A speed planning method and system for an electric vehicle in a dynamic traffic environment

Abstract

The application discloses a kind of electric vehicle speed planning method and system under dynamic traffic environment, comprising the following steps: obtaining road traffic information;Static speed planning, calculate speed curve according to vehicle position and road traffic information;Long-term planning, convert speed curve from position domain into time domain, and it is as the input of target green light phase of next traffic signal light;Target green light phase adaptive selection, the speed curve of time domain is integrated, and the time required for vehicle to reach next traffic signal light is obtained;Dynamic speed planning;Short-term planning based on preset rule;Environment prediction, calculate the predicted speed of preceding vehicle, generate solution space, and limit automobile position based on this.The computational complexity of the application is lower than two-dimensional discrete dynamic programming by several orders of magnitude, and by introducing short-term planning, it can be applied to dynamic traffic scenarios and is more consistent with actual road scenarios.

Description

Technical Field

[0001] This invention relates to a vehicle speed planning method and system, and more particularly to a speed planning method and system for electric vehicles in dynamic traffic environments. Background Technology

[0002] Discrete dynamic programming (DLP) is commonly used for vehicle speed planning. To ensure optimality, at least three dimensions (time, position, and speed) must be considered as state variables, leading to unacceptably high computation time in real-time applications. To reduce computation time, some existing methods omit the time dimension and incorporate it into the cost function. However, this approach does not guarantee optimality, especially when speed constraints change, where suboptimal performance becomes particularly pronounced. Alternatives to DLP include either Model Predictive Control (MPC) or rule-based optimization techniques. MPC requires a solution space constructed based on the selected green light phase of the upcoming traffic light. MPC determines the speed profile within this solution space to minimize a given cost function. Rule-based optimization, on the other hand, leverages the fact that minimizing the energy at the wheels yields a constant speed profile without any edge constraints; therefore, rule-based optimization is less practical and unsuitable for dynamic traffic environments. Summary of the Invention

[0003] Purpose of the invention: The first objective of this invention is to provide a speed planning method for electric vehicles with low computational workload that can be applied to dynamic traffic environments.

[0004] A second objective of this invention is to provide a system for electric vehicle speed planning in dynamic traffic environments.

[0005] Technical solution: This invention discloses a speed planning method for electric vehicles in a dynamic traffic environment, comprising the following steps: Obtain road traffic information; Static speed planning calculates speed curves based on vehicle position and road traffic information; Long-term planning involves converting the speed curve from the location domain to the time domain and using it as input for the target green phase of the next traffic light. The target green light phase adaptive selection integrates the speed curve in the time domain to obtain the time required for the vehicle to reach the next traffic light. Based on the state of the traffic light, the ratio of vehicle coasting and retraction is dynamically adjusted through an adaptive algorithm. Dynamic speed planning continuously optimizes the speed curve through inner and outer iterative loops until it approaches the optimal speed curve, ensuring that the vehicle arrives at the traffic light within the target green light phase and minimizes the energy consumption of the vehicle's wheels. Short-term planning, based on preset rules, has the following functions in the longitudinal control block: Function A is predictive deceleration maneuver; Function B is a travel time-oriented pulse and coasting strategy; Function C is to initiate coasting events based on the static speed curve; Function D is to set the speed according to the dynamic speed planning algorithm. Environmental prediction involves calculating the predicted speed of the vehicle ahead, generating a solution space, and constraining the vehicle's position based on this space.

[0006] Furthermore, the road traffic information includes map data, traffic light data, and traffic density.

[0007] Furthermore, the steps for calculating the velocity curve are as follows: First, calculate the maximum speed v using the following formula. max ,

[0008]

[0009] in Refers to the legal speed limit. This refers to the maximum speed for handling curves. This refers to the maximum speed taking into account the stop sign. This refers to the maximum speed at which a vehicle can pass over a speed bump. This refers to the maximum lateral acceleration. Refers to the turning radius; The formula for calculating the velocity curve is as follows:

[0010]

[0011] in Deceleration curve, used to indicate approaching speed reduction; factor speed difference It consists of two parts: gliding and recovery. The gliding and recovery strategy is used to reduce approach speed, that is, through... Reduce speed in gliding mode; the remaining speed difference Captured in recycling mode; The acceleration during energy recovery.

