CN118706146A - A hybrid vehicle path planning method and device based on minimum energy consumption - Google Patents

Abstract

The invention discloses a hybrid electric vehicle path planning method based on minimum energy consumption, which comprises the following steps: calculating a reference SOC value of the hybrid electric vehicle at a road node according to the input vehicle speed information V; taking the reference SOC value and the initial SOC value as inputs, taking the minimum equivalent fuel consumption on each road as a target, calculating optimal equivalent factors under different initial SOCs, and establishing a table look-up model of the equivalent factors with respect to the roads and the initial SOCs; and obtaining equivalent factor values by all the available path nodes according to a table look-up model, calculating equivalent fuel consumption between two adjacent path nodes, determining the accumulated minimum equivalent fuel consumption and an optimal path pointer at all the path nodes, and reversely pushing the optimal path pointer to a starting point by a terminal point to obtain an optimal path. According to the invention, the driving path is rapidly solved and planned based on the minimum energy consumption of the road, so that the vehicle driving path with better economical efficiency can be obtained, and the calculated amount is reduced.

Description

A hybrid vehicle path planning method and device based on minimum energy consumption

Technical Field

The present invention relates to the technical field of path guidance, and in particular to a hybrid electric vehicle path planning method and device based on minimum energy consumption.

Background Art

Since hybrid vehicles have multiple power sources, they can adjust power output according to road conditions to improve driving efficiency. The current driving route planning for hybrid vehicles is basically the same as the traditional planning method, which is based on vehicle speed information and the shortest time consumption. However, the time-based planning method may cause greater energy consumption, and users who focus on economic costs may prefer the route planning method based on minimum energy consumption.

The vehicle route selection method based on energy optimization in the existing technology is mainly aimed at traditional fuel vehicles. For hybrid vehicles, different power distribution of the engine and the motor on each road will lead to different energy consumption effects, affecting the final route selection.

Summary of the invention

In view of the phenomenon that the route planning scheme of hybrid electric vehicles based on time cost may cause great economic cost, the present invention provides a hybrid electric vehicle route planning method and device based on minimum energy consumption to solve the problem of energy consumption optimal route selection of hybrid electric vehicles.

The technical solution of the present invention is as follows:

A hybrid vehicle path planning method based on minimum energy consumption comprises the following steps:

Calculate a reference SOC value of the hybrid vehicle at a road node according to the input vehicle speed information V;

Taking the reference SOC value and the initial SOC value as input, taking the minimum equivalent fuel consumption on each road as the target, calculating the optimal equivalent factor under different initial SOCs, and establishing a table lookup model of the equivalent factor with respect to the road and the initial SOC;

From all reachable path nodes, the equivalent factor value is obtained according to the table lookup model and the equivalent fuel consumption between two adjacent path nodes is calculated. The cumulative minimum equivalent fuel consumption and the optimal path pointer at all path nodes are determined, and the optimal path is obtained by reversely deducing from the end point to the starting point according to the optimal path pointer.

Furthermore, the method includes the following steps: obtaining the equivalent factor according to the optimal path based on the position information and instantaneous SOC value obtained by the vehicle in real time, using a table lookup model, solving the engine power and motor power that minimize the equivalent fuel consumption, and completing the vehicle energy distribution.

Furthermore, the reference SOC calculation formula for each driving point in a single path is:

In the formula, soc _in is the initial SOC value, soc _en is the final SOC value, f is the average required torque of the vehicle calculated by V, l is the road mileage, D is the mileage traveled, n is the number of roads traveled, k is the total number of roads, j is the road number, and the calculation formula of the reference SOC value soc _Pi2 of a single node Pi2 in the feasible path _Li1 is

Wherein, i1 represents the feasible path number, i2 represents the node number, _n1 represents the number of times a node is passed in all driving paths, and the reference SOC value of the hybrid vehicle at the road node is soc _r = [soc _P1 , soc _P2 , …, soc _Pi2 ].

Furthermore, the calculation formula of the minimum equivalent fuel consumption is:

Where, _Pe is the engine power, _Pm is the motor power, _ηe is the engine efficiency, _ηm is the motor efficiency, _ηb is the battery efficiency, _Hf is the fuel calorific value, and s is the equivalent factor;

The calculation of the optimal equivalent factor at different initial SOCs is performed according to the following objective function and constraints.

