN) bus, or via Ethernet or other vehicle communication protocols.

[0049] The aforementioned data acquisition device 102 is used to collect various types of information during the vehicle's operation and send the collected information to the control device 101.

[0050] Specifically, the data acquisition device 102 is used to acquire the vehicle's navigation path information, the mileage and estimated travel time of each path segment, the vehicle's current location, the real-time state of charge of the power battery, and the vehicle's operating status parameters.

[0051] The data acquisition device 102 may include an in-vehicle navigation system, a battery management system, a global positioning system, and vehicle status sensors.

[0052] The in-vehicle navigation system provides a planned route from origin to destination, along with information on traffic congestion index, road gradient, and travel time at various points along the route. The battery management system monitors and provides feedback on the real-time state of charge, voltage, current, and temperature of the power battery. The global positioning system (GPS) is used to determine the vehicle's real-time location to ascertain its current route segment.

[0053] Vehicle status sensors are used to collect operating status parameters such as vehicle speed, acceleration, engine speed, and torque.

[0054] The actuator 103 is used to receive control commands sent by the control device 101 and to perform power generation control on the engine on the first target path segment according to the control commands.

[0055] The actuator 103 is used to determine the engine control command based on the target optimization value determined by the control device 101 and the estimated travel time of the first target path segment. The engine control command indicates the control speed and control torque of the engine, and then sends the control command to the engine control unit for execution.

[0056] For example, the execution device 103 is used to obtain the estimated travel time of the first target path segment when the vehicle is about to reach the starting point of the first target path segment, obtain the target average power of the engine by dividing the target optimization value by the estimated travel time, determine the engine speed range according to the current vehicle speed and transmission ratio, find the optimal speed and torque combination corresponding to the average power based on the offline calibrated engine optimal energy consumption curve, generate control speed and control torque commands, and send the commands to the engine control unit through the controller local area network bus.

[0057] Since the control command executed by the actuator 103 is generated based on the target optimization value obtained by global optimization, which is the optimal value selected by the control device 101 from the discrete energy value sequence by minimizing the cumulative fuel consumption and the estimated lower limit of remaining fuel consumption, the actuator 103 can make the engine work in the optimal energy output state on each path segment when executing the control command, thereby minimizing the global fuel consumption.

[0058] In a specific implementation, the actuator 103 may include an engine control unit and a motor controller. The engine control unit is used to control the engine's speed and torque output, and the motor controller is used to control the power output of the drive motor. When the engine's output mechanical energy is insufficient to meet the energy demand of the path segment, the motor controller controls the drive motor to discharge and supplement the difference in energy; when the engine's output mechanical energy exceeds the energy demand of the path segment, the motor controller controls the drive motor to convert the excess mechanical energy into electrical energy and store it in the power battery.

[0059] In addition, the actuator 103 can also dynamically fine-tune the engine control commands during vehicle operation based on the actual state of charge fed back in real time by the acquisition device 102, so as to eliminate the deviation between prediction and actual driving.

[0060] In one possible way, the actuator 103 can reacquire the target optimization value updated by the control device 101 based on the current actual state of charge at the beginning of each path segment, and generate the engine control command for that path segment based on the updated target optimization value.

[0061] Optionally, the data acquisition device 102 integrates an in-vehicle navigation system and a battery management system. The data acquisition device 102 acquires the vehicle's real-time location, remaining mileage of the current route segment, real-time state of charge of the power battery, and vehicle speed information at a preset frequency (e.g., once every 100 milliseconds), and sends the above information to the control device 101 in real time.

[0062] The data acquisition device 102 is used to send the actual state of charge fed back by the battery management system to the control device 101 after the vehicle has traveled a certain route segment. This allows the control device 101 to use the actual state of charge as the new starting state of charge, take the remaining untraveled route segment as the new planned route, and re-execute the energy management method of the hybrid vehicle for rolling optimization.

[0063] As one possible approach, when the execution device 103 is about to reach the end of the current path segment, it requests the target optimization value of the next path segment from the control device 101 in advance to ensure the continuity of the control command and avoid execution interruption due to the calculation delay of the control device 101.

[0064] Optionally, the control device 101 is used to determine the target discrete energy value sequence according to the energy demand of each path segment sent by the acquisition device 102, and based on the target discrete energy value sequence, the state of charge of the power battery at the starting point of the current path segment and the objective function, determine the target optimization value corresponding to the engine output mechanical energy of the current path segment, and send the target optimization value to the execution device 103.

[0065] The control device 101 is also used to determine whether the deviation between the actual state of charge and the planned state of charge exceeds a preset threshold based on the real-time feedback from the acquisition device 102. If the deviation exceeds the preset threshold, the control device 101 takes the current actual state of charge as the starting state of charge, uses the remaining untraveled path segment as the new planned path, and re-executes the steps of determining the target discrete energy value sequence, determining the target optimization value, and generating control commands to achieve rolling optimization.

[0066] It should be understood that the control device 101, the acquisition device 102, and the execution device 103 can be independent physical devices or integrated into the same vehicle controller.

[0067] In the embodiments of this application, the physical implementation of the above-mentioned devices is not limited, as long as they can achieve their respective functions.

[0068] This application does not limit the specific physical form of the control device 101.

[0069] The control device 101 may be a vehicle controller, a hybrid power controller, or an on-board central computing unit.

[0070] The vehicle controller is the core control unit of a hybrid vehicle, responsible for the vehicle's power distribution and energy management.

[0071] A hybrid power controller is a control unit specifically designed for hybrid power systems, responsible for the coordinated control of the engine and the electric motor.

[0072] The onboard central computing unit is a high-performance computing platform that integrates navigation, perception, decision-making, and control functions in the next generation of intelligent connected vehicles.

[0073] Regardless of the physical form adopted, the control device 101 should include a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the energy management method of the hybrid vehicle described above.

[0074] Figure 2 This is a flowchart illustrating an energy management method for a hybrid vehicle according to an embodiment of this application, with reference to... Figure 2 The energy management method for this hybrid vehicle includes: S201. Determine the energy requirements of the first target path segment.

[0075] The first target path segment is one of the multiple path segments divided into the planned path for hybrid vehicles.

[0076] Specifically, the hybrid vehicle first obtains navigation path information from the starting point to the destination.

[0077] Navigation route information can be provided by the in-vehicle navigation system. The hybrid vehicle divides the planned route into multiple route segments. For each route segment, the hybrid vehicle obtains the mileage and estimated travel time of the route segment, and calculates the energy demand of the route segment based on the offline calibrated energy consumption rate per unit mile.

[0078] The energy consumption rate per unit mileage mentioned above can be calibrated offline based on vehicle curb weight, rolling resistance coefficient, air resistance coefficient, frontal area, and transmission system efficiency.

[0079] Specifically, the formula for calculating energy demand is: Energy demand = Energy consumption rate per unit distance × Distance.

[0080] S202. Based on energy demand, determine the target discrete energy value sequence.

[0081] The aforementioned target discrete energy value sequence is a set of multiple energy levels discretized from 0 to the engine's maximum permissible output mechanical energy according to a preset energy step size.

[0082] In one possible implementation, the first energy value is determined by the product of energy demand and a first adjustment coefficient, the second energy value is determined by the product of the engine's rated power and the expected maximum driving time of the first target path segment, the larger of the first and second energy values ​​is determined as the engine's maximum permissible mechanical energy output, and the energy closed interval between 0 and the engine's maximum permissible mechanical energy output is discretized according to a preset energy step size to obtain a target discrete energy value sequence.

[0083] The aforementioned preset energy step size is the interval value when discretizing the engine output mechanical energy, used to divide the continuous engine output mechanical energy range into a finite number of discrete energy levels.

[0084] The determination of the preset energy step size is based on at least one of the following: the total energy demand of the current road segment, the computing power of the controller, the preset number of discretization points, and the balance requirements between accuracy and computing efficiency.

[0085] In one possible implementation, the preset energy step size is determined based on the ratio of the total energy demand of the current road segment to the preset number of discretization points.

