Hybrid vehicle driving range algorithm

Abstract

The invention discloses a driving range algorithm of a hybrid vehicle. The driving range algorithm comprises the following steps: S1, carrying out filtering calculation on a residual oil quantity mean value; s2, updating the oil quantity; s3, performing oil quantity arbitration; s4, calculating average energy consumption per hundred kilometers; and S5, calculating the comprehensive driving range of the vehicle, smoothing the fuel quantity fluctuation caused by the shaking of the fuel tank through a mean filtering algorithm, arbitrating the initial value of the residual fuel quantity of the vehicle under ramp, non-ramp and refueling working conditions through a ramp identification strategy, and then subtracting the fuel injection quantity in the engine task scheduling period to obtain the final arbitrated residual fuel quantity. And the severe fluctuation of the driving range caused by repeatedly taking the actual residual oil quantity value is avoided. The hybrid vehicle driving range algorithm provided by the invention has the effect of accurately calculating the driving range.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid vehicles, and in particular to a hybrid vehicle driving range algorithm. Background Art

[0002] Based on the collaborative relationship between different controllers of hybrid vehicles, the vehicle's energy consumption and driving range are calculated through signal interaction between the instrument panel, vehicle controller and engine controller. The calculation of fuel and pure electric driving range is independent of each other.

[0003] In this technology, the pure electric driving range is obtained by multiplying the corresponding parameters under different working conditions and the current SOC of the high-voltage battery pack. The fuel driving range is calculated based on the relationship between the engine injection amount and the remaining fuel amount. The pure electric and fuel driving ranges are added together to get the comprehensive driving range.

[0004] The existing technology ignores the oil-to-electricity conversion problem that is unique to hybrid vehicles. When the SOC of the high-voltage battery pack is at a low level during vehicle driving, fuel can be consumed to generate electricity through the motor to replenish the high-voltage battery pack, thereby extending the pure electric driving range while shortening the fuel driving range. Secondly, the oil level in the fuel tank shakes during driving, causing the oil level to fluctuate too much, and the calculated fuel driving range has a large deviation. If a strong filtering strategy is used, the driving range will be seriously distorted; and once the vehicle stops on a slope, due to the layout of the oil level sensor, there is a large difference between the collected oil level and the actual oil level, which will cause a large error in the calculation of the driving range, and the final result is very unreliable. Summary of the invention

[0005] In view of the above problems, a hybrid vehicle driving range algorithm is now provided to solve the problems existing in the prior art.

[0006] The specific technical solutions are as follows:

[0007] A hybrid vehicle driving range algorithm comprises the following steps:

[0008] S1, remaining oil amount mean value filtering calculation;

[0009] S2, oil quantity update;

[0010] S3, oil quantity arbitration;

[0011] S4. Calculate the average energy consumption per 100 kilometers;

[0012] S5. Calculate the comprehensive driving range of the vehicle.

[0013] The above hybrid vehicle driving range algorithm also has the following characteristics: step S1 includes the following steps:

[0014] S11, define the oil quantity array a[], the array length is 10;

[0015] S12, push a real-time oil level into the array every 0.01s, the first oil level is counted as a[1], a[2]…a

[10] ;

[0016] S13, when there are only two fuel levels a[1] and a[2] in the array, the current remaining fuel level is the real-time fuel level;

[0017] S14. When a[3] is pushed into the array, a[3], a[1] and a[2] are sorted to select the maximum and minimum values. At this time, the remaining oil volume = (a[1]+a[2]+a[3]-maximum oil volume-minimum oil volume) / 1;

[0018] S15. When a[4] is pushed into the array, a[4] is sorted with a[1], a[2], and a[3] to select the maximum and minimum values. At this time, the remaining oil volume = (a[1]+a[2]+a[3]+a[4]-maximum oil volume-minimum oil volume) / 2;

[0019] S16, and so on until the array is full of 10 oil level values, that is, a[1] to a

[10] all have values;

[0020] S17. When the array is filled for the first time, if a new oil amount is pushed in, the left shift assignment strategy can be used to set a[1]=a[2], until a[9]=a

[10] , and finally replace a

[10] with the new oil amount. Each time the actual oil amount is pushed into the array, the array values ​​will be reordered.

