Method and hybrid system for determining locations for charging stations

Abstract

The invention relates to a method and a hybrid system for determining locations for charging stations for electric vehicles. Furthermore, the invention relates to a road planning module.

Description

[0001] The invention relates to a method and a hybrid system for determining locations for charging stations for electric vehicles.

[0002] An electrically powered vehicle comprises an electrical energy storage device that must be regularly connected to a charging station in order to charge the energy storage device with electrical energy from a power grid, in particular from a public power supply grid.

[0003] To date, charging station expansion has been implemented using a large-scale, usually oversized approach. This usually means that charging stations cover the entire region in question. Furthermore, charging stations are placed randomly based on site quality.

[0004] Static simulation methods such as Markov chains are generally used to plan charging station expansion. Agent-based simulations are also used to position charging stations.

[0005] However, these locations may be unsuitable for EV traffic and / or charging requirements, with recharging typically taking several hours.

[0006] DE 102013202059 A1 discloses a system for the location selection of charging stations in an area, the method comprising: a plurality of vehicle sensors distributed in an area; a motion detection module that collects the traffic data from the vehicle sensors; a map adaptation module that maps the traffic detected in the area; a vehicle flow module that characterizes the mapped traffic flow in the area in a time-dependent manner; and an evaluation device for the requirements of electric vehicles, which determines a number of charging stations and the corresponding locations for the location selection of the charging stations in the area.

[0007] It is therefore an object of the invention to provide an improved method for a charging infrastructure of charging stations in an area in the form of improved positioning of charging stations, as well as a hybrid system for this purpose. Furthermore, it is an object of the invention to provide an improved road planning module.

[0008] The object is achieved by a method having the features of claim 1 and a hybrid system having the features of claim 7 as well as a road planning module having the features of claim 12.

[0009] Advantageous embodiments emerge from the dependent patent claims, the description and the figures.

[0010] The task is solved by a method for determining locations for charging stations for electric vehicles comprising the following steps: - Providing a digital computer, - Providing a set of possible locations i, i = 1,.. NL of charging stations as a matrix graph representing the road network, with matrix nodes w i,k where the matrix nodes w i,k the utilization of a road section between a first location i and a second location k, i with i, k ∈ 1, .. N L describe in which digital computers, - Determination of an expected usage, which is specified as a usage function, at each charging station taking into account the respective road load, which is determined by the matrix nodes w i,k is given, using the following binary variables: xi={1, if a charging station is installed at location i0, otherwise - Providing an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer, - Generating an energy function based on the usage function for each charging station under the constraint that each charging station at a location has a limited charging capacity, - Generating an initial state for the adiabatic quantum system based on the energy function and initializing the energy landscape based on the initial state and changing the initial state using the energy function in the quantum computer to an energetic ground state, whereby the energetic ground state indicates an optimal expected utility based on the minimized utility function, which corresponds to an optimal number of charging stations as well as an optimal location of each charging station, - Measuring the basic energetic state by the digital computer after adiabatic change and encoding the measurement by the digital computer and determining the optimal locations of the charging stations and the optimal number of charging stations based on the encoding.

[0011] Where N L an integer and corresponds to the maximum number of locations.

[0012] The optimal number of charging stations is determined indirectly via the optimal locations.

[0013] The initial state of the quantum computer typically corresponds to a solvable quantum system in which the qubits are initialized to the ground state. This initial state is then transferred to the matrix of the problem, which ideally also brings the qubits to the ground state of the problem.

[0014] In the adiabatic quantum computer, the matrix is ​​translated into a coupling strength between the qubits.

[0015] The operating principle of a quantum computer has been known as a theoretical concept for a long time and has been implemented in practice for some time.

[0016] While in conventional digital computers, information is represented in bits, which are essentially switches in an on or off position, the information in quantum computers, which is also essentially binary, is represented by quantum mechanical states. These can typically be the spin of an electron, energy levels of atoms, or the current direction, charge, or magnetic flux in a superconductor. Regardless of the choice of physical implementation, such a quantum mechanical two-state system is called a qubit.

[0017] A quantum annealer, such as a D-Wave machine, can be used as a quantum computer.

[0018] At the beginning of the calculation, the coupling of the quantum annealer qubits is initialized with the initial state and changed to the lowest energy ground state.

