CN109919369B - Battery exchange station site selection and electric vehicle path planning method - Google Patents
Battery exchange station site selection and electric vehicle path planning method Download PDFInfo
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Abstract
The invention discloses a battery exchange station site selection and electric vehicle path planning method, which comprises the steps of firstly generating an initial path plan and a battery exchange station site selection plan corresponding to the initial path plan; solving an optimal battery exchange station site selection plan by adopting a BPSO algorithm or a local search algorithm according to a preset probability; then, solving an optimal path plan by adopting a variable neighborhood search algorithm VNS; and judging whether a termination condition is met, outputting an optimal battery exchange station site selection plan and a corresponding path plan when the termination condition is met, and returning to continue iteration if the termination condition is not met. The invention applies the pareto optimality concept to the electric automobile path model to improve the sequence selection efficiency of the battery exchange station, has stronger global search capability and can avoid falling into local optimality. The method has good stability and convergence under two extended remedial measures, robustness under different remedial measures and good performance.
Description
Technical Field
The invention belongs to the technical field of battery exchange station site selection and electric vehicle path planning, and particularly relates to a method for battery exchange station site selection and electric vehicle path planning.
Background
The large amount of waste gas generated by land transportation can seriously harm the atmosphere and further influence the change of climate. Thus, china, the united states, japan, the european union, and many other countries and regions are vigorously spreading Electric Vehicles (EVs) and encouraging the use of electric vehicles in the supply chain to build a sustainable logistics distribution network. Therefore, it becomes increasingly important to design efficient routes for electric vehicles. The electric vehicle path planning problem can be regarded as an extension of the green vehicle path problem, and the goal is to find a minimum-cost electric vehicle path plan to meet the needs of all customers. Unlike the conventional automobile, the electric automobile has a short driving distance because of its limited battery capacity. Therefore, the electric vehicle may need to access a charging station to charge the battery, or a battery exchange station (BSS) to obtain a battery in a full charge state. Now, the electric vehicle path planning problem is combined with the battery exchange station addressing problem, based on the fact that the electric vehicle routing problem (LRP) is a hybrid problem.
However, the existing research on the path planning problem of the electric automobile is carried out under a deterministic environment, and uncertain factors influencing the problem are ignored. Indeed, uncertainty factors such as multi-product economic production issues, inventory issues, and supply chain integration optimization issues have begun to be considered in many supply chain issues. In fact, in many practical urban logistics applications, the customer's needs are still unknown until the arrival of the vehicle, such as warm oil delivery to the home service and the distribution of beer.
Although there have been some studies on the address-route problem and the use of electric vehicles has been considered in some research models. However, the existing addressing-routing model including the electric automobile mostly assumes that the customer requirements are known and does not meet the actual application scenario. When the transport vehicle arrives at the customer site, the customer's actual demand may exceed the amount of remaining cargo of the current transport vehicle, resulting in a failure of the routing service.
Disclosure of Invention
The invention aims to provide a method for battery exchange station site selection and electric vehicle path planning, and provides two extended remedial measures aiming at the problem of path service failure, wherein the extended remedial measures are utilized to ensure that an electric vehicle keeps certain electric quantity during the driving period of the electric vehicle, and the expected transportation cost is reduced.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method for battery exchange station site selection and electric vehicle path planning comprises the following steps:
generating an initial path plan and a battery exchange station site selection plan corresponding to the initial path plan;
solving an optimal battery exchange station site selection plan by adopting a BPSO algorithm or a local search algorithm according to a preset probability;
solving an optimal path plan by adopting a variable neighborhood search algorithm VNS;
and judging whether a termination condition is met, outputting an optimal battery exchange station site selection plan and a corresponding path plan when the termination condition is met, and returning to continue iteration if the termination condition is not met.
Further, the local search algorithm and the variable neighborhood search algorithm VNS include:
if an electric vehicle with a certain transport capacity is able to select an optimal battery exchange station sequence to the next customer based on the current charge and to return from the customer to the warehouse, the electric vehicle will serve the next customer directly; otherwise, it needs to be returned to the warehouse for battery and cargo replenishment.