[0021] The above hybrid vehicle driving range algorithm also has the following characteristics: step S2 comprises the following steps:

[0022] S21, key 0n gear, charging, remote wake-up power-on fast update of fuel level;

[0023] S22, determining that refueling is completed and quickly updating the oil volume.

[0024] The above hybrid vehicle driving range algorithm also has the following characteristics: step S22 includes the following steps:

[0025] S221, judging that the vehicle is stationary according to the vehicle speed, the actual required torque and the engine speed;

[0026] S222, if the oil volume change exceeds a certain threshold within a fixed time, it is determined that refueling is in progress;

[0027] S223: If the oil volume change within a fixed time is less than a certain threshold, it is determined that the refueling is completed.

[0028] The above hybrid vehicle driving range algorithm also has the following characteristics: step S3 includes the following steps:

[0029] S31, when the controller is in sleep mode, the remaining oil volume is updated to 0;

[0030] S32, before the controller is powered off and hibernated, the current remaining oil volume data is stored in the NVRAM area of ​​the memory, and when the controller is powered on again, the oil volume data value stored in the NVRAM area of ​​the memory is used as the initial oil volume;

[0031] S33, when the conditions of key 0n position, charging, remote wake-up and power-on rapid fuel quantity update are met, arbitration calculation is performed;

[0032] S34, when the refueling completion fast update condition is met, the current actual fuel volume is credible, and the fuel volume after mean filtering is updated to the initial fuel volume;

[0033] S35, arbitration remaining fuel amount = updated initial fuel amount - engine fuel injection amount during task scheduling cycle.

[0034] The above hybrid vehicle driving range algorithm also has the following characteristics: step S33 includes the following steps:

[0035] S331, if the absolute value of the difference between the oil volume after mean filtering and the oil volume in the NVRAM area of ​​the memory is within a certain threshold range, and there is no fail in the NVRAM area of ​​the memory, it is determined that the vehicle is on a slope, and the oil volume value stored in the NVRAM area of ​​the memory is used as the initial oil volume;

[0036] S332: If the absolute value of the difference between the oil volume after the mean filter and the oil volume in the NVRAM area of ​​the memory is less than the lower limit of the threshold range, it is determined that the road is not on a slope, and the current actual oil volume is credible, and the oil volume after the mean filter is updated to the initial oil volume;

[0037] S333. If the absolute value of the difference between the oil amount after mean filtering and the oil amount in the NVRAM area of ​​the memory is greater than the upper limit of the threshold range, it is judged to be a refueling condition, and the current actual oil amount is credible. The oil amount after mean filtering is updated to the initial oil amount.

[0038] The above hybrid vehicle driving range algorithm also has the following characteristics: step S4 comprises the following steps:

[0039] S41, calculating the average fuel consumption per 100 kilometers, recording the vehicle speed integral accumulated mileage within a fixed time, recording the engine fuel injection integral accumulated fuel consumption within a fixed time,

[0040]

[0041] Where k is the weight coefficient;

[0042] S42, calculating the average power consumption per 100 kilometers, recording the accumulated mileage of the vehicle speed integral within a fixed time, and recording the power integral obtained by multiplying the battery pack voltage and current within a fixed time to obtain the accumulated power consumption,

[0043]

[0044] Where k is the weight coefficient;

[0045] S43, calculate the average energy consumption per 100 kilometers (fuel consumption unit) = average fuel consumption per 100 kilometers + average electricity consumption per 100 kilometers / fuel point conversion coefficient;

[0046] S44. Calculate the average energy consumption per 100 kilometers (electricity consumption unit) = average electricity consumption per 100 kilometers + average oil consumption per 100 kilometers × oil-to-electricity conversion coefficient.

[0047] The above hybrid vehicle driving range algorithm also has the following characteristics: step S5 comprises the following steps:

[0048] S51,

[0049] S52,

[0050] S53, calculating the comprehensive driving range = fuel driving range + pure electric driving range.

