Fuzzy control-based extended-range commercial vehicle energy management method

Abstract

The invention relates to an extended-range commercial vehicle energy management method based on fuzzy control. The method comprises the steps that according to the SOC of a power battery, the working mode of the vehicle is divided into a power enhancement mode and an electric quantity maintaining mode; different fuzzy controllers are applied in different vehicle working modes, the battery SOC decline rate and the driving motor demand power serve as the input control quantity of the fuzzy controllers, and the auxiliary unit demand power serves as the output control quantity of the fuzzy controllers; performing fuzzy representation on the input control quantity and the output control quantity, establishing a fuzzy control rule base by taking optimization of the required power of the auxiliary unit as a target, and reasonably outputting the required power of the auxiliary unit; according to the method, the high-power demand scene of the range extender and the scene that the battery electric quantity is low and has the charging demand are comprehensively considered, the influence of the battery SOC reduction rate and the drive motor demand power on energy management is comprehensively considered, and the dynamic property and economical efficiency of the whole vehicle can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobiles, and particularly to an energy management method for a range-extended commercial vehicle based on fuzzy control. Background Art

[0002] Compared with traditional fuel commercial vehicles, range-extended commercial vehicles have significantly improved fuel economy, and the complex mechanisms of traditional mechanical transmission systems are removed; compared with pure electric commercial vehicles, the driving range is increased under the condition of achieving the same efficient pure electric operation, and they have good development prospects. The energy management method is an important part of the control of range-extended commercial vehicles. Traditional energy management methods for range-extended commercial vehicles include the SOC following method and the power following method. The SOC following method can avoid damage to the battery caused by overcharging and over-discharging, but it cannot meet the high-power requirements of the whole vehicle. The power following method increases the power output under high-power requirements to meet the dynamic requirements, and maintains in the high-efficiency area during stable driving, giving priority to meeting the economy, but this method may damage the battery.

[0003] The SOC following method and the power following method respectively reflect two application scenarios of the range extender: one is to charge the battery when the power battery is insufficient, and the other is to output high power when the whole vehicle has high-power requirements. However, using the above two traditional methods alone cannot meet the energy management requirements of range-extended commercial vehicles. Therefore, a new energy management method is needed to make full use of the advantages of the range extender in the above scenarios, reasonably respond to the power requirements of the whole vehicle under different working conditions on the premise of ensuring the health of the battery power, improve fuel economy, and achieve higher dynamic and economic goals. Summary of the Invention

[0004] The purpose of the present invention is to provide an energy management method for a range-extended commercial vehicle based on fuzzy control in view of the deficiencies of the prior art.

[0005] The present invention is implemented by adopting the following technical solutions: An energy management method for a range-extended commercial vehicle based on fuzzy control, the method comprising: Dividing the vehicle working mode into a power enhancement mode and a power retention mode according to the SOC of the power battery; Applying different fuzzy controllers in different vehicle working modes, taking the SOC drop rate of the battery and the required power of the drive motor as the input control quantities of the fuzzy controller, and taking the required power of the auxiliary unit as the output control quantity of the fuzzy controller; Performing fuzzy representation on the input control quantity and the output control quantity, establishing a fuzzy control rule base with the optimization of the required power of the auxiliary unit as the goal, and reasonably outputting the required power of the auxiliary unit.

[0006] As a further description of the invention, the vehicle operating mode is divided into a power boost mode and a power hold mode according to the state of charge (SOC) of the power battery. The steps include: Collect the SOC of the vehicle's power battery and determine whether the SOC of the power battery is less than a set value; If the SOC of the power battery is less than the set value, control the vehicle to operate in the power hold mode; If the SOC of the power battery is greater than the set value, control the vehicle to operate in the power boost mode.

[0007] As a further description of the invention, the rate of decrease of the battery SOC is used as the input control quantity of the fuzzy controller. The steps include: Keep the change range of the rate of decrease of the battery SOC within =[0, 1], After the quantization factor Convert it into a discrete integer domain, that is, ={0, 1……9, 10}, thus serving as the input control quantity of the fuzzy controller.