Further, the local search algorithm and the variable neighborhood search algorithm VNS include:
if an electric vehicle with a certain transport capacity can select an optimal battery exchange station sequence to reach the next customer according to the current electric quantity and can return to the warehouse from the customer, and the transport cost of the electric vehicle is smaller than that of the electric vehicle which returns to the warehouse for battery and goods replenishment, the electric vehicle is directly served to the next customer, otherwise, the electric vehicle returns to the warehouse for battery and goods replenishment.
Further, the selecting an optimal battery exchange station sequence includes:
a pareto optimization method is used to select an optimal battery exchange station sequence.
Further, the solving of the optimal battery exchange station site selection plan by adopting a BPSO algorithm or a local search algorithm according to the preset probability further includes:
updating and solving the percentage B of the improvement of the optimal solution after the optimal battery exchange station site selection planimprove；
The method for solving the optimal path plan by adopting the variable neighborhood search algorithm VNS further comprises the following steps:
percentage of optimal solution improvement after updating and solving optimal path planRatio Rimprove；
The preset probability calculation formula is as follows:
wherein P isBPSOIs a preset probability.
Further, the solving of the optimal battery exchange station site selection plan by adopting a BPSO algorithm or a local search algorithm according to the preset probability includes:
randomly generating a random number between 0 and 1, and calculating PBPSOComparing if the random number is less than PBPSOThen the local search algorithm is executed, otherwise the BPSO algorithm is executed.
The invention provides a method for battery exchange station site selection and electric vehicle path planning, and provides a site selection and path model of an electric vehicle logistics distribution system facing random demands. The model comprises the contents of optimal location of the battery exchange station and path planning of the electric automobile. Traditional remedial measures have been extended by taking into account both battery capacity and transport capacity. Then, the pareto optimality concept is applied to the electric vehicle path model to improve the battery exchange station sequence selection efficiency. In order to solve the electric automobile path planning model, the advantages of discrete binary particle swarm optimization and a variable neighborhood search algorithm are combined, a hybrid variable neighborhood search algorithm is provided, and the problems of site selection and path of an electric automobile logistics distribution system are solved in an interactive iteration mode. The invention has good solving performance when solving the problems.
Drawings
FIG. 1 is a flow chart of a battery exchange station site selection and electric vehicle path planning method of the present invention;
FIG. 2 is a diagram illustrating an address selection plan and corresponding path plan vectors for a battery exchange station according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a variable neighborhood search algorithm VNS according to an embodiment of the present invention;
FIG. 4 is a detailed flowchart of a battery exchange station site selection and electric vehicle path planning method according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an extended passive remedial action according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an extended preventive remedy according to an embodiment of the present invention;
fig. 7 is a schematic diagram of pareto optimization according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The battery exchange station site selection and electric vehicle path planning method can be regarded as combination of a battery exchange station site selection problem and a vehicle path problem oriented to random requirements, and an application scene comprises a warehouse D, a group of electric vehicles K, a group of customers C and a group of battery exchange stations B which can be built. An electric car with a transport capacity Q starts with the warehouse filled with goods and full charge, then serves all customers, and finally returns to the warehouse. During transportation, the electric quantity of the electric automobile cannot be lower than L. The electric vehicle may arrive at the battery exchange station to obtain a full charge of battery. It is assumed that electric vehicles can also obtain full charge from a warehouse, and additionally the actual demand before the electric vehicle reaches the customer is unknown. The demand probability distributions among the customers are known and independent of each other, and the order in which the electric vehicles serve the customers depends on a prior path plan in which each customer is visited only once.