[0051] In summary, the beneficial effects of this scheme are:

[0052] In the hybrid vehicle driving range algorithm provided by the present invention, the oil volume fluctuation caused by the shaking of the fuel tank is smoothed by the mean filtering algorithm, and the initial value of the remaining oil volume of the vehicle under the ramp, non-ramp and refueling conditions is arbitrated by identifying the ramp strategy, and then the injection volume in the engine task scheduling cycle is subtracted to obtain the final arbitrated remaining oil volume, thereby avoiding the drastic fluctuation of the driving range caused by repeatedly using the actual remaining oil volume value. The hybrid vehicle driving range algorithm provided by the present invention has the effect of accurately calculating the driving range. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be clearly and completely described below in conjunction with 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 are within the scope of protection of the present invention.

[0054] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0055] The present invention will be further described below in conjunction with specific embodiments, but the present invention is not limited thereto.

[0056] The hybrid vehicle driving range algorithm provided in this embodiment includes the following steps:

[0057] S1, remaining oil amount mean value filtering calculation;

[0058] S2, oil quantity update;

[0059] S3, oil quantity arbitration;

[0060] S4. Calculate the average energy consumption per 100 kilometers;

[0061] S5. Calculate the comprehensive driving range of the vehicle.

[0062] In the above embodiment, step S1 includes the following steps:

[0063] S11, define the oil quantity array a[], the array length is 10;

[0064] S12, push a real-time oil level into the array every 0.01s, the first oil level is counted as a[1], a[2]…a

[10] ;

[0065] S13, when there are only two fuel levels a[1] and a[2] in the array, the current remaining fuel level is the real-time fuel level;

[0066] S14. When a[3] is pushed into the array, a[3], a[1] and a[2] are sorted to select the maximum and minimum values. At this time, the remaining oil volume = (a[1]+a[2]+a[3]-maximum oil volume-minimum oil volume) / 1;

[0067] S15. When a[4] is pushed into the array, a[4] is sorted with a[1], a[2], and a[3] to select the maximum and minimum values. At this time, the remaining oil volume = (a[1]+a[2]+a[3]+a[4]-maximum oil volume-minimum oil volume) / 2;

[0068] S16, and so on until the array is full of 10 oil level values, that is, a[1] to a

[10] all have values;

[0069] S17. When the array is filled for the first time, if a new oil amount is pushed in, the left shift assignment strategy can be used to set a[1]=a[2], until a[9]=a

[10] , and finally replace a

[10] with the new oil amount. Each time the actual oil amount is pushed into the array, the array values ​​will be reordered.

[0070] In the above embodiment, step S2 includes the following steps:

[0071] S21, key 0n gear, charging, remote wake-up power-on fast update of fuel level;

[0072] S22, determining that refueling is completed and quickly updating the oil volume.

[0073] In the above embodiment, step S22 includes the following steps:

[0074] S221, judging that the vehicle is stationary according to the vehicle speed, the actual required torque and the engine speed;

[0075] S222, if the oil volume change exceeds a certain threshold within a fixed time, it is determined that refueling is in progress;

[0076] S223: If the oil volume change within a fixed time is less than a certain threshold, it is determined that the refueling is completed.

[0077] In the above embodiment, step S3 includes the following steps:

[0078] S31, when the controller is in sleep mode, the remaining oil volume is updated to 0;

[0079] S32, before the controller is powered off and hibernated, the current remaining oil volume data is stored in the NVRAM area of ​​the memory, and when the controller is powered on again, the oil volume data value stored in the NVRAM area of ​​the memory is used as the initial oil volume;

[0080] S33, when the conditions of key 0n position, charging, remote wake-up and power-on rapid fuel quantity update are met, arbitration calculation is performed;

[0081] S34, when the refueling completion fast update condition is met, the current actual fuel volume is credible, and the fuel volume after mean filtering is updated to the initial fuel volume;

[0082] S35, arbitration remaining fuel amount = updated initial fuel amount - engine fuel injection amount during task scheduling cycle.