[0086] Specifically, the mileage and energy consumption rate per unit mileage of the current road segment are obtained, and the total energy demand of the current road segment is calculated. A preset number of discretization points is determined based on the controller's computing power (e.g., the maximum number of evaluation points the controller can handle is 10). The total energy demand is divided by the preset number of discretization points to obtain the preset energy step size. For example, if the total energy demand of the current road segment is 5 kWh and the preset number of discretization points is 10, then the preset energy step size is 0.5 kWh.

[0087] The preset number of discretization points refers to the number of divisions required when dividing the engine's maximum permissible output mechanical energy range into discrete energy levels. This determines the discretization step size, directly affecting the number of candidate energy values ​​contained in the target discrete energy value sequence. A larger preset number of discretization points results in a smaller discretization step size, finer intervals between candidate energy values, and higher search accuracy, but also increases the state space and computational load. Conversely, a smaller preset number of discretization points results in a larger discretization step size, coarser intervals between candidate energy values, and lower search accuracy, but also reduces the state space and improves computational efficiency. The preset number of discretization points can be pre-set based on the controller's computational capabilities. For example, if the controller's maximum capacity for evaluation points is 10, then the preset number of discretization points is 10.

[0088] Optionally, the preset number of discretization points can also be adaptively determined according to the balance requirements of accuracy and real-time performance, and this application does not limit this.

[0089] Another possible implementation involves setting the energy step size to a fixed value, such as 0.5 kWh. The fixed step size method is simple to implement and suitable for operating conditions where energy demand varies little.

[0090] In another possible implementation, the preset energy step size is adaptively determined based on the balance requirements between accuracy and computational efficiency. When the total energy demand of a road segment is large, a larger step size is used to reduce the number of states and improve computational efficiency; when the total energy demand of a road segment is small, a smaller step size is used to improve discretization accuracy and ensure optimization results.

[0091] Optionally, the preset energy step size is also limited by the minimum controllable energy change of the engine, that is, the step size is not less than the minimum energy increment that the engine can precisely control.

[0092] It should be noted that a smaller preset energy step size results in higher discretization accuracy and a search result closer to the theoretical optimum, but also increases the number of states and computational load; a larger preset energy step size results in higher computational efficiency, but may lead to the loss of the optimal solution. Those skilled in the art can flexibly choose the above method to determine the preset energy step size according to the actual application scenario (such as controller computing power, real-time requirements, and accuracy requirements), all of which fall within the protection scope of this application.

[0093] S203. Based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment and the objective function, determine the target optimization value corresponding to the engine output mechanical energy of the first target path segment.

[0094] In one possible implementation, the constraints of the objective function are determined, and the optimal objective value is determined based on the objective discrete energy value sequence, the state of charge, the objective function, and the constraints. The constraints include at least: the total fuel consumption corresponding to the total engine output mechanical energy is less than the remaining fuel of the hybrid vehicle at the starting point of the planned path; and the state of charge is between an under-voltage and an over-voltage state.

[0095] The target optimization value is a value in the target discrete energy value sequence.

[0096] The objective function is used to minimize the first fuel consumption and the second fuel consumption.

[0097] The first fuel consumption is the cumulative value of the fuel consumption for each segment of the second target path.

[0098] The fuel consumption for the second target path segment is determined based on the optimal value of the engine's output mechanical energy for that segment. Specifically, fuel consumption = optimal value of engine output mechanical energy / (engine's power generation efficiency × lower heating value of fuel).

[0099] The second target path segment is any path segment that precedes the first target path segment among multiple path segments.

[0100] Specifically, for each candidate value in the target discrete energy value sequence, the hybrid vehicle calculates the segment fuel consumption corresponding to that candidate value (as part of the second fuel consumption), and combines it with the accumulated first fuel consumption and the estimated lower limit of remaining fuel consumption to calculate an evaluation value. The candidate value with the smallest evaluation value is determined as the target optimization value for the first target path segment.

[0101] The second fuel consumption is the fuel consumption corresponding to the minimum allowable value of the engine's output mechanical energy for the first target path segment.

[0102] The minimum allowable value is determined based on the cumulative energy demand from the starting point of the first target path segment to the end point of the planned path, as well as the difference in state of charge between the state of charge and the target state of charge.

[0103] Specifically, when the state of charge difference is greater than or equal to 0, the first mechanical energy corresponding to the first electrical energy is determined based on the state of charge difference, the total capacity of the power battery, and the charging and discharging efficiency of the power battery, and the energy difference between the cumulative energy demand and the first mechanical energy is determined as the minimum allowable value.

[0104] When the state of charge difference is less than 0, the second mechanical energy corresponding to the second electrical energy is determined based on the state of charge difference, the total capacity of the power battery and the charging and discharging efficiency of the power battery, and the sum of the cumulative energy demand and the second mechanical energy is determined as the minimum allowable value.

[0105] Optionally, the process of determining the state of charge includes: determining the target state of charge of the power battery at the starting point of the third target path segment, where the third target path segment is the path segment preceding the first target path segment; determining the target energy difference between the energy demand of the third target path segment and the target optimization value corresponding to the engine output mechanical energy of the third target path segment; determining the change in state of charge of the third target path segment based on the target energy difference and the charging and discharging efficiency of the power battery; if the target energy difference is greater than 0, determining the difference between the target state of charge and the change in state of charge as the state of charge; if the target energy difference is not greater than 0, determining the sum between the target state of charge and the change in state of charge as the state of charge.

[0106] For example, suppose the energy demand of the current path segment is 5 kWh and the target discrete energy value sequence is {0,0.5,1.0,…,4.0} kWh.

[0107] The hybrid vehicle has consumed a total of 2 liters of fuel from the starting point to the current route's starting point. The total energy demand for the remaining route is 10 kWh. The current state of charge (SOC) is 60%, the target SOC is 50%, the motor discharge efficiency is 0.9, and the total battery capacity is 10 kWh. The SOC difference = 60% - 50% = 10%, which releases 10% × 10 kWh = 1 kWh of electrical energy. After conversion based on the motor discharge efficiency, this can replace 1 × 0.9 = 0.9 kWh of mechanical energy. The minimum allowable value = max(0, 10 - 0.9) = 9.1 kWh of fuel consumption. For the candidate value of 1.5 kWh, its corresponding evaluation value = accumulated fuel consumption + current route fuel consumption + remaining fuel consumption lower limit. The hybrid vehicle calculates the evaluation value for each candidate value sequentially, and determines the candidate value with the smallest evaluation value as the target optimization value.

[0108] It should be understood that the above examples are only used to illustrate the process of determining the target optimization value and do not constitute a limitation on the technical solution of the present invention. In practical applications, the specific values ​​of each parameter depend on the vehicle's physical parameters and driving conditions.

[0109] It should be noted that the first fuel consumption value in the objective function is a predetermined value (determined by the decision results of previous path segments), while the second fuel consumption value is the fuel consumption corresponding to different engine output mechanical energy values ​​selected in the current path segment. By minimizing this objective function, the optimal engine output mechanical energy value (i.e., the target optimization value) can be selected from the target discrete energy value sequence.

[0110] S204. Using the target optimization value, the engine is controlled to generate electricity on the first target path segment.

[0111] In one possible implementation, when approaching the starting point of the first target path segment, the estimated travel time of the first target path segment is determined; based on the target optimization value and the estimated travel time, the engine control command corresponding to the first target path segment is determined; based on the engine control command, the engine is used for power generation control on the first target path segment. The engine control command indicates the engine's control speed and control torque.

[0112] Specifically, the hybrid vehicle divides the target optimization value by the estimated travel time to obtain the target average power of the engine; then, it determines the engine speed range based on the vehicle's current speed and transmission ratio, finds the optimal speed and torque combination corresponding to the average power based on the offline calibrated engine optimal energy consumption curve, generates control speed and control torque commands, and sends the commands to the engine control unit for execution through the controller area network bus.