The method adopts remedial measures to solve the problem of path service failure, and expands passive remedial measures and preventive remedial measures, namely expanded passive remedial measures and expanded preventive remedial measures, aiming at two factors of battery capacity and transportation capacity of the electric automobile. Since a path service failure problem may occur, an actual electric vehicle route cannot be determined in advance. In this case, the electric quantity of the electric vehicle in transit cannot be determined in advance. Therefore, in the extended remedial measure, the electric automobile adjusts the route according to the current electric quantity. For example, if the electric vehicle is currently low, it will go to the nearest battery exchange station to exchange batteries. Therefore, when the electric vehicle travels between customers or between a customer and a warehouse, it may visit a series of battery exchange stations. This embodiment defines the series of battery exchange stations as a sequence of battery exchange stations, which may also be battery charging stations. If the electric vehicle selects a different battery exchange station sequence, its driving costs will also be different. In the proposed extended passive and extended preventive remedies, the electric vehicle always selects the optimal battery exchange station sequence according to the current charge.
In the present embodiment, the following set is defined:
d: a warehouse; b: a battery exchange station that may be built; c: a set of customers; n: a set of nodes, N ═ C ═ D; k: a group of electric automobiles.
And the following input variables:
cBSS: the cost of construction of the battery exchange station;
q: maximum transport capacity of the electric vehicle;
s: battery capacity of electric vehicles;
l: the minimum electric quantity when the electric automobile runs;
q: current transportation capacity of the electric vehicle;
s: the current electric quantity of the electric automobile;
ξc: an integer random variable representing the requirements of customer c;
pc(ξ): the probability that the c-th customer's demand is ξ may also be expressed as: p is a radical ofc(ξ)＝Pr{ξc＝ξ}；
ξ c: a lower demand limit for the c-th customer;
m: infinity.
And the following decision variables:
xb: if the b-th battery exchange station has been established, x b1, otherwise xb＝0；
πk: representing a prior path through the kth electric vehicle, on the path
In (1),
is the warehouse and the other nodes are the customers, the electric vehicle will go from the warehouse and return to the warehouse.
This example adopts
And
indicates that the k-th electric vehicle is
And the node respectively adopts an extended passive remedial measure or an extended preventive remedial measure to complete the expected transportation cost required by the remaining journey, q is the current transportation capacity, and s is the current electric quantity. By using
Represents a path pikExpected transportation costs. The total cost, including the construction cost and the expected transportation cost of the battery exchange station, is minimized using the following objective function:
the method for battery exchange station site selection and electric vehicle path planning aims to minimize the total cost expressed by the objective function of the formula (1).
In one embodiment, as shown in fig. 1, there is provided a battery exchange station site selection and electric vehicle path planning method, including:
and step S1, generating an initial path plan and a battery exchange station addressing plan corresponding to the initial path plan.
In the case of a known application scenario, namely a known warehouse D, a group of electric vehicles K, a group of clients C, and a group of battery exchange stations B that may be built, when generating the initial path plan, the clients may be randomly selected from the client group to form a path, and multiple paths are planned to cover all the clients, so as to generate the initial path plan. Or starting from the warehouse, selecting a client c closest to the warehouse1Then the distance c is selected1A nearest client c2And repeating the steps until the electric automobile is filled up and returns to the warehouse to form a path; after a path is planned, starting from the warehouse, the path is regenerated by adopting the same method (excluding the selected customers) until all the customers are covered, and thus an initial path plan is generated.
After the initial path plan is generated, one battery exchange station is sequentially excluded from the battery exchange stations B that may be built, and then it is checked whether the path plan is feasible. Since the electric vehicle will go to the nearest battery exchange station to exchange the battery or charge when the current power is low, if the electric vehicle can run the route with the support of the remaining battery exchange stations, i.e., the power when the electric vehicle is running is not lower than the minimum power L when the electric vehicle is running, it is considered that the excluded battery exchange station does not need to be constructed. Then, excluding a battery exchange station, and detecting whether the path plan is feasible again until a battery exchange station can not be excluded, wherein the rest battery exchange stations are the initial battery exchange station address plan. The present embodiment is not limited to the order of excluding the battery exchange stations, and B minus one battery exchange station may be randomly selected from the battery exchange stations B that may be built, to detect whether the path plan is feasible, and so on until an initial battery exchange station site plan is generated.