[0083] In the above embodiment, step S33 includes the following steps:

[0084] S331, if the absolute value of the difference between the oil volume after mean filtering and the oil volume in the NVRAM area of ​​the memory is within a certain threshold range, and there is no fail in the NVRAM area of ​​the memory, it is determined that the vehicle is on a slope, and the oil volume value stored in the NVRAM area of ​​the memory is used as the initial oil volume;

[0085] S332: If the absolute value of the difference between the oil volume after the mean filter and the oil volume in the NVRAM area of ​​the memory is less than the lower limit of the threshold range, it is determined that the road is not on a slope, and the current actual oil volume is credible, and the oil volume after the mean filter is updated to the initial oil volume;

[0086] S333. If the absolute value of the difference between the oil amount after mean filtering and the oil amount in the NVRAM area of ​​the memory is greater than the upper limit of the threshold range, it is judged to be a refueling condition, and the current actual oil amount is credible. The oil amount after mean filtering is updated to the initial oil amount.

[0087] In the above embodiment, step S4 includes the following steps:

[0088] S41, calculating the average fuel consumption per 100 kilometers, recording the vehicle speed integral accumulated mileage within a fixed time, recording the engine fuel injection integral accumulated fuel consumption within a fixed time,

[0089]

[0090] Where k is the weight coefficient;

[0091] S42, calculating the average power consumption per 100 kilometers, recording the accumulated mileage of the vehicle speed integral within a fixed time, and recording the power integral obtained by multiplying the battery pack voltage and current within a fixed time to obtain the accumulated power consumption,

[0092]

[0093] Where k is the weight coefficient;

[0094] S43, calculate the average energy consumption per 100 kilometers (fuel consumption unit) = average fuel consumption per 100 kilometers + average electricity consumption per 100 kilometers / fuel point conversion coefficient;

[0095] S44. Calculate the average energy consumption per 100 kilometers (electricity consumption unit) = average electricity consumption per 100 kilometers + average oil consumption per 100 kilometers × oil-to-electricity conversion coefficient.

[0096] It should be noted that the weight coefficient k is a coefficient of a first-order low-pass filter, which can be obtained by looking up a table based on the fuel volume and SOC, and its value range is between 0 and 1.

[0097] In the above embodiment, step S5 includes the following steps:

[0098] S51,

[0099] S52,

[0100] S53, calculating the comprehensive driving range = fuel driving range + pure electric driving range.

[0101] Working principle: The fuel fluctuation caused by the shaking of the fuel tank is smoothed by the mean filtering algorithm. The initial value of the remaining fuel of the vehicle under ramp, non-ramp and refueling conditions is arbitrated by identifying the ramp strategy, and then the injection amount in the engine task scheduling cycle is subtracted to obtain the final arbitrated remaining fuel. This avoids the drastic fluctuation of the driving range caused by repeated use of the actual remaining fuel value. The concept of average energy consumption is introduced, and the conversion efficiency of fuel and electricity is considered to obtain the average energy consumption used to calculate the driving range, which effectively improves the calculation accuracy of the driving range and can calculate the driving range more accurately under different working conditions.

[0102] The above are only preferred embodiments of the present invention, and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of the specification of the present invention should be included in the protection scope of the present invention.

Claims

1. A hybrid vehicle driving range algorithm, characterized in that: The following steps are involved: S1, remaining oil amount mean value filtering calculation; S2, oil quantity update; S3, oil quantity arbitration; S4. Calculate the average energy consumption per 100 kilometers; S5. Calculate the comprehensive driving range of the vehicle.

2. A hybrid vehicle driving range algorithm according to claim 1, characterized in that: The step S1 comprises the following steps: S11, define the oil quantity array a[], the array length is 10; S12, push a real-time oil level into the array every 0.01s, the first oil level is counted as a[1], a[2]…a[10]; S13, when there are only two fuel levels a[1] and a[2] in the array, the current remaining fuel level is the real-time fuel level; S14. When a[3] is pushed into the array, a[3], a[1] and a[2] are sorted to select the maximum and minimum values. At this time, the remaining oil volume = (a[1]+a[2]+a[3]-maximum oil volume-minimum oil volume) / 1; S15. When a[4] is pushed into the array, a[4] is sorted with a[1], a[2], and a[3] to select the maximum and minimum values. At this time, the remaining oil volume = (a[1]+a[2]+a[3]+a[4]-maximum oil volume-minimum oil volume) / 2; S16, and so on until the array is full of 10 oil level values, that is, a[1] to a[10] all have values; S17. When the array is filled for the first time, if a new oil amount is pushed in, the left shift assignment strategy can be used to set a[1]=a[2], until a[9]=a[10], and finally replace a[10] with the new oil amount. Each time the actual oil amount is pushed into the array, the array values ​​will be reordered.