[0113] Optionally, during vehicle operation, the hybrid vehicle monitors the actual state of charge of the power battery at a preset frequency. When the deviation between the actual state of charge and the planned state of charge exceeds a preset threshold, the hybrid vehicle takes the current actual state of charge as the starting point, uses the remaining untraveled path segment as the new planned path, and re-executes steps S201 to S204 for rolling optimization to eliminate the deviation between prediction and actual driving.

[0114] Optionally, when all path segments have been traversed and the final state of charge meets the target state of charge, the hybrid vehicle records the optimal engine output mechanical energy value for each selected path segment through reverse backtracking, forming an optimal discrete energy value sequence. This sequence can be used as reference data for subsequent driving tasks to optimize energy management strategies under similar operating conditions.

[0115] Based on the aforementioned technical means, this application discretizes the engine output mechanical energy of each path segment into multiple candidate energy values, and selects the target optimization value from these based on an objective function for power generation control. This achieves precise planning of engine output energy, solving the execution deviation problem caused by the prior art's inability to specify the specific output energy due to only controlling the engine's start-stop state. Simultaneously, the objective function minimizes the amount of fuel consumed and the lower limit of remaining fuel consumption determined based on remaining energy demand and state-of-charge deviation, ensuring that energy allocation decisions for each path segment consider the global optimum, effectively reducing overall vehicle fuel consumption.

[0116] In one embodiment, the process of determining the minimum allowable value includes the following steps: Ⅰ: When the state of charge difference is less than 0, the second mechanical energy corresponding to the second electrical energy is determined based on the state of charge difference, the total capacity of the power battery, and the charging and discharging efficiency of the power battery.

[0117] The second type of electrical energy is the energy that the power battery needs to replenish to adjust its state of charge to the target state of charge.

[0118] Specifically, when the state-of-charge (SOC) difference is less than 0, it indicates that the current remaining charge of the power battery is lower than the target charge required at the end of the planned route, meaning there is a charge gap in the battery. To ensure that the vehicle's SOC meets the target requirements upon reaching the destination, it is necessary to replenish the power battery during subsequent driving. The second amount of electrical energy to be replenished is determined by multiplying the SOC difference (i.e., the difference between the target SOC and the current SOC) by the total battery capacity. Since there is efficiency loss in the process of converting mechanical energy into electrical energy, the engine needs to output more mechanical energy to convert it into enough electrical energy to charge the battery. Therefore, the second mechanical energy equals the second electrical energy divided by the charging efficiency of the power battery.

[0119] In one possible implementation, the formula for calculating the second mechanical energy is: Second mechanical energy = (Target state of charge - Current state of charge) × Total capacity of power battery / 100 / Motor charging efficiency.

[0120] II: The sum of the cumulative energy demand and the second mechanical energy is determined as the minimum allowable value.

[0121] Specifically, the minimum allowable value represents the minimum total mechanical energy that the engine needs to output on that path segment.

[0122] It should be understood that when the current battery level is lower than the target, the engine not only needs to meet all the energy required for the remaining road travel (cumulative energy demand), but also needs to output additional mechanical energy to supplement the battery's charge gap. Therefore, the minimum allowable value is equal to the sum of the cumulative energy demand and the second mechanical energy.

[0123] In one possible implementation, the cumulative energy demand is the sum of the energy demands of all remaining road segments from the starting point of the path segment to the end point of the planned path.

[0124] Minimum allowable value = cumulative energy demand + second mechanical energy. It should be understood that by determining this energy sum as the minimum allowable value, it is ensured that during subsequent driving, the mechanical energy output by the engine is sufficient to drive the vehicle to complete the remaining distance and to replenish the battery charge from the current state of charge to the target state of charge, thus avoiding insufficient battery charge upon arrival at the destination.

[0125] When the actual mechanical energy output by the engine is exactly equal to the minimum allowable value, the state of charge of the power battery at the destination is exactly equal to the target state of charge; when the actual mechanical energy output by the engine is greater than the minimum allowable value, the state of charge of the power battery at the destination will be higher than the target state of charge.

[0126] It should be noted that in actual decision-making, the objective function will comprehensively evaluate different engine output mechanical energy values ​​and their corresponding fuel consumption, and select the globally optimal value.

[0127] In another embodiment, the process of determining the minimum allowable value may further include the following steps: Ⅰ: When the state of charge difference is less than 0, the second mechanical energy corresponding to the second electrical energy is determined based on the state of charge difference, the total capacity of the power battery, and the charging and discharging efficiency of the power battery.

[0128] The second type of electrical energy is the energy that the power battery needs to replenish to adjust its state of charge to the target state of charge.

[0129] Specifically, when the state-of-charge difference (current state of charge minus target state of charge) is less than 0, it indicates that the remaining charge of the current power battery is lower than the target charge required at the planned destination, meaning there is a charge gap in the battery. To ensure that the state of charge meets the requirements when the vehicle reaches the destination, it is necessary to replenish the battery with electrical energy during subsequent driving. The second electrical energy to be replenished is equal to the absolute value of the state-of-charge difference multiplied by the total capacity of the power battery divided by 100. Since there is an efficiency loss in the process of the motor converting mechanical energy into electrical energy, the additional mechanical energy output by the engine should be greater than this electrical energy value. Specifically, the second mechanical energy = |state-of-charge difference| × total capacity of the power battery / 100 / motor charging efficiency.

[0130] II: The sum of the cumulative energy demand and the second mechanical energy is determined as the minimum allowable value.

[0131] Specifically, when the current battery level is lower than the target, the engine not only needs to provide all the energy required for the remaining journey (cumulative energy demand), but also needs to output additional mechanical energy to compensate for the battery charge shortfall. Therefore, the minimum allowable value = cumulative energy demand + second mechanical energy, which is the minimum mechanical energy the engine needs to output for this route segment.

[0132] It should be noted that the cumulative energy demand is the sum of the energy demands of all remaining path segments from the starting point of the first target path segment to the end point of the planned path. By determining this energy sum as the minimum allowable value, it is ensured that the mechanical energy output by the engine during subsequent driving is sufficient to meet the energy demands of vehicle operation and replenish the power battery to the target state of charge, providing the necessary energy reserves for energy optimization in subsequent path segments. When the actual mechanical energy output by the engine is exactly equal to this minimum allowable value, the battery state of charge at the end point is exactly equal to the target state of charge; when the actual output is greater than this value, the state of charge at the end point will be higher than the target value.

[0133] In actual decision-making, the objective function will comprehensively evaluate the fuel consumption and power balance results of each candidate energy value and select the global optimal value.

[0134] In one embodiment, the process of determining the fuel consumption of the second target path segment includes: Based on the optimization value of the engine output mechanical energy, the engine power generation efficiency, and the lower heating value of the fuel in the second target path segment, the fuel consumption of the second target path segment is determined.

[0135] Specifically, the fuel consumption of the second target path segment = the optimization value of the engine output mechanical energy of the second target path segment / (engine power generation efficiency × fuel lower heating value).

[0136] Among them, the optimal value of the engine output mechanical energy is A. The search algorithm determines the optimal engine output mechanical energy value on the second target path segment, in kilowatt-hours; the engine's power generation efficiency is the efficiency of the engine in converting mechanical energy into electrical energy, dimensionless, and usually between 0.85 and 0.95; the lower heating value of fuel is the heat released when a unit mass or unit volume of fuel is completely burned, for example, the lower heating value of gasoline is about 9.444 kWh / L, and the lower heating value of diesel is about 9.96 kWh / L.

[0137] It should be noted that the power generation efficiency of an engine is not a fixed constant; its specific value depends on the engine's speed and torque at the current operating point.

[0138] In one possible implementation, the engine's power generation efficiency is determined by querying an engine efficiency MAP based on the engine's speed and torque at the current operating point.

[0139] The engine efficiency MAP is a two-dimensional table pre-calibrated through bench testing that records the engine efficiency values ​​under different speed and torque combinations. The efficiency value corresponding to the current operating condition can be obtained online in real time by looking up the table, so as to calculate fuel consumption more accurately.

