Hybrid vehicle energy economy real-time management method based on vehicle speed planning

Abstract

The invention relates to the technical field of hybrid electric vehicle control, in particular to a hybrid electric vehicle energy economy real-time management method based on vehicle speed planning, which is used for managing a hybrid electric vehicle comprising an engine, a driving motor, a power battery and a data acquisition module, and comprises the following steps: S1, information acquisition; s2, generating a safe vehicle speed constraint; s3, generating a reference time track; s4, carrying out integrated rolling optimization solution; s5, performing control execution and periodic rolling; according to the method, vehicle speed track optimization and power system power distribution are subjected to integrated collaborative optimization through an economic model predictive control framework on the premise of only depending on local sensing information, so that the energy economy of the whole vehicle is remarkably improved while the driving safety and maneuverability are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of hybrid vehicle control technology, and in particular to a real-time energy economy management method for hybrid vehicles based on vehicle speed planning. Background Technology

[0002] The public knowledge, as attached Figure 1 As shown, hybrid vehicles can effectively reduce fuel consumption and emissions through the coordinated work of the engine and electric motor. Their performance largely depends on the energy management strategy. Traditional energy management strategies are mainly divided into two categories: rule-based strategies and optimization-based strategies. Rule-based strategies, such as deterministic rules and fuzzy logic control, are simple and have good real-time performance, but their control logic is usually preset and fixed, and cannot be dynamically adjusted according to real-time driving conditions. It is difficult to achieve global or near-global optimal fuel economy under complex and ever-changing actual road conditions. Optimization-based strategies, especially dynamic programming, can obtain the theoretical optimal solution under known global conditions. However, they require prior knowledge of the complete driving conditions, have high computational complexity, and belong to offline optimization methods, which cannot be directly used for real-time control.

[0003] In actual driving, vehicles can only obtain road information (slope, curvature, traffic conditions, etc.) for a limited distance ahead through onboard sensors such as cameras, radar, and GPS. They cannot predict the road conditions for the entire journey, which makes optimization methods that rely on global information ineffective. Although some existing real-time optimization methods do not rely on global information, they usually treat speed planning and power distribution as two independent or decoupled problems. This decoupled optimization fails to fully consider the deep coupling relationship between speed changes and energy flow. For example, reasonable pre-deceleration can maximize braking energy recovery efficiency, and a stable speed helps the engine operate in its high-efficiency range. In addition, existing strategies do not adequately consider the dynamic safety boundaries of vehicles under complex road conditions (such as high-curvature curves and low-adhesion surfaces), and their handling of travel time constraints is relatively rigid.

[0004] Therefore, the existing technology has the following shortcomings: First, it is difficult to balance real-time performance and global economy; second, vehicle speed planning and energy allocation are decoupled, and the optimization potential is not fully explored; third, it lacks effective coordination with real-time road safety constraints and travel time requirements. There is an urgent need for a real-time energy management method that can comprehensively utilize limited forward-looking information, simultaneously optimize vehicle speed and energy allocation, and meet safety and mobility constraints. Summary of the Invention

[0005] To overcome the shortcomings of the prior art and solve the existing technical problems, this invention discloses a real-time energy economy management method for hybrid vehicles based on vehicle speed planning. It can integrate vehicle speed trajectory optimization and power system power distribution through an economic model predictive control framework, relying only on local perception information, thereby significantly improving the energy economy of the whole vehicle while ensuring driving safety and maneuverability.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A real-time energy economy management method for hybrid vehicles based on vehicle speed planning is used to manage hybrid vehicles including an engine, drive motor, power battery, and data acquisition module. The method is executed cyclically in each control cycle and includes the following steps: S1, Information Acquisition: The data acquisition module acquires the vehicle's current state information, road environment information within the forward prediction domain, and travel target information. The vehicle's current state information includes at least the current vehicle speed v and the battery's state of charge (SOC). The road environment information includes at least the road curvature, slope, and road surface adhesion coefficient at each sampling point within the prediction domain. The travel target information includes the total travel distance. and expected total travel time S2. Safe Speed ​​Constraint Generation: Based on a pre-generated stable limit speed MAP map that correlates road surface adhesion coefficient, road curvature, and slope, interpolation queries are performed using the road environment information obtained in S1 within the prediction domain to obtain the real-time stable limit speed at each sampling point within the prediction domain. Combined with legal speed limits on roads Determine the upper limit constraint of the comprehensive vehicle speed at each sampling point within the prediction domain.