3. A hybrid vehicle driving range algorithm according to claim 2, characterized in that: The step S2 comprises the following steps: S21, key 0n gear, charging, remote wake-up power-on fast update of fuel level; S22, determining that refueling is completed and quickly updating the oil volume.

4. A hybrid vehicle driving range algorithm according to claim 3, characterized in that: The step S22 includes the following steps: S221, judging that the vehicle is stationary according to the vehicle speed, the actual required torque and the engine speed; S222, if the oil volume change exceeds a certain threshold within a fixed time, it is determined that refueling is in progress; S223: If the oil volume change within a fixed time is less than a certain threshold, it is determined that the refueling is completed.

5. A hybrid vehicle driving range algorithm according to claim 4, characterized in that: The step S3 comprises the following steps: S31, when the controller is in sleep mode, the remaining oil volume is updated to 0; S32, before the controller is powered off and hibernated, the current remaining oil volume data is stored in the NVRAM area of ​​the memory, and when the controller is powered on again, the oil volume data value stored in the NVRAM area of ​​the memory is used as the initial oil volume; S33, when the conditions of key 0n position, charging, and remote wake-up power-on rapid fuel quantity update are met, arbitration calculation is performed; S34, when the refueling completion fast update condition is met, the current actual fuel volume is credible, and the fuel volume after mean filtering is updated to the initial fuel volume; S35, arbitration remaining fuel amount = updated initial fuel amount - engine fuel injection amount during task scheduling cycle.

6. A hybrid vehicle driving range algorithm according to claim 5, characterized in that: The step S33 comprises the following steps: S331, if the absolute value of the difference between the oil volume after mean filtering and the oil volume in the NVRAM area of ​​the memory is within a certain threshold range, and there is no fail in the NVRAM area, it is determined that the vehicle is on a slope, and the oil volume value stored in the NVRAM area of ​​the memory is used as the initial oil volume; S332: If the absolute value of the difference between the oil volume after mean filtering and the NV oil volume is less than the lower limit of the threshold range, it is determined that the vehicle is not on a slope and the current actual oil volume is credible, and the oil volume after mean filtering is updated to the initial oil volume; S333. If the absolute value of the difference between the oil quantity after mean filtering and the NV oil quantity is greater than the upper limit of the threshold range, it is judged as a refueling condition, the current actual oil quantity is credible, and the oil quantity after mean filtering is updated to the initial oil quantity.

7. A hybrid vehicle driving range algorithm according to claim 6, characterized in that: The step S4 comprises the following steps: S41, calculating the average fuel consumption per 100 kilometers, recording the vehicle speed integral accumulated mileage within a fixed time, recording the engine fuel injection integral accumulated fuel consumption within a fixed time, Where k is the weight coefficient; S42, calculating the average power consumption per 100 kilometers, recording the accumulated mileage of the vehicle speed integral within a fixed time, and recording the power integral obtained by multiplying the battery pack voltage and current within a fixed time to obtain the accumulated power consumption, Where k is the weight coefficient; S43, calculate the average energy consumption per 100 kilometers (fuel consumption unit) = average fuel consumption per 100 kilometers + average electricity consumption per 100 kilometers / fuel point conversion coefficient; S44. Calculate the average energy consumption per 100 kilometers (electricity consumption unit) = average electricity consumption per 100 kilometers + average oil consumption per 100 kilometers × oil-to-electricity conversion coefficient.

8. A hybrid vehicle driving range algorithm according to claim 7, characterized in that: The step S5 comprises the following steps: S51、 S52、 S53, calculating the comprehensive driving range = fuel driving range + pure electric driving range.

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