[0140] In another possible implementation, to simplify calculations and improve real-time performance, the engine's power generation efficiency is set to a preset fixed value, such as 0.9. This fixed efficiency approach is suitable for applications with high real-time requirements and where engine efficiency varies little across the entire operating range. It can reduce computational complexity and improve algorithm speed.

[0141] In one possible implementation, when the optimization value of the engine's output mechanical energy is zero, the fuel consumption of the second target path segment is zero, indicating that the vehicle is driving in pure electric mode on that path segment and the engine is not working.

[0142] It should be understood that the fuel consumption determined in the above manner reflects the physical correspondence between the engine's output mechanical energy and fuel consumption. This fuel consumption, as a component of the first fuel consumption, is accumulated in the objective function, ensuring that the objective function's value accurately represents the actual amount of fuel consumed from the starting point to the current path segment's starting point. In subsequent objective function optimization, the accumulated fuel consumption will be used as a fixed cost in the evaluation value calculation, ensuring that the decision-making process optimizes in the direction of minimizing cumulative fuel consumption globally.

[0143] Figure 3 This is a flowchart illustrating another energy management method for a hybrid vehicle according to an embodiment of this application, referred to... Figure 3 The energy management method for this hybrid vehicle includes: S301. Determine the energy requirements of the first target path segment.

[0144] For details, please refer to S201; further details will not be provided here.

[0145] S302. The first energy value is determined based on the product of the energy demand and the first adjustment coefficient.

[0146] The aforementioned first adjustment coefficient is a value greater than 1, used to ensure that the engine's maximum permissible output mechanical energy can cover the actual energy demand of the path segment, and to reserve a certain energy margin for charging the power battery when necessary.

[0147] Specifically, the first adjustment coefficient can be pre-calibrated based on vehicle parameters and operating conditions, for example, a value of 1.2. The first energy value = energy demand of the first target path segment × the first adjustment coefficient. By multiplying the energy demand by an adjustment coefficient greater than 1, the engine's maximum permissible output mechanical energy is slightly higher than the actual energy required for driving on the path segment, providing space for the engine to operate in its high-efficiency range or for battery recharging.

[0148] It should be understood that the value of the first adjustment coefficient should not be too large, otherwise it will lead to an excessively high upper limit for the discrete energy value sequence, increasing the state space and computational load; nor should it be too small, otherwise it will not leave enough margin for battery charging or efficient engine operation. Those skilled in the art can flexibly set it according to the actual application scenario.

[0149] S303. The product of the engine's rated power and the estimated maximum travel time of the first target path segment is determined as the second energy value.

[0150] The estimated maximum travel time mentioned above is the maximum estimated time required for the vehicle to travel on the first target route segment. The estimated maximum travel time can be determined based on the mileage of the route segment and the minimum permitted speed for that route segment, where the minimum speed can be provided by the in-vehicle navigation system based on historical traffic data or real-time traffic information. For example, in severely congested areas, the minimum speed may be as low as 5 km / h; in uncongested areas, the minimum speed may be 30 km / h.

[0151] Specifically, the second energy value = engine rated power × estimated maximum driving time. The second energy value represents the maximum mechanical energy that the engine can output when it continuously operates at its rated power for the estimated maximum driving time within this path segment. This value reflects the upper limit constraint of the engine's physical capabilities on the output energy.

[0152] It should be understood that if the first energy value is greater than the second energy value, it means that the energy demand of the path segment or its adjustment value exceeds the physical output capacity of the engine. At this time, the engine cannot provide enough energy in the path segment and requires electric motor assistance or adjustment of driving strategy. If the second energy value is greater than the first energy value, the engine's physical capacity is sufficient and the maximum allowable output mechanical energy is determined by the energy demand of the path segment.

[0153] S304. The larger of the first energy value and the second energy value is determined as the maximum permissible mechanical energy output of the engine for the first target path segment.

[0154] The larger of the first and second energy values ​​is used as the engine's maximum permissible mechanical energy output. This ensures that the engine's maximum permissible mechanical energy output can at least meet the adjusted energy demand of the path segment (first energy value), while also ensuring that it does not exceed the engine's physical output capacity within the expected maximum driving time (second energy value). The larger value is chosen over the smaller one because when the energy demand of the path segment exceeds the engine's physical output capacity, the upper limit of the physical output capacity must be used as the maximum permissible value to ensure that the subsequent discretization interval includes all feasible solutions. Conversely, when the engine's physical capacity is sufficient, the adjusted energy demand value of the path segment is used as the upper limit to avoid including unnecessarily large energy values ​​in the search interval, thereby reducing the state space and improving search efficiency.

[0155] S305. Discretize the energy closed interval between 0 and the maximum permissible output mechanical energy of the engine according to the preset energy step size to obtain the target discrete energy value sequence.

[0156] The aforementioned preset energy step size is the interval value used when discretizing the engine's output mechanical energy, and is used to divide the continuous range of engine output mechanical energy into a finite number of discrete energy levels. The preset energy step size is determined based on at least one of the following: the total energy demand of the current road segment, the controller's computing power, the preset number of discretization points, and the balance requirements between accuracy and computing efficiency.

[0157] In one possible implementation, the preset energy step size is determined based on the ratio of the total energy demand of the current road segment to a preset number of discretization points. Specifically, the mileage and energy consumption rate per unit mileage of the current road segment are obtained, and the total energy demand of the current road segment is calculated; the preset number of discretization points is determined based on the controller's computing power (e.g., the maximum number of evaluation points the controller can handle is 10); the total energy demand is divided by the preset number of discretization points to obtain the preset energy step size. For example, if the total energy demand of the current road segment is 5 kWh and the preset number of discretization points is 10, then the preset energy step size is 0.5 kWh.

[0158] Another possible implementation involves setting the energy step size to a fixed value, such as 0.5 kWh. The fixed step size method is simple to implement and suitable for operating conditions where energy demand varies little.

[0159] In another possible implementation, the preset energy step size is adaptively determined based on the balance requirements between accuracy and computational efficiency. When the total energy demand of a road segment is large, a larger step size is used to reduce the number of states and improve computational efficiency; when the total energy demand of a road segment is small, a smaller step size is used to improve discretization accuracy and ensure optimization results. Optionally, the preset energy step size is also limited by the minimum controllable energy change of the engine, that is, the step size is not less than the minimum energy increment that the engine can precisely control.

[0160] It should be noted that a smaller preset energy step size results in higher discretization accuracy and a search result closer to the theoretical optimum, but also increases the number of states and computational load; a larger preset energy step size results in higher computational efficiency, but may lead to the loss of the optimal solution. Those skilled in the art can flexibly choose the above method to determine the preset energy step size according to the actual application scenario (such as controller computing power, real-time requirements, and accuracy requirements), all of which fall within the protection scope of this application.

[0161] After obtaining the target discrete energy value sequence, this sequence will serve as a set of candidate energy values ​​for subsequent objective function optimization steps. For example, if the engine's maximum permissible output mechanical energy is 4 kilowatt-hours and the preset energy step size is 0.5 kilowatt-hours, then the target discrete energy value sequence is {0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0}. Each value in this sequence represents a candidate energy level for that path segment, from which subsequent steps will determine the optimal value.

[0162] S306. Based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment and the objective function, determine the target optimization value corresponding to the engine output mechanical energy of the first target path segment.

[0163] For details, please refer to S203; further details will not be provided here.

[0164] S307. Using the target optimization value, the engine is controlled to generate electricity on the first target path segment.

[0165] For details, please refer to S204; further details will not be provided here.

[0166] Figure 4 This is a flowchart illustrating another energy management method for a hybrid vehicle according to an embodiment of this application, referred to... Figure 4 The energy management method for this hybrid vehicle includes: S401. Determine the energy requirements of the first target path segment.

[0167] For details, please refer to S201; further details will not be provided here.

[0168] S402. Based on energy demand, determine the target discrete energy value sequence.

[0169] For details, please refer to S202; further details will not be provided here.

[0170] S403. Determine the constraints of the objective function.

