Extended-range hybrid power system optimization control method based on demand power prediction

Abstract

The invention provides an extended-range hybrid power system optimization control method based on demand power prediction. The extended-range hybrid power system optimization control method comprises an energy management optimization control method in a driving state and a braking state of an extended-range hybrid power vehicle. In the vehicle driving state, the driving demand power of the vehicle is predicted, the power generation state of the range extender and the charge state of the power battery are obtained, and the power ratio of power generation of the range extender and power battery discharging participating in driving is determined through logical algorithm judgment. In the vehicle braking state, according to the charge state of the power battery, whether the power battery needs to be subjected to kinetic energy recovery or range extender synchronous auxiliary charging or not is judged and determined through a logical algorithm. The performance of the range extender and the performance of the power battery can be efficiently exerted, and the energy utilization efficiency of the range-extended hybrid power system is improved.

Description

Technical Field

[0001] The present invention relates to the field of optimization control methods for extended-range hybrid systems, and in particular to an optimization control method for extended-range hybrid systems based on demand power prediction. Background Art

[0002] The extended-range hybrid system is used in the field of highway freight. The transportation conditions are relatively complex. The extended-range hybrid vehicle frequently accelerates and brakes during transportation, resulting in frequent transfer and conversion of system energy. If the energy supply process of the extended-range hybrid vehicle is not reasonably controlled, a large amount of unnecessary power loss will occur. The single power supply capacity of the power battery is not enough to meet the energy demand of the extended-range hybrid vehicle during rapid acceleration. Therefore, the range extender needs to intervene at an appropriate time to assist in powering the drive motor, and the kinetic energy of the extended-range hybrid vehicle during braking can also be recovered by the power battery. A reasonably designed extended-range hybrid system optimization control method can improve the energy utilization efficiency of the entire system during operation and further improve the economy of the system. Summary of the invention

[0003] In view of this, an object of the present invention is to propose an optimization control method for an extended-range hybrid system based on demand power prediction.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] An optimization control method for a range-extended hybrid system based on demand power prediction, the specific steps are as follows:

[0006] Step 1: The optimization controller determines the driving state of the extended-range hybrid vehicle, determines whether the extended-range hybrid vehicle is in a driving state or a braking state, predicts the driving power demand of the extended-range hybrid vehicle, and obtains the power generation state of the range extender and the charge state signal of the power battery. If the extended-range hybrid vehicle is in a driving state, the process proceeds to step 2; if the extended-range hybrid vehicle is in a braking state, the process proceeds to step 3;

[0007] Step 2: The optimization controller obtains the current driving state of the extended-range hybrid vehicle and predicts the driving demand power of the extended-range hybrid vehicle. The optimization controller obtains the power generation state of the range extender and the charge state signal of the power battery, and determines the power ratio of the range extender power generation and the power battery discharge to participate in the driving according to the logic algorithm;

[0008] Step three, the optimization controller determines whether the power battery needs to recover kinetic energy or the range extender needs to be synchronously charged according to the current braking power of the extended-range hybrid vehicle and the power battery charge state information, and determines the power ratio of the range extender power generation and the power battery kinetic energy recovery according to the logic algorithm.

[0009] The specific process of step one is as follows:

[0010] S101: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle;

[0011] S102: The optimization controller obtains the required power signal Pm of the driving motor of the extended-range hybrid vehicle. If the required power Pm of the driving motor is a positive value, it is determined that the extended-range hybrid vehicle is in a driving state and the process goes to step two. If the required power Pm of the driving motor is a negative value, it is determined that the extended-range hybrid vehicle is in a braking state and the process goes to step three.

[0012] The specific process of step 2 is as follows:

[0013] S201: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle;

[0014] S202: optimizing the controller to predict the driving power demand of the extended-range hybrid vehicle;

[0015] S203: The optimization controller obtains the power generation state of the range extender and the charge state signal of the power battery;

[0016] S204: If the power battery state of charge SOC is within the preset high-efficiency SOC range, and the predicted driving demand power P m Less than the upper limit of power battery discharge power P bs,max , then the predicted driving demand power P m All provided by power battery discharge;

[0017] S205: If the power battery state of charge SOC is within the preset high-efficiency SOC range, and the predicted driving demand power Pm is greater than the power battery discharge power upper limit P bs,max , the optimization controller issues a control instruction, the power battery starts the maximum power discharge state, and the remaining driving power required is provided by the discharge of the range extender;

[0018] S206: If the power battery state of charge SOC is less than the lower limit of the preset high-efficiency SOC range, and the predicted driving demand power P m Greater than the upper limit of the range extender power generation power P es,max , the optimization controller issues a control instruction, the range extender starts the maximum power generation state, and the remaining driving power is provided by the power battery discharge;

[0019] S207: If the power battery state of charge SOC is less than the lower limit of the preset high-efficiency SOC range, and the predicted driving demand power P m Less than the upper limit of the range extender power generation power Pes,max , the optimization controller issues a control instruction, and the range extender generates electricity to provide all the required power;

[0020] S208: After the current driving process is completed, return to step 1.