[0008] As a further description of the invention, the required power of the drive motor is used as the input control quantity of the fuzzy controller. The steps include: The change range of the required power of the drive motor is =[-20, 90], After passing through the quantization factor Convert it into a discrete integer domain, that is, ={-2, -3……0, 1……8, 9}, thus serving as the input control quantity of the fuzzy controller; Among them, the operating mode of the drive motor is divided into a drive mode and a regenerative braking mode. When the required power of the drive motor is negative, it means the motor is operating in the regenerative braking mode. When the required power of the drive motor is non - negative, it means the motor is operating in the drive mode.

[0009] As a further description of the invention, the required power of the auxiliary unit is used as the output control quantity of the fuzzy controller. The steps include: The value range of the required power of the auxiliary unit is the value range ={0, 1……7}, and through the scale factor Convert it into the actual required power of the auxiliary unit, that is, =[0, 35].

[0010] As a further description of the invention, taking the rate of decrease of the battery SOC ={0, 1……9, 10} as the input control quantity, and using the membership function to divide the fuzzy subsets. The steps include: The set {0, 1……9, 10} is divided into 7 fuzzy subsets, namely {EL, VL, LO, ST, HI, VH, EH}; Among them, EL represents extremely low, VL represents very low, LO represents relatively low, ST represents normal, HI represents relatively high, VH represents very high, and EH represents extremely high; the membership function adopts a normal distribution function.

[0011] As a further description of the invention, the required power of the drive motor ={-2, -3……0, 1……8, 9} is used as the input control quantity, and the fuzzy subsets are divided by using the membership function. The steps include: The set {-2, -3……0, 1……8, 9} is divided into 9 fuzzy subsets, which are {NB, NS, ZE, PS, PM, PB, PVB, PEB, PVEB}; Among them, NB represents negative large, NS represents negative small, ZE represents positive zero, PS represents positive small, PM represents positive medium, PB represents positive large, PVB represents positive very large, PEB represents positive extremely large; PVEB represents positive super large; the membership function adopts a normal distribution function.

[0012] As a further description of the invention, the required power of the auxiliary unit =[0, 35] is used as the output control quantity, and the fuzzy subsets are divided by using the membership function. The steps include: The set [0, 35] is divided into 8 fuzzy subsets, namely {ES, VS, SM, MI, BG, VB, EB, VEB}; Among them, ES represents extremely small, VS represents very small, SM represents relatively small, MI represents medium, BG represents relatively large, VB represents very large, EB represents extremely large, and VEB represents super large; the membership function adopts a normal distribution function.

[0013] As a further description of the invention, a fuzzy control rule base is established with the optimization of the required power of the auxiliary unit as the goal to reasonably output the required power of the auxiliary unit. The steps include: In the power boost mode, when the battery SOC drop rate is low and the required power of the drive motor is low, the required power of the auxiliary unit is low; when the battery SOC drop rate is low and the required power of the drive motor is high, the required power of the auxiliary unit is high; In the charge sustaining mode, when the battery SOC drop rate is low and the required power of the drive motor When it is low, the required power of the auxiliary unit is low; when the battery SOC decline rate is low, the required power of the drive motor When it is high, the required power of the auxiliary unit is high; and when the motor working mode is the regenerative braking mode, the required power of the auxiliary unit is low.

[0014] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention divides the vehicle working mode into a power enhancement mode and a power retention mode, corresponding to the vehicle high-power demand scenario and the scenario where the battery power is low and there is a charging demand respectively, enabling the vehicle to execute a control strategy more suitable for the current scenario. At the same time, considering the influence of the battery SOC decline rate and the drive motor required power on vehicle energy management, a fuzzy control rule base is formulated with the optimization of the auxiliary unit required power economy and power performance as the goal in different modes, and the auxiliary unit required power is reasonably output to achieve the purpose of improving power output or maintaining the power; improving the power performance and economy of the whole vehicle. Description of the Drawings

