Intelligent driving vehicle energy-saving control method, device and system

Abstract

The invention relates to the technical field of intelligent driving, and discloses an energy-saving control method, device and system for an intelligent driving vehicle, and the method comprises the steps: taking energy consumption cost as an optimization target, and building a future vehicle speed planning model considering the dynamic interaction characteristics of a driving scene; based on the interaction strength of the driving scene, the vehicle speed planning model is solved through vehicle speed planning rolling optimization oriented to the multi-mode long period-short time domain, and an optimal vehicle speed curve is obtained; according to the planned vehicle speed, the traction power requirement is calculated, power distribution between an engine and a motor in the hybrid power system is optimized, and the optimal track of the working torque and the rotating speed of the engine and the motor is obtained. According to the method, the vehicle driving energy consumption and the driving scene interaction strength are comprehensively considered, the energy consumption of the intelligent driving vehicle can be reduced in the urban complex driving scene, and meanwhile, the driving safety is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to an energy-saving control method, device and system for an intelligent driving vehicle. Background Art

[0002] In complex urban roads, driving scenarios are diverse and highly time-varying. How to optimize energy consumption while maintaining the driving safety of intelligent driving vehicles is an important challenge currently faced. Therefore, it is urgent to study intelligent driving energy-saving optimization control methods for complex and multi-scenario scenarios to ensure the safety of intelligent vehicle driving and maximize the optimal energy consumption. However, the existing hybrid power system energy-saving control methods mainly focus on energy management at the vehicle power system level, which cannot adapt to the complex and changeable characteristics of urban scenarios.

[0003] For example, the invention patent published on March 8, 2022: Patent No.: CN114148351B, a hybrid energy management system and method, the method includes: obtaining vehicle positioning information and target vehicle speed, obtaining map information, calculating the predictive neutral gliding distance and energy consumption, performing predictive vehicle speed / gear planning, and calculating the optimal energy consumption and the corresponding vehicle speed curve, and arbitrating the predictive gliding and predictive vehicle speed / gear planning. Among them, calculating the predictive neutral gliding distance and energy consumption includes: obtaining the vehicle position, driving distance and driving time; checking whether there is a speed limit change or a downhill slope on the road ahead according to the map information; if so, starting to calculate the vehicle speed trajectory curve when the gearbox is in neutral according to the vehicle dynamics model; if the calculated end speed value of the future vehicle speed trajectory falls within a sufficiently small range based on the target vehicle speed, the predictive gliding function is activated; when the actual gliding distance reaches the pre-estimated gliding distance, the predictive gliding is judged to be completed. This method takes advantage of the autonomous calculation and decision-making of longitudinal and lateral acceleration of autonomous driving, uses the perception function to predict and judge the movement trajectory of the front and surrounding vehicles, makes full use of the slope, speed limit and curvature information in the map information, and uses navigation information to ensure vehicle fuel saving. However, due to the complex and changeable information of urban driving scenes, this method may have poor adaptability and poor energy consumption optimization effect. Summary of the invention

[0004] In order to solve the above problems, the present application provides an energy-saving control method, device and system for intelligent driving vehicles, so as to effectively reduce driving energy consumption in scenarios with complex urban road conditions and multi-vehicle interference and interaction.

[0005] In order to achieve the above purpose, the technical solution adopted in this application is as follows:

[0006] In a first aspect, the present application provides an energy-saving control method for an intelligent driving vehicle, the method comprising:

[0007] Taking energy consumption cost as the optimization target, a future vehicle speed planning model considering the dynamic interaction characteristics of driving scenarios is established; based on the interaction strength of driving scenarios, the future time domain vehicle speed planning model considering the dynamic interaction characteristics of driving scenarios is solved by using multi-mode long-cycle-short-time domain vehicle speed planning rolling optimization to obtain the planned vehicle speed;

[0008] The traction power demand is calculated according to the planned vehicle speed, and a rule-based global power allocation strategy is established to optimize the power distribution between the engine and the motor in the hybrid system to obtain the optimal trajectory of the engine and motor working torque and speed.