[0012] Furthermore, the steps for converting the velocity curve from the position domain to the time domain are as follows: Determine the vehicle's velocity profile in the position domain, i.e., the velocity profile of the vehicle when it is in its current position. ; Calculate the time required for the vehicle to reach the designated position based on its current speed and acceleration. By using integration or numerical methods, the location information is converted into time information to obtain the vehicle's speed when it reaches the specified location. .

[0013] Furthermore, regarding The time required for a vehicle to reach the next traffic light is calculated by accumulating points. If new traffic lights appear that are not part of the road traffic information, then... calculate Choose the earliest green light phase.

[0014] Furthermore, the steps for selecting the earliest green light phase are as follows: If a vehicle arrives when the new traffic light is green, then that green light phase is the target green light phase. If a vehicle arrives during a red light at a new traffic light, the next green light phase of that new traffic light becomes the target green light phase. The iterations increase, and each simulation iteration step calculates the arrival time of the next target green light stage.

[0015] Furthermore, the calculation formulas for the inner and outer iterative loops are as follows:

[0016]

[0017] in t is the target arrival time of the next traffic light. actual For the current time, The distance between the traffic light and the vehicle. The time difference between the current time and the target arrival time. These are reference values ​​for dynamic velocity planning calculations. set up For the speed curve of long-term planning, if for all ,in They all Then the optimal velocity curve is considered to be... To minimize energy consumption at the wheels; if The optimal velocity curve is then found through the following two iterative steps. : Inner iteration: Greater than Partial replacement , The time to arrive at the traffic light is ; Outer iteration: This reduces vehicle speed and delays the time it takes for vehicles to reach the next traffic light. The reduced portion is stretched over time.

[0018] Furthermore, the calculation formula for the dynamic speed planning set speed in the longitudinal control block is as follows: , Will and Transmitted to short-term planning.

[0019] Furthermore, the predicted speed of the preceding vehicle. The calculation formula is as follows:

[0020] Where P H It is the predicted time range. , It is the length of the prediction range. The actual speed of the car in front This refers to the simulated speed of the vehicle in front; The solution space is determined by the predicted speed of the preceding vehicle. ,function sum function Composition to restrict vehicle position,

[0021]

[0022] in The predicted position of the vehicle in front. Indicates the location of the next traffic light. This refers to the time when the next traffic light will switch.

[0023] Based on the same inventive concept, this invention also discloses an electric vehicle speed planning system for dynamic traffic environments, comprising, The input module is used to obtain road traffic information; The static speed planning module calculates the speed curve based on vehicle position and road traffic information; The long-term planning module converts the speed curve from the location domain to the time domain and uses it as input for the target green light phase of the next traffic light. The adaptive selection module integrates the velocity curve in the time domain to obtain the time required for the vehicle to reach the next traffic light. Based on the state of the traffic light, it dynamically adjusts the ratio of vehicle coasting and retraction through an adaptive algorithm. The dynamic speed planning module continuously optimizes the speed curve through inner and outer iterative loops until it approaches the optimal speed curve, ensuring that the vehicle arrives at the traffic light within the target green light phase and minimizes the energy consumption of the vehicle's wheels. The short-term planning module, based on rules, has the following functions in the longitudinal control block: Function A is predictive deceleration maneuver; Function B is a travel time-oriented pulse and coasting strategy; Function C is to initiate coasting events based on the static speed curve; Function D is to set the speed according to the dynamic speed planning algorithm. The environment prediction module is used to calculate the predicted speed of the vehicle in front, generate a solution space, and constrain the vehicle's position based on this space.