[0171] Specifically, before determining the target optimization value, it is first necessary to determine the constraints of the objective function to limit the feasible domain of the optimization problem and ensure that the optimization result meets the vehicle's physical limitations and driving safety requirements.

[0172] The above constraints include at least the following: Constraint 1: Total fuel consumption is less than remaining fuel. The total fuel consumption corresponding to the total engine output mechanical energy is less than the remaining fuel of the hybrid vehicle at the starting point of the planned path. The total engine output mechanical energy is obtained by summing the target optimization values ​​corresponding to the engine output mechanical energy of multiple path segments. This constraint ensures that the cumulative fuel consumption of all path segments during the entire journey does not exceed the total amount of fuel carried by the vehicle at the start, preventing the vehicle from failing to reach its destination due to running out of fuel.

[0173] Specifically, total fuel consumption = Σ (target optimization value of engine output mechanical energy for each path segment) / (engine power generation efficiency × lower heating value of fuel). During the optimization process, if a candidate energy value sequence causes the total fuel consumption to exceed the vehicle's initial fuel reserve, the candidate sequence is determined to be infeasible, and the corresponding path segment is not considered.

[0174] Constraint 2: The state of charge (SOC) must be within a safe range. The SOC must be between an undervoltage state of charge and an overvoltage state of charge. The undervoltage state of charge corresponds to the undervoltage protection threshold voltage of the battery; the overvoltage state of charge corresponds to the overvoltage protection threshold voltage of the battery. This constraint ensures that the battery will not trigger undervoltage protection and cause power loss due to excessively low voltage, nor will it trigger overvoltage protection and cause safety hazards due to excessively high voltage during the entire driving process.

[0175] The aforementioned undervoltage and overvoltage states of charge are determined by the physical characteristics of the power battery. The undervoltage state of charge is the lower limit protection value set by the power battery management system. When the battery state of charge falls below this value, the battery management system will cut off the power battery output, and the vehicle will not be able to operate normally. The overvoltage state of charge is the upper limit protection value set by the power battery management system. When the battery state of charge exceeds this value, it may cause safety hazards such as battery thermal runaway. The specific values ​​of the undervoltage and overvoltage states of charge are set by the battery manufacturer at the factory based on the battery materials and electrochemical characteristics. For example, the undervoltage state of charge can be set to 20%, and the overvoltage state of charge can be set to 90%. It should be understood that the undervoltage protection threshold and the overvoltage protection threshold corresponding to the state of charge may differ for different specifications of power batteries, and this application does not impose any limitations on this.

[0176] Optionally, the constraints can be further extended to: the engine output mechanical energy in each path segment does not exceed the maximum permissible output mechanical energy of the engine in that path segment; the engine output mechanical energy in each path segment is not lower than the minimum permissible value for that path segment; and the real-time state-of-charge rate of the power battery does not exceed a preset rate of change threshold. These extended constraints can be flexibly configured according to actual application scenarios.

[0177] S404. Determine the state of charge.

[0178] Specifically, the state of charge (SOC) is the state of charge of the power battery at the starting point of the first target path segment, and its determination process depends on the energy distribution results of the previous path segment (i.e., the third target path segment). By recursion, the SOC at the end of the previous path segment is taken as the starting SOC of the current path segment.

[0179] In one possible implementation, S404 above, determining the state of charge, includes the following steps: S4041. Determine the target state of charge of the power battery at the starting point of the third target path segment.

[0180] The third target path segment is the path segment preceding the first target path segment.

[0181] Since the energy distribution for the third target path segment has been completed, its state of charge at the starting point is known and can be directly obtained from the previous calculation results. When the first target path segment is the first segment of the planned path, its state of charge is the preset starting state of charge, which is determined by the actual state of charge when the vehicle starts.

[0182] S4042. Determine the target energy difference between the energy demand of the third target path segment and the target optimization value corresponding to the engine output mechanical energy of the third target path segment.

[0183] Specifically, the target energy difference = energy demand of the third target path segment - target optimization value corresponding to the engine output mechanical energy of the third target path segment. This difference represents the mechanical energy that the motor needs to provide or absorb in the third target path segment. When the difference is positive, it indicates that the engine output is insufficient and the motor needs to discharge to compensate; when the difference is negative, it indicates that the engine output is excessive and the motor generates electricity to charge the battery.

[0184] S4043. Based on the target energy difference and the charging and discharging efficiency of the power battery, determine the change in state of charge of the third target path segment.

[0185] Specifically, when the target energy difference is greater than 0 (motor discharging), the change in state of charge is negative, and its absolute value is the target energy difference divided by the motor discharge efficiency, then divided by the total battery capacity, multiplied by 100%. When the target energy difference is less than 0 (motor charging), the change in state of charge is positive, and its value is the absolute value of the target energy difference multiplied by the motor charging efficiency, then divided by the total battery capacity, multiplied by 100%. When the target energy difference is equal to 0, the change in state of charge is 0.

[0186] S4044-a. When the target energy difference is greater than 0, the difference between the target state of charge and the change in state of charge is determined as the state of charge.

[0187] Specifically, when the target energy difference is greater than 0, the motor is in discharge mode and the battery power decreases. Therefore, the state of charge at the starting point of the first target path segment = the target state of charge at the starting point of the third target path segment - |change in state of charge|.

[0188] S4044-b: When the target energy difference is not greater than 0, the sum between the target state of charge and the change in state of charge is determined as the state of charge.

[0189] Specifically, when the target energy difference is not greater than 0, the motor is in charging mode or non-working mode, and the battery charge increases or remains unchanged. Therefore, the state of charge at the starting point of the first target path segment = the target state of charge at the starting point of the third target path segment + the change in state of charge (the change is 0 when the difference is equal to 0).

[0190] S405. Based on the target discrete energy value sequence, state of charge, objective function and constraints, determine the target optimization value corresponding to the engine output mechanical energy of the first target path segment.

[0191] Specifically, after determining the target discrete energy value sequence, state of charge, objective function, and constraints, the hybrid vehicle evaluates each candidate value in the target discrete energy value sequence in turn.

[0192] The evaluation process includes: determining whether the candidate value meets the total fuel consumption limit and the state of charge safety boundary based on the constraints; calculating the objective function value (i.e., the sum of the cumulative fuel consumption and the estimated lower limit of the remaining fuel consumption) among the candidate values ​​that meet the constraints; and determining the candidate value with the smallest objective function value as the target optimization value corresponding to the engine output mechanical energy of the first target path segment.

[0193] More specifically, for the j-th candidate value in the target discrete energy value sequence, the hybrid vehicle performs the following evaluation sub-step: Ⅰ: Calculate the fuel consumption of the current path segment corresponding to the candidate value, and update the cumulative fuel consumption from the starting point to the end point of the current path segment.

[0194] II: Based on the total energy demand of the remaining path segment and the deviation between the current state of charge and the target state of charge, calculate the estimated lower limit of the remaining fuel consumption.

[0195] III: Calculate the sum of the cumulative fuel consumption and the estimated lower limit of remaining fuel consumption as the evaluation value of the candidate value.

[0196] IV: Compare the evaluation values ​​of all candidate values ​​and determine the candidate value with the smallest evaluation value as the target optimization value.

[0197] It should be understood that this optimization process is essentially a heuristic search in the discrete state space, using pruning strategies to eliminate candidate values ​​that do not meet the constraints, thereby reducing computational complexity.

[0198] It should be understood that the charge state of the starting point of the first target path segment determined in S404 serves as the initial state for the optimization decision of that path segment, providing the current charge state data for the objective function calculation in S405; the constraints determined in S403 are used in S405 to limit the legality of each candidate value in the target discrete energy value sequence, ensuring that all candidate values ​​participating in the evaluation are feasible solutions.

[0199] Figure 5 This is a flowchart illustrating another energy management method for a hybrid vehicle according to an embodiment of this application, referred to... Figure 5 The energy management method for this hybrid vehicle includes: S501. Determine the energy requirements of the first target path segment.

[0200] For details, please refer to S201; further details will not be provided here.