[0009] In a second aspect, the present application provides an energy-saving control device for an intelligent driving vehicle, the device comprising:

[0010] The vehicle speed curve planning unit is configured to establish a future vehicle speed planning model that takes into account the dynamic interaction characteristics of driving scenarios with energy consumption cost as an optimization target; based on the interaction strength of the driving scenarios, the future time domain vehicle speed planning model that takes into account the dynamic interaction characteristics of driving scenarios is solved by using a multi-mode long-period-short-time domain vehicle speed planning rolling optimization to obtain a planned vehicle speed;

[0011] The optimal trajectory determination unit is configured to calculate the traction power demand according to the planned vehicle speed, establish a rule-based global power distribution strategy to optimize the power distribution between the engine and the motor in the hybrid system, and obtain the optimal trajectory of the engine and motor working torque and speed.

[0012] In a third aspect, the present application provides an intelligent driving vehicle energy-saving control system, the system comprising: a memory for storing a computer program; a processor for executing the computer program to implement the intelligent driving vehicle energy-saving control method as described above.

[0013] This application has at least the following beneficial effects:

[0014] (1) In view of the changing characteristics of driving scenarios in urban environments and taking into account the strength of scene interactions, this application designs a long-period-short-time domain speed planning rolling optimization solution strategy, which makes the speed planning in the intelligent driving mode more reasonable and better able to adapt to the complex urban traffic environment.

[0015] (2) This application proposes an energy management optimization control scheme for a power-split hybrid system of an intelligent driving heavy truck. By optimizing the power distribution between the engine and the motor in the hybrid system, the optimal trajectory of the working torque and speed of the engine and the motor is obtained, thereby minimizing fuel consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is an overall flow chart of the energy-saving control method for an intelligent driving vehicle provided in an embodiment of the present application.

[0017] Figure 2 A schematic diagram of the classification of interaction strength levels for complex urban driving scenarios provided in an embodiment of the present application.

[0018] Figure 3 A schematic diagram of long-period-short-time domain rolling optimization provided for an embodiment of the present application.

[0019] Figure 4 This is a flow chart of energy-saving optimization control of a heavy-duty truck hybrid system provided in an embodiment of the present application.

[0020] Figure 5 This is a framework diagram of the energy-saving control device for intelligent driving vehicles provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0022] The specific implementation of the present application is further described in detail below in conjunction with the drawings and examples.

[0023] The embodiment of the present application provides an energy-saving control method for an intelligent driving vehicle, which can be configured in an intelligent terminal of the vehicle (such as a control chip carried by the vehicle) for implementation to achieve intelligent control of the vehicle, thereby achieving the purpose of reducing energy consumption. It should be noted that this method can be applied to any vehicle capable of performing intelligent driving control, including but not limited to intelligent driving heavy trucks. In some embodiments, the energy-saving optimization control method can also be applied to other vehicles with hybrid systems.

[0024] Specifically, Figure 1 As shown, it is a flow chart of the energy-saving control method of an intelligent driving vehicle provided in an embodiment of the present application. The energy-saving control method of an intelligent driving vehicle includes the following steps S1 to S2, which are described in detail as follows.

[0025] S1, multi-mode intelligent driving speed planning considering changes in driving scenarios: taking energy consumption cost as the optimization target, a future time-domain speed planning model considering the dynamic interaction characteristics of driving scenarios is established; based on the interaction strength of driving scenarios, the future time-domain speed planning model considering the dynamic interaction characteristics of driving scenarios is solved by using the multi-mode long-cycle-short-time domain speed planning rolling optimization to obtain the planned speed.