[0021] The specific process of step three is as follows:

[0022] S301: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle;

[0023] S302: optimizing the controller to predict the braking driving power requirement of the extended-range hybrid vehicle;

[0024] S303: If the power battery state of charge SOC is less than the preset maximum value SOC max , and the predicted driving demand power P m Greater than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control instruction, the power battery starts the charging state, and recovers the vehicle braking energy at the maximum charging power;

[0025] S304: If the power battery state of charge SOC is less than the preset maximum value SOC max And greater than the preset minimum SOC min , and the predicted driving demand power P m Less than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control instruction, and the power battery starts charging to recover all braking energy;

[0026] S305: If the power battery state of charge SOC is less than a preset minimum value SOC min , and the required power P of the drive motor m Less than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control command, the power battery starts the charging state to recover all the braking energy, and at the same time starts the range extender to synchronously assist in charging, so that the power battery charging power reaches the maximum charging power P bs,c,max To replenish the power battery;

[0027] S306: After the braking process is completed, return to step 1. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention is a flow chart of an optimization control method for an extended-range hybrid system based on demand power prediction. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0030] like Figure 1 As shown, an optimization control method for an extended-range hybrid system based on demand power prediction is provided, and the specific steps are as follows:

[0031] Step 1: The optimization controller determines the driving state of the extended-range hybrid vehicle, determines whether the extended-range hybrid vehicle is in a driving state or a braking state, predicts the driving power demand of the extended-range hybrid vehicle, and obtains the power generation state of the range extender and the charge state signal of the power battery. If the extended-range hybrid vehicle is in a driving state, the process proceeds to step 2; if the extended-range hybrid vehicle is in a braking state, the process proceeds to step 3;

[0032] Step 2: The optimization controller obtains the current driving state of the extended-range hybrid vehicle and predicts the driving demand power of the extended-range hybrid vehicle. The optimization controller obtains the power generation state of the range extender and the charge state signal of the power battery, and determines the power ratio of the range extender power generation and the power battery discharge to participate in the driving according to the logic algorithm;

[0033] Step three, the optimization controller determines whether the power battery needs to recover kinetic energy or the range extender needs to be synchronously charged according to the current braking power of the extended-range hybrid vehicle and the power battery charge state information, and determines the power ratio of the range extender power generation and the power battery kinetic energy recovery according to the logic algorithm.

[0034] The specific process of step one is as follows:

[0035] S101: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle;

[0036] S102: The optimization controller obtains the required power signal Pm of the driving motor of the extended-range hybrid vehicle. If the required power Pm of the driving motor is a positive value, it is determined that the extended-range hybrid vehicle is in a driving state and the process goes to step two. If the required power Pm of the driving motor is a negative value, it is determined that the extended-range hybrid vehicle is in a braking state and the process goes to step three.

[0037] The specific process of step 2 is as follows:

[0038] S201: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle;

[0039] S202: optimizing the controller to predict the driving power demand of the extended-range hybrid vehicle;

[0040] S203: The optimization controller obtains the power generation state of the range extender and the charge state signal of the power battery;

[0041] S204: If the power battery state of charge SOC is within the preset high-efficiency SOC range, and the predicted driving demand power P m Less than the upper limit of power battery discharge power P bs,max , then the predicted driving demand power P m All provided by power battery discharge;

[0042] S205: If the power battery state of charge SOC is within the preset high-efficiency SOC range, and the predicted driving demand power Pm is greater than the power battery discharge power upper limit P bs,max , the optimization controller issues a control instruction, the power battery starts the maximum power discharge state, and the remaining driving power required is provided by the discharge of the range extender;

[0043] S206: If the power battery state of charge SOC is less than the lower limit of the preset high-efficiency SOC range, and the predicted driving demand power P m Greater than the upper limit of the range extender power generation power P es,max , the optimization controller issues a control instruction, the range extender starts the maximum power generation state, and the remaining driving power is provided by the power battery discharge;

[0044] S207: If the power battery state of charge SOC is less than the lower limit of the preset high-efficiency SOC range, and the predicted driving demand power P m Less than the upper limit of the range extender power generation power P es,max , the optimization controller issues a control instruction, and the range extender generates electricity to provide all the required power;

[0045] S208: After the current driving process is completed, return to step 1.