[0015] Figure 1 is a flow chart of the present invention. Detailed Embodiments

[0016] As Figure 1 shown, a method for energy management of a range-extended commercial vehicle based on fuzzy control, the method includes: Dividing the vehicle working mode into a power enhancement mode and a power retention mode according to the power battery SOC (the battery SOC is the state of charge, used to reflect the remaining capacity of the battery); Applying different fuzzy controllers in different vehicle working modes, taking the battery SOC decline rate and the drive motor required power as the input control quantities of the fuzzy controller, and taking the auxiliary unit required power (i.e., the vehicle auxiliary power APU) as the output control quantity of the fuzzy controller; Performing fuzzy representation on the input control quantity and the output control quantity, establishing a fuzzy control rule base with the optimization of the auxiliary unit required power as the goal, and reasonably outputting the auxiliary unit required power.

[0017] Dividing the vehicle working mode into a power enhancement mode and a power retention mode according to the power battery SOC, the steps include: Collecting the vehicle power battery SOC, and judging whether the power battery SOC is less than the set value; If the power battery SOC is less than the set value, controlling the vehicle to run in the power retention mode; If the power battery SOC is greater than the set value, controlling the vehicle to run in the power enhancement mode.

[0018] In this embodiment, when the SOC of the power battery is higher than 20%, the vehicle working mode is the power enhancement mode. At this time, the required power of the auxiliary unit mainly meets the power performance requirements of the whole vehicle. While taking into account the impact of the decrease in the SOC of the power battery, it makes a more aggressive response to the required power of the drive motor. When the SOC of the power battery is lower than 20%, the vehicle working mode is the power retention mode. At this time, the required power of the auxiliary unit is mainly used to charge the power battery to make up for the energy consumption of the power battery, and the response to the required power of the drive motor weakens.

[0019] Taking the battery SOC decline rate as the input control quantity of the fuzzy controller, the steps include: Keep the change range of the battery SOC decline rate within =[0, 1], Through the quantization factor Convert it into a discrete integer domain, that is ={0, 1... 9, 10}, so as to be used as the input control quantity of the fuzzy controller. Among them, the conversion formula is: , the quantization factor The calculation formula is: .

[0020] Taking the required power of the drive motor as the input control quantity of the fuzzy controller, the steps include: The change range of the required power of the drive motor is =[-20, 90], After passing through the quantization factor Convert it into a discrete integer domain, that is ={-2, -3... 0, 1... 8, 9}, so as to be used as the input control quantity of the fuzzy controller; Among them, the working mode of the drive motor is divided into the drive mode and the regenerative braking mode. When the required power of the drive motor is negative, it means that the motor is working in the regenerative braking mode. When the required power of the drive motor is non - negative, it means that the motor is working in the drive mode. Among them, the conversion formula is , the quantization factor The calculation formula is: .

[0021] Taking the required power of the auxiliary unit as the output control quantity of the fuzzy controller, the steps include: The value range of the required power of the auxiliary unit is the value range ={0, 1... 7}, through the scale factor Convert it into the actual required power of the auxiliary unit, that is =[0, 35]. Among them, the conversion formula is: , the scale factor Is: .

[0022] Take the battery SOC decline rate ={0, 1……9, 10} as the input control variable, and use the membership function to divide the fuzzy subsets. The steps include: Divide ={0, 1……9, 10} into 7 fuzzy subsets, namely {EL, VL, LO, ST, HI, VH, EH}; Among them, EL represents extremely low, VL represents very low, LO represents relatively low, ST represents normal, HI represents relatively high, VH represents very high, and EH represents extremely high; the membership function adopts the normal distribution type function; that is, the normal distribution center values corresponding to {EL, VL, LO, ST, HI, VH, EH} are {0, 1……9, 10}.

