An energy control method and control system for an unmanned pure electric vehicle

Abstract

The application discloses an energy control method and a control system for an unmanned pure electric vehicle, and comprises the following steps: S1, acquiring front road condition information; S2, judging vehicle driving demand according to an instruction sent by an automatic driving controller; and S3, controlling a power system according to the front road condition information and the vehicle driving demand. By adopting the technical scheme, the energy consumption of the vehicle can be reduced and the cruising range can be prolonged under the premise of ensuring the safety and power performance of the vehicle.

Description

Technical Field

[0001] This invention belongs to the field of pure electric vehicle technology, and in particular relates to an energy control method and control system for an unmanned pure electric vehicle. Background Technology

[0002] The development of pure electric vehicles is rapid. To meet user needs, pure electric vehicles need to maintain good power performance under different temperatures and ensure sufficient range to avoid range anxiety. However, due to the inherent characteristics of current batteries, performance degrades at both high and low temperatures. Furthermore, features like comfort and entertainment consume a lot of energy, and excessively high or low battery temperatures affect power output, impacting vehicle power and fuel economy, causing inconvenience for users. Currently, energy management in the powertrain of pure electric vehicles mainly involves controlling motor torque. Acceleration and energy recovery torque demands are determined based on the driver's accelerator and brake pedal inputs, and the motor responds accordingly. Torque demand is primarily dependent on the driver, limiting the potential for energy saving. Thermal management and air conditioning control depend mainly on the driver's air conditioning needs and the heat dissipation requirements of components such as the motor and battery. The heat dissipation requirements of the motor and battery, in turn, depend on the overall system condition and are largely determined by the driver's driving style. Therefore, in the presence of a driver, the scope for energy management is limited, and the potential for energy saving is also small. Currently, autonomous vehicles are mainly considered in three dimensions: perception, decision-making, and execution. The perception layer mainly uses radar and cameras to identify surrounding traffic conditions. The decision-making layer makes judgments based on the identified traffic conditions and then controls the vehicle speed, acceleration, and deceleration. The execution layer executes control commands. Throughout the process, the main considerations are driving safety, travel efficiency, and drivability, while energy consumption is given less attention. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an energy control method and control system for an unmanned pure electric vehicle, which can reduce vehicle energy consumption and extend driving range while ensuring vehicle safety and power performance.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] An energy control method for an autonomous pure electric vehicle includes:

[0006] Step S1: Obtain road condition information ahead;

[0007] Step S2: Determine the vehicle's driving needs based on the instructions sent by the autonomous driving controller;

[0008] Step S3: Control the power system based on the road conditions ahead and the vehicle's driving needs.

[0009] Preferably, in step S1, based on the weather data sent by the navigation system, the weather is categorized into two states: severe weather (rain, snow, fog) and good weather (sunny / partly cloudy). The average speed V of the traffic flow ahead is also considered based on the data sent by the perception system. avg There are three types: V avg <30km / h, 30km / h≤V avg <80km / h, 80km / h≤V avg。

[0010] Preferably, in step S2, the driving demand state is determined based on the driving commands and driving torque sent by the autonomous driving controller.

[0011] As a preferred option, step S3 involves controlling the power system based on the road conditions ahead and the vehicle's driving needs, using both drive control and braking control strategies.

[0012] This invention also provides an energy control system for an unmanned pure electric vehicle, comprising: a navigation system, a perception system, a VCU system, and an autonomous driving controller; wherein,

[0013] Navigation and sensing systems are used to obtain information about road conditions ahead;

[0014] The VCU system is used to determine the vehicle's driving needs based on instructions sent by the autonomous driving controller, and to control the power system based on road conditions and the vehicle's driving needs.

[0015] As a preferred method, based on weather data sent by the navigation system, two weather conditions are distinguished: severe weather (rain, snow, fog) and good weather (sunny / partly cloudy). The average speed V of the traffic flow ahead is also considered based on data sent by the perception system. avg There are three types: V avg <30km / h, 30km / h≤V avg <80km / h, 80km / h≤V avg。

[0016] Preferably, the VCU system determines the driving demand state based on the driving commands and driving torque sent by the autonomous driving controller.