[0026] In an exemplary embodiment, the influence of dynamic changes in driving scenarios on the efficiency and SOC of the hybrid power system is analyzed, and a hybrid power system energy consumption model based on a deep neural network considering battery SOC and system efficiency is established (Formula (1));

[0027]

[0028] in, represents the instantaneous energy consumption cost of the powertrain, f DNN represents the nonlinear mapping function describing the relationship between driving state and optimal energy consumption, v is the vehicle speed, a is the vehicle acceleration, SOC is the battery state of charge, η is the system efficiency, including the motor, engine and converter efficiency;

[0029] In an exemplary embodiment, based on the hybrid power system energy consumption model, the safety risk constraints of the surrounding leading vehicles and the traffic efficiency constraints of the signal phase are considered, and the energy consumption cost is used as the optimization target to establish a future vehicle speed planning model that considers the dynamic interaction characteristics of the driving scene, as shown in equations (2)-(7):

[0030]

[0031] u=a(t),x=[v(t),x(t),SOC(t)] T (3)

[0032]

[0033] Wherein, formula (2) represents the energy consumption target, which is the energy consumption integral in the time domain, T0 represents the starting point of the vehicle speed planning, T d represents the end point of the vehicle speed planning; Formula (3) indicates that the control variable is acceleration, and the state variables are speed, distance and battery power; Formulas (4)-(6) are the transfer equations of the state variables, f NNS Q is a nonlinear mapping function that describes the relationship between driving state and optimal battery current. b is the battery capacity; Formula (14) is the constraint condition, including speed constraint, acceleration constraint, SOC constraint, following distance constraint and traffic light constraint, s p is the distance traveled by the preceding vehicle, δ s For safe distance, It indicates the time that the i-th traffic light has been running in its periodic clock when the vehicle starts. is the travel time required for a vehicle to pass the i-th intersection, expressed as Calculate the passing time of the vehicle in the signal cycle clock, x(t) represents the state variable at time t, u(t) represents the control variable at time t, v(t) represents the speed at time t, x represents the state variable set, u represents the control variable set, a(t) represents the acceleration at time t, s(t) represents the position at time t, v(t) represents the speed at time t, a(t) represents the acceleration at time t, SOC(t) represents the battery power at time t, v min (s) represents the minimum speed at the position s on the road, v max (s) represents the maximum speed at position s, a min represents the minimum acceleration of the vehicle, a max Indicates the maximum acceleration of the vehicle, SOC min Indicates the minimum battery charge value, SOC max Maximum battery charge value.

[0034] In an exemplary embodiment, Figure 2 As shown, it is a schematic diagram of the interaction strength and weakness level classification of the urban complex driving scene provided by the embodiment of the present application. When there are no environmental vehicles in all 8 directions around the vehicle and within the longitudinal front and rear distance of X meters (in this embodiment, X=150), the driving scene is defined as a non-interaction scene; when there are at least two environmental vehicles (n≥2) in all 8 directions around the vehicle and within the longitudinal front and rear distance of X meters, and the distance between the vehicle and the front vehicle is within the safe distance range, the driving scene is defined as a strong interaction scene; otherwise, when the conditions of the non-interaction scene and the strong interaction scene are not met, it is defined as a weak interaction scene.

[0035] In an exemplary embodiment, Figure 3 As shown, it is a schematic diagram of long-term-short-term rolling optimization provided by an embodiment of the present application. Based on the interaction strength of the driving scene, the future time domain speed planning model considering the dynamic interaction characteristics of the driving scene is solved by using the multi-mode long-term-short-term speed planning rolling optimization to obtain the planned speed, including:

[0036] Analyze the changing characteristics of different interaction strengths and weaknesses in the city and determine the appropriate time domain length I for long-term vehicle speed planning, where [t0, t0+I]∈[T0, T d ], t0 is the current moment; in the short time domain H of driving scenes with different interaction strengths within the long period range, a time-based rolling optimization solution strategy is established for the strong interaction driving scene, and the rolling step is ΔT; for the non-interaction and weak interaction driving scenes, a distance-based rolling optimization solution strategy is established, and the rolling step is Δs, as shown in formula (8); where H = [t0, t d ], t d is the end time of the driving scene type identified at time t0, t d -t0≤I;

[0037]

[0038] Among them, d rolling represents the rolling step, Δs represents the rolling distance step, and ΔT represents the rolling time step;

[0039] The rolling optimization solution strategy uses the current time t0 as the starting point and uses the dynamic programming algorithm to solve the optimal vehicle speed planning within the long period time domain length I; according to the rolling advancement step length d rolling , the long-term time window moves forward, and the optimal speed planning within the next long-term time domain length is solved again; the long-term time window is continuously rolled and the speed planning is optimized and solved until the end time point of the entire planning task is reached T d .