And step S2, solving the optimal battery exchange station site selection plan by adopting a BPSO algorithm or a local search algorithm according to the preset probability.
The step is the battery exchange station site selection stage. In this embodiment, the battery exchange station addressing plan and the corresponding path plan are represented by two vectors, the first vector is used for representing the battery exchange station addressing plan (i.e. the battery exchange station vector), and the second vector is used for representing the prior path plan (i.e. the path vector).
For example, one representation scheme X would include a battery exchange station vector XB＝{b1,...,bBAnd the path vector XR＝{r1,...,rR}. For any i e 1i1 denotes that the b thiA battery exchange station, otherwise not built. For any j ∈ 1jIf the path plan of the current electric automobile is finished, and a new path plan is restarted, otherwise, the path plan of the current electric automobile needs to be added with the r < th > valuejAnd (4) each client.
Figure 2 gives an example of a vector-based representation scheme. In this example, there are 9 customers, 8 candidate battery exchange stations (of which 3 are built), and the path of 3 electric vehicles is planned. The path vector represents a path plan. For example, after customer number 7 is served, the electric vehicle may access battery exchange station number 3; however, the customer object for the next service must be customer number 3.
The PSO algorithm is an optimization algorithm based on the bird swarm, and inspiration of the PSO algorithm is derived from social behaviors of the bird swarm. The PSO algorithm has the advantage of being easy to implement and can achieve a high quality solution in a short time. In each iteration, the search direction of the PSO algorithm is determined by the historical information and the global information. However, the basic PSO algorithm is only suitable for solving the continuous optimization problem, but the battery exchange site addressing problem involves discrete binary variables. Therefore, the basic PSO algorithm cannot directly solve the battery exchange site addressing problem. The present embodiment applies the BPSO algorithm, i.e., the discrete binary version of the PSO algorithm, to the problem of addressing battery exchange stations.
A particle can be seen as a point in the | B | dimensional space that has both position and velocity characteristics. The position of the ith particle can be expressed as a vector
The speed can be expressedIs a vector
In the present embodiment, the first and second electrodes are,
in the form of a binary variable, the variable,
indicating that the b-th battery exchange station is not constructed, otherwise indicating that it has been constructed. The algorithm begins with the random generation of a population of particles. In each subsequent iteration, each particle adjusts the velocity according to its previous best position and the best position among all particles. Then, the position of each particle is updated.
Although the BPSO algorithm has a strong global search capability, it is sometimes difficult to converge due to its weak local search capability. The randomness becomes more pronounced as the iteration time is extended. In the present embodiment, a local search algorithm is fused to overcome this drawback.
In order to avoid the local search algorithm from causing the overall algorithm to be trapped in local optimum, the BPSO algorithm and the local search algorithm are switched by adopting a probability mechanism. The probability of executing the BPSO algorithm is calculated as follows:
PBPSOis the probability of executing the BPSO algorithm. B isimproveAnd RimproveDefined as the percentage of the best solution improvement in the battery exchange station site selection phase and the path planning phase, respectively. If the BPSO algorithm is not executed, a local search algorithm is executed to improve the battery exchange site siting plan.
In a specific implementation, a random number between 0 and 1 can be randomly generated, and the calculated PBPSOComparing if the random number is less than PBPSOThen the local search algorithm is executed, otherwise the BPSO algorithm is executed. It is also possible to directly generate a random number between 0 and 1 at random,when the random number is smaller than a set threshold (preset probability), a local search algorithm is executed, otherwise, a BPSO algorithm is executed.
It will be readily appreciated that P is usedBPSOWhen the selection is made, after the end of this step, B is updatedimproveAnd after step S3, R is to be updatedimprove。
And step S3, solving the optimal path plan by adopting a variable neighborhood search algorithm VNS.
This step is a path planning stage, and a VNS algorithm is used to solve the path problem. The basic idea of the VNS algorithm is to disturb the current solution and then search to reach the local optimum, so that the quality of the solution is improved. First, a new solution is selected from the neighborhood of the current solution, and then a local search is performed from the location of this new solution. If a locally optimal solution is obtained that is better than the current solution, the current solution is replaced. Otherwise, the VNS algorithm will restart from the original solution again. The main process of the VNS algorithm is shown in the path planning phase of fig. 3, and is an example that includes three local search operations.