[0017] As a preferred option, the VCU system controls the powertrain based on the road conditions ahead and the vehicle's driving needs, using drive control and braking control strategies.

[0018] This invention can rationally control the electric drive assembly, air conditioning system, and thermal management system based on road information ahead, combined with the current vehicle status and assembly status. Under the premise of ensuring safe driving of the whole vehicle, it can achieve rational use of vehicle energy, reduce waste, reduce vehicle power consumption, and extend vehicle range. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A schematic diagram of the energy management system for an autonomous pure electric vehicle;

[0021] Figure 2 A schematic diagram of the power system of an autonomous pure electric vehicle;

[0022] Figure 3 This is a flowchart of an energy control method for an unmanned pure electric vehicle according to an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Example 1:

[0026] This invention provides an energy control method for an autonomous pure electric vehicle, wherein, as shown in the embodiments of the present invention... Figure 1As shown, the energy management system of an autonomous pure electric vehicle includes: a vehicle controller (VCU), a perception system, an air conditioning controller, a battery management system (BMS), a motor controller (MCU), a cooling fan, an autonomous driving controller, an electric water pump, a parking brake system (EPB), and an in-vehicle navigation system. The perception system sends information such as the distance to the traffic light, the traffic light status, the distance to the intersection, the speed of the vehicle ahead, acceleration / deceleration, and the road speed limit to the VCU. The air conditioning controller sends ambient temperature, air conditioning on / off status, air conditioning compressor load, and passenger compartment temperature signals to the VCU. The VCU sends air conditioning compressor load commands to the air conditioning controller. The BMS sends battery temperature, battery SOC, battery fault status, and available battery charge / discharge power information to the VCU. The motor controller (MCU) sends motor temperature, motor fault status, and available battery charge / discharge power information to the VCU. The engine's actual speed and torque, and maximum torque capacity are sent to the VCU. The VCU sends the motor torque command to the motor controller. The cooling fan sends its actual fan load to the VCU, and the VCU sends the fan load command to the cooling fan. The autonomous driving controller sends vehicle braking commands, drive commands, drive torque requests, braking torque requests, and stop commands to the VCU. The VCU sends the vehicle energy management flag and the actual motor torque to the autonomous driving controller. The electric water pump sends its actual speed to the VCU, and the VCU sends the electric water pump speed command to the electric water pump. The parking brake (EPB) system sends its parking brake status to the VCU, and the VCU sends its parking brake command to the EPB. The navigation system sends the average speed of the traffic ahead, the distance of the vehicle to the destination, the estimated travel time to the destination, and the weather conditions to the VCU. Figure 2 As shown, the power system of an autonomous pure electric vehicle includes: a motor, a reducer, a battery, a DC-DC converter, a VCU, an MCU, a BMS, an autonomous driving controller, an air conditioning controller, and a navigation system. The battery powers the motor and drives the vehicle. The battery is connected to a DC-DC converter (DCDC) to power the MCU, VCU, BMS, autonomous driving controller, air conditioning controller, and navigation system.

[0027] like Figure 3 As shown, the energy control method includes:

[0028] Step 1: Determine the road conditions ahead based on the information sent by the navigation system and the perception system.

[0029] Based on weather data sent by the navigation system, the weather is categorized into two states: severe weather (rain, snow, fog) and good weather (sunny / partly cloudy). The average speed V of the traffic ahead is also considered based on data from the perception system. avg There are three types: V avg <30km / h, 30km / h≤Vavg <80km / h, 80km / h≤V avg The vehicle speed value mentioned is a preferred value, not the only one. The above-mentioned weather and average vehicle speed combinations result in 6 operating conditions, as shown in Table 1.

[0030] Table 1

[0031]

[0032] Step 2: The VCU determines the vehicle's driving needs based on the instructions sent by the autonomous driving controller.

[0033] The VCU determines the drive demand status based on the drive commands and drive torque sent by the autonomous driving controller. A drive command of 1 indicates a drive demand, while a drive command of 0 indicates no drive demand. Based on the magnitude of the drive torque, there are three states: T_drive < 1 / 3 * T_max, 1 / 3 * T_max ≤ T_drive < 2 / 3 * T_max, and 2 / 3 * T_max ≤ T_drive ≤ T_max, where T_max is the maximum torque capability of the electric drive. Specific combinations are shown in Table 2 below.