[0201] S502. Based on energy demand, determine the target discrete energy value sequence.

[0202] For details, please refer to S202; further details will not be provided here.

[0203] S503. Based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment and the objective function, determine the target optimization value corresponding to the engine output mechanical energy of the first target path segment.

[0204] For details, please refer to S203; further details will not be provided here.

[0205] S504. When approaching the starting point, determine the estimated travel time for the first target path segment.

[0206] Specifically, when the vehicle is about to reach the starting point of the first target route segment, the hybrid vehicle obtains the estimated travel time for that route segment from the onboard navigation system.

[0207] The estimated travel time can be predicted by the vehicle navigation system based on real-time traffic information, historical traffic data, and traffic flow characteristics of the current time period.

[0208] For example, when the distance between the vehicle's current location and the starting point of the first target path segment is less than a preset distance threshold, the vehicle can trigger the operation of obtaining the estimated travel time. Alternatively, the estimated travel time of the next path segment can be obtained in advance when the current path segment is about to end, to ensure continuity.

[0209] It should be understood that the estimated travel time changes dynamically with real-time traffic conditions. Therefore, obtaining the estimated travel time at the moment of imminent arrival at the starting point can improve the accuracy of the travel time prediction, thereby enhancing the accuracy of engine control commands.

[0210] S505. Based on the target optimization value and the estimated travel time, determine the engine control command corresponding to the first target path segment.

[0211] Among them, the engine control command indicates the engine's control speed and control torque.

[0212] Specifically, the hybrid vehicle divides the target optimization value by the estimated travel time (converted to hours) to obtain the engine's target average power for that route segment. Target average power = target optimization value / (estimated travel time / 3600), in kilowatts.

[0213] The average power is the average output power that the engine needs to maintain within this path segment. Operating at this power can meet the energy demand of this path segment and achieve optimal global fuel consumption.

[0214] In one possible implementation, the specific steps for determining the engine control commands include: First, based on the vehicle's current speed and transmission ratio, the selectable speed range of the engine is determined. Then, based on the offline calibrated engine optimal energy consumption curve (i.e., the economic curve on the engine universal characteristic curve), the optimal speed and torque combination corresponding to the target average power is found within this speed range. The optimal speed and torque combination is the operating point that minimizes the fuel consumption rate of the engine under this power output condition. Finally, the found optimal speed and torque combination is generated as engine control commands.

[0215] Optionally, if the target optimization value is zero, the engine control command instructs the engine to stop operating, and the vehicle travels in pure electric mode for that route. If the target optimization value is greater than the energy demand for that route, the engine control command instructs the engine to output excess mechanical energy, which is used to charge the battery.

[0216] S506. Based on engine control commands, the engine performs power generation control on the first target path segment.

[0217] Specifically, hybrid vehicles send engine control commands (including target speed and target torque) to the engine control unit via a controller area network bus.

[0218] The engine control unit adjusts the engine's excitation current and throttle opening (or fuel injection quantity) according to the received instructions, so that the engine runs at the target speed and target torque in the instructions, and outputs the mechanical energy corresponding to the target optimization value on the path segment.

[0219] During vehicle operation, the engine control unit monitors the engine's actual speed and torque in real time and maintains them near the target value through closed-loop feedback control to ensure the accuracy of energy output.

[0220] In one possible implementation, during travel on the first target path segment, the hybrid vehicle continuously monitors the actual state of charge of the power battery and changes in vehicle speed. When the deviation between the actual travel time and the expected travel time exceeds a preset time threshold, the vehicle recalculates the target average power based on the actual travel time and dynamically corrects the engine control commands.

[0221] For example, if an extended travel time leads to a decrease in average power demand, the engine speed can be appropriately reduced to maintain its operation within the high-efficiency range; conversely, if a shortened travel time leads to an increase in average power demand, the engine output power can be appropriately increased. The corrected control commands are sent to the engine control unit for execution in real time.

[0222] Optionally, during the journey along the route, the engine control commands remain constant (i.e., constant speed and constant torque control), allowing the engine to operate at a stable operating point. This approach helps the engine maintain efficient operation and reduces efficiency losses and emissions increases caused by transient operating conditions. Alternatively, the engine speed and torque can be dynamically adjusted within a certain range based on real-time vehicle speed changes, while maintaining the engine's average output power within the route at the target average power.

[0223] Optionally, if the engine is inefficient under low load, and the engine operating point corresponding to the target optimization value deviates from the high-efficiency range, the engine output power can be appropriately increased while meeting the energy requirements of the path segment. The excess energy can be used to charge the power battery, allowing the engine to operate at a more efficient operating point, thereby reducing overall energy consumption.

[0224] Understandably, by combining the target optimization value with the estimated travel time to obtain the average power, and then translating it into specific speed and torque commands based on the optimal energy consumption curve, a precise mapping from energy optimization results to actual engine control signals is achieved. This allows the global optimization results to be directly applied to the vehicle execution layer, ensuring the executability of the optimization scheme. Simultaneously, determining the speed and torque based on the optimal energy consumption curve ensures that the engine always operates within its efficient operating range during this path segment, further reducing fuel consumption.

[0225] In one embodiment, the path segment division process for the above-mentioned planned path division includes: Based on the changes in traffic congestion index, road gradient, and / or travel time per unit mile along the planned route, the planned route is divided into multiple route segments.

[0226] The aforementioned traffic congestion index change points refer to nodes on the planned route where the traffic congestion index changes significantly. The traffic congestion index is a quantitative indicator reflecting the degree of road traffic congestion and can be provided in real-time by in-vehicle navigation systems. When the difference in congestion index between adjacent road segments on the planned route exceeds a preset congestion change threshold, that location is considered a traffic congestion index change point. For example, when a road segment changes from a "smooth" state (congestion index 0-2) to a "congested" state (congestion index 6-8), a change point is formed at the boundary of the state change. Dividing the route into segments at these change points can separate road segments with significantly different traffic conditions, making the driving conditions within the same route segment relatively consistent.

[0227] The aforementioned road slope change points refer to nodes on the planned path where the road slope undergoes a significant change. Road slope information can be provided by navigation map data or high-precision map data. When the difference in road slope between adjacent locations on the planned path exceeds a preset slope change threshold, that location is designated as a road slope change point. For example, when a road changes from flat (0% slope) to uphill (5% slope), a change point is formed at the starting position of the slope change. Dividing the path into segments at these change points separates road segments with different slope characteristics, ensuring relatively stable vehicle driving force demand within the same path segment and avoiding deviations in energy demand estimation due to sudden slope changes.

[0228] The aforementioned points of change in travel time per unit mileage refer to nodes on the planned route where the travel time per kilometer changes significantly. The travel time per unit mileage can be calculated based on the estimated travel time and mileage provided by the vehicle navigation system, reflecting the traffic efficiency of that road segment. When the difference in travel time per kilometer between adjacent locations on the planned route exceeds a preset time change threshold, that location is considered a point of change in travel time per unit mileage. For example, when a road changes from a smooth traffic segment (1 minute travel time per kilometer) to a congested segment (5 minutes travel time per kilometer), a change point is formed at the starting point of the time abrupt change. Dividing the route into segments at these change points allows for the separation of road segments with significant differences in traffic efficiency.

[0229] It is understood that the three points of change mentioned above can be obtained in real time by the vehicle navigation system or high-precision map data, without the need for calculation by this application. By integrating real-time traffic information, historical road condition data, and map data, the vehicle navigation system can provide the congestion level, road gradient, and estimated travel time for each location, providing an accurate data basis for route segmentation.

[0230] Optionally, when dividing route segments, one or more of the above three types of change points can be selected as the division basis according to actual needs. For example, when driving in plains, the division can be mainly based on the change points of traffic congestion index and the change points of travel time per unit distance; when driving in mountainous or hilly areas, the change points of road slope can be added as the division basis to ensure that the division results can reflect the actual driving characteristics.