[0024] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: The present invention can not only find the same solution as the three-dimensional discrete dynamic programming under arbitrary speed limits, but its computational complexity is also several orders of magnitude lower than that of two-dimensional discrete dynamic programming; The present invention introduces short-run programming, which can be applied to dynamic traffic scenarios and is more in line with actual road scenarios; The present invention, through the inner and outer iterative algorithms of dynamic speed programming, can find the speed curve that minimizes wheel energy, is applicable to arbitrary speed limits, and can reach the upcoming traffic light of the selected target green light phase at the same time; The present invention innovatively proposes coasting and recovery factors, which enable adaptive adjustment of the coasting and recovery ratios. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the present invention; Figure 2 This is a schematic diagram of an inner iterative loop and an outer iterative loop for the dynamic velocity planning of this invention. Detailed Implementation

[0026] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0027] Example 1 like Figure 1 and Figure 2 As shown, the electric vehicle speed planning method under dynamic traffic conditions according to the present invention includes the following steps: 1. Obtain road traffic information; which includes map data, traffic light data, and traffic density; preferably, road traffic information can be obtained through GPS.

[0028] 2. Static speed planning: Calculate speed curves based on vehicle location and road traffic information; The steps for calculating the velocity curve are as follows: First, calculate the maximum speed v using the following formula. max ,

[0029]

[0030] in Refers to the legal speed limit. This refers to the maximum speed for handling curves. This refers to the maximum speed taking into account the stop sign. This refers to the maximum speed at which a vehicle can pass over a speed bump. This refers to the maximum lateral acceleration. Refers to the turning radius; The formula for calculating the velocity curve is as follows:

[0031]

[0032] in Deceleration curve, used to indicate approaching speed reduction; factor speed difference It consists of two parts: gliding and recovery. The gliding and recovery strategy is used to reduce approach speed, that is, through... Reduce speed in gliding mode; the remaining speed difference Captured in recycling mode; The acceleration during energy recovery. Preferred .

[0033] 3. Long-term planning involves converting the speed curve from the location domain to the time domain and using it as input for the target green light phase of the next traffic light; the speed obtained from the location domain in static speed planning is then used... speed converted to the time domain The steps for converting the velocity curve from the position domain to the time domain are as follows: Determine the vehicle's velocity profile in the position domain, i.e., the velocity profile of the vehicle when it is in its current position. ; Calculate the time required for the vehicle to reach the designated position based on its current speed and acceleration. By using integration or numerical methods, the location information is converted into time information to obtain the vehicle's speed when it reaches the specified location. .

[0034] 4. Adaptive selection of target green light phase: Integrate the speed curve in the time domain to obtain the time required for the vehicle to reach the next traffic light. Based on the state of the traffic light, dynamically adjust the ratio of vehicle coasting and retraction through an adaptive algorithm. Specifically, for The time required for a vehicle to reach the next traffic light is calculated by accumulating points. If new traffic lights appear that are not part of the road traffic information, then... calculate The vehicle selects the earliest green light phase; that is, when a vehicle encounters a new traffic light ahead, which may have been previously invisible or unrecognized by the system, but is now within the vehicle's perception range or recognized by the system, the vehicle needs to recalculate its speed profile in order to decide how to safely and efficiently pass through this new traffic light.

[0035] The steps for selecting the earliest green light phase are as follows: If a vehicle arrives when the new traffic light is green, then that green light phase is the target green light phase. If a vehicle arrives during a red light at a new traffic light, the next green light phase of that new traffic light becomes the target green light phase. The simulation is iterated and each iteration calculates the arrival time of the next target green light stage. That is, when a vehicle arrives at a new traffic light, if it happens to be a red light, the vehicle's control system will not select the current red light stage as the target, but will select the immediately following green light stage as the target green light stage.