Where soc is the instantaneous SOC value, soc _lo is the SOC lower limit, soc _up is the SOC upper limit, T _m is the motor torque, _Te is the engine torque, ω _m is the motor torque, ω _e is the engine torque, the superscripts min and max represent the minimum and maximum values of the variables, and a is a constant.

Furthermore, the calculation formula for obtaining the equivalent factor value based on the table lookup model and calculating the equivalent fuel consumption between two adjacent path nodes is:

Where _t1 is the road driving time, _Pe is the engine power, _Pm is the motor power, _ηe is the engine efficiency, _ηm is the motor efficiency, _ηb is the battery efficiency, _Hf is the fuel calorific value, and s is the equivalent factor;

The calculation formula for determining the cumulative minimum equivalent fuel consumption and the optimal path pointer at all path nodes is:

L _n2-1 =mon(J(k ₁ )+L _n2 (k ₁ ))

k*＝argminL _n2-1

Where _Ln2 is the cumulative minimum equivalent fuel consumption at the previous node, _k1 is the feasible path, and k ^* is the optimal path pointer.

Another technical solution of the present invention is:

A hybrid vehicle path planning device based on minimum energy consumption, comprising:

Reference SOC planning module: calculates the reference SOC value of the hybrid vehicle at the road node according to the input vehicle speed information V;

Offline optimization module: taking the reference SOC value and the initial SOC value as input, taking the minimum equivalent fuel consumption on each road as the goal, calculating the optimal equivalent factor under different initial SOCs, and establishing a lookup table model of the equivalent factor with respect to the road and the initial SOC;

And, the path planning module: from all reachable path nodes, the equivalent factor value is obtained according to the table lookup model and the equivalent fuel consumption between two adjacent path nodes is calculated, the cumulative minimum equivalent fuel consumption and the optimal path pointer at all path nodes are determined, and the optimal path is obtained by reversely deducing from the end point to the starting point according to the optimal path pointer.

Further, including:

Energy management control module: Based on the optimal path, the vehicle's real-time location information and instantaneous SOC value are obtained, and the equivalent factor is obtained using the table lookup model to solve the engine power and motor power that minimize the equivalent fuel consumption;

And, the whole vehicle model module: controls the vehicle to complete the vehicle energy distribution based on the engine power and the motor power.

Compared with the prior art, the present invention has the following advantages:

1. The present invention performs driving route planning based on the characteristics of the hybrid power source of the hybrid vehicle and the minimum energy consumption of the road, and can obtain a vehicle driving route with better economic efficiency;

2. The present invention establishes an offline table lookup model for each road, and can quickly solve the driving route with the minimum energy consumption when the initial vehicle state is determined, thereby reducing the calculation time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a module schematic diagram of a hybrid vehicle path planning device based on minimum energy consumption according to an embodiment of the present invention.

FIG. 2 is a simplified diagram of a road according to an embodiment of the present invention.

FIG. 3 is a flow chart of the path planning module performing optimal path solving in an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention will be further described below in conjunction with the embodiments, but are not intended to be limiting of the present invention.

Please refer to Figure 1 , the hybrid vehicle path planning device based on minimum energy consumption of this embodiment includes: a reference SOC planning module 1, a path planning module 2, an offline optimization module 3, an energy management control module 4 and a whole vehicle model module 5.

The reference SOC planning module 1 is used to calculate the reference SOC value of the hybrid vehicle at the road node according to the input vehicle speed information V.

The offline optimization module 3 is used to take the reference SOC value and the initial SOC value as input, take the minimum equivalent fuel consumption on each road as the target, calculate the optimal equivalent factor under different initial SOCs, and establish a lookup table model of the equivalent factor with respect to the road and the initial SOC.

The path planning module 2 is used to obtain the equivalent factor value and calculate the equivalent fuel consumption between two adjacent path nodes from all reachable path nodes according to the table lookup model, determine the cumulative minimum equivalent fuel consumption and the optimal path pointer at all path nodes, and reversely infer from the end point to the starting point according to the optimal path pointer to obtain the optimal path.

As a preferred embodiment, the hybrid vehicle path planning device based on minimum energy consumption in this embodiment further includes an energy management control module 4 and a vehicle model module 5, wherein the energy management control module 4 is used to obtain the equivalent factor based on the optimal path according to the position information and instantaneous SOC value obtained by the vehicle in real time, and to solve the engine power and motor power that minimize the equivalent fuel consumption by using the table lookup model. The vehicle model module 5 is used to control the vehicle to complete the vehicle energy distribution based on the engine power and motor power.