[0040] S2, energy management optimization control of power-split hybrid system for intelligent heavy trucks: Calculate the traction power demand according to the planned vehicle speed, establish a rule-based global power distribution strategy to optimize the power distribution between the engine and the motor in the hybrid system, and thereby obtain the optimal trajectory of the engine and motor working torque and speed, and control according to the optimal trajectory;

[0041] Traction power requirement P t The calculation formula is:

[0042]

[0043] Where P t (k) represents the required traction power, k∈N, N is the index set of the speed vector, M represents the equivalent mass of the vehicle, represents the vehicle acceleration, C d represents the drag coefficient, ρ represents the air density, A represents the frontal area, v(k) represents the vehicle speed, m represents the static mass of the vehicle, g represents the acceleration of gravity, and is the road coefficient that is the combination of the rolling resistance coefficient f and the road slope θ.

[0044] In an exemplary embodiment, the rule-based global power allocation strategy is to divide the future driving period into four stages according to the required power and the planned vehicle speed, namely, the electric energy regeneration stage, the high power demand stage, the low speed stage and the power balance stage, and determine the required power of the engine in stages.

[0045] The four stages are:

[0046] (1) Power regeneration stage: Calculate P t (k) is a negative index set N r ={k∈N|P t (k)<0}, the total regenerative electric energy is calculated as in is the maximum power that can be regenerated based on the GMB operating boundary, P e (k)=0.

[0047] (2) High power demand stage: In this stage, the driving power is provided by the engine and the battery at the same time. Calculate P t (k) Indicator set exceeding high power requirements And calculate the total power required as P e (k) = P up .

[0048] (3) Low-speed stage: Find the index set of the low-speed area and make where σ is a given threshold value related to the absolute value of the energy difference between the terminal SOC and the initial SOC, P e (k)=0.

[0049] (4) Power balancing stage: the remaining power P t (k) Sort from small to large and find the low power P low region, so that P t (i)<P low , i∈[1,k l ], and the equilibrium point k b satisfy Then the remaining required power can be divided into three stages (motor stage N b1 ={i∈[1,k b ]}, Hybrid stage N b2 ={i∈[k b +1, k l ]}, engine stage N b3 ={i>k l}), the required engine power is P e (k) = {0(k∈N b1 );P low (k∈N b2 );P t (k)(k∈N b3 )}.

[0050] The above mentioned P up and P low It is selected according to the engine diagram, P up and P low Fuel consumption rate between g e Small and changes slowly with engine power.

[0051] In an exemplary embodiment, a rule-based global power distribution strategy is used to optimize the power distribution between the engine and the motor in the hybrid system, and the optimal trajectory of the working torque and speed of the engine and the motor is obtained by the following method:

[0052] The torque and speed combination is obtained based on the engine demand power and engine map obtained by the rule-based global power allocation strategy The optimal trajectory of the speed and torque of the drive motor and generator is calculated using the planetary gear ratio formula and the hybrid system dynamics equation. in, Indicates the torque and speed corresponding to the optimal operating point of the engine, T e represents the engine torque, ω e Indicates the engine speed, Indicates the optimal torque of the driving motor, Indicates the optimal speed of the drive motor. represents the optimal torque of the generator, The optimal speed of the generator, the speed ratio formula of the planetary gear set, and the dynamic equation of the hybrid system are expressed as:

[0053]

[0054] Where K = R r / R s , represents the planetary gear coefficient, which is the ratio of the radius of the sun gear to the ring gear, i r is the speed ratio of the main reducer, R tire is the tire radius, I e ,I B , U A are the rotational inertia of the engine, generator and drive motor respectively, T e , T B , T A , T c , T s , T r , T t are the torques at the engine, generator, drive motor, planet carrier, sun gear, ring gear and wheel, respectively; η represents the engine efficiency diagram, is the engine angular acceleration, is the generator angular acceleration, is the angular acceleration of the drive motor, is the vehicle acceleration, m is the static mass of the vehicle, M is the equivalent mass of the vehicle, g is the gravitational acceleration, C d is the drag coefficient, ρ is the air density, A is the frontal area, and β=fcos(θ)+sin(θ) is the road coefficient combining the rolling resistance coefficient f and the road slope θ.