[0008] A real-time energy economy management method for hybrid vehicles based on vehicle speed planning is used to manage hybrid vehicles including an engine, drive motor, power battery, and data acquisition module. The method is executed cyclically in each control cycle and includes the following steps: S1, Information Acquisition: The data acquisition module acquires the vehicle's current state information, road environment information within the forward prediction domain, and travel target information. The vehicle's current state information includes at least the current vehicle speed v and the battery's state of charge (SOC). The road environment information includes at least the road curvature, slope, and road surface adhesion coefficient at each sampling point within the prediction domain. The travel target information includes the total travel distance. and expected total travel time S2. Safe Speed ​​Constraint Generation: Based on a pre-generated stable limit speed MAP map that correlates road surface adhesion coefficient, road curvature, and slope, interpolation queries are performed using the road environment information obtained in S1 within the prediction domain to obtain the real-time stable limit speed at each sampling point within the prediction domain. Combined with legal speed limits on roads Determine the upper limit constraint of the comprehensive vehicle speed at each sampling point within the prediction domain.

[0009] Where k is the index of the sampling point within the prediction domain; S3, Reference time trajectory generation: based on the distance the vehicle has traveled. Used travel time Total travel distance and expected total travel time The reference time trajectory is generated at discrete points in the prediction domain space. S4. Integrated rolling optimization solution: Establish an economic optimization problem with minimizing total energy consumption as the core, and perform rolling solution under physical constraints including the comprehensive vehicle speed limit constraint and reference time trajectory to obtain the optimal control quantity for the current cycle and execute it; S5. Control execution and cycle rolling: Send the control quantity obtained in S4 to the vehicle actuator for execution; after reaching the next control cycle, return to S1.

[0010] Furthermore, in S3, the generation of the reference time trajectory The specific method is as follows: the reference time corresponding to the i-th spatial sampling point in the prediction domain is calculated using the following formula:

[0011] ,in, Let i be the sampling interval step size of the spatial prediction domain, i = 0, 1, 2, ..., N, where N is the number of sampling points within the length of the prediction domain.

[0012] Furthermore, in S4, the integrated rolling optimization solution specifically includes: S4.1, constructing a discrete spatial domain prediction model, with the state variable being the square of the vehicle speed. And the battery SOC value, the control variable is the drive motor torque. and engine output power S4.2 Set constraints, including at least: vehicle speed constraint, SOC constraint, motor torque constraint, engine power constraint, and battery power constraint; S4.3 Construct an economic optimization objective function J, which includes a fuel consumption term, a trip time tracking term, and a battery SOC tracking term; S4.4 In each control cycle, using the current measured state as the initial value, solve for the sequence of control variables that minimizes the objective function J under all constraints in the prediction domain, and apply the first control variable in the sequence to the vehicle.

[0013] Furthermore, the discrete spatial domain prediction model is as follows:

[0014]

[0015] Where k is the spatial domain step count, and m is the vehicle mass. Where r is the transmission ratio and r is the wheel radius. For rolling resistance, For air resistance, For slope resistance, This is the battery open-circuit voltage. This refers to the battery's internal resistance. For battery capacity, Battery power; mechanical braking torque As an auxiliary control quantity, it only operates when the vehicle speed exceeds the limit or the regenerative braking power exceeds the battery's maximum charging power.

[0016] Furthermore, the economic optimization objective function J is:

[0017]

[0018] in, Let k be the fuel consumption at time k. As a weighting factor, To predict the reference time at the end of the domain, To ensure that the reference SOC at the end of the prediction domain is consistent with the initial SOC, Let k be the system energy loss at time k.

[0019] Furthermore, in S2, the stable limit speed MAP is generated in the following way: based on the vehicle dynamics model, for different road surface adhesion coefficients μ, road curvature ρ and slope angle θ, the critical speed that ensures the vehicle's steady-state driving safety is calculated, and a multi-dimensional lookup table is formed.

[0020] Furthermore, in S1, the road environment information within the forward prediction domain is obtained through the fusion of vehicle-mounted visual sensors, radar, high-precision maps, and positioning modules.

