New energy automobile energy recovery self-learning method

Abstract

The invention relates to the field of vehicle control of new energy vehicles, in particular to a new energy vehicle energy recovery self-learning method, which comprises the following steps: step S1, a torque-based determination step: acquiring a vehicle weight signal, a vehicle speed signal and a brake pedal opening signal of a vehicle in real time; according to the vehicle weight signal, a basic recovery torque MAP corresponding to the current vehicle weight tonnage is selected; the method comprises the following steps: S1, acquiring a vehicle speed signal and a brake pedal opening signal, looking up a table in a selected basic recovery torque MAP based on the vehicle speed signal and the brake pedal opening signal, and determining a basic recovery torque value, S2, carrying out gradient correction, and obtaining a current road gradient signal of the vehicle in real time, and S3, carrying out self-learning adjustment, and when the vehicle is in a downhill state and an energy recovery function is activated, carrying out self-learning adjustment on the vehicle. The accelerator pedal opening degree and the duration thereof are monitored in real time, and the problems that influences of the vehicle weight and the gradient are not considered at the present stage and the fault coping strategy is single are solved.

Description

Technical Field

[0001] This invention relates to the field of energy recovery in new energy vehicles, and specifically to a self-learning method for energy recovery in new energy vehicles. Background Technology

[0002] With the popularization of new energy vehicles, improving energy utilization efficiency has become one of the key technologies. Braking energy recovery systems can effectively extend the driving range of vehicles by converting the kinetic energy of a vehicle during braking or coasting into electrical energy for storage.

[0003] Currently, most vehicles' regenerative braking strategies determine a fixed regenerative torque based on vehicle speed and brake pedal opening (or deceleration request) by looking up a table. However, this strategy has the following drawbacks:

[0004] 1. The influence of vehicle weight is not taken into account. The vehicle inertia is different when it is unloaded and fully loaded. The same recovery torque will result in completely different deceleration experience, which cannot provide a comfortable and consistent braking feel under various load conditions. It also affects the optimization of recovery efficiency.

[0005] 2. The impact of slope is not taken into account. On slopes, a fixed recovery torque strategy can lead to a poor experience and even safety hazards. When going downhill, insufficient recovery strength may cause the vehicle to accelerate, requiring the driver to frequently press the mechanical brake, wasting energy recovery opportunities. When going uphill, excessive recovery torque may cause the vehicle to stall prematurely, affecting driving smoothness and safety.

[0006] 3. The fault response strategy is too simplistic. When the signals of key sensors (such as weight sensors and slope sensors) fail, the system can only report an error or exit the energy recovery function, which reduces the vehicle's energy efficiency and the system's robustness.

[0007] 4. Lack of intelligent adaptability: The existing system cannot dynamically adjust according to the driver's real-time intentions. During downhill recovery, if the driver intends to accelerate, the system cannot intelligently reduce the recovery intensity to meet the driving needs.

[0008] 5. Lack of safety when turning. When turning, under certain conditions of adhesion coefficient, when in energy recovery state, it may cause insufficient steering force, resulting in unsafe phenomena such as fishtailing.

[0009] In summary, a self-learning method for energy recovery in new energy vehicles is proposed to address the problems mentioned in the background. Summary of the Invention

[0010] The purpose of this invention is to provide a self-learning method for energy recovery in new energy vehicles, which can dynamically adjust the energy recovery torque according to real-time vehicle weight, road slope and driver intention, thereby achieving a balance between recovery efficiency, driving safety and comfort.

[0011] A self-learning method for energy recovery in new energy vehicles, applied to the vehicle controller, comprises the following steps:

[0012] Step S1, Torque-based determination step: Real-time acquisition of vehicle weight signal, vehicle speed signal, and brake pedal opening signal; Based on the vehicle weight signal, select the basic recovery torque MAP corresponding to the current vehicle weight tonnage; Based on the vehicle speed signal and brake pedal opening signal, look up the table in the selected basic recovery torque MAP to determine the basic recovery torque value.

[0013] Step S2: Perform slope correction and acquire the vehicle's current road slope signal in real time; compare the slope signal with a preset slope threshold.