[0055] In an exemplary embodiment, regarding the energy-saving optimization control flow chart of the heavy truck hybrid system, as shown in FIG. Figure 4 As shown, the torque and speed combination is obtained according to the engine demand power obtained by the global power distribution and the engine map The optimal trajectory of motor speed and torque is obtained from the hybrid system dynamics equation and the speed and torque relationship of the planetary gear set. The first set of torque values As the control input of the hybrid system.

[0056] The present application also provides an intelligent driving vehicle energy saving control device, such as Figure 5 As shown, the device comprises:

[0057] The vehicle speed curve determining unit 501 is configured to establish a future vehicle speed planning model that takes into account the dynamic interaction characteristics of driving scenarios with energy consumption cost as an optimization target; based on the interaction strength of the driving scenarios, the future time domain vehicle speed planning model that takes into account the dynamic interaction characteristics of driving scenarios is solved by using a multi-mode long-period-short-time domain vehicle speed planning rolling optimization to obtain a planned vehicle speed;

[0058] The optimal trajectory determination unit 502 is configured to calculate the traction power demand according to the planned vehicle speed, establish a rule-based global power distribution strategy to optimize the power distribution between the engine and the motor in the hybrid system, and obtain the optimal trajectory of the engine and motor working torque and speed.

[0059] It should be noted that the device described in this embodiment and the method described previously belong to the same technical concept, have the same technical principles, and can achieve the same beneficial effects, so they will not be repeated here.

[0060] An embodiment of the present application also provides an intelligent driving vehicle energy-saving control system, the system comprising: a memory for storing a computer program; a processor for executing the computer program to implement the intelligent driving vehicle energy-saving control method as described in any of the above embodiments.

[0061] The above implementation modes are only used to illustrate the present application, and are not intended to limit the present application. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions also belong to the scope of the present application, and the scope of patent protection of the present application shall be limited by the claims.

Claims

1. An energy-saving control method for an intelligent driving vehicle, characterized in that: The method comprises: Taking energy consumption cost as the optimization target, a future vehicle speed planning model considering the dynamic interaction characteristics of driving scenarios is established; based on the interaction strength of driving scenarios, the future time domain vehicle speed planning model considering the dynamic interaction characteristics of driving scenarios is solved by using multi-mode long-cycle-short-time domain vehicle speed planning rolling optimization to obtain the planned vehicle speed; The traction power demand is calculated according to the planned vehicle speed, and a rule-based global power allocation strategy is established to optimize the power distribution between the engine and the motor in the hybrid system to obtain the optimal trajectory of the engine and motor working torque and speed.

2. The intelligent driving vehicle energy saving control method according to claim 1, characterized in that: Taking energy consumption cost as the optimization target, a vehicle speed planning model for different future cycles is established, including: Based on the influence of dynamic changes in driving scenarios on the efficiency and SOC of the hybrid system, a hybrid system energy consumption model based on a deep neural network considering battery SOC and system efficiency is established, as shown in formula (1): in, represents the instantaneous energy consumption cost of the powertrain, f DNN represents the nonlinear mapping function describing the relationship between driving state and optimal energy consumption, v is the vehicle speed, a is the vehicle acceleration, SOC is the battery state of charge, η is the system efficiency, including the motor, engine and converter efficiency; Based on the hybrid system energy consumption model, the safety risk constraints of the surrounding leading vehicles and the traffic efficiency constraints of the signal phase are considered, and the future vehicle speed planning model considering the dynamic interaction characteristics of the driving scene is established with the energy consumption cost as the optimization target, as shown in equations (2)-(7): u=a(t),x=[v(t),x(t),SOC(t)] T (3) Wherein, formula (2) represents the energy consumption target, which is the energy consumption integral in the time domain, T0 represents the starting point of the vehicle speed planning, T d represents the end point of the vehicle speed planning; Formula (3) indicates that the control variable is acceleration, and the state variables are speed, distance and battery power; Formulas (4)-(6) are the transfer equations of the state variables, f NNS Q is a nonlinear mapping function that describes the relationship between driving state and optimal battery current. b is the battery capacity; Formula (14) is the constraint condition, including speed constraint, acceleration constraint, SOC constraint, following distance constraint and traffic light constraint, s p is the distance traveled by the preceding vehicle, δ s For safe distance, It indicates the time that the i-th traffic light has been running in its periodic clock when the vehicle starts. is the travel time required for a vehicle to pass the i-th intersection, expressed as Calculate the passing time of the vehicle in the signal cycle clock, x(t) represents the state variable at time t, u(t) represents the control variable at time t, v(t) represents the speed at time t, x represents the state variable set, u represents the control variable set, a(t) represents the acceleration at time t, s(t) represents the position at time t, v(t) represents the speed at time t, a(t) represents the acceleration at time t, SOC(t) represents the battery power at time t, v min (s) represents the minimum speed at the position s on the road, v max (s) represents the maximum speed at position s, a min represents the minimum acceleration of the vehicle, a max Indicates the maximum acceleration of the vehicle, SOC min Indicates the minimum battery charge value, SOC max Maximum battery charge value.