[0021] Furthermore, the control system of the hybrid vehicle includes: a data acquisition module for acquiring vehicle status, road environment, and travel target information; a safety monitoring module for storing a stable limit speed MAP and generating safe speed constraints based on real-time road environment information; a reference time generator for generating a reference time trajectory based on travel target information; and an online rolling optimization module, integrated into the vehicle controller, for solving the economic optimization problem based on the safe speed constraints, reference time trajectory, vehicle status, and prediction model, and outputting the optimal control command to act on the vehicle.

[0022] By employing the technical solution described above, the present invention has the following beneficial effects:

[0023] The present invention discloses a real-time energy economy management method for hybrid vehicles based on vehicle speed planning, which achieves real-time global near-optimal performance. It adopts an economic model predictive control framework and performs rolling optimization within a limited prediction domain. This method utilizes future local road condition information while avoiding the dependence of dynamic programming on global information and the huge amount of computation, thus achieving a good balance between real-time performance and economy.

[0024] This invention discloses a real-time energy economy management method for hybrid vehicles based on vehicle speed planning. It creatively integrates vehicle speed planning and power allocation into a single optimization problem, achieving deep coupling and collaborative optimization between the two. The system can proactively plan reasonable speed changes to assist energy management, maximize regenerative braking, and allow the engine to operate more frequently in its high-efficiency range. Through a safety monitoring module, the vehicle dynamics stability boundary is transformed into a speed limit constraint in real time and directly embedded into the optimization problem, ensuring that the optimized speed trajectory meets dynamic safety requirements under any operating condition, significantly improving driving safety in complex road conditions. A reference time generator transforms the user's total travel time requirement into a smooth reference time trajectory in the spatial domain, which is tracked as a soft constraint in the objective function. This allows the system to flexibly adjust local vehicle speeds to optimize energy consumption while ensuring on-time arrival, resolving the conflict between economy and mobility. Furthermore, the entire strategy relies solely on the vehicle's existing sensors and controllers, requiring no prior knowledge of global path information, and can adapt to various complex and time-varying urban and suburban road conditions, demonstrating promising engineering application prospects. Attached Figure Description

[0025] Figure 1 This is a schematic block diagram of a series hybrid vehicle powertrain system to which the present invention is applicable;

[0026] Figure 2 This is an overall control framework diagram of the energy economy real-time management method described in this invention;

[0027] Figure 3 This is a flowchart illustrating the specific implementation steps of the real-time energy management method described in this invention;

[0028] Figure 4 This is a flowchart of the safety supervision module in this invention;

[0029] Figure 5 This is a flowchart of the closed-loop rolling optimization process of the online rolling optimization module in this invention. Detailed Implementation

[0030] The technical solution of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0031] A real-time energy economy management method for hybrid vehicles based on vehicle speed planning is used to manage hybrid vehicles, including the engine, drive motor, power battery, and data acquisition module, as shown in the attached figure. Figure 1 As shown, the series hybrid system mainly includes an engine, a generator, a drive motor, a power battery, a power converter, a main reducer, and wheels. The engine drives the generator to generate electricity, and the generated electricity, together with the battery, or separately supplies the drive motor to drive the vehicle. The vehicle controller is responsible for implementing the energy management strategy of this invention.

[0032] As attached Figure 2 As shown, the control system of a hybrid vehicle includes: a data acquisition module for acquiring vehicle status, road environment, and travel target information; a safety monitoring module for storing a stable limit speed MAP and generating safe speed constraints based on real-time road environment information; a reference time generator for generating a reference time trajectory based on travel target information; and an online rolling optimization module, integrated into the vehicle controller, for solving the economic optimization problem based on the safe speed constraints, reference time trajectory, vehicle status, and prediction model, and outputting the optimal control command to act on the vehicle.

[0033] Combined with appendix Figure 3 As shown, the method of the present invention is executed cyclically in each control cycle, including the following steps:

[0034] Step 1: Information Collection: The data collection module acquires the vehicle's current status information, road environment information within the prediction domain, and travel target information. The vehicle's current status information includes at least the current vehicle speed v and the battery's state of charge (SOC). The road environment information includes at least the road curvature, slope, and road surface adhesion coefficient at each sampling point within the prediction domain, obtained through fusion of onboard vision sensors, radar, high-precision maps, and the positioning module. The travel target information includes the total travel distance. and expected total travel time .