In the following, please refer to FIG. 2 to illustrate the hybrid vehicle path planning method based on minimum energy consumption using a simplified road example with 16 nodes.

The simplified road includes 16 road nodes from P1 to P16, 12 transverse roads from R11 to R43, 12 longitudinal roads from H11 to H34, and 20 driving paths from L ₁ to L _20. In this embodiment, path planning is performed with P1 as the starting point and P12 as the end point, and the vehicle speed corresponding to each road is _VR11 , _VR12 ,…, _VH11 , _VH21 ,….

The reference SOC planning module 1 calculates the reference SOC value of the hybrid vehicle at the road node according to the input vehicle speed information V. The vehicle speed information V is the vehicle speed corresponding to each road, that is, V = [ _VR11 , _VR12 , ..., _VH11 , _VH21 , ...]. The reference SOC calculation formula for each driving point in a single path is:

In the formula, soc _in is the initial SOC value, soc _en is the final SOC value, and f is the average required torque of the vehicle calculated from V.

_t1 is the road driving time, m is the curb weight of the controlled vehicle, g is the gravitational acceleration, _fg is the rolling resistance coefficient, _Cd is the air resistance coefficient of the controlled vehicle, ρ is the air density, A is the frontal area of the controlled vehicle, _vt is the instantaneous speed of the vehicle, _vt∈V , θ is the road slope, and δ is the rotational mass conversion coefficient of the controlled vehicle. l is the road mileage, D is the mileage traveled, n is the number of roads traveled, k is the total number of roads, and j is the road number. According to formula (1), the reference SOC value soc _Pi2 of a single node in the feasible path L _i1 can be calculated, i1 represents the feasible path number, i2 represents the node number, and considering all feasible paths, the reference SOC value of each node can be calculated as follows:

In the formula, soc _Pi2 represents the reference SOC value of a single node, n ₁ is the number of times a node is passed in all driving paths, is the reference SOC value of node Pi2 in the feasible path L _i1 .

After calculating the reference SOC value of each node, the reference SOC planning module 1 outputs the node reference SOC value soc _r =[soc _P1 , soc _P2 , . . . , soc _P16 ] to the offline optimization module 3 and the energy management control module 4 .

The offline optimization module 3 takes the node reference SOC value soc _r and the initial SOC value soc _in as input to optimize the equivalent factor s. The instantaneous equivalent fuel consumption of the vehicle can be obtained by formula (4).

Where _Pe is the engine power, _Pm is the motor power, _ηe is the engine efficiency, _ηm is the motor efficiency, _ηb is the battery efficiency, and _Hf is the fuel calorific value. Taking the minimum equivalent fuel consumption on each road as the goal, the optimal equivalent factor under different initial SOCs can be calculated, and the objective function constraints are as follows:

Wherein, soc is the instantaneous SOC value, soc _lo is the lower limit of SOC, soc _up is the upper limit of SOC, T _m is the motor torque, _Te is the engine torque, ω _m is the motor torque, ω _e is the engine torque, the superscripts min and max represent the minimum and maximum values of the variables, a is a constant, and soc _in is taken at intervals of 1% during the optimization process. The offline optimization module 3 calculates the equivalent factor of each road at different initial SOCs through formula (4), and finally establishes a lookup table model of the equivalent factor with respect to the road and the initial SOC, as shown in Table 1, in which A ₁ to D _t are the equivalent factor constant values obtained by optimization. The offline optimization module 3 outputs the lookup table model to the path planning module 2 and the energy management control module 4.

Table 1 Lookup model

As shown in FIG3 , the path planning module 2 uses the table lookup model and the vehicle speed information V as input, uses a dynamic programming algorithm to optimize and output the optimal driving path L _b , and outputs it to the energy management control module 4. The path planning module 2 uses the node position as the state variable and the road selection at the node as the control variable, and reversely solves the optimal path from the last node P16. The specific steps include:

1. Determine the node reference SOC value soc _r .

2. Filter the reachable nodes. For example, in this embodiment, the reachable nodes at P16 are P15 and P12.

3. Obtain the equivalent factor value based on the table lookup model and calculate the arc cost, that is, calculate the equivalent fuel consumption from the previous node to this node. The calculation formula is:

In the embodiment, for example, the equivalent fuel consumption from node P16 to node P15 is J _R43 , and the corresponding equivalent factor value can be found in Table 1 according to the reference SOC value soc _P15 at node P15, assuming it is E ₁ , and substituted into formula (6) to calculate J _R43 as

4. Calculate the cumulative minimum equivalent fuel consumption _Ln2 and the optimal path pointer k ^* at the node. The calculation formula is:

L _n2-1 =min(K(k ₁ )+L _n2 (k ₁ ))

k ^* ＝ _argminLn2-1 (8)

For example, in this embodiment, the minimum equivalent fuel consumption and the optimal path pointer at node P15 are:

For node P11, the minimum equivalent fuel consumption and the optimal path pointer are:

5. Repeat steps 2 to 4 to reversely calculate the minimum equivalent fuel consumption and optimal path pointers of all nodes until the starting point P1 is reached, and connect all optimal path pointers to obtain the optimal path L _b .

The energy management control module 4 takes the position information, the table lookup model, the optimal path L _b , the node reference SOC value soc _r and the instantaneous SOC value soc obtained from the vehicle model module 5 as inputs. The vehicle takes the optimal path L _b as the driving route. According to the position information obtained in real time by the vehicle, the table lookup module is used to solve the equivalent factor s. Based on formula (3), the engine power _Pe and the motor power P _m that minimize the equivalent fuel consumption are solved and output to the vehicle model module 5 to complete the vehicle energy distribution.

Claims (10)

1. The hybrid electric vehicle path planning method based on the minimum energy consumption is characterized by comprising the following steps of:

calculating a reference SOC value of the hybrid electric vehicle at a road node according to the input vehicle speed information V;

Taking the reference SOC value and the initial SOC value as inputs, taking the minimum equivalent fuel consumption on each road as a target, calculating optimal equivalent factors under different initial SOCs, and establishing a table look-up model of the equivalent factors with respect to the roads and the initial SOCs;

And obtaining equivalent factor values by all the available path nodes according to a table look-up model, calculating equivalent fuel consumption between two adjacent path nodes, determining the accumulated minimum equivalent fuel consumption and an optimal path pointer at all the path nodes, and reversely pushing the optimal path pointer to a starting point by a terminal point to obtain an optimal path.

2. The minimum energy consumption-based hybrid vehicle path planning method according to claim 1, comprising the steps of: and obtaining an equivalent factor by utilizing a table look-up model according to the position information and the instantaneous SOC value obtained in real time by the optimal path, solving the engine power and the motor power which enable the equivalent fuel consumption to be minimum, and completing the energy distribution of the vehicle.

3. The hybrid vehicle path planning method based on minimum energy consumption according to claim 1 or 2, characterized in that the reference SOC calculation formula for each traveling point in a single path is

Wherein, SOC _in is the initial SOC value, SOC _en is the final SOC value, f is the average required torque of the vehicle calculated by V, L is the road mileage, D is the travelled mileage, n is the travelled road number, k is the total road number, j is the road number, and the calculation formula of the reference SOC value SOC _Pi2 of the single node Pi2 in the feasible path L _i1 is

Where i1 represents a feasible path number, i2 represents a node number, n ₁ is the number of times of passing a certain node in all driving paths, and the hybrid electric vehicle is located at a reference SOC value SOC _r＝[soc_P1,soc_P2,…,soc_Pi2 at a road node.

4. The hybrid vehicle path planning method based on minimum energy consumption according to claim 1 or 2, characterized in that the calculation formula of the minimum equivalent fuel consumption is

Wherein P _e is engine power, P _m is motor power, eta _e is engine efficiency, eta _m is motor efficiency, eta _b is battery efficiency, H _f is fuel heating value, and s is an equivalent factor;

the calculation is performed according to the following objective function and constraint when the optimal equivalent factors under different initial SOCs

F＝argmin{∫H(s，soc_in＝a,a∈[soc_lo,soc_up])dt}

Wherein, SOC is an instantaneous SOC value, SOC _lo is a lower SOC limit, SOC _up is an upper SOC limit, T _m is a motor torque, T _e is an engine torque, ω _m is a motor torque, ω _e is an engine torque, superscripts min and max represent variable minimum and maximum values, and a is a constant.