[0034] Table 2

[0035]

[0036] The VCU determines braking demand based on the braking commands and required braking torque sent by the autonomous driving controller. A braking command of 1 indicates a braking demand, while a braking command of 0 indicates no braking demand. The VCU determines the magnitude of the braking demand based on the braking torque, categorizing it into four cases: T_brake ≤ 0.2*m (light braking), 0.2*m < T_brake ≤ 0.7*m (medium braking), and 0.7*m < T_brake (emergency braking). Here, m represents the vehicle mass, and T_brake is the wheel-end braking torque demand sent by the autonomous driving controller. Specific combinations are shown in Table 3 below.

[0037] Table 3

[0038]

[0039] The VCU determines parking demand based on the vehicle parking command sent by the autonomous driving controller. When the parking command is 1, it indicates that there is a parking demand, and when the parking command is 0, it indicates that there is no parking demand.

[0040] Step 3: Based on the road condition information ahead determined in Step 1 and the vehicle driving requirements determined in Step 2, the VCU controls the powertrain system.

[0041] (1) When the vehicle needs to be parked, regardless of the operating conditions, the VCU sends a parking lock command to the EPB, and the EPB executes the parking command to lock the wheels. At the same time, the VCU sends a motor torque command of 0, and the motor does not output torque.

[0042] (2) When the vehicle has a driving requirement, the VCU sends a command to release the parking lock, and the EPB unlocks. The electric drive torque is controlled according to the driving requirements in Table 2 and the operating condition combination in Table 1, as shown in Table 4 below.

[0043] Table 4

[0044]

[0045]

[0046] The specific drive control strategy 1 is as follows:

[0047] The automatic driving controller sends a motor torque demand T_drive, and the upper and lower limits of the torque are defined as Torque_scope1 = [T_drive - ΔT_drive]. 1 ,T_drive+ΔT_drive 1 ], ΔT_drive 1 The preferred value is 20 Nm, but it is not the only value. This range is less than Torque_scope' = [0, T_max], where T_max is the motor's maximum torque capacity, i.e., T_drive - ΔT_drive. 1 ≥0, T_drive+ΔT_drive 1 ≤T_max. Within the above Torque_scope1 range, divide the torque range at regular intervals of torque value ΔT1 (preferably 5Nm) to determine n1 driving torques as T_drive1 = T_drive - ΔT_drive 1 =T_drive1-20, T_drive2=

[0048] T_drive1+ΔT=T_drive1+5, continuing until T_drive_n=T_drive+ΔT_drive 1 .

[0049] For example, if T_drive = 100 Nm, ΔT_drive 1 =20Nm, ΔT=5Nm, then n1 is 9. The split torque is [80,85,90,95,100,105,110,115,120] 9 data.

[0050] Based on the torque segmentation described above, calculate the drive power requirement corresponding to each torque.

[0051] P_drive = T_drive * n_motor / eff / 9549, where the motor speed n_motor is sent to the VCU by the motor controller MCU, and the motor efficiency eff is calculated by interpolation based on torque and speed, which is a prior art technique. This yields n drive powers P_drive_1, P_drive_2, P_drive_3….P_drive_n. The minimum drive power P_drive_x = min[P_drive_1, P_drive_2, P_drive_3….P_drive_n] is determined, and its corresponding drive torque T_drive_x is the target drive torque. The VCU sends this torque command to the MCU for execution. Simultaneously, the VCU sets this energy management flag to 1 and sends it to the autonomous driving controller, feeding back this torque to the autonomous driving controller. The autonomous driving controller considers this response torque to have taken energy management into account and is considered normal torque, without fault determination.

[0052] This control method can appropriately adjust the required torque based on driving needs, aiming for optimal efficiency and improving fuel economy. At the same time, the adjusted torque is not significantly different from the torque required for autonomous driving, thus having minimal impact on power and drivability.