[0231] For example, suppose a hybrid vehicle travels from location A to location B, and the total planned route length provided by the in-vehicle navigation system is 50 kilometers. The system divides the route at 15 kilometers (congestion index changes from 2 to 6) and at 30 kilometers (congestion index changes from 6 to 3) based on changes in the traffic congestion index; it also divides the route at 22 kilometers (gradient changes from 0% to 4%) based on changes in road gradient; and it further divides the route at 40 kilometers (time per kilometer changes from 1.2 minutes to 3.5 minutes) based on changes in travel time per kilometer. Combining these division points, the planned route is divided into 5 segments: 0-15 kilometers (unobstructed traffic), 15-22 kilometers (congested traffic), 22-30 kilometers (uphill traffic), 30-40 kilometers (slow traffic), and 40-50 kilometers (congested traffic). The driving characteristics within each segment are relatively consistent, facilitating accurate estimation of subsequent energy requirements.

[0232] It should be noted that when the same location is identified as a change point by multiple criteria, a path segment can be divided at that location to avoid generating invalid path segments that are too short. Additionally, if the distance between adjacent change points is less than the preset minimum path segment length (e.g., 500 meters), these adjacent change points can be merged to avoid an excessive number of path segments leading to increased computational complexity.

[0233] It should also be understood that the specific method of route segmentation can be flexibly adjusted according to the actual application scenario. For example, when vehicles are traveling on urban roads, due to frequent changes in traffic conditions, the main basis for segmentation can be the points of change in the traffic congestion index; when vehicles are traveling on highways, due to relatively stable traffic conditions, the basis for segmentation can be the points of change in road gradient and the points of change in travel time per unit mileage. Those skilled in the art can make adaptive adjustments according to the actual situation, and these all fall within the scope of protection of this application.

[0234] By employing the above-described division method, the traffic congestion level, road gradient, and traffic efficiency within each path segment are kept relatively stable. This avoids deviations in energy demand estimation caused by drastic changes in road conditions, thereby improving the prediction accuracy of energy demand for each path segment and providing more accurate input data for subsequent global optimization. Simultaneously, by reasonably setting the threshold for change points and the minimum path segment length, the number of path segments can be effectively controlled, reducing computational complexity while ensuring optimization accuracy, thus balancing optimization effectiveness and computational efficiency.

[0235] Figure 6 This is a block diagram illustrating an energy management device for a hybrid vehicle according to an embodiment of this application, with reference to... Figure 6 The energy management device of the hybrid vehicle includes: a first determining module 601, a second determining module 602, a third determining module 603, and a control module 604.

[0236] The system comprises a first determining module 601, a second determining module 602, a third determining module 603, and a control module 604.

[0237] The first determining module 601 is used to determine the energy demand of the first target path segment; wherein, the first target path segment is one of the multiple path segments divided into the planned path of the hybrid vehicle. The second determining module 602 is used to determine the target discrete energy value sequence based on energy demand; The third determining module 603 is used to determine the target optimization value corresponding to the engine output mechanical energy of the first target path segment based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment and the objective function. The control module 604 is used to control the engine to generate electricity on the first target path segment by using the target optimization value.

[0238] The target optimization value is a value in the target discrete energy value sequence; The objective function is used to minimize the first fuel consumption and minimize the second fuel consumption; The first fuel consumption is the cumulative value of the fuel consumption of each of the second target path segments; the fuel consumption of the second target path segment is determined based on the optimization value of the engine output mechanical energy of the second target path segment; the second target path segment is the path segment that precedes the first target path segment among multiple path segments. The second fuel consumption is the fuel consumption corresponding to the minimum allowable value of the engine output mechanical energy for the first target path segment. The minimum allowable value is determined based on the cumulative energy demand from the starting point of the first target path segment to the end point of the planned path, as well as the difference in state of charge between the state of charge and the target state of charge.

[0239] The second determining module 602 mentioned above is also used to determine the first energy value based on the product of the energy demand and the first adjustment coefficient; The second energy value is determined by multiplying the engine's rated power by the estimated maximum travel time of the first target path segment. The larger of the first energy value and the second energy value is determined as the maximum permissible mechanical energy output of the engine for the first target path segment; The energy closed interval between 0 and the engine's maximum permissible output mechanical energy is discretized according to a preset energy step size to obtain the target discrete energy value sequence.

[0240] The control module 604 is also used to determine the first mechanical energy corresponding to the first electrical energy based on the state of charge difference, the total capacity of the power battery and the charging and discharging efficiency of the power battery when the state of charge difference is greater than or equal to 0; wherein, the first electrical energy is the energy that the power battery needs to release to adjust the state of charge to the target state of charge. The energy difference between the cumulative energy demand and the first mechanical energy is determined as the minimum allowable value.

[0241] The control module 604 is also used to determine the second mechanical energy corresponding to the second electrical energy based on the state of charge difference, the total capacity of the power battery and the charging and discharging efficiency of the power battery when the state of charge difference is less than 0; wherein, the second electrical energy is the energy that the power battery needs to replenish to adjust the state of charge to the target state of charge. The sum of the cumulative energy demand and the second mechanical energy is determined as the minimum allowable value.

[0242] The control module 604 is also used to determine the fuel consumption of the second target path segment based on the optimization value of the engine output mechanical energy, the engine power generation efficiency, and the lower heating value of the fuel.

[0243] The second determining module 602 mentioned above is also used to determine the constraints of the objective function; Based on the target discrete energy value sequence, state of charge, objective function, and constraints, the target optimization value corresponding to the engine output mechanical energy of the first target path segment is determined.

[0244] The above constraints include at least the following: The total fuel consumption corresponding to the total engine output mechanical energy is less than the remaining fuel of the hybrid vehicle at the starting point of the planned path; the total engine output mechanical energy is obtained by summing the target optimization values ​​corresponding to the engine output mechanical energy of multiple path segments; The state of charge is between the undervoltage state of charge and the overvoltage state of charge; the undervoltage state of charge is the state of charge corresponding to the undervoltage protection threshold voltage of the power battery; the overvoltage state of charge is the state of charge corresponding to the overvoltage protection threshold voltage of the power battery. The second determining module 602 is further configured to determine the target state of charge (SOC) of the power battery at the starting point of the third target path segment; wherein the third target path segment is the path segment preceding the first target path segment; the target energy difference between the energy demand of the third target path segment and the target optimization value corresponding to the engine output mechanical energy of the third target path segment; based on the target energy difference and the charging and discharging efficiency of the power battery, the change in SOC of the third target path segment is determined; if the target energy difference is greater than 0, the difference between the target SOC and the change in SOC is determined as the SOC; if the target energy difference is not greater than 0, the sum between the target SOC and the change in SOC is determined as the SOC.

[0245] The aforementioned control module 604 is also used to determine the estimated travel time of the first target path segment when the starting point is about to be reached; Based on the target optimization value and the estimated travel time, the engine control command corresponding to the first target path segment is determined; wherein, the engine control command indicates the control speed and control torque of the engine; based on the engine control command, the engine performs power generation control on the first target path segment.

[0246] The aforementioned first determining module 601 is also used to divide the planned path into multiple path segments based on the traffic congestion index change points, road slope change points, and / or travel time per unit mileage change points on the planned path.

[0247] Regarding the apparatus in the above embodiments, the specific methods of execution of each module have been described in detail in the embodiments of the network maintenance method, and will not be elaborated here.

[0248] Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device includes, but is not limited to, a processor 701 and a memory 702.

[0249] The memory 702 described above is used to store the executable instructions of the processor 701. It is understood that the processor 701 is configured to execute instructions to implement the network maintenance method described in the above embodiments.

[0250] It should be noted that those skilled in the art will understand that Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 7This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0251] Processor 701 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 702, and by calling data stored in memory 702, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 701 may include one or more processing units. Processor 701 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 701.

[0252] The memory 702 can be used to store software programs and various data. The memory 702 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as deterministic components, integrated components, etc.), etc. Furthermore, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0253] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 702 including instructions, which can be executed by a processor 701 of an electronic device to implement the methods in the above embodiments.