[0046] The specific process of step three is as follows:

[0047] S301: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle;

[0048] S302: optimizing the controller to predict the braking driving power requirement of the extended-range hybrid vehicle;

[0049] S303: If the power battery state of charge SOC is less than the preset maximum value SOC max , and the predicted driving demand power Pm Greater than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control instruction, the power battery starts the charging state, and recovers the vehicle braking energy at the maximum charging power;

[0050] S304: If the power battery state of charge SOC is less than the preset maximum value SOC max And greater than the preset minimum SOC min , and the predicted driving demand power P m Less than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control instruction, and the power battery starts charging to recover all braking energy;

[0051] S305: If the power battery state of charge SOC is less than a preset minimum value SOC min , and the required power P of the drive motor m Less than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control command, the power battery starts the charging state to recover all the braking energy, and at the same time starts the range extender to synchronously assist in charging, so that the power battery charging power reaches the maximum charging power P bs,c,max To replenish the power battery;

[0052] S306: After the braking process is completed, return to step 1.

Claims

1. An optimization control method for an extended-range hybrid system based on demand power prediction, characterized in that: The specific steps are as follows: Step 1: The optimization controller determines the driving state of the extended-range hybrid vehicle, determines whether the extended-range hybrid vehicle is in a driving state or a braking state, predicts the driving power demand of the extended-range hybrid vehicle, and obtains the power generation state of the range extender and the charge state signal of the power battery. If the extended-range hybrid vehicle is in a driving state, the process proceeds to step 2; if the extended-range hybrid vehicle is in a braking state, the process proceeds to step 3; Step 2: The optimization controller obtains the current driving state of the extended-range hybrid vehicle and predicts the driving demand power of the extended-range hybrid vehicle. The optimization controller obtains the power generation state of the range extender and the charge state signal of the power battery, and determines the power ratio of the range extender power generation and the power battery discharge to participate in the driving according to the logic algorithm; Step three, the optimization controller determines whether the power battery needs to recover kinetic energy or the range extender needs to be synchronously charged according to the current braking power of the extended-range hybrid vehicle and the power battery charge state information, and determines the power ratio of the range extender power generation and the power battery kinetic energy recovery according to the logic algorithm.

2. The method for optimizing and controlling a range-extended hybrid system based on power demand prediction according to claim 1, characterized in that: The specific process of step one is as follows: S101: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle; S102: The optimization controller obtains the required power signal P of the driving motor of the extended-range hybrid vehicle m , if the required power of the drive motor is P m is a positive value, it is determined that the extended-range hybrid vehicle is in the driving state and the process goes to step 2. If the required power P of the driving motor m is a negative value, it is determined that the extended-range hybrid vehicle is in a braking state and the process goes to step three.

3. The method for optimizing and controlling a range-extended hybrid system based on power demand prediction according to claim 1, characterized in that: The specific process of step 2 is as follows: S201: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle; S202: optimizing the controller to predict the driving power demand of the extended-range hybrid vehicle; S203: The optimization controller obtains the power generation state of the range extender and the charge state signal of the power battery; S204: If the power battery state of charge SOC is within the preset high-efficiency SOC range, and the predicted driving demand power P m Less than the upper limit of power battery discharge power P bs,max , then the predicted driving demand power P m All provided by power battery discharge; S205: If the power battery state of charge SOC is within the preset high-efficiency SOC range, and the predicted driving demand power P m Greater than the upper limit of power battery discharge power P bs,max , the optimization controller issues a control instruction, the power battery starts the maximum power discharge state, and the remaining driving power required is provided by the discharge of the range extender; S206: If the power battery state of charge SOC is less than the lower limit of the preset high-efficiency SOC range, and the predicted driving demand power P m Greater than the upper limit of the range extender power generation power P es,max , the optimization controller issues a control instruction, the range extender starts the maximum power generation state, and the remaining driving power is provided by the power battery discharge; S207: If the power battery state of charge SOC is less than the lower limit of the preset high-efficiency SOC range, and the predicted driving demand power P m Less than the upper limit of the range extender power generation power P es,max , the optimization controller issues a control instruction, and the range extender generates electricity to provide all the required power; S208: After the current driving process is completed, return to step 1.

4. The method for optimizing and controlling a range-extended hybrid system based on power demand prediction according to claim 1, characterized in that: The specific process of step three is as follows: S301: Optimizing the controller to obtain the current driving state of the extended-range hybrid vehicle; S302: optimizing the controller to predict the braking driving power requirement of the extended-range hybrid vehicle; S303: If the power battery state of charge SOC is less than the preset maximum value SOC max , and the predicted driving demand power P m Greater than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control instruction, the power battery starts the charging state, and recovers the vehicle braking energy at the maximum charging power; S304: If the power battery state of charge SOC is less than the preset maximum value SOC max And greater than the preset minimum SOC min , and the predicted driving demand power P m Less than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control instruction, and the power battery starts charging to recover all braking energy; S305: If the power battery state of charge SOC is less than a preset minimum value SOC min , and the required power P of the drive motor m Less than the maximum charging power P of the power battery bs,c,max , the optimization controller issues a control command, the power battery starts the charging state to recover all the braking energy, and at the same time starts the range extender to synchronously assist in charging, so that the power battery charging power reaches the maximum charging power P bs,c,max To replenish the power battery; S306: After the braking process is completed, return to step 1.

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