[0053] The specific drive control strategy 2 is as follows:

[0054] The automatic driving controller sends a motor torque demand T_drive, and the upper and lower limits of the torque are defined as Torque_scope2 = [T_drive - ΔT_drive]. 2 ,T_drive],ΔT_drive 2 The preferred value is 30 Nm, but it is not the only value. This range is less than Torque_scope' = [0, T_max], where T_max is the motor's maximum torque capacity, i.e., T_drive - ΔT_drive. 2 ≥0, T_drive≤T_max. Within the above Torque_scope2 range, divide the torque range at regular intervals of torque value ΔT2 (preferably 3Nm) to determine n2 driving torques as T_drive1=T_drive-ΔT_drive 2 =T_drive1-30, T_drive2=T_drive1+ΔT=T_drive1+3, and so on until T_drive_n=T_drive.

[0055] For example, if T_drive = 200 Nm, ΔT_drive 2=30Nm, ΔT=3Nm, then n2 is 11. Then the split torque is [170,173,176,179,182,185,188,191,194,197,200] 11 data.

[0056] Based on the torque segmentation described above, calculate the drive power requirement corresponding to each torque.

[0057] P_drive = T_drive * n_motor / eff / 9549, where the motor speed n_motor is sent to the VCU by the motor controller MCU, and the motor efficiency eff is calculated by interpolation based on torque and speed, which is a prior art technique. Correspondingly, n2 drive powers P_drive_1, P_drive_2, P_drive_3….P_drive_n2 can be obtained. The minimum drive power P_drive_x = min[P_drive_1, P_drive_2, P_drive_3….P_drive_n2] is compared, and its corresponding drive torque T_drive_x is the target drive torque. The VCU sends this torque command to the MCU for execution. Simultaneously, the VCU sets this energy management flag to 1 and sends it to the autonomous driving controller, feeding back this torque to the autonomous driving controller. The autonomous driving controller considers this response torque to have taken energy management into account and is a normal torque, without performing fault determination.

[0058] The specific drive control strategy 3 is as follows:

[0059] The automatic driving controller sends a motor torque demand T_drive, and the upper and lower limits of the torque are defined as Torque_scope3 = [T_drive - ΔT_drive]. 3 ,T_drive+ΔT_drive 3 ], ΔT_drive 3 Less than ΔT_drive in control strategy 1 1 The preferred value is 10 Nm, but it is not the only value. This range is less than Torque_scope' = [0, T_max], where T_max is the motor's maximum torque capacity, i.e., T_drive - ΔT_drive. 3 ≥0, T_drive+ΔT_drive 3 ≤T_max. Within the above Torque_scope3 range, divide the torque range at regular intervals of torque value ΔT3 (preferably 2.5Nm) to determine n3 driving torques as T_drive1 = T_drive - ΔT_drive 3=T_drive1-10, T_drive2=T_drive1+ΔT3=T_drive1-7.5, and so on until T_drive_n=T_drive+ΔT_drive 3 .

[0060] For example, if T_drive = 100 Nm, ΔT_drive 3 =10Nm, ΔT3=2.5Nm, then n3 is 9. Then the split torque is [90,92.5,95,97.5,100,102.5,105,107.5,110] 9 data.

[0061] Based on the torque segmentation described above, calculate the drive power requirement corresponding to each torque.

[0062] P_drive = T_drive * n_motor / eff / 9549, where the motor speed n_motor is sent to the VCU by the motor controller MCU, and the motor efficiency eff is calculated by interpolation based on torque and speed, which is a prior art technique. This yields n drive powers P_drive_1, P_drive_2, P_drive_3….P_drive_n. The minimum drive power P_drive_x = min[P_drive_1, P_drive_2, P_drive_3….P_drive_n] is determined, and its corresponding drive torque T_drive_x is the target drive torque. The VCU sends this torque command to the MCU for execution. Simultaneously, the VCU sets this energy management flag to 1 and sends it to the autonomous driving controller, feeding back this torque to the autonomous driving controller. The autonomous driving controller considers this response torque to have taken energy management into account and is considered normal torque, without fault determination.