[0023] Take the driving motor demand power ={-2, -3……0, 1……8, 9} as the input control variable, and use the membership function to divide the fuzzy subsets; including; Divide ={-2, -3……0, 1……8, 9} into 9 fuzzy subsets, namely {NB, NS, ZE, PS, PM, PB, PVB, PEB, PVEB}; Among them, NB represents negative large, NS represents negative small, ZE represents positive zero, PS represents positive small, PM represents positive medium, PB represents positive large, PVB represents positive very large, PEB represents positive extremely large; PVEB represents positive super large; the membership function adopts the normal distribution type function. That is, the normal distribution center values corresponding to {NB, NS, ZE, PS, PM, PB, PVB, PEB, PVEB} are {-2, -3……0, 1……8, 9}.

[0024] Take the auxiliary unit demand power =[0, 35] as the output control variable, and divide the fuzzy subsets; including: Divide =[0, 35] into 8 fuzzy subsets, namely {ES, VS, SM, MI, BG, VB, EB, VEB}; Among them, ES represents extremely small, VS represents very small, SM represents relatively small, MI represents medium, BG represents relatively large, VB represents very large, EB represents extremely large, and VEB represents super large; the membership function adopts the normal distribution type function. That is, the normal distribution center values corresponding to {ES, VS, SM, MI, BG, VB, EB, VEB} are {0, 5, 10……35}.

[0025] Establish a fuzzy control rule base with the optimization of the auxiliary unit demand power as the goal, and reasonably output the auxiliary unit demand power, including: In the power boost mode, when the battery SOC decline rate is low and the required power of the drive motor is low, the required power of the auxiliary unit is low; when the battery SOC decline rate is low and the required power of the drive motor is high, the required power of the auxiliary unit is high; based on simulation tests and long-term accumulated experience, a fuzzy control rule base is formulated with the goal of optimizing the economy and dynamic performance of the auxiliary unit required power. There are 63 rules in total, and the specific rules are as follows: Fuzzy control rule table in power boost mode ; In the charge sustaining mode, when the battery SOC decline rate is low and the required power of the drive motor is low, the required power of the auxiliary unit is low; when the battery SOC decline rate is low and the required power of the drive motor is high, the required power of the auxiliary unit is high; and when the motor working mode is the regenerative braking mode, the required power of the auxiliary unit is low. Based on simulation tests and long-term accumulated experience, a fuzzy control rule base is formulated with the goal of optimizing the economy and dynamic performance of the auxiliary unit required power. There are 63 rules in total, and the specific rules are as follows; Fuzzy control rule table in charge sustaining mode ; The above method divides the vehicle working mode into a power boost mode and a charge sustaining mode, corresponding to the vehicle's high-power demand scenario and the scenario where the battery has a low charge and a charging demand respectively, enabling the vehicle to execute a control strategy more suitable for the current scenario. At the same time, it comprehensively considers the impact of the battery SOC decline rate and the required power of the drive motor on vehicle energy management, formulates a fuzzy control rule base with the goal of optimizing the economy and dynamic performance of the auxiliary unit required power in different modes, and reasonably outputs the required power of the auxiliary unit to achieve the purpose of improving power output or maintaining the battery charge, thereby improving the dynamic performance and economy of the whole vehicle.

[0026] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An energy management method for a range-extended commercial vehicle based on fuzzy control, characterized in that, The method includes: Dividing the vehicle working mode into a power enhancement mode and a power retention mode according to the state of charge (SOC) of the power battery; Applying different fuzzy controllers in different vehicle working modes, taking the battery SOC decline rate and the drive motor demand power as the input control variables of the fuzzy controller, and taking the auxiliary unit demand power as the output control variable of the fuzzy controller; Performing fuzzy representation on the input control variables and the output control variable, establishing a fuzzy control rule base with the optimization of the auxiliary unit demand power as the goal, and reasonably outputting the auxiliary unit demand power.

2. The energy management method for a range-extended commercial vehicle based on fuzzy control according to claim 1, wherein Dividing the vehicle working mode into a power enhancement mode and a power retention mode according to the state of charge (SOC) of the power battery, the steps include: Collecting the SOC of the vehicle power battery and judging whether the SOC of the power battery is less than the set value; If the SOC of the power battery is less than the set value, controlling the vehicle to run in the power retention mode; If the SOC of the power battery is greater than the set value, controlling the vehicle to run in the power enhancement mode.