[0036] 5. Dynamic speed planning: Through inner and outer layer iterative loops, the speed curve is continuously optimized until it approaches the optimal speed curve, so that the vehicle arrives at the traffic light within the target green light phase and the energy consumption of the vehicle's wheels is minimized. Specifically, the calculation formulas for the inner and outer iterative loops are as follows:

[0037]

[0038] in t is the target arrival time of the next traffic light. actual For the current time, The distance between the traffic light and the vehicle. The time difference between the current time and the target arrival time. These are reference values ​​for dynamic velocity planning calculations. set up For the speed curve of long-term planning, if for all ,in They all Then the optimal velocity curve is considered to be... To minimize energy consumption at the wheels; if The optimal velocity curve is then found through the following two iterative steps. : Inner iteration: Greater than Partial replacement , The time to arrive at the traffic light is ; Outer iteration: This reduces vehicle speed and delays the time it takes for vehicles to reach the next traffic light. The reduced portion is stretched over time.

[0039] 6. Short-term planning: Based on preset rules, short-term planning in the longitudinal control block has the following functions: Function A is predictive deceleration maneuver, preferably implemented through a coasting recovery strategy, which achieves the highest energy savings compared to other deceleration curves; Function B is a travel time-oriented pulse and coasting strategy, which is only activated when the travel time does not increase; Function C is to initiate a coasting event based on the static speed curve. Since the coasting curve may deviate from the calculated coasting curve due to unconsidered wind speed and road gradient, the time to switch from coasting to recovery is calculated within the longitudinal control block; Function D is to set the speed according to the dynamic speed planning algorithm; where preset rules refer to some set rules that can be set according to the actual situation, such as needing to decelerate when there is an object in front of the vehicle during driving. Specifically, the calculation formula for the dynamic velocity planning setpoint in the longitudinal control block is as follows: , Will and Transmitted to short-term planning.

[0040] 7. Environmental prediction: Calculate the predicted speed of the vehicle in front, generate a solution space, and restrict the vehicle's position based on this. By restricting the vehicle's position, the vehicle's speed can be indirectly controlled.

[0041] Specifically, the predicted speed of the vehicle in front. The calculation formula is as follows:

[0042] Where P H It refers to the time range of the prediction, that is, the time interval from the current moment to the end of the prediction. , It is the length of the prediction range. The actual speed of the car in front This refers to the simulated speed of the vehicle in front; The solution space is determined by the predicted speed of the preceding vehicle. ,function sum function Composition to restrict vehicle position,

[0043]

[0044] in The predicted position of the vehicle in front. Indicates the location of the next traffic light. This refers to the time when the next traffic light will switch.

[0045] In summary, this invention proposes an automated longitudinal control system comprising a novel long-term planning algorithm and a rule-based short-term planning algorithm, while satisfying vehicle dynamics requirements and considering actual road conditions. The core of the longitudinal control system is a speed planning algorithm, which calculates a speed curve to arrive at the upcoming traffic light at a given time while minimizing energy on the wheels. Dynamic speed planning incorporates adaptive techniques, adjusting the ratio of coasting and regenerative braking in static speed planning. Short-term planning primarily considers the prediction errors of dynamic traffic scenarios and long-term planning. This method avoids suboptimal solutions and has low computational complexity, thus possessing strong potential for real-world vehicle applications. This invention, through its inner and outer iterative algorithms of dynamic speed planning, can find a speed curve that minimizes wheel energy, applicable to arbitrary speed limits, and capable of arriving at the upcoming traffic light at the selected target green light phase simultaneously. This invention not only finds the same solution as three-dimensional discrete dynamic programming under arbitrary speed limits but also reduces computational complexity by several orders of magnitude compared to two-dimensional discrete dynamic programming. This invention innovatively proposes coasting and regenerative braking factors, enabling adaptive adjustment of the coasting and regenerative braking ratios. The introduction of short-term planning allows for application to dynamic traffic scenarios, better reflecting real-world road conditions.

[0046] Example 2 The present invention discloses an electric vehicle speed planning system under dynamic traffic conditions, comprising an input module, a static speed planning module, a long-term planning module, an adaptive selection module, a dynamic speed planning module, a short-term planning module, and an environmental prediction module.

[0047] The input module is used to obtain road traffic information, which includes map data, traffic light data, and traffic density.