[0035] Step 2: Generating safe speed constraints: (See attached) Figure 4 As shown, based on vehicle stability analysis, a MAP (Modular Map of Stable Limit Speeds) is pre-generated under different combinations of road surface adhesion coefficients, road curvature, and slope. Specifically, based on a two-degree-of-freedom vehicle dynamics model or a monorail model, the critical steady-state speed that ensures the vehicle does not skid or overturn is calculated for different road surface adhesion coefficients μ, road curvature ρ, and slope angle θ, forming a multi-dimensional lookup table. Based on the road environment information within the prediction domain obtained in step one, the MAP is interpolated to obtain the real-time stable limit speeds at each sampling point within the prediction domain. Combined with legal speed limits on roads Determine the upper limit constraint of the comprehensive vehicle speed at each sampling point within the prediction domain. , where k is the index of the sampling point within the prediction domain.

[0036] Step 3: Reference Time Trajectory Generation: Based on the distance the vehicle has traveled Used travel time Total travel distance and expected total travel time The reference time trajectory is generated at discrete points in the prediction domain space. ;

[0037] Generate reference time trajectory The specific method is as follows: the reference time corresponding to the i-th spatial sampling point in the prediction domain is calculated using the following formula:

[0038] ,in, Let i be the sampling interval step size of the spatial prediction domain, i = 0, 1, 2, ..., N, where N is the number of sampling points within the length of the prediction domain.

[0039] Step 4: Integrated rolling optimization solution: as shown in the appendix Figure 5 As shown, an economic optimization problem centered on minimizing total energy consumption is established, and a rolling solution is performed under the aforementioned comprehensive vehicle speed limit constraint, reference time trajectory, and other physical constraints to obtain the optimal control quantity for the current cycle and execute it; specifically, the integrated rolling optimization solution includes:

[0040] Construct a discrete spatial domain prediction model, with the state variable being the square of the vehicle speed. And the battery SOC value, the control variable is the drive motor torque. and engine output power Mechanical braking torque As an auxiliary control quantity, it only operates when the vehicle speed exceeds the limit or the regenerative braking power exceeds the battery's maximum charging power; the discrete spatial domain prediction model is:

[0041]

[0042] Where k is the spatial domain step count, and m is the vehicle mass. Where r is the transmission ratio and r is the wheel radius. For rolling resistance, For air resistance, For slope resistance, This is the battery open-circuit voltage. This refers to the battery's internal resistance. For battery capacity, For battery power, and and Related;

[0043] Set constraints, including at least:

[0044] Speed ​​constraints: ;

[0045] SOC constraints: ;

[0046] Motor torque constraint: ;

[0047] Engine power constraints: ;

[0048] Battery power constraints: ;

[0049] Mechanical braking torque constraint: ;

[0050] Construct an economic optimization objective function J, which includes fuel consumption, travel time tracking, and battery SOC tracking terms; the economic optimization objective function J is:

[0051]

[0052] in, Fuel consumption at time k, and Related, As a weighting factor, To predict the reference time at the end of the domain, This is a predicted value for the cumulative travel time. The SOC reference value at the end of the prediction domain is usually set as the initial SOC. Let k be the system energy loss at time k;

[0053] Rolling optimization is used to solve the sequence of control variables that minimizes the objective function J within the prediction domain, using the current measured state as the initial value, while satisfying all constraints. , Only the first control variable in the optimal sequence is output to the vehicle system, and the remaining control variables are discarded; numerical optimization algorithms are used to solve the economic optimization problem, including but not limited to the sequential quadratic programming method, the interior point method, or the pseudospectral method.

[0054] Step 5, Control Execution and Cycle Rolling: The control quantity obtained in Step 4 is sent to the vehicle engine controller and motor controller for execution; after the next control cycle is reached, return to Step 1, and repeat Steps 2 to 5 with the new vehicle state and road information as the starting point to achieve closed-loop rolling optimization control; as needed, at the next sampling time, combine the feedback information (error between actual vehicle speed, SOC and predicted value) to correct the prediction model, and repeat the above rolling optimization process to improve control accuracy and real-time performance.