[0014] Step S3: Perform self-learning adjustment. When the vehicle is going downhill and the energy recovery function is activated, monitor the accelerator pedal opening and its duration in real time. If the accelerator pedal opening is detected to exceed the set opening threshold for a predetermined time, it is determined that the driver has the intention to accelerate, triggering the self-learning mechanism to adjust the weighted average of the currently used basic recovery torque MAP.

[0015] Further, it also includes fault safety monitoring, continuously monitoring the effectiveness of the vehicle weight signal and the slope signal; when it is determined that the vehicle weight signal or the slope signal is invalid or exceeds a reasonable range, steps S1 and S2 are ignored, and a preset safety recovery torque value with a lower intensity level is adopted instead.

[0016] Further specifying, in step S3, all torque values ​​in the basic recovery torque MAP are attenuated by a fixed ratio to generate and apply a new self-learning recovery torque MAP.

[0017] Furthermore, the safe recovery torque value is a fixed value, and its intensity is lower than the recovery torque under normal operating conditions.

[0018] Further specifying, step S2 compares the slope signal with a preset slope threshold. If the slope signal indicates that the vehicle is downhill and the slope value is greater than the first positive threshold, the basic recovery torque value is multiplied by a strengthening coefficient greater than 1 to obtain the first corrected torque. If the slope signal indicates that the vehicle is uphill and the slope value is greater than the second positive threshold, the basic recovery torque value is multiplied by a weakening coefficient less than 1 to obtain the first corrected torque.

[0019] Furthermore, the vehicle controller detects vehicle speed and steering wheel angle signals in real time. It uses vehicle speed and steering wheel angle as inputs and a correction coefficient as the output MAP. When the vehicle speed is high and the steering wheel angle is large, a coefficient less than 1 is obtained. When driving at high speed and making large turns, safety is prioritized, and the regenerative torque is actively reduced to avoid unsafe phenomena such as fishtailing, thereby enhancing vehicle driving safety.

[0020] Furthermore, the basic energy recovery torque MAP is strongly correlated with the vehicle's driving mode, which includes ECO mode, NORMAL mode, and SPORT mode. The basic energy recovery MAP trend gradually decreases for the three driving modes. When the driver switches driving modes, a torque gradient change strategy is adopted to avoid sudden changes in energy recovery torque.

[0021] Furthermore, in step S2, when going uphill, the weakening coefficient is configured to ensure that the vehicle does not suffer a severe speed loss due to energy recovery, thus maintaining the smoothness of uphill driving.

[0022] The advantages of this invention compared to the prior art are as follows:

[0023] This invention significantly improves the intelligence and overall performance of energy recovery systems in new energy vehicles by introducing three core mechanisms: vehicle weight adaptation, dynamic slope correction, and driver intention self-learning. First, the multi-MAP lookup strategy based on vehicle weight signals fundamentally solves the problem of inconsistent braking feel under different load conditions, providing deceleration curves that meet driver expectations under both unloaded and fully loaded conditions, maximizing both ride comfort and energy recovery efficiency. Second, the slope correction module, through bidirectional adjustment of enhancement and weakening coefficients, enhances recovery capability on downhill slopes and suppresses power loss on uphill slopes, effectively avoiding energy waste or driving jerks in slope scenarios caused by traditional fixed strategies, significantly improving driving safety and handling confidence in complex road conditions. Furthermore, the self-learning adjustment mechanism overcomes the passive response limitations of traditional energy recovery systems. By monitoring accelerator pedal behavior in real time to identify the driver's acceleration intentions, it adaptively reduces the weight of the recovery torque MAP accordingly. This allows for gradual optimization of the match between recovery intensity and driving intentions while ensuring safe downhill speeds. In addition, the introduction of a fault safety monitoring module ensures system degradation in the event of critical sensor failure, maintaining basic energy efficiency with conservative, safe recovery torque and avoiding a sharp drop in range due to complete loss of function, thus enhancing the system's robustness and reliability. Finally, the integration of driving mode association and torque gradient change strategies unifies energy recovery characteristics with the overall vehicle's stylistic positioning. The smooth transition during mode switching further eliminates discomfort caused by sudden torque changes, comprehensively achieving synergistic optimization of energy recovery efficiency, driving safety, and ride comfort. Attached Figure Description