[0048] The static speed planning module can calculate the speed curve based on vehicle position and road traffic information; the calculation steps for the speed curve are as follows: First, calculate the maximum speed v using the following formula. max ,

[0049]

[0050] That Refers to the legal speed limit. This refers to the maximum speed for handling curves. This refers to the maximum speed taking into account the stop sign. This refers to the maximum speed at which a vehicle can pass over a speed bump. This refers to the maximum lateral acceleration. Refers to the turning radius; The formula for calculating the velocity curve is as follows:

[0051]

[0052] in Deceleration curve, used to indicate approaching speed reduction; factor speed difference It consists of two parts: gliding and recovery. The gliding and recovery strategy is used to reduce approach speed, that is, through... Reduce speed in gliding mode; the remaining speed difference Captured in recycling mode; The acceleration during energy recovery. Preferred .

[0053] The long-term planning module can convert the velocity curve from the position domain to the time domain, and convert the velocity obtained from the position domain in static velocity planning into a time domain. speed converted to the time domain And use it as the input for the target green phase of the next traffic light.

[0054] Specifically, the velocity obtained from the position domain in static velocity planning speed converted to the time domain The steps for converting the velocity curve from the position domain to the time domain are as follows: Determine the vehicle's velocity profile in the position domain, i.e., the velocity profile of the vehicle when it is in its current position. ; Calculate the time required for the vehicle to reach the designated position based on its current speed and acceleration. By using integration or numerical methods, the location information is converted into time information to obtain the vehicle's speed when it reaches the specified location. .

[0055] The adaptive selection module integrates the velocity curve in the time domain to obtain the time required for the vehicle to reach the next traffic light. Based on the traffic light status, it dynamically adjusts the ratio of vehicle coasting to retraction using an adaptive algorithm. Specifically, for... The time required for a vehicle to reach the next traffic light is calculated by accumulating points. If new traffic lights appear that are not part of the road traffic information, then... calculate The vehicle selects the earliest green light phase; that is, when a vehicle encounters a new traffic light ahead, which may have been previously invisible or not recognized by the system, but is now within the vehicle's perception range or recognized by the system, the vehicle needs to recalculate its speed profile in order to decide how to safely and efficiently pass through the new traffic light. The steps for selecting the earliest green light phase are as follows: If a vehicle arrives when the new traffic light is green, then that green light phase is the target green light phase. If a vehicle arrives during a red light at a new traffic light, the next green light phase of that new traffic light becomes the target green light phase. The simulation is iterated and each iteration calculates the arrival time of the next target green light stage, i.e., adaptive adjustment; that is, when a vehicle arrives at a new traffic light, if it happens to be a red light, the vehicle's control system will not select the current red light stage as the target, but will select the immediately following green light stage as the target green light stage.

[0056] The dynamic speed planning module can continuously optimize the speed curve through inner and outer iterative loops until it approaches the optimal speed curve, enabling the vehicle to reach the traffic light within the target green light phase and minimizing the energy consumption of the vehicle's wheels. Specifically, the calculation formulas for the inner and outer iterative loops are as follows:

[0057]

[0058] in t is the target arrival time of the next traffic light. actual For the current time, The distance between the traffic light and the vehicle. The time difference between the current time and the target arrival time. These are reference values ​​for dynamic velocity planning calculations. set up For the speed curve of long-term planning, if for all ,in They all Then the optimal velocity curve is considered to be... To minimize energy consumption at the wheels; if The optimal velocity curve is then found through the following two iterative steps. : Inner iteration: Greater than Partial replacement , The time to arrive at the traffic light is ; Outer iteration: This reduces vehicle speed and delays the time it takes for vehicles to reach the next traffic light. The reduced portion is stretched over time.