Three local search operations, flip, swap and insert, are used in fig. 3. The flip operation is a segment of the flip path vector, the swap operation is a swap of two elements in the path vector, and the insert operation is a selection of several elements and an insertion of them in front of the path vector.
And step S4, judging whether the termination condition is met, outputting the optimal battery exchange station addressing plan and the corresponding path plan when the termination condition is met, and returning to the step S2 to continue iteration if the termination condition is not met.
The termination condition set in this embodiment is that a preset maximum iteration number is reached, and when the preset maximum iteration number is reached, the iteration is terminated, and the optimal battery exchange station addressing plan and the path plan corresponding to the optimal battery exchange station addressing plan are output, otherwise, the step S2 is returned to continue the iteration.
Fig. 4 shows an embodiment of a method for battery exchange station address selection and electric vehicle path planning according to the present application, which shows that in both a local search algorithm and a variable neighborhood search algorithm VNS, a current solution needs to be determined or improved. For example, in a local search algorithm, the optimal solution needs to be improved; in the variable neighborhood search algorithm VNS, a path plan needs to be promoted, and it needs to be determined whether a current path plan is due to a previous path plan. In these steps, the construction cost and the expected transportation cost of the battery exchange station need to be calculated, and due to the path service failure problem, remedial measures need to be taken to deal with the path service failure problem. In remedial action, the electric vehicle will adjust the route according to the current amount of power. For example, if the electric vehicle is currently low, it will go to the nearest battery exchange station to exchange batteries. Therefore, when the electric vehicle travels between customers or between a customer and a warehouse, it may visit a series of battery exchange stations. This embodiment defines the series of battery exchange stations as a battery exchange station sequence, and the electric vehicle always selects an optimal battery exchange station sequence according to the current amount of electricity.
In one embodiment, with extended passive remedial action, the next action of the electric vehicle will depend on the current charge and transport capacity. If an electric vehicle with a certain transport capacity is able to select an optimal battery exchange station sequence to the next customer based on the current charge and to return from the customer to the warehouse, the electric vehicle will serve the next customer directly; otherwise, it needs to be returned to the warehouse for battery and cargo replenishment. Between any two nodes, the electric vehicle will always select an optimal battery exchange station sequence to reduce transportation costs.
From equations (2) - (4), path π can be calculatedkExpected transportation cost of (a):
the boundary constraints are as follows:
equation (2) represents the path plan πkThe expected transportation cost of a fully loaded and charged electric vehicle is equal to the expected transportation cost of leaving the warehouse, then satisfying all customer needs, and finally returning to the warehouse.
From equation (3), the expected transportation cost required to meet the remaining customer demand when the electric vehicle leaves one customer or warehouse and travels to the next customer can be calculated. Equation (3) is calculated mainly from the auxiliary equations (5) and (6). If the electric vehicle has a certain transport capacity and can reach the next customer through the optimal battery exchange station sequence depending on the current charge and can return to the warehouse from the customer, the electric vehicle will choose to reach the next customer first. Otherwise, the electric vehicle will return to the warehouse to be recharged with batteries and goods before starting the trip.
Equation (4) shows that when all customer requirements are met, the electric vehicle will return the battery exchange station sequence with the lowest selection cost to the warehouse. If the electric vehicle cannot return to the warehouse through any battery exchange station sequence depending on the current charge, the expected transportation cost will be set to an infinite value M.