[0024] Figure 1 The following is a flowchart of the logic control of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0026] Example:

[0027] like Figure 1 As shown, a self-learning method for energy recovery in new energy vehicles is applied to the vehicle control unit (VCU). The overall enabling conditions for the energy recovery function within the VCU need to be met, including but not limited to the following conditions:

[0028] 1. Vehicle speed requirements: The vehicle speed must be higher than the minimum threshold for energy recovery to start (usually 5-10 km / h) to ensure the effectiveness and safety of recovery;

[0029] 2. Gear condition: The vehicle is in a drive gear (such as D gear) or a specific coasting retraction gear, not reverse gear (R gear) or parking gear (P gear).

[0030] 3. High-voltage system status: The high-voltage system has been powered on and there are no fatal faults in the main high-voltage components.

[0031] 4. Power Battery System Status: The Battery Management System (BMS) reports no serious faults, and the battery's state of charge (SOC) is lower than the set maximum recovery limit (e.g., SOC < 90%), to ensure that the battery has sufficient capacity to absorb and recover energy.

[0032] 5. Motor system status: The motor controller (MCU) and drive motor report no faults, and the temperature is within the allowable operating range, and the generator torque command can be executed normally;

[0033] 6. Braking system status: The anti-lock braking system (ABS) or electronic stability program (ESP) is not activated or has not reported any faults affecting energy recovery, ensuring the coordination between the braking system and electric braking;

[0034] 7. Vehicle Fault Level: The vehicle's fault diagnosis system does not report a high-level fault that would disable energy recovery.

[0035] When all the above conditions are met, the VCU will enable the energy recovery function and then execute the following steps. If any condition is not met, the energy recovery function will be disabled or deactivated to ensure vehicle safety. The specific steps are as follows:

[0036] Step S1, based on the torque determination step, the VCU continuously collects various sensor signals via the CAN bus, including: vehicle weight signals from air suspension sensors or load calculation models, slope signals from integrated navigation modules (such as GPS+IMU) or slope sensors, vehicle speed signals calculated by wheel speed sensors, and brake pedal opening and accelerator pedal opening signals from pedal sensors. The VCU first determines whether the vehicle weight and slope signals are valid and reasonable. If invalid (e.g., signal timeout, value exceeding physical limits), a preset safe recovery torque is adopted. This torque can be a small fixed value to ensure the most basic recovery function and safety. If valid, the core adjustment process begins. The system selects the corresponding basic recovery torque MAP based on the current vehicle weight (e.g., 1.5 tons, 2.0 tons, 2.5 tons, etc.). Each tonnage has a preset two-dimensional MAP, with the horizontal axis representing vehicle speed and the vertical axis representing brake pedal opening. The basic recovery torque T_base can be obtained by looking up the table using the current vehicle speed and brake pedal opening.

[0037] Step S2: Perform slope correction and acquire the current road slope signal of the vehicle in real time; compare the slope signal with the preset slope threshold. If the slope is >3%, it is defined as downhill, and T_base is multiplied by a strengthening coefficient of 1.2; if the slope is <-2%, it is defined as uphill, and T_base is multiplied by a weakening coefficient of 0.7 to obtain the first correction torque T_mod1.

[0038] Step S3: Perform self-learning adjustment. When the vehicle is downhill and the energy recovery function is activated, monitor the accelerator pedal opening and its duration in real time. If the vehicle is detected to be downhill and energy recovery is currently being performed (i.e., T_mod1>0), and the accelerator pedal opening is >20% and lasts for more than 2 seconds, the self-learning mechanism is triggered. If the vehicle is equipped with an IMU, and the Z-term acceleration sensor signal fluctuates drastically, for example, changing by 1 m / s² within 1 second (calibrated value), and the VCU detects that the accelerator pedal opening is greater than a certain value for a certain period of time, and the accelerator pedal change is greater than a certain value (calibrated value, indicating that the accelerator pedal changes too quickly in a short period of time, which is outside the normal driving range), the energy recovery intensity self-learning mechanism will not be triggered. The VCU will reduce all values ​​in the currently used basic recovery torque MAP by 25% and update the MAP. This updated MAP will be used for subsequent lookup processes until the system is reset or is learned and updated again. The VCU will send the finally determined target recovery torque command to the motor controller (MCU), and the MCU will execute precise torque control to achieve energy recovery.

