CN105509760A - Electric vehicle - Google Patents

Abstract

An electric vehicle comprises a vehicle body and an automatic navigation system mounted on the vehicle body; the automatic navigation system comprises a signal module, a processing module and a generation module. According to the electric vehicle, an optimized path algorithm is adopted, all cost factors in a vehicle running process are taken into consideration, the optimization effect is good, the solution efficiency is high, the property is stable, the global search capability is improved, the vehicle running cost is reduced to the utmost extent, and an excellent energy-saving effect is realized.

Description

Electric automobile

Technical Field

The invention relates to the field of automobiles, in particular to an electric automobile.

Background

Automatic navigation systems are mostly installed on the existing electric automobiles. The automatic navigation system is used for monitoring the current position of a vehicle by an on-board GPS (global positioning system) receiver, comparing the data with a user-defined destination, calculating a driving route by referring to an electronic map, and providing information to a driver in real time. At present, autonomous navigation widely applied in the world is realized by using a self-contained electronic map of vehicle-mounted navigation equipment, and positioning and navigation functions are completely completed by the vehicle-mounted equipment.

In actual use, drivers often have more than one destination and require different times to reach each destination. Therefore, how to select a vehicle running route which can save cost to the maximum extent according to different destinations is an urgent problem to be solved.

Disclosure of Invention

In order to solve the problems, the invention provides an electric automobile.

The purpose of the invention is realized by adopting the following technical scheme:

an electric automobile comprises an automobile body and an automatic navigation system arranged on the automobile body, and is characterized by comprising a signal module, a processing module and a generating module;

the signal module is used for receiving one or more destinations input by a user and predicted required time for reaching each destination and inquiring whether the owner returns to the starting place after leaving the last destination;

the processing module is used for selecting an optimal path according to the destination and the geographic environment information input in advance, and specifically comprises the following steps:

a building module:

$\begin{array}{c}\min S=\underset{m=1}{\overset{m}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}{b}_0{\mathrm{\ω}}_0{\mathrm{\Φ}}_0{f}_{ij}{y}_{ijk}+\underset{m=1}{\overset{m}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}\underset{i=0}{\overset{U}{\Σ}}{b}_0{\mathrm{\ω}}_0\frac{{\mathrm{\Φ}}^{*}-{\mathrm{\Φ}}_0}{H}{c}_i{f}_{ij}{y}_{ijk}+T_1\underset{i=0}{\overset{U}{\Σ}}(G_i-t_i)\\ +T_2\underset{i=0}{\overset{U}{\Σ}}(t_i-O_i)\end{array}$

wherein minS is the lowest cost in the running process of the vehicle; m is the total number of the departure vehicles, is determined by nearby vehicle signals received by the GPS system, and if signals of other vehicles are not received, m is 1; u is the number of destinations; b₀Carbon emission cost per unit distance; omega₀Is the carbon emission coefficient; phi (₀Fuel consumption per unit distance when no load exists; f. of_ijDistance between destination i (i ═ 1,2, …, U) to destination j (j ═ 1,2, …, U); c is the load capacity of the vehicle; h is the maximum load capacity of the vehicle; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the vehicle to arrive at the loss factor in advance, for cost penalty in arriving at destination i earlier at time G, T₂In order for the vehicle to be late in loss factor,for cost losses when the destination i is reached at the late arrival time O, the early arrival loss factor and the late arrival loss factor are used to account for the punctual situation, T, at which the vehicle arrives at each destination₁And T₂A coefficient set artificially;

an opportunity module: assuming a total of R nodes, γ_ij(t) represents the intensity of the tracker between node i and node j at time t, γ_ij(0) K (K is a constant with a small value), the vehicle selects the transfer direction according to the intensity of the tracker during the movement process, and then the vehicle K (K is a constant with a small value)1, 2.. m) the probability of transitioning from node i to node j is:

wherein, g ∈ A_k；A_k＝{0,1,…,R-1}-B_kRepresenting the set of points that the vehicle k is allowed to select next, dynamically changing over time, B_k(k ═ 1,2, …, m) is a taboo list for the kth vehicle, used to record the points that vehicle k has passed;the heuristic factor represents the expected degree of the t time from the node i to the node j, and is generally takenψ is an information heuristic factor, μ is an expected heuristic factor, α (i, j) is a time degree of the next destination;relative importance of time degree for next destination;

an optimization module: introducing an optimization variable X_ij(t) satisfying X_ij(t+1)＝σX(t)[1-X_ij(t)]And obtaining an optimized tracker updating rule by taking sigma as a control variable:

wherein,when the user returns to the departure place after leaving the last destination by the selection of the signal module, a cycle is formed, then