Equation (5) corresponds to the first term in equation (3), indicating that the electric vehicle serves the next customer first and that an optimal combination of battery exchange station sequences needs to be considered. Due to the possibility of a path service failure, the replenishment trip between the next customer and the warehouse must be considered. The electric vehicle first visits the next customer and implements a replenishment trip in the event of a possible path service failure. This means that the optimal combination requires consideration of three battery exchange station sequences. The formula (6) corresponds to the second term in the formula (3), and represents that the electric vehicle firstly returns to the warehouse for battery replenishment and then is delivered. The first term of equation (6) indicates that if replenishment is to be done before the electric vehicle serves the next customer, an optimal combination of battery exchange station sequences needs to be considered to minimize transportation costs. In this case, the electric vehicle will pass through a sequence of two battery exchange stations in total. This means that the optimal combination needs to consist of two battery exchange station sequences. The second term of equation (6) indicates that the electric vehicle will not be able to complete the task according to the current battery exchange station addressing scheme or because the current charge is too low, at which point the expected cost is set to an infinite value M.
As shown in fig. 5, it is assumed that there are 5 customers, only one electric vehicle is used, and 4 battery exchange stations are established. We set the value of the electric car transport capacity to 9, and when the battery is fully charged, i.e. the charge is S, there are 11 units of charge. When the electric quantity is L, the electric automobile only has 2 unit electric quantities. The prior route is {0, 1, 2, 3, 4, 5, 0}, and the actual customer demand is 5, 4, 3, 6, 3 in sequence. Before the electric vehicle serves customer # 2, it needs to replace a fully charged battery at battery exchange station a. After the electric automobile serves the number 2 customer, the electric automobile must return to a warehouse for replenishment due to insufficient transportation capacity. The total cost of this route is 46 +3+2+9+3+3+2+5+7+3+ 6.
In another embodiment, extended preventative remedial action is taken and the next action of the electric vehicle will depend on the current capacity and charge. Unlike extended passive remedial actions, electric vehicles return to the warehouse when the vehicle cargo is not depleted or the current charge is sufficient for the electric vehicle to reach the next customer. That is, if an electric vehicle with a certain transportation capacity can select an optimal battery exchange station sequence according to the current electric quantity to reach the next customer and can return to the warehouse from the customer, and the transportation cost of the electric vehicle is smaller than that of returning to the warehouse for battery and goods replenishment, the electric vehicle is directly served to the next customer, otherwise, the electric vehicle returns to the warehouse for battery and goods replenishment.
Between any two nodes, the electric vehicle will always select the optimal battery exchange station sequence to reduce transportation costs.
Path pikThe expected transportation cost of (c) is calculated by equations (7) - (9):
the boundary conditions are as follows:
as can be seen from equation (7), the path plan πkThe expected cost of transportation is equal to the expected cost of transportation for a fully loaded and charged electric vehicle leaving the warehouse, then meeting all customer needs, and finally returning to the warehouse.
From equation (8), the expected transportation cost required to meet the remaining customer demand when the electric vehicle leaves one customer or warehouse and travels to the next customer can be calculated. In the process, the extended preventive remedial action and the extended passive remedial action have two options, namely, the option of reaching the next customer first or the option of first recharging the battery and goods. But unlike extended preventative remedial actions, extended passive remedial actions always choose actions that are expected to be less costly to transport. The formula (8) can be calculated by the auxiliary formulas (10) and (11).
Equation (9) indicates that the electric vehicle will return to the warehouse the optimal battery exchange station sequence with the lowest selection cost when all customer requirements are met. If the electric vehicle cannot select the optimal battery exchange station sequence to return to the warehouse based on the current charge, then the expected transportation cost will be set to an infinite value M.
Equation (10) corresponds to the first term in equation (8) and indicates that the electric vehicle will choose to arrive at the next customer first and choose an optimal combination of battery exchange station sequences for its journey. Similar to equation (5), this optimal combination requires consideration of three battery exchange station sequences. If this action cannot be performed, the expected cost of transportation is set to an infinite value M, as shown in the second term of equation (10). Equation (11) corresponds to the second term in equation (8), and indicates that the electric vehicle will be returned to the warehouse for replenishment. The first term of equation (11) indicates that if the electric vehicle is replenished before it serves the next customer, then it will need to select an optimal combination of battery exchange station sequences for its trip. Similar to equation (6), the optimal combination consists of two battery exchange station sequences. The second term of equation (11) indicates that the electric vehicle will not be able to accomplish this task according to the current battery exchange site selection scheme or because the current charge is too low, when the expected cost is set to an infinite value M.