5. The hybrid electric vehicle path planning method based on minimum energy consumption according to claim 1 or 2, wherein the calculation formula for obtaining the equivalent factor value and calculating the equivalent fuel consumption between two adjacent path nodes according to the table look-up model is as follows

Wherein t ₁ is road driving time, P _e is engine power, P _m is motor power, eta _e is engine efficiency, eta _m is motor efficiency, eta _b is battery efficiency, H _f is fuel heating value, and s is an equivalent factor;

the calculation formula for determining the accumulated minimum equivalent fuel consumption and the optimal path pointer at all path nodes is as follows

L_n2-1＝mon(J(k₁)+L_n2(k₁))

k^*＝argmin L_n2-1

Where L _n2 is the cumulative minimum equivalent fuel consumption at the previous node, k ₁ is the feasible path, and k ^* is the optimal path pointer.

6. A hybrid vehicle path planning apparatus based on minimum energy consumption, comprising:

Reference SOC planning module: calculating a reference SOC value of the hybrid electric vehicle at a road node according to the input vehicle speed information V;

And an offline optimization module: taking the reference SOC value and the initial SOC value as inputs, taking the minimum equivalent fuel consumption on each road as a target, calculating optimal equivalent factors under different initial SOCs, and establishing a table look-up model of the equivalent factors with respect to the roads and the initial SOCs;

And a path planning module: and obtaining equivalent factor values by all the available path nodes according to a table look-up model, calculating equivalent fuel consumption between two adjacent path nodes, determining the accumulated minimum equivalent fuel consumption and an optimal path pointer at all the path nodes, and reversely pushing the optimal path pointer to a starting point by a terminal point to obtain an optimal path.

7. The minimum energy consumption-based hybrid vehicle path planning apparatus according to claim 6, comprising:

Energy management control module: obtaining an equivalent factor by utilizing a table look-up model according to the position information and the instantaneous SOC value obtained in real time by the optimal path and solving the engine power and the motor power which enable the equivalent fuel consumption to be minimum;

And, a whole vehicle model module: and controlling the vehicle to complete vehicle energy distribution based on the engine power and the motor power.

8. The minimum energy consumption-based hybrid vehicle path planning apparatus according to claim 6 or 7, wherein the reference SOC calculation formula for each traveling point in a single path is

Wherein, SOC _in is the initial SOC value, SOC _en is the final SOC value, f is the average required torque of the vehicle calculated by V, L is the road mileage, D is the travelled mileage, n is the travelled road number, k is the total road number, j is the road number, and the calculation formula of the reference SOC value SOC _Pi2 of the single node Pi2 in the feasible path L _i1 is

Where i1 represents a feasible path number, i2 represents a node number, n ₁ is the number of times of passing a certain node in all driving paths, and the hybrid electric vehicle is located at a reference SOC value SOC _r＝[soc_P1,soc_P2,…,soc_Pi2 at a road node.

9. The minimum energy consumption-based hybrid vehicle path planning apparatus according to claim 6 or 7, wherein the calculation formula of the minimum equivalent fuel consumption is

Wherein P _e is engine power, P _m is motor power, eta _e is engine efficiency, eta _m is motor efficiency, eta _b is battery efficiency, H _f is fuel heating value, and s is an equivalent factor;

the calculation is performed according to the following objective function and constraint when the optimal equivalent factors under different initial SOCs

F＝argmin{∫H(s，sco_in＝a,a∈[soc_lo,soc_up])dt}

Wherein, SOC is an instantaneous SOC value, SOC _lo is a lower SOC limit, SOC _up is an upper SOC limit, T _m is a motor torque, T _e is an engine torque, ω _m is a motor torque, ω _e is an engine torque, superscripts min and max represent variable minimum and maximum values, and a is a constant.

10. The minimum energy consumption-based hybrid vehicle path planning apparatus according to claim 6 or 7, wherein the calculation formula for obtaining the equivalent factor value and calculating the equivalent fuel consumption between two adjacent path nodes according to the table look-up model is

Wherein t ₁ is road driving time, P _e is engine power, P _m is motor power, eta _e is engine efficiency, eta _m is motor efficiency, eta _b is battery efficiency, H _f is fuel heating value, and s is an equivalent factor;

the calculation formula for determining the accumulated minimum equivalent fuel consumption and the optimal path pointer at all path nodes is as follows

L_n2-1＝min(J(k₁)+L_n2(k₁))

k^*＝argminL_n2-1

Where L _n2 is the cumulative minimum equivalent fuel consumption at the previous node, k ₁ is the feasible path, and k ^* is the optimal path pointer.