[0039] The above provides a detailed description of a self-learning method for energy recovery in new energy vehicles. The specific embodiments are only used to help understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from it, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A self-learning method for energy recovery in new energy vehicles, applied to a vehicle controller, characterized in that: The specific steps are as follows: Step S1, Torque-based determination step: Real-time acquisition of vehicle weight signal, vehicle speed signal, and brake pedal opening signal; Based on the vehicle weight signal, select the basic recovery torque MAP corresponding to the current vehicle weight tonnage; Based on the vehicle speed signal and brake pedal opening signal, look up the table in the selected basic recovery torque MAP to determine the basic recovery torque value. Step S2: Perform slope correction and acquire the vehicle's current road slope signal in real time; compare the slope signal with a preset slope threshold. Step S3: Perform self-learning adjustment. When the vehicle is going downhill and the energy recovery function is activated, monitor the accelerator pedal opening and its duration in real time. If the accelerator pedal opening is detected to continuously exceed the set opening threshold for a predetermined time, it is determined that the driver intends to accelerate, triggering the self-learning mechanism to downweight the currently used base recovery torque MAP.

2. The self-learning method for energy recovery in new energy vehicles according to claim 1, characterized in that: It also includes fault safety monitoring, which continuously monitors the effectiveness of the vehicle weight signal and the slope signal; when it is determined that the vehicle weight signal or the slope signal is invalid or exceeds a reasonable range, steps S1 and S2 are ignored, and a preset safety recovery torque value with a lower intensity level is adopted instead.

3. The self-learning method for energy recovery in new energy vehicles according to claim 1, characterized in that: In step S3, all torque values ​​in the basic recovery torque MAP are attenuated by a fixed ratio to generate and apply a new self-learning recovery torque MAP.

4. The self-learning method for energy recovery in new energy vehicles according to claim 2, characterized in that: The safe recovery torque value is a fixed value, and its intensity is lower than the recovery torque under normal operating conditions.

5. The self-learning method for energy recovery in new energy vehicles according to claim 1, characterized in that... In step S2, the slope signal is compared with a preset slope threshold. If the slope signal indicates that the vehicle is going downhill and the slope value is greater than the first positive threshold, the basic recovery torque value is multiplied by a strengthening coefficient greater than 1 to obtain the first corrected torque. If the slope signal indicates that the vehicle is going uphill and the slope value is greater than the second positive threshold, the basic recovery torque value is multiplied by a weakening coefficient less than 1 to obtain the first corrected torque.

6. The self-learning method for energy recovery in new energy vehicles according to claim 1, characterized in that: The vehicle controller detects vehicle speed and steering wheel angle signals in real time. It uses vehicle speed and steering wheel angle as inputs and a correction coefficient as the output MAP. When the vehicle speed is high and the steering wheel angle is large, a coefficient less than 1 is obtained. When driving at high speed and making large turns, safety is prioritized, and the regenerative torque is actively reduced to avoid unsafe phenomena such as fishtailing, thereby enhancing vehicle driving safety.

7. The self-learning method for energy recovery in new energy vehicles according to claim 4, characterized in that: The basic energy recovery torque MAP is strongly correlated with the vehicle's driving mode, which includes ECO mode, NORMAL mode, and SPORT mode. The basic energy recovery MAP trend gradually decreases for the three driving modes. When the driver switches driving modes, a torque gradient change strategy is adopted to avoid sudden changes in energy recovery torque.

8. The self-learning method for energy recovery in new energy vehicles according to claim 5, characterized in that: In step S2, when going uphill, the weakening coefficient is configured to ensure that the vehicle does not suffer a severe speed loss due to energy recovery, thus maintaining the smoothness of uphill driving.

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