[0059] The short-term planning module, based on preset rules, has the following functions in the longitudinal control block: Function A is predictive deceleration maneuver, preferably implemented through a coasting recovery strategy, which achieves the highest energy savings compared to other deceleration curves; Function B is a travel time-oriented pulse and coasting strategy, which is only activated when the travel time does not increase; Function C is to initiate a coasting event based on the static speed curve. Since the coasting curve may deviate from the calculated coasting curve due to unconsidered wind speed and road gradient, the time to switch from coasting to recovery is calculated within the longitudinal control block; Function D is to set the speed according to the dynamic speed planning algorithm. The preset rules refer to some predefined rules that can be set according to actual conditions, such as the need to decelerate when there is an object in front of the vehicle. Specifically, the calculation formula for the dynamic velocity planning setpoint in the longitudinal control block is as follows: , Will and Transmitted to short-term planning.

[0060] The environment prediction module calculates the predicted speed of the vehicle in front, generates a solution space, and restricts the vehicle's position based on this space. By restricting the vehicle's position, the vehicle's speed can be indirectly controlled; the predicted speed of the vehicle in front... The calculation formula is as follows:

[0061] in , It is the length of the prediction range. The actual speed of the car in front This refers to the simulated speed of the vehicle in front; The solution space is determined by the predicted speed of the preceding vehicle. ,function sum function Composition to restrict vehicle position,

[0062]

[0063] in The predicted position of the vehicle in front. Indicates the location of the next traffic light. This refers to the time when the next traffic light will switch.

Claims

1. A speed planning method for electric vehicles in a dynamic traffic environment, characterized in that: Includes the following steps: Obtain road traffic information; Static speed planning calculates speed curves based on vehicle position and road traffic information; Long-term planning involves converting the speed curve from the location domain to the time domain and using it as input for the target green phase of the next traffic light. The target green light phase adaptive selection integrates the speed curve in the time domain to obtain the time required for the vehicle to reach the next traffic light. Based on the state of the traffic light, the ratio of vehicle coasting and retraction is dynamically adjusted through an adaptive algorithm. Dynamic speed planning continuously optimizes the speed curve through inner and outer iterative loops until it approaches the optimal speed curve, ensuring the vehicle arrives at the traffic light within the target green light phase and minimizing the energy consumption of the vehicle's wheels. The calculation formulas for the inner and outer iterative loops are as follows: ， ， in t is the target arrival time of the next traffic light. actual For the current time, The distance between the traffic light and the vehicle. The time difference between the current time and the target arrival time. These are reference values ​​for dynamic velocity planning calculations. set up For the speed curve of long-term planning, if for all ,in They all Then the optimal speed curve is considered to be... To minimize energy consumption at the wheels; if The optimal velocity curve is then found through the following two iterative steps. : Inner iteration: Greater than Partial replacement , The time to arrive at the traffic light is ; Outer iteration: This reduces vehicle speed and delays the time it takes for vehicles to reach the next traffic light. The lowered portion is stretched over time; Short-term planning, based on preset rules, has the following functions in the longitudinal control block: Function A is predictive deceleration maneuver; Function B is a travel time-oriented pulse and coasting strategy; Function C is to initiate coasting events based on the static speed curve; Function D is to set the speed according to the dynamic speed planning algorithm. Environmental prediction involves calculating the predicted speed of the vehicle ahead, generating a solution space, and constraining the vehicle's position based on this space.

2. The electric vehicle speed planning method under dynamic traffic conditions according to claim 1, characterized in that: The road traffic information includes map data, traffic light data, and traffic density.

3. The electric vehicle speed planning method under dynamic traffic conditions according to claim 1, characterized in that: The steps for calculating the velocity curve are as follows: First, calculate the maximum speed v using the following formula. max , ， ， in Refers to the legal speed limit. This refers to the maximum speed for handling curves. This refers to the maximum speed taking into account the stop sign. This refers to the maximum speed at which a vehicle can pass over a speed bump. This refers to the maximum lateral acceleration. Refers to the turning radius; The formula for calculating the velocity curve is as follows: ， ， in The deceleration curve is used to indicate a decrease in speed as you approach a vehicle. The acceleration during energy recovery, factor speed difference It consists of two parts: gliding and recovery. The gliding and recovery strategy is used to reduce approach speed, that is, through... Reduce speed in gliding mode; the remaining speed difference Captured in recycling mode; The acceleration during energy recovery.