[0055] To implement the real-time energy economy management method for hybrid vehicles based on vehicle speed planning described in this invention, the following steps are performed within each control cycle (e.g., 100ms):

[0056] Step 1: Information Acquisition (corresponding to the data acquisition module); The current vehicle speed v and battery SOC are acquired via the CAN bus. By fusing data from cameras, millimeter-wave radar, GPS / IMU, and high-precision maps, the road curvature ρ(s), slope angle θ(s), and estimated road adhesion coefficient μ(s) curves within a prediction domain at a certain distance (e.g., 150 meters) ahead of the vehicle are obtained. Simultaneously, the total travel distance is acquired from the navigation system. And the expected total travel time set by the user or estimated by the system. .

[0057] Step 2: Generate security constraints (corresponding to the security supervision module), such as... Figure 4 As shown, this module pre-stores a "Stable Limit Speed ​​MAP" obtained through offline simulation calculations. This is a database that takes μ, ρ, and θ as inputs and the critical safe speed as the output. During online execution, based on μ(s), ρ(s), and θ(s) obtained in step one, it calculates the stable limit speed corresponding to each spatial point in the prediction domain in real time through three-dimensional linear interpolation. Then, it compares the stable limit speed with the legal speed limit of the road and takes the smaller value as the final speed limit for that point. And pass it to the optimization module.

[0058] Step 3: Reference Time Generation (corresponding to the reference time generator); current distance traveled is known. Used travel time According to the formula This calculates the "reference time" that should be reached at each spatial sampling point within the prediction domain in the future. This reference time curve reflects the intention to evenly distribute the remaining time over the remaining distance, providing a time tracking target for the optimization problem.

[0059] Step 4: Integrated rolling optimization solution (corresponding to the online rolling optimization module), this is the core step;

[0060] 1. Initialization: Set the current measured state [ [(0), SOC(0)] is set as the initial state of the optimization problem.

[0061] 2. Problem Construction: Establish an optimization problem framework that includes a prediction model, various physical constraints, and a vehicle speed limit constraint.

[0062] 3. Define the objective: Construct the objective function J, where the fuel consumption term... With engine power Correlation is achieved through the engine's universal characteristic MAP diagram; the time tracking term penalizes the actual predicted time t(k) and the reference time. Deviation; SOC term penalizes the deviation between the terminal SOC and the expected value; loss term This includes efficiency losses in components such as motors and batteries.

[0063] 4. Numerical solution: The above nonlinear optimization problem is solved in each control cycle. Since the prediction domain is finite (N is usually small, such as 20-30 steps) and the model is carefully designed, an efficient sequential quadratic programming (SQP) solver can be used to complete the calculation in milliseconds and obtain the optimal control sequence for the next N steps.

[0064] 5. Implementation and Rollout: Only the first step of the control sequence is taken, i.e., the motor torque command and engine power command for the next moment, and sent to the underlying actuator; at the same time, depending on whether mechanical braking is required, a decision is made... After entering the next control cycle, the above process is repeated, starting from the new measurement state, and optimized again to form a closed-loop control of "rolling time domain and feedback correction".

[0065] Through the above process, the vehicle can plan a safe (without exceeding stability limits and legal speed limits), punctual (tracking reference time), and energy-efficient speed trajectory in real time, even when the road conditions are unknown throughout the journey, and simultaneously provide the optimal power distribution command for the power system.

[0066] The parts of this invention not described in detail are prior art. It will be apparent to those skilled in the art that this invention is not limited to the details of the above exemplary embodiments, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the above embodiments should be regarded as exemplary and non-limiting in all respects. The scope of this invention is defined by the appended claims rather than the foregoing description. Therefore, it is intended to include all changes that fall within the meaning and scope of the equivalents of the claims within this invention, and no reference numerals in the claims should be regarded as limiting the content of the claims.