3. The intelligent driving vehicle energy saving control method according to claim 2, characterized in that: Based on the interaction strength of the driving scene, the future speed planning model considering the dynamic interaction characteristics of the driving scene is solved by using the multi-mode long-period-short-time domain speed planning rolling optimization to obtain the planned speed, including: When there are no environmental vehicles in all eight directions around the vehicle and within the longitudinal front and rear distance of X meters, the driving scene is defined as a non-interaction scene; when there are at least two environmental vehicles in all eight directions around the vehicle and within the longitudinal front and rear distance of X meters, and the distance between the vehicle and the front vehicle is within the safe distance range, the driving scene is defined as a strong interaction scene; when the conditions of the non-interaction scene and the strong interaction scene are not met, it is defined as a weak interaction scene; where X is a set constant value; Analyze the changing characteristics of different interaction strengths and weaknesses in the city and determine the appropriate time domain length I for long-term vehicle speed planning, where [t0, t0+I]∈[T0,T d ], t0 is the current moment; in the short time domain H of driving scenes with different interaction strengths in the long period range, a time-based rolling optimization solution strategy is established for the strong interaction driving scene, and the rolling step is ΔT. For the non-interaction and weak interaction driving scenes, a distance-based rolling optimization solution strategy is established, and the rolling step is Δs, as shown in formula (8); where H = [t0, t d ], t d is the end time of the driving scene type identified at time t0, t d -t0≤I; Among them, d rolling represents the rolling step, Δs represents the rolling distance step, and ΔT represents the rolling time step; The rolling optimization solution strategy uses the current time t0 as the starting point and uses the dynamic programming algorithm to solve the optimal vehicle speed planning within the long period time domain length I; according to the rolling advancement step length d rolling , the long-term time window moves forward, and the optimal speed planning within the next long-term time domain length is solved again; the long-term time window is continuously rolled and the speed planning is optimized and solved until the end time point of the entire planning task is reached T d .

4. The intelligent driving vehicle energy saving control method according to claim 1, characterized in that: According to the planned vehicle speed, the traction power requirement is calculated by the following formula: Where P t (k) represents the required traction power, k∈N, N is the index set of the speed vector, M represents the equivalent mass of the vehicle, represents the vehicle acceleration, C d represents the drag coefficient, ρ represents the air density, A represents the frontal area, v(k) represents the vehicle speed, m represents the static mass of the vehicle, g represents the acceleration of gravity, and β=fcos(θ)+sin(θ) is the road coefficient which is the combination of the rolling resistance coefficient f and the road slope θ.