[0254] In actual implementation, Figure 6 The functions of the first determining module 601, the second determining module 602, the third determining module 603, and the control module 604 can all be derived from... Figure 7 The processor 701 calls the computer program stored in the memory 702 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0255] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device. In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 701 of an electronic device to perform the methods in the above embodiments.

[0256] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of the electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0257] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0258] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only 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 apparatus, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0259] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0260] Furthermore, the functional units in the various embodiments of this application 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.

[0261] 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 readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0262] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the above method embodiments.

[0263] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method in the method flow shown in the above method embodiments.

[0264] The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, registers, hard disks, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0265] Since the network maintenance device, computer-readable storage medium, and computer program product in the embodiments of this application can be applied to the above method, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0266] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy management method for a hybrid vehicle, characterized in that, The energy management method for the hybrid vehicle includes: Determine the energy demand of the first target path segment; wherein the first target path segment is one of multiple path segments divided into the planned path of the hybrid vehicle; Based on the energy requirements, determine the target discrete energy value sequence; Based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment and the objective function, the target optimization value corresponding to the engine output mechanical energy of the first target path segment is determined. Using the target optimization value, the engine is controlled to generate electricity on the first target path segment; Wherein, the target optimization value is a value in the target discrete energy value sequence; The objective function is used to minimize the first fuel consumption and minimize the second fuel consumption; The first fuel consumption is the cumulative value of the fuel consumption of each of the second target path segments; the fuel consumption of the second target path segment is determined based on the optimization value of the engine output mechanical energy of the second target path segment; the second target path segment is each path segment that is located before the first target path segment among the plurality of path segments; The second fuel consumption is the fuel consumption corresponding to the minimum allowable value of the engine output mechanical energy corresponding to the first target path segment; the minimum allowable value is determined based on the cumulative energy demand from the starting point of the first target path segment to the ending point of the planned path, and the difference in state of charge between the state of charge and the target state of charge. The process of determining the target discrete energy value sequence includes: The first energy value is determined based on the product of the energy requirement and the first adjustment coefficient. The product of the engine's rated power and the estimated maximum travel time of the first target path segment is determined as the second energy value; The larger of the first energy value and the second energy value is determined as the maximum permissible mechanical energy output of the engine for the first target path segment; The energy closed interval between 0 and the maximum permissible output mechanical energy of the engine is discretized according to a preset energy step size to obtain the target discrete energy value sequence.

2. The energy management method for hybrid vehicles according to claim 1, characterized in that, The process of determining the minimum allowable value includes: When the state of charge difference is greater than or equal to 0, the first mechanical energy corresponding to the first electrical energy is determined based on the state of charge difference, the total capacity of the power battery, and the charge and discharge efficiency of the power battery; wherein, the first electrical energy is the energy that the power battery needs to release to adjust the state of charge to the target state of charge; The energy difference between the cumulative energy demand and the first mechanical energy is determined as the minimum allowable value.

3. The energy management method for hybrid vehicles according to claim 1, characterized in that, The process of determining the minimum allowable value includes: When the state of charge difference is less than 0, the second mechanical energy corresponding to the second electrical energy is determined based on the state of charge difference, the total capacity of the power battery, and the charge and discharge efficiency of the power battery; wherein, the second electrical energy is the energy that the power battery needs to replenish to adjust the state of charge to the target state of charge; The sum of the cumulative energy demand and the second mechanical energy is determined as the minimum allowable value.

4. The energy management method for hybrid vehicles according to claim 1, characterized in that, The process of determining the fuel consumption of the second target path segment includes: Based on the optimization value of the engine output mechanical energy of the second target path segment, the power generation efficiency of the engine, and the lower heating value of the fuel, the fuel consumption of the second target path segment is determined.

5. The energy management method for a hybrid vehicle according to any one of claims 1-4, characterized in that, The step of determining the target optimization value corresponding to the engine output mechanical energy of the first target path segment based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment, and the objective function includes: Determine the constraints of the objective function; Based on the target discrete energy value sequence, the state of charge, the objective function, and the constraints, the target optimization value corresponding to the engine output mechanical energy of the first target path segment is determined.

6. The energy management method for a hybrid vehicle according to claim 5, characterized in that, The constraints include at least the following: The total fuel consumption corresponding to the total engine output mechanical energy is less than the remaining fuel of the hybrid vehicle at the starting point of the planned path; the total engine output mechanical energy is obtained by summing the target optimization values ​​corresponding to the engine output mechanical energy of the multiple path segments; The state of charge is between an undervoltage state of charge and an overvoltage state of charge; wherein, the undervoltage state of charge is the state of charge corresponding to the undervoltage protection threshold voltage of the power battery; and the overvoltage state of charge is the state of charge corresponding to the overvoltage protection threshold voltage of the power battery.

7. The energy management method for a hybrid vehicle according to claim 5, characterized in that, The process of determining the state of charge includes: Determine the target state of charge of the power battery at the starting point of the third target path segment; wherein the third target path segment is the path segment preceding the first target path segment; The target energy difference between the energy demand of the third target path segment and the target optimization value corresponding to the engine output mechanical energy of the third target path segment; Based on the target energy difference and the charging and discharging efficiency of the power battery, the change in state of charge of the third target path segment is determined. When the target energy difference is greater than 0, the difference between the target state of charge and the change in state of charge is determined as the state of charge. If the target energy difference is not greater than 0, the sum of the target state of charge and the change in state of charge is determined as the state of charge.

8. The energy management method for a hybrid vehicle according to claim 5, characterized in that, The step of using the target optimization value to control the engine's power generation on the first target path segment includes: When approaching the starting point, determine the estimated travel time for the first target path segment; Based on the target optimization value and the estimated travel time, the engine control command corresponding to the first target path segment is determined; wherein, the engine control command indicates the control speed and control torque of the engine; Based on the engine control command, the engine is controlled to generate electricity on the first target path segment.

9. The energy management method for a hybrid vehicle according to claim 1, characterized in that, The path segment division process for the planned path division includes: Based on the traffic congestion index change points, road slope change points, and / or travel time per unit mileage change points along the planned route, the planned route is divided into the multiple route segments.

10. An energy management device for a hybrid vehicle, characterized in that, The energy management device of the hybrid vehicle includes: The first determining module is used to determine the energy demand of the first target path segment; wherein the first target path segment is one of multiple path segments divided into the planned path of the hybrid vehicle. The second determining module is used to determine a target discrete energy value sequence based on the energy demand; The third determining module is used to determine the target optimization value corresponding to the engine output mechanical energy of the first target path segment based on the target discrete energy value sequence, the state of charge of the power battery of the hybrid vehicle at the starting point of the first target path segment and the objective function. The control module is used to control the engine to generate electricity on the first target path segment using the target optimization value; Wherein, the target optimization value is a value in the target discrete energy value sequence; The objective function is used to minimize the first fuel consumption and minimize the second fuel consumption; The first fuel consumption is the cumulative value of the fuel consumption of each of the second target path segments; the fuel consumption of the second target path segment is determined based on the optimization value of the engine output mechanical energy of the second target path segment; the second target path segment is the path segment that precedes the first target path segment among the plurality of path segments. The second fuel consumption is the fuel consumption corresponding to the minimum allowable value of the engine output mechanical energy corresponding to the first target path segment; the minimum allowable value is determined based on the cumulative energy demand from the starting point of the first target path segment to the ending point of the planned path, and the difference in state of charge between the state of charge and the target state of charge; wherein, the process of determining the target discrete energy value sequence includes: The first energy value is determined based on the product of the energy requirement and the first adjustment coefficient. The product of the engine's rated power and the estimated maximum travel time of the first target path segment is determined as the second energy value; The larger of the first energy value and the second energy value is determined as the maximum permissible mechanical energy output of the engine for the first target path segment; The energy closed interval between 0 and the maximum permissible output mechanical energy of the engine is discretized according to a preset energy step size to obtain the target discrete energy value sequence.

11. A vehicle, characterized in that, The vehicle includes the energy management device for a hybrid vehicle as described in claim 10.

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