[0063] The specific drive control strategy 4 is as follows:

[0064] The automatic driving controller sends a motor torque demand T_drive, and the upper and lower limits of the torque are defined as Torque_scope4 = [T_drive - ΔT_drive]. 4 ,T_drive],ΔT_drive 4 The preferred value is 20 Nm, but it is not the only value. This range is less than Torque_scope' = [0, T_max], where T_max is the motor's maximum torque capacity, i.e., T_drive - ΔT_drive. 4 ≥0, T_drive≤T_max. Within the Torque_scope4 range mentioned above, the torque is divided at certain intervals of torque value ΔT4 (preferably 2Nm) to determine n4 driving torques.

[0065] For example, if T_drive = 200 Nm, ΔT_drive 4 =20Nm, ΔT4=2Nm, then n4 is 11. Then the split torque is [180,182,184,186,188,190,192,194,196,198,200] 11 data.

[0066] Based on the torque segmentation described above, calculate the drive power requirement corresponding to each torque.

[0067] P_drive = T_drive * n_motor / eff / 9549, where the motor speed n_motor is sent to the VCU by the motor controller MCU, and the motor efficiency eff is calculated by interpolation based on torque and speed, which is a prior art technique. This results in n4 drive powers P_drive_1, P_drive_2, P_drive_3….P_drive_n4. The minimum drive power P_drive_x = min[P_drive_1, P_drive_2, P_drive_3….P_drive_n2] is obtained through comparison, and its corresponding drive torque T_drive_x is the target drive torque. The VCU sends this torque command to the MCU for execution. Simultaneously, the VCU sets this energy management flag to 1 and sends it to the autonomous driving controller, feeding back this torque to the autonomous driving controller. The autonomous driving controller considers this response torque to have taken energy management into account and is considered normal torque, without performing fault determination.

[0068] The above-mentioned drive control strategy can find the drive torque with the minimum power near the target torque requirement, thereby saving energy. At the same time, it does not significantly change the torque required by the autonomous driving controller and has little impact on drivability and power.

[0069] (3) When the vehicle has braking requirements, the VCU sends a command to release the parking lock, and the EPB unlocks. The electric drive torque and service braking system are controlled according to the braking requirements in Table 3 and the operating condition combination in Table 1, as shown in Table 5 below.

[0070] Table 5

[0071]

[0072]

[0073] The specific braking control strategy 1 is as follows.

[0074] In this scenario, all braking requirements are met by electric drive, with no mechanical or hydraulic intervention. The wheel-end braking torque T_brake_dmd_wheel is converted into a torque at the motor end.

[0075] T_brake_dmd_motor = T_brake_dmd_wheel / i, where i is the speed ratio of the reducer. The upper and lower limits of the braking torque at the motor end are defined as brake_torque_scope1 = [T_brake_dmd_motor, T_brake_dmd_motor + ΔT_drive]. 1 ], ΔT_drive 1 The preferred value is 20 Nm, but it is not the only value. This range is less than 'brake_torque_scope' = [0, T_max_brake], where T_max_brake is the maximum braking torque capacity of the motor, i.e., T_brake_dmd_motor + ΔT_drive. 1 ≤T_max_brake. Within the above-mentioned brake_torque_scope1 range, divide the torque range at regular intervals of torque value ΔT1 (preferably 2Nm) to determine n1 driving torques.

[0076] For example, if T_brake_dmd_motor = 200 Nm, ΔT_drive 1 =20Nm, ΔT1=2Nm, then n1 is 11. Then the split torque is [200,202,204,206,208,210,212,214,216,218,220] 11 data.

[0077] Based on the aforementioned torque segmentation, the regenerative braking power requirement P_brake = T_brake_dmd_motor * n_motor * eff / 9549 is calculated for each torque. The motor speed n_motor is sent to the VCU by the motor controller MCU. The motor efficiency eff is calculated by interpolation based on the torque and speed, which is a prior art technique. This yields n1 corresponding regenerative braking powers P_brake_1, P_brake_2, P_brake_3….P_brake_n1. The maximum regenerative braking power P_brake_x = max[P_brake_1, P_brake_2, P_brake_3….P_brake_n1] is obtained. The corresponding braking torque P_brake_x is the target braking torque. The VCU sends this torque command to the MCU for execution. Simultaneously, the VCU sets the energy management flag to 1 and sends it to the autopilot controller, feeding back this torque to the autopilot controller. The autopilot controller considers this response torque to have taken energy management into account and is considered normal torque, without fault determination.