3. The energy management method for an extended-range commercial vehicle based on fuzzy control according to claim 2, characterized in that Taking the battery SOC decline rate as the input control variable of the fuzzy controller, the steps include: Keep the change range of the battery SOC decline rate within =[0, 1], After being transformed by the quantization factor it is converted into a discrete integer domain, that is ={0, 1... 9, 10}, thus serving as the input control quantity of the fuzzy controller.

4. The energy management method for an extended-range commercial vehicle based on fuzzy control according to claim 3, characterized in that, Taking the drive motor demand power as the input control variable of the fuzzy controller, the steps include: The required power change range of the drive motor is =[-20, 90], After passing through the quantization factor it is transformed into a discrete integer domain, that is ={-2, -3……0, 1……8, 9}, thus serving as the input control quantity of the fuzzy controller; Wherein the drive motor working mode is divided into a drive mode and a regenerative braking mode. When the drive motor demand power is negative, it means the motor is working in the regenerative braking mode. When the drive motor demand power is non - negative, it means the motor is working in the drive mode.

5. The energy management method for an extended-range commercial vehicle based on fuzzy control according to claim 4, wherein Taking the auxiliary unit demand power as the output control variable of the fuzzy controller, the steps include: The value range of the required power of the auxiliary unit is ={0, 1……7}, and through the scale factor it is converted into the actual required power of the auxiliary unit, that is =[0, 35].

6. The energy management method for an extended-range commercial vehicle based on fuzzy control according to claim 5, characterized in that Take the battery SOC degradation rate ={0, 1……9, 10} as the input control variable, and use the membership function to divide the fuzzy subsets. The steps are as follows: Partition {0, 1……9, 10} into 7 fuzzy subsets, namely {EL, VL, LO, ST, HI, VH, EH}; Wherein, EL represents extremely low, VL represents very low, LO represents low, ST represents normal, HI represents high, VH represents very high, EH represents extremely high; the membership function adopts a normal distribution function.

7. The energy management method for an extended-range commercial vehicle based on fuzzy control according to claim 6, characterized in that, Take the required power of the drive motor ={-2, -3……0, 1……8, 9} as the input control quantity, and use the membership function to divide the fuzzy subsets. The steps include: ={-2, -3……0, 1……8, 9} is divided into 9 fuzzy subsets, Is {NB, NS, ZE, PS, PM, PB, PVB, PEB, PVEB}; Wherein, NB represents negative large, NS represents negative small, ZE represents positive zero, PS represents positive small, PM represents positive medium, PB represents positive large, PVB represents positive very large, PEB represents positive extremely large; PVEB represents positive super large; the membership function adopts a normal distribution function.

8. The energy management method for a range-extended commercial vehicle based on fuzzy control according to claim 7, wherein Take the auxiliary unit required power =[0, 35] as the output control quantity, and use the membership function to divide the fuzzy subsets. The steps are as follows: Divide [0, 35] into 8 fuzzy subsets, which are {ES, VS, SM, MI, BG, VB, EB, VEB}; Wherein, ES represents extremely small, VS represents very small, SM represents small, MI represents medium, BG represents large, VB represents very large, EB represents extremely large, VEB represents super large; the membership function adopts a normal distribution function.

9. The energy management method for a range-extended commercial vehicle based on fuzzy control according to claim 8, characterized in that, Establishing a fuzzy control rule base with the optimization of the auxiliary unit demand power as the goal and reasonably outputting the auxiliary unit demand power, the steps include: In the power boost mode, when the battery SOC degradation rate is low and the required power of the drive motor is low, the required power of the auxiliary unit is low; when the battery SOC degradation rate is low and the required power of the drive motor is high, the required power of the auxiliary unit is high. In the power holding mode, when the battery SOC drop rate is low and the required power of the drive motor is low, the required power of the auxiliary unit is low; when the battery SOC drop rate is low and the required power of the drive motor is high, the required power of the auxiliary unit is high; and when the motor operating mode is the regenerative braking mode, the required power of the auxiliary unit is low.

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