4. The electric vehicle speed planning method under dynamic traffic conditions according to claim 3, characterized in that: The steps for converting the velocity curve from the position domain to the time domain are as follows: Determine the vehicle's velocity profile in the position domain, i.e., the velocity profile of the vehicle when it is in its current position. ; Calculate the time required for the vehicle to reach the designated position based on its current speed and acceleration. By using integration or numerical methods, the location information is converted into time information to obtain the vehicle's speed when it reaches the specified location. .

5. The electric vehicle speed planning method under dynamic traffic conditions according to claim 4, characterized in that: right The time required for a vehicle to reach the next traffic light is calculated by accumulating points. If new traffic lights appear that are not part of the road traffic information, then... calculate Choose the earliest green light phase.

6. The electric vehicle speed planning method under dynamic traffic conditions according to claim 5, characterized in that: The steps for selecting the earliest green light phase are as follows: If a vehicle arrives when the new traffic light is green, then that green light phase is the target green light phase. If a vehicle arrives during a red light at a new traffic light, the next green light phase of that new traffic light becomes the target green light phase. The iterations increase, and each simulation iteration step calculates the arrival time of the next target green light stage.

7. The electric vehicle speed planning method under dynamic traffic conditions according to claim 6, characterized in that: The calculation formula for the dynamic speed planning set speed in the longitudinal control block is as follows: ， Will and Transmitted to short-term planning.

8. The electric vehicle speed planning method under dynamic traffic conditions according to claim 6, characterized in that: The predicted speed of the vehicle in front The calculation formula is as follows: ， Where P H It is the predicted time range. , It is the length of the prediction range. The actual speed of the car in front This refers to the simulated speed of the vehicle in front; The solution space is determined by the predicted speed of the preceding vehicle. ,function sum function Composition to restrict vehicle position, ， ， in The predicted position of the vehicle in front. Indicates the location of the next traffic light. This refers to the time when the next traffic light will switch.

9. A speed planning system for electric vehicles in a dynamic traffic environment, characterized in that: include, The input module is used to obtain road traffic information; The static speed planning module calculates the speed curve based on vehicle position and road traffic information; The long-term planning module converts the speed curve from the location domain to the time domain and uses it as input for the target green light phase of the next traffic light. The adaptive selection module integrates the velocity curve in the time domain to obtain the time required for the vehicle to reach the next traffic light. Based on the state of the traffic light, it dynamically adjusts the ratio of vehicle coasting and retraction through an adaptive algorithm. The dynamic speed planning module continuously optimizes the speed curve through inner and outer iterative loops until it approaches the optimal speed curve, ensuring the vehicle arrives at the traffic light within the target green light phase and minimizing the energy consumption of the vehicle's wheels. The calculation formulas for the inner and outer iterative loops are as follows: ， ， in t is the target arrival time of the next traffic light. actual For the current time, The distance between the traffic light and the vehicle. The time difference between the current time and the target arrival time. These are reference values ​​for dynamic velocity planning calculations. set up For the speed curve of long-term planning, if for all ,in They all Then the optimal speed curve is considered to be... To minimize energy consumption at the wheels; if The optimal velocity curve is then found through the following two iterative steps. : Inner iteration: Greater than Partial replacement , The time to arrive at the traffic light is ; Outer iteration: This reduces vehicle speed and delays the time it takes for vehicles to reach the next traffic light. The lowered portion is stretched over time; The short-term planning module, based on rules, has the following functions in the longitudinal control block: Function A is predictive deceleration maneuver; Function B is a travel time-oriented pulse and coasting strategy; Function C is to initiate coasting events based on the static speed curve; Function D is to set the speed according to the dynamic speed planning algorithm. The environment prediction module is used to calculate the predicted speed of the vehicle in front, generate a solution space, and constrain the vehicle's position based on this space.

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