[0078] The braking control strategy 2 is described in detail below:

[0079] The difference from braking control strategy 1 is ΔT_drive 1 Compared to braking control strategy 1, ΔT_drive 1 The value should be small, preferably 10 Nm. ΔT1 should be smaller than ΔT1 in braking control strategy 1, preferably 1 Nm. Everything else is the same as braking control strategy 1.

[0080] The braking control strategy 3 is described in detail below:

[0081] In this scenario, all braking requirements are met by electric drive, with no mechanical or hydraulic intervention. The wheel-end braking torque T_brake_dmd_wheel is converted into a torque at the motor end.

[0082] T_brake_dmd_motor = T_brake_dmd_wheel / i, where i is the speed ratio of the reducer. The braking requirement T_brake_dmd_motor at the motor end is directly sent to the MCU by the VCU for execution. Conditions 4, 5, and 6 correspond to severe weather, and the braking intensity corresponding to braking requirement 2 is relatively large. This control strategy does not add to the basic braking requirement, which can effectively ensure braking safety.

[0083] The braking control strategy 4 is described in detail below:

[0084] In this situation, it is an emergency braking. To ensure safety, the electric drive no longer participates in braking, and all braking needs are met by mechanical hydraulic braking.

[0085] Example 2:

[0086] This invention also provides an energy control system for an autonomous pure electric vehicle, comprising: a navigation system, a perception system, a VCU system, and an autonomous driving controller; wherein,

[0087] Navigation and sensing systems are used to obtain information about road conditions ahead;

[0088] The VCU system is used to determine the vehicle's driving needs based on instructions sent by the autonomous driving controller, and to control the power system based on road conditions and the vehicle's driving needs.

[0089] As one embodiment of the present invention, based on weather data sent by the navigation system, two weather states are distinguished: severe weather (rain, snow, fog) and good weather (sunny / partly cloudy). The average speed V of the traffic flow ahead is also considered based on data sent by the perception system. avg There are three types: V avg <30km / h, 30km / h≤V avg <80km / h, 80km / h≤V avg。

[0090] As one embodiment of the present invention, the VCU system determines the driving demand state based on the driving commands and driving torque sent by the autonomous driving controller.

[0091] As one embodiment of the present invention, the VCU system controls the power system based on the road conditions ahead and the vehicle's driving needs, using drive control strategies and braking control strategies.