F_kIs the length of the path taken by the kth vehicle in the cycle, I is a constant of the intensity of the tracker,showing the intensity of the tracker left on the path (i, j) by the kth vehicle in the cycle, wherein zeta is the global volatility factor of the tracker and is zeta ∈ [0, 1%]And ζ is a parameter that is dynamically adjusted according to the following formula:wherein ζ_minIs a minimum value set manually; delta gamma_ij(t) represents the sum of the intensities of the tracers left by all vehicles on the path (i, j) in the current cycle, ч is an adjustable coefficient;

an initial module: initializing parameters and adjusting tracking elements of each path by setting the iteration number DD to be 0; produce a range of [0,1]If p < given constant p₀The next node j is selected according to the following equation: wherein l ∈ A_k(ii) a Otherwise, selecting the next node j according to the probability formula in the opportunity module, and adding the j into the array B_kRepeating the steps until all the node tasks are completed to obtain an initial set S of the simulation algorithm_i；

A solving module: generating a new set of feasible solutions S from the current initial set_jThe target value variation Δ S is S_j-S_iIf Δ S < 0, then accept the new feasible solution S_jIs the optimal solution; otherwise the effect of the deviation is taken into account: r ═ exp (- Δ S/N (t)), where N is the amount that changes over time, and S is accepted if r > 1_jIs the optimal solution, otherwise does not accept the new feasible solution, and the optimal solution is still S_i；

A judging module: when the current optimal solution is smaller than a certain specific value, updating the tracker; if the current round lists B_kIf there is no data update, then a [0,1 ] is generated]Random number of range u, if e₁+e₂+,…,e_i-1＜u＜e₁+e₂+,…,e_iThen the probability of selection is e_iThe candidate vehicle of (2) as a next target node;

a generation module: outputting the calculated optimal path by making the iteration number DD equal to DD +1, and if DD is less than DD_maxClearing B according to a formula N (t +1) ═ N (t) v according to a tracker updating rule_kList, where v ∈ [0,1]Returning to the initial module to regenerate the random number p; if DD is DD ═ DD_maxAnd outputting the optimal solution as the optimal path.

The invention has the beneficial effects that: the invention adopts an optimized path algorithm, considers various cost factors in the running process of the vehicle, has good optimization effect, high solving efficiency and stable performance, enhances the global searching capability, can save the running cost of the vehicle to the maximum extent according to a plurality of destinations and the arrival time required, and can play a good energy-saving effect.

Drawings

The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.

Fig. 1 is a block diagram of the present invention.

Reference numerals: a signal module-2; establishing a module-4; opportunity module-6; an optimization module-8; an initial module-10; a solving module-12; a judgment module-14; a generating module-16.

Detailed Description

The invention is further described with reference to the following examples.

An electric automobile comprises an automobile body and an automatic navigation system arranged on the automobile body, and is characterized by comprising a signal module 2, a processing module and a generating module 16;

the signal module 2 is used for receiving one or more destinations input by a user and predicted required time for reaching each destination, and inquiring whether the owner returns to the starting place after leaving the last destination;

the processing module is used for selecting an optimal path according to the destination and the geographic environment information input in advance, and specifically comprises the following steps:

and a building module 4:

$\begin{array}{c}\min S=\underset{m=1}{\overset{m}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}{b}_0{\mathrm{\&omega;}}_0{\mathrm{\&Phi;}}_0{f}_{ij}{y}_{ijk}+\underset{m=1}{\overset{m}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}\underset{i=0}{\overset{U}{\&Sigma;}}{b}_0{\mathrm{\&omega;}}_0\frac{{\mathrm{\&Phi;}}^{*}-{\mathrm{\&Phi;}}_0}{H}{c}_i{f}_{ij}{y}_{ijk}+T_1\underset{i=0}{\overset{U}{\&Sigma;}}(G_i-t_i)\\ +T_2\underset{i=0}{\overset{U}{\&Sigma;}}(t_i-O_i)\end{array}$

wherein minS is the lowest cost in the running process of the vehicle; m is the total number of the departure vehicles, is determined by nearby vehicle signals received by the GPS system, and if signals of other vehicles are not received, m is 1; u is the number of destinations; b₀Carbon emission cost per unit distance; omega₀Is the carbon emission coefficient; phi (₀Fuel consumption per unit distance when no load exists; f. of_ijDistance between destination i (i ═ 1,2, …, U) to destination j (j ═ 1,2, …, U); c is the load capacity of the vehicle; h is the maximum load of the vehicleAn amount; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the vehicle to arrive at the loss factor in advance, for cost penalty in arriving at destination i earlier at time G, T₂In order for the vehicle to be late in loss factor,for cost losses when the destination i is reached at the late arrival time O, the early arrival loss factor and the late arrival loss factor are used to account for the punctual situation, T, at which the vehicle arrives at each destination₁And T₂A coefficient set artificially;