Claims

1. A real-time energy economy management method for hybrid vehicles based on vehicle speed planning, used to manage hybrid vehicles including an engine, drive motor, power battery, and data acquisition module, characterized in that, The method is executed cyclically in each control cycle, and includes the following steps: S1. Information Acquisition: The data acquisition module acquires the vehicle's current status information, road environment information within the forward prediction domain, and travel target information. The vehicle's current status information includes at least the current vehicle speed v and the battery state of charge (SOC). The road environment information includes at least the road curvature, slope, and road surface adhesion coefficient at each sampling point within the prediction domain. The travel target information includes the total travel distance. and expected total travel time ; S2. Safe Speed ​​Constraint Generation: Based on a pre-generated stable limit speed MAP map that correlates road surface adhesion coefficient, road curvature, and slope, interpolation queries are performed using the road environment information obtained in S1 within the prediction domain to obtain the real-time stable limit speed at each sampling point within the prediction domain. Combined with legal speed limits on roads Determine the upper limit constraint of the comprehensive vehicle speed at each sampling point within the prediction domain. , where k is the index of the sampling point in the prediction domain; S3. Reference Time Trajectory Generation: Based on the distance the vehicle has traveled. Used travel time Total travel distance and expected total travel time The reference time trajectory is generated at discrete points in the prediction domain space. ; S4. Integrated rolling optimization solution: Establish an economic optimization problem with minimizing total energy consumption as the core, and perform rolling solution under physical constraints including the comprehensive vehicle speed limit constraint and reference time trajectory to obtain the optimal control quantity for the current cycle and execute it. S5. Control Execution and Cycle Rolling: Send the control quantity obtained in S4 to the vehicle actuator for execution; after the next control cycle is reached, return to S1.

2. The real-time energy economy management method for hybrid vehicles based on vehicle speed planning according to claim 1, characterized in that: In S3, the generation of the reference time trajectory The specific method is as follows: the reference time corresponding to the i-th spatial sampling point in the prediction domain is calculated using the following formula: ,in, Let i be the sampling interval step size of the spatial prediction domain, i = 0, 1, 2, ..., N, where N is the number of sampling points within the length of the prediction domain.

3. The real-time energy economy management method for hybrid vehicles based on vehicle speed planning according to claim 1, characterized in that: In S4, the integrated rolling optimization solution specifically includes: S4.1 Construct a discrete spatial domain prediction model, with the state variable being the square of the vehicle speed. And the battery SOC value, the control variable is the drive motor torque. and engine output power ; S4.2 Set constraints, including at least: vehicle speed constraint, SOC constraint, motor torque constraint, engine power constraint, and battery power constraint; S4.3 Construct an economic optimization objective function J, which includes a fuel consumption term, a trip time tracking term, and a battery SOC tracking term; S4.4 In each control cycle, with the current measured state as the initial value, solve the sequence of control variables that minimizes the objective function J under all constraints in the prediction domain, and apply the first control variable in the sequence to the vehicle.

4. The real-time energy economy management method for hybrid vehicles based on vehicle speed planning according to claim 3, characterized in that: The discrete spatial domain prediction model is as follows: Where k is the spatial domain step count, and m is the vehicle mass. Where r is the transmission ratio and r is the wheel radius. For rolling resistance, For air resistance, For slope resistance, This is the battery open-circuit voltage. This refers to the battery's internal resistance. For battery capacity, Battery power; mechanical braking torque As an auxiliary control quantity, it only operates when the vehicle speed exceeds the limit or the regenerative braking power exceeds the battery's maximum charging power.

5. The real-time energy economy management method for hybrid vehicles based on vehicle speed planning according to claim 3, characterized in that: The economic optimization objective function J is: in, Let k be the fuel consumption at time k. As a weighting factor, To predict the reference time at the end of the domain, To ensure that the reference SOC at the end of the prediction domain is consistent with the initial SOC, Let k be the system energy loss at time k.

6. The real-time energy economy management method for hybrid vehicles based on vehicle speed planning according to claim 1, characterized in that: In S2, the stable limit speed MAP is generated in the following way: based on the vehicle dynamics model, for different road surface adhesion coefficients μ, road curvature ρ and slope angle θ, the critical speed that ensures the vehicle's steady-state driving safety is calculated, and a multi-dimensional lookup table is formed.

7. The real-time energy economy management method for hybrid vehicles based on vehicle speed planning according to claim 1, characterized in that: In S1, the road environment information within the forward prediction domain is obtained by fusing vehicle-mounted visual sensors, radar, high-precision maps, and positioning modules.

8. The real-time energy economy management method for hybrid vehicles based on vehicle speed planning according to claim 1, characterized in that: The control system of the hybrid vehicle includes: The data acquisition module is used to obtain information on vehicle status, road environment, and travel destination. The safety monitoring module is used to store a stable limit speed MAP and generate safe speed constraints based on real-time road environment information. Reference time generator, used to generate reference time trajectories based on travel target information; The online rolling optimization module, integrated into the vehicle controller, is used to solve the economic optimization problem based on the safe speed constraint, reference time trajectory, vehicle status and prediction model, and output the optimal control command to be applied to the vehicle.

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