[0092] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An energy control method for an unmanned pure electric vehicle, characterized in that, include: Step S1: Obtain road condition information ahead; Step S2: Determine the vehicle's driving needs based on the instructions sent by the autonomous driving controller; Step S3: Control the power system based on road conditions ahead and the vehicle's driving needs; In step S1, based on the weather data sent by the navigation system, the weather is categorized into two states: severe weather (rain, snow, fog) and good weather (sunny / partly cloudy). The average speed V of the traffic flow ahead is also determined based on the data sent by the perception system. avg There are three types: V avg <30km / h, 30km / h≤V avg <80km / h, 80km / h≤V avg ; In step S2, the driving demand status is determined based on the driving commands and driving torque sent by the autonomous driving controller. The VCU determines the drive demand state based on the drive commands and drive torque sent by the autonomous driving controller; it is divided into three states according to the magnitude of the drive torque: T_drive < 1 / 3 * T_max, 1 / 3 * T_max ≤ T_drive < 2 / 3 * T_max, 2 / 3 * T_max ≤ T_drive ≤ T_max, where T_max is the maximum torque capability of the electric drive. The VCU determines the braking demand based on the braking command and required braking torque sent by the autonomous driving controller. It then determines the magnitude of the braking demand based on the braking torque, classifying it into three cases: T_brake ≤ 0.2*m, 0.2*m < T_brake ≤ 0.7*m, and 0.7*m < T_brake. ； m represents the total vehicle mass, and T_brake represents the wheel-end braking torque requirement sent by the autonomous driving controller. Step S3: Based on the road conditions ahead and the vehicle's driving needs, control the powertrain system using drive control and braking control strategies; specifically, the drive control strategy includes: The specific details of drive control strategy 1 are as follows: The autonomous driving controller sends a motor torque demand T_drive, with the upper and lower limits of the torque defined as Torque_scope1=[T_drive-ΔT_drive]. 1 , T_drive+ΔT_drive 1 This range is less than Torque_scope' = [0, T_max], where T_max is the motor's maximum torque capacity, i.e., T_drive - ΔT_drive. 1 ≥0, T_drive+ΔT_drive 1 ≤T_max; Within the Torque_scope1 range, divide the torque range at regular intervals of torque value ΔT1 (preferably 5 Nm) to determine n1 driving torques as T_drive1 = T_drive - ΔT_drive 1 = T_drive1-20, T_drive2= T_drive1+ΔT= T_drive1+5, and so on until T_drive_n= T_drive+ΔT_drive 1 ; Drive control strategy 2 is as follows: The motor torque demand T_drive sent by the autonomous driving controller is defined as Torque_scope2=[T_drive-ΔT_drive] 2 [T_drive]; This range is less than Torque_scope' = [0, T_max], where T_max is the maximum torque capacity of the motor, i.e., T_drive - ΔT_drive. 2 ≥0, T_drive≤T_max; Within the Torque_scope2 range, divide the torque range at regular intervals of ΔT2 to determine n2 driving torques as T_drive1 = T_drive - ΔT_drive 2 = T_drive1-30, T_drive2= T_drive1+ΔT= T_drive1+3, until T_drive_n= T_drive; The specific details of drive control strategy 3 are as follows: The automatic driving controller sends a motor torque demand T_drive, and the upper and lower limits of the torque are defined as Torque_scope3=[T_drive-ΔT_drive]. 3 , T_drive+ΔT_drive 3 ], ΔT_drive 3 Less than ΔT_drive in control strategy 1 1 This range is less than Torque_scope' = [0, T_max], where T_max is the motor's maximum torque capacity, i.e., T_drive - ΔT_drive. 3 ≥0, T_drive+ΔT_drive 3 ≤T_max; Within the Torque_scope3 range, divide the torque range at regular intervals of ΔT3 to determine n3 driving torques as T_drive1 = T_drive - ΔT_drive 3 = T_drive1-10, T_drive2= T_drive1+ΔT3= T_drive1-7.5, and so on up to T_drive_n= T_drive+ΔT_drive 3 ; The specific drive control strategy 4 is as follows: The autonomous driving controller sends a motor torque demand T_drive, with the upper and lower limits of the torque defined as Torque_scope4 = [T_drive - ΔT_drive]. 4 [T_drive]; This range is less than Torque_scope' = [0, T_max], where T_max is the maximum torque capacity of the motor, i.e., T_drive - ΔT_drive. 4 ≥0, T_drive≤T_max; Divide the Torque_scope4 range at certain torque values ​​ΔT4 to determine n4 drive torques; By employing drive control strategies, the drive torque with the minimum power is found near the target required torque. Furthermore, when the vehicle requires braking, the VCU sends a command to release the parking lock, and the EPB unlocks; the electric drive torque and service braking system are controlled according to the braking requirements and operating conditions.

2. An energy control system for an unmanned pure electric vehicle that implements the energy control method for an unmanned pure electric vehicle as described in claim 1, characterized in that, include: Navigation system, perception system, VCU system, and autonomous driving controller; among which, Navigation and sensing systems are used to obtain information about road conditions ahead; The VCU system is used to determine the vehicle's driving needs based on instructions sent by the autonomous driving controller, and to control the power system based on road conditions and the vehicle's driving needs. Based on weather data sent by the navigation system, the weather is categorized into two states: severe weather (rain, snow, fog) and good weather (sunny / partly cloudy). The average speed V of the traffic flow ahead is also considered based on data from the perception system. avg There are three types: V avg <30km / h, 30km / h≤V avg <80km / h, 80km / h≤V avg ; The VCU system determines the driving demand status based on the driving commands and driving torque sent by the autonomous driving controller. The VCU system controls the powertrain based on road conditions and vehicle driving needs, using drive and braking control strategies.

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