the opportunity module 6: assuming a total of R nodes, γ_ij(t) represents the intensity of the tracker between node i and node j at time t, γ_ij(0) If the vehicle selects a transfer direction during movement according to the intensity of the tracker, the probability that the vehicle K (K ═ 1, 2.., m) transfers from node i to node j is:

wherein, g ∈ A_k；A_k＝{0,1,…,R-1}-B_kRepresenting the set of points that the vehicle k is allowed to select next, dynamically changing over time, B_k(k ═ 1,2, …, m) is a taboo list for the kth vehicle, used to record the points that vehicle k has passed;representing the node at the time t as a heuristic factorThe desired degree of i to node j is generally takenψ is an information heuristic factor, μ is an expected heuristic factor, α (i, j) is a time degree of the next destination;relative importance of time degree for next destination;

and an optimization module 8: introducing an optimization variable X_ij(t) satisfying X_ij(t+1)＝σX(t)[1-X_ij(t)]And obtaining an optimized tracker updating rule by taking sigma as a control variable:

wherein,when the user returns to the departure place after leaving the last destination by the selection of the signal module 2, a loop is formed, then

F_kIs the length of the path taken by the kth vehicle in the cycle, I is a constant of the intensity of the tracker,showing the intensity of the tracker left on the path (i, j) by the kth vehicle in the cycle, wherein zeta is the global volatility factor of the tracker and is zeta ∈ [0, 1%]And ζ is a parameter that is dynamically adjusted according to the following formula:wherein ζ_minIs a minimum value set manually; delta gamma_ij(t) shows the paths of all vehicles in the present cycle(i, j) the sum of the intensities of the trackins left over, ч is an adjustable factor;

an initial module 10: initializing parameters and adjusting tracking elements of each path by setting the iteration number DD to be 0; produce a range of [0,1]If p < given constant p₀The next node j is selected according to the following equation:

wherein l ∈ A_k(ii) a Otherwise, selecting the next node j according to the probability formula in the opportunity module 6, and adding the j into the array B_kRepeating the steps until all the node tasks are completed to obtain an initial set S of the simulation algorithm_i；

The solving module 12: generating a new set of feasible solutions S from the current initial set_jThe target value variation Δ S is S_j-S_iIf Δ S < 0, then accept the new feasible solution S_jIs the optimal solution; otherwise the effect of the deviation is taken into account: r ═ exp (- Δ S/N (t)), where N is the amount that changes over time, and S is accepted if r > 1_jIs the optimal solution, otherwise does not accept the new feasible solution, and the optimal solution is still S_i；

The judging module 14: when the current optimal solution is smaller than a certain specific value, updating the tracker; if the current round lists B_kIf there is no data update, then a [0,1 ] is generated]Random number of range u, if e₁+e₂+,…,e_i-1＜u＜e₁+e₂+,…,e_iThen the probability of selection is e_iThe candidate vehicle of (2) as a next target node;

the generation module 16: outputting the calculated optimal path by making the iteration number DD equal to DD +1, and if DD is less than DD_maxClearing B according to a formula N (t +1) ═ N (t) v according to a tracker updating rule_kList, where v ∈ [0,1]Returning to the initial module 10, and regenerating the random number p; if DD is DD ═ DD_maxAnd outputting the optimal solution as the optimal path.

The invention has the beneficial effects that: the invention adopts an optimized path algorithm, considers various cost factors in the running process of the vehicle, has good optimization effect, high solving efficiency and stable performance, enhances the global searching capability, can save the running cost of the vehicle to the maximum extent according to a plurality of destinations and the arrival time required, and can play a good energy-saving effect.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (1)

1. An electric automobile comprises an automobile body and an automatic navigation system arranged on the automobile body, and is characterized by comprising a signal module, a processing module and a generating module;

the signal module is used for receiving one or more destinations input by a user and predicted required time for reaching each destination and inquiring whether the owner returns to the starting place after leaving the last destination;

the processing module is used for selecting an optimal path according to the destination and the geographic environment information input in advance, and specifically comprises the following steps:

a building module:

$\begin{array}{c}\min S=\underset{m=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{U}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{U}{\mathrm{\&Sigma;}}}{b}_0{\mathrm{\&omega;}}_0{\mathrm{\&Phi;}}_0{f}_{ij}{y}_{ijk}+\underset{m=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{U}{\mathrm{\&Sigma;}}}\underset{i=0}{\overset{U}{\mathrm{\&Sigma;}}}{b}_0{\mathrm{\&omega;}}_0\frac{{\mathrm{\&Phi;}}^{*}-{\mathrm{\&Phi;}}_0}{H}{c}_i{f}_{ij}{y}_{ijk}+T_1\underset{i=0}{\overset{U}{\mathrm{\&Sigma;}}}(G_i-t_i)\\ +T_2\underset{i=0}{\overset{U}{\mathrm{\&Sigma;}}}(t_i-O_i)\end{array}$

wherein minS is the lowest cost in the running process of the vehicle; m is the total number of the departure vehicles, is determined by nearby vehicle signals received by the GPS system, and if signals of other vehicles are not received, m is 1; u is the number of destinations; b₀Carbon emission cost per unit distance; omega₀Is the carbon emission coefficient; phi (₀Fuel consumption per unit distance when no load exists; f. of_ijDistance between destination i (i ═ 1,2, …, U) to destination j (j ═ 1,2, …, U); c is the load capacity of the vehicle; h is the maximum load capacity of the vehicle; phi (^*Fuel consumption per unit distance when fully loaded;

T₁in order for the vehicle to arrive at the loss factor in advance, $T_1{\mathrm{\&Sigma;}}_{i=0}^U(G_i-$ $t_i)$ for cost penalty in arriving at destination i earlier at time G, T₂In order for the vehicle to be late in loss factor,for cost losses when the destination i is reached at the late arrival time O, the early arrival loss factor and the late arrival loss factor are used to account for the punctual situation, T, at which the vehicle arrives at each destination₁And T₂A coefficient set artificially;

an opportunity module: assuming a total of R nodes, γ_ij(t) represents the intensity of the tracker between node i and node j at time t, γ_ij(0) If the vehicle selects a transfer direction during movement according to the intensity of the tracker, the probability that the vehicle K (K ═ 1, 2.., m) transfers from node i to node j is:

wherein, g ∈ A_k；A_k＝{0,1,…,R-1}-B_kRepresenting the set of points that the vehicle k is allowed to select next, dynamically changing over time, B_k(k ═ 1,2, …, m) is a taboo list for the kth vehicle, used to record the points that vehicle k has passed;the heuristic factor represents the expected degree of the t time from the node i to the node j, and is generally takenψ is an information heuristic factor, μ is an expected heuristic factor, α (i, j) is a time degree of the next destination;relative importance of time degree for next destination;

an optimization module: introducing an optimization variable X_ij(t) satisfying X_ij(t+1)＝σX(t)[1-X_ij(t)]And obtaining an optimized tracker updating rule by taking sigma as a control variable:

γ_ij(t+1)＝(1-ζ)γ_ij(t)+Δγ_ij(t)+чX_ij(t)

wherein,when the user returns to the departure place after leaving the last destination by the selection of the signal module, a cycle is formed, then

F_kIs the length of the path taken by the kth vehicle in the cycle, I is a constant of the intensity of the tracker,showing the intensity of the tracker left on the path (i, j) by the kth vehicle in the cycle, wherein zeta is the global volatility factor of the tracker and is zeta ∈ [0, 1%]And ζ is a parameter that is dynamically adjusted according to the following formula:wherein ζ_minIs a minimum value set manually; delta gamma_ij(t) represents the sum of the intensities of the tracers left by all vehicles on the path (i, j) in the current cycle, ч is an adjustable coefficient;

an initial module: initializing parameters and adjusting tracking elements of each path by setting the iteration number DD to be 0; produce a range of [0,1]If p < given constant p₀The next node j is selected according to the following equation: j ═ argmax { [ γ (i, l)]ψ×Wherein l ∈ A_k(ii) a Otherwise, selecting the next node j according to the probability formula in the opportunity module, and adding the j into the array B_kRepeating the steps until all the node tasks are completed to obtain an initial set S of the simulation algorithm_i；

A solving module: generating a new set of feasible solutions S from the current initial set_jThe target value variation Δ S is S_j-S_iIf Δ S < 0, then accept the new feasible solution S_jIs the optimal solution; otherwise the effect of the deviation is taken into account: r ═ exp (- Δ S/N (t)), where N is the amount that changes over time, and S is accepted if r > 1_jIs the optimal solution, otherwise does not accept the new feasible solution, and the optimal solution is still S_i；

A judging module: when the current optimal solution is smaller than a certain specific value, updating the tracker; if the current round lists B_kIf there is no data update, then a [0,1 ] is generated]Random number of range u, if e₁+e₂+,…,e_i-1＜u＜e₁+e₂+,…,e_iThen the probability of selection is e_iThe candidate vehicle of (2) as a next target node;

a generation module: outputting the calculated optimal path by making the iteration number DD equal to DD +1, and if DD is less than DD_maXClearing B according to a formula N (t +1) ═ N (t) v according to a tracker updating rule_kList, where v ∈ [0,1]Returning to the initial module to regenerate the random number p; if DD is DD ═ DD_maxAnd outputting the optimal solution as the optimal path.