As shown in fig. 6, the example settings for implementing the extended preventative remedial action are the same as the previous extended passive remedial action, except for the final actual route. Unlike extended passive remedial measures, after the electric vehicle has served customer # 3, it, still having a certain transport capacity, will choose to return to the warehouse for restocking. Then, the electric vehicle starts from the warehouse again to serve the rest of the customers. The total cost of the route is 3+3+2+9+3+3+5+1+3+ 6-38.
In the remedial action described above, the electric vehicle will select the optimal battery exchange station sequence based on the amount of power on the current trip. In the selection of the battery exchange station sequence, the electric vehicle needs to make a trade-off between two goals of transportation cost and remaining capacity. A better battery exchange station sequence must have less transportation costs and be able to reserve more power for the electric vehicle. Pareto optimization may be used for screening of battery exchange station sequences to save computation time and resources. By examining all alternatives according to a multi-objective function, a pareto optimal set can be constructed, which has non-dominant properties (none of its elements can be dominated by other alternatives). Assuming that there is a pareto-optimal battery exchange station sequence, then the pareto-optimal battery exchange station sequence has at least one target superior or any target not inferior to the other battery exchange station sequences.
Figure 7 shows an example of a sequence of 4 battery exchange stations. When the electric quantity is equal to S, the electric vehicle has 11 units of electric quantity. When the electric quantity is equal to L, the electric vehicle has 2 unit electric quantities. If the electric vehicle starts from customer a to customer B with an initial charge of 6 units, the electric vehicle cannot reach customer B by selecting the R4 route. The total running costs of the routes R1, R2, R3 are 11, 10, 12, respectively, and the battery remaining capacity at the customer B is 9, 8 unit capacities, respectively. The travel cost of route R3 is greater than route R2, but the remaining capacity is the same after both routes. Therefore, the electric vehicle considers only the route R1 or R2 from the customer a to the customer B.
Let | B | be the number of battery exchange stations, the number of battery exchange station sequences being equal to
For example, if there are currently 6 battery exchange stations, the number of battery exchange station sequences is 1957. When an electric vehicle is started from one location to the next, an optimal battery exchange station sequence needs to be selected. If all battery exchange station sequences are enumerated to find this optimal sequence, it will take a significant amount of computing time. In fact, the final charge depends only on the last visited battery exchange station before the electric vehicle reaches the next customer. In other words, if the last visited battery exchange station is the same, the final charge of the electric vehicle remains unchanged. Therefore, the possible number of different final amounts of power is less than or equal to | B | +1 (including that the electric vehicle does not access any battery traffic)A station change). Assuming the existence of a pareto optimal battery exchange station sequence RxAnd any battery exchange station sequence RyResidual capacity and R ofxSame, then RyThe transportation cost is not higher than Rx. Furthermore, if some pareto optimal battery exchange station sequences have the same transportation cost and the same remaining capacity, the electric vehicle only needs to consider one of them. Thus, the number of pareto-optimal battery exchange station sequences to consider is less than or equal to | B | + 1.
In summary, if only the battery exchange station sequence with the minimum transportation cost corresponding to each different final electric quantity is considered, the pareto optimal battery exchange station sequence can be obtained quickly, and the calculation burden caused by enumerating all the battery exchange station sequences can be avoided. This problem of finding a pareto optimal battery exchange station sequence can be viewed as a single source shortest path problem that solves for the shortest path from one starting point to all other points. Similarly, the initial charge may be considered a starting point and the last visited battery exchange station corresponding to a different final charge may be considered a source point. The present embodiment solves this problem with a widely recognized shortest path algorithm, the Dijkstra algorithm. The complexity of the Dijkstra algorithm is O (| B |2+2| B | + 1).