5. The intelligent driving vehicle energy-saving control method according to claim 4, characterized in that: The rule-based global power allocation strategy is: According to the required power and planned vehicle speed, the future driving period is divided into four stages, and the required engine power is determined in stages: Electric energy regeneration stage: Find P t (k) is a negative index set N r ={k∈N|P t (k)<0}, the total regenerative electric energy is calculated as in is the maximum power that can be regenerated based on the generator operating boundary, P e (k) = 0, η B is the generator efficiency, Δs(k) is the travel distance of the kth stage of the planned vehicle speed; High power demand stage: The driving power is provided by the engine and the motor at the same time. Find P t (k) Indicator set exceeding high power requirements And calculate the total power required as P e (k) = P up , P up It is the highest power requirement selected according to the engine map; Low-speed stage: Find the index set of the low-speed area where v low According to the formula Determine, σ is a given threshold value related to the absolute value of the energy difference between the terminal SOC and the initial SOC, P e (k) = 0; Power balancing stage: the remaining power P t (k) Sort from small to large and find the low power P low region, so that P t (i) <P low , i∈[1,k l ],k l is the number of required power values ​​falling in the low power area, and the equilibrium point k b satisfy The remaining required power is divided into three stages: motor stage N b1 ={i∈[1,k b ]}, hybrid stage N b2 ={i∈[k b +1,k l ]}, engine stage N b3 ={i>k l }), the required engine power is P e (k) = {0(k∈N b1 );P low (k∈N b2 );P t (k)(k∈N b3 )}; Among them, P low is the minimum power requirement selected according to the engine map, k l represents the number of required power values ​​falling in the low power area, η A Indicates the drive motor efficiency.

6. The energy-saving control method for an intelligent driving vehicle according to claim 5, characterized in that: The rule-based global power allocation strategy is used to optimize the power distribution between the engine and the motor in the hybrid system. The optimal trajectory of the engine and motor working torque and speed is obtained by the following method: The torque and speed combination is obtained based on the engine demand power and engine map obtained by the rule-based global power allocation strategy The optimal trajectory of the speed and torque of the drive motor and generator is calculated using the planetary gear ratio formula and the hybrid system dynamics equation. in, Indicates the torque and speed corresponding to the optimal operating point of the engine, T e represents the engine torque, ω e Indicates the engine speed, Indicates the optimal torque of the driving motor, Indicates the optimal speed of the drive motor. represents the optimal torque of the generator, represents the optimal speed of the generator; wherein the planetary gear set speed ratio formula and the hybrid system dynamics equation are respectively expressed as: Where K = R r / R s , represents the planetary gear coefficient, which is the ratio of the radius of the sun gear to the ring gear, i r is the speed ratio of the main reducer, R tire is the tire radius, I e ,I B ,I A are the rotational inertia of the engine, generator and drive motor respectively, T e , T B , T A , T c , T s , T r , T t are the torques at the engine, generator, drive motor, planet carrier, sun gear, ring gear and wheel, respectively; η represents the engine efficiency diagram, is the engine angular acceleration, is the generator angular acceleration, is the angular acceleration of the drive motor, is the vehicle acceleration, m is the static mass of the vehicle, M is the equivalent mass of the vehicle, g is the gravitational acceleration, C d is the drag coefficient, ρ is the air density, A is the frontal area, and β=fcos(θ)+sin(θ) is the road coefficient combining the rolling resistance coefficient f and the road slope θ.

7. An energy-saving control device for an intelligent driving vehicle, characterized in that: The device comprises: The vehicle speed curve planning unit is configured to establish a future vehicle speed planning model that takes into account the dynamic interaction characteristics of driving scenarios with energy consumption cost as an optimization target; based on the interaction strength of the driving scenarios, the future time domain vehicle speed planning model that takes into account the dynamic interaction characteristics of driving scenarios is solved by using a multi-mode long-period-short-time domain vehicle speed planning rolling optimization to obtain a planned vehicle speed; The optimal trajectory determination unit is configured to calculate the traction power demand according to the planned vehicle speed, establish a rule-based global power distribution strategy to optimize the power distribution between the engine and the motor in the hybrid system, and obtain the optimal trajectory of the engine and motor working torque and speed.

8. An intelligent driving vehicle energy-saving control system, characterized in that: The system comprises: a memory for storing a computer program; and a processor for executing the computer program to implement the energy-saving control method for an intelligent driving vehicle according to any one of claims 1 to 6.

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