In this case, the pareto optimal battery exchange station sequences are routes R1 and R2. The electric vehicle selects the optimal battery exchange station sequence from the pareto optimal battery exchange station sequences, so that the expected transportation cost can be rapidly calculated. It is noted that if the electric vehicle leaves or enters a different node, the pareto optimal battery exchange station sequence will also be different. The fourth section introduces a method of extracting a pareto optimal battery exchange station sequence.
Through experimental verification, under the extended passive remedial measures, the expected cost fluctuation is caused by the increase of the maximum transportation capacity of the electric automobile, but the overall fluctuation trend is reduced. Increasing the maximum transport capacity of an electric vehicle may reduce the expected cost under extended preventive remedial action. Under two kinds of extended remedial measures, the expected cost can be reduced by increasing the maximum driving distance of the electric automobile.
In fact, as the maximum transport capacity of an electric car increases, it is able to serve more customers before a path service failure occurs. In the case of extended preventive remedial action, the electric vehicle can select a better replenishment route or an original replenishment route, and therefore the expected transportation cost of the replenishment route does not increase. However, under extended passive remedial action, the results may vary. At this time, the battery replenishment service is not performed unless a path service failure occurs. Therefore, the electric vehicle may perform a poor restocking trip instead, resulting in fluctuations in the expected transportation cost of the restocking trip.
Experimental results show that the method has strong global search capability and can avoid falling into local optimum. The method has good stability and convergence under two extended remedial measures, robustness under different remedial measures and good performance.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (4)
1. A method for battery exchange station site selection and electric vehicle path planning is characterized in that the method for battery exchange station site selection and electric vehicle path planning comprises the following steps:
generating an initial path plan and a battery exchange station site selection plan corresponding to the initial path plan;
solving an optimal battery exchange station site selection plan by adopting a BPSO algorithm or a local search algorithm according to a preset probability;
solving an optimal path plan by adopting a variable neighborhood search algorithm VNS;
judging whether a termination condition is met, outputting an optimal battery exchange station site selection plan and a corresponding path plan when the termination condition is met, and returning to continue iteration if the termination condition is not met;
wherein, according to the preset probability, the optimal battery exchange station site selection plan is solved by adopting a BPSO algorithm or a local search algorithm, and the method further comprises the following steps:
updating and solving the percentage B of the improvement of the optimal solution after the optimal battery exchange station site selection planimprove；
The method for solving the optimal path plan by adopting the variable neighborhood search algorithm VNS further comprises the following steps:
updating the percentage R of improvement of the optimal solution after solving the optimal path planimprove；
The preset probability calculation formula is as follows:
wherein P isBPSOIs a preset probability;
then, the solving of the optimal battery exchange station site selection plan by adopting the BPSO algorithm or the local search algorithm according to the preset probability includes:
randomly generating a random number between 0 and 1, and calculating PBPSOComparing if the random number is less than PBPSOThen the local search algorithm is executed, otherwise the BPSO algorithm is executed.
2. The battery exchange station site selection and electric vehicle path planning method of claim 1, wherein the local search algorithm, the variable neighborhood search algorithm VNS, comprises:
if an electric vehicle with a certain transport capacity is able to select an optimal battery exchange station sequence to the next customer based on the current charge and to return from the customer to the warehouse, the electric vehicle will serve the next customer directly; otherwise, it needs to be returned to the warehouse for battery and cargo replenishment.
3. The battery exchange station site selection and electric vehicle path planning method of claim 1, wherein the local search algorithm, the variable neighborhood search algorithm VNS, comprises:
if an electric vehicle with a certain transport capacity can select an optimal battery exchange station sequence to reach the next customer according to the current electric quantity and can return to the warehouse from the customer, and the transport cost of the electric vehicle is smaller than that of the electric vehicle which returns to the warehouse for battery and goods replenishment, the electric vehicle is directly served to the next customer, otherwise, the electric vehicle returns to the warehouse for battery and goods replenishment.
4. The battery exchange station addressing and electric vehicle path planning method of claim 2 or 3, wherein said selecting an optimal battery exchange station sequence comprises:
a pareto optimization method is used to select an optimal battery exchange station sequence.
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