[0019] Qubits are a quantum mechanical system with two states (quantum superposition), whereby a qubit can be in a superposition of these two states and can also be entangled with other qubits. A change in the coupling of the qubits thus simultaneously affects the state of all other entangled qubits.

[0020] Entangled qubits thus behave over any distance as if they were connected to each other. For example, a measurement of the spin of one electron determines the spin state of the other. Thus, if one qubit is changed, the entangled qubit adopts a corresponding change without any time delay and regardless of the distance.

[0021] According to the invention, the problem is approximated in such a way that it can be described by a maximally binary energy function.

[0022] According to the invention, a sufficiently accurate approximation must be found for the task to be input into the quantum annealer so that an adiabatic optimization is possible.

[0023] According to the invention, it was recognized that up to now only a predetermined number of charging stations have been distributed across a road network.

[0024] Until now, it was not possible to determine both the required number and their locations simultaneously / synchronously; this is a task that cannot be solved by a normal computer.

[0025] According to the invention, a digital computer and an adiabatic quantum system (quantum annealer) are provided for this purpose, wherein the quantum annealer is associated with a corresponding energy landscape, and wherein the quantum annealer is coupled to the digital computer.

[0026] According to the invention, a set of possible locations i, i = 1,.. N L of charging stations as a matrix graph representing the road network, with matrix nodes w i,k generated, where the matrix nodes w i,k the utilization of a road section between a first location i and a second location k, i, with i, k ∈ 1,.. N L describe.

[0027] The matrix entries or weights w i,kdescribe the utilization of the road section between location i and location k. This could, for example, be the average number of electric cars expected per hour in this section. Alternatively, the peak value of the number of electric cars expected per hour in the road sections can be chosen.

[0028] Subsequently, the expected usage is calculated as a usage function U(i) at each charging station, taking into account the respective road load, which is determined by the matrix nodes w i,k is given, using the following binary variables: xi={1, if a charging station is installed at location i0, otherwise determined.

[0029] Subsequently, an energy function based on the usage function U(i) is generated for each charging station under the constraint that each charging station at a location i has a limited charging capacity.

[0030] Using such a generated energy function, the problem is approximated according to the invention so that it can be solved on a quantum computer. Using the energy function, an initial state, and thus the energy landscape, is then initialized. By changing the energy landscape to the ground state, after encoding by the digital computer, both the optimal location for a given charging station and the optimal number of charging stations are determined and specified.

[0031] Thus, using the method according to the invention, it is possible not only to distribute an existing number of charging stations in a road network, but also to simultaneously determine the optimal number of charging stations and their locations.

[0032] Furthermore, the method according to the invention is not subject to any restrictions regarding the number of possible locations or the number of charging stations. Thus, a large number of locations and a large road network can be considered, something that would be impossible to solve using a conventional computer (even with a large number of locations and a long computation time).

[0033] In further development, the set of locations i, i = 1,.. N L of charging stations as a matrix graph representing the road network, with matrix nodes w i,k provided, where the matrix nodes w i,k the utilization of a road section between a first location i and a second location k, i with i, k ∈ 1,.. N L and where the load of a road section is considered to be equal in both directions. This means that the matrix nodes w i,k equal to the matrix node w k,ibe set so that w k,i = w i,k This allows the matrix to be simplified.

[0034] In further development, the expected usage, as a usage function U(i) at each charging station, taking into account the respective road load, is determined by: U(i) :=∑k∈{1,...,NL}wk,ixi−c∑k∈{1,...,NL}wk,ixk with i= 1,.. N L equal to the number of possible locations, c a constant which, depending on the surrounding charging stations around the location i, indicates a reduced demand for charging at the charging station at the location i, with xi={1, if a charging station is installed at location i0, otherwise and the matrix node w i,k , where w i,k the utilization of a road section between a first location i and a second location k with i, k ∈ 1,.. N L indicates.

[0035] This means that the load factor is defined as the road load leading to location i, minus the charging performed by charging stations located in the neighborhood, i.e., minus the number of vehicles charging in the neighborhood. This is weighted by the constant c.

[0036] In further development, the energy function H is given by: H=−(∑i∈{1,...,NL}U(i))2

[0037] This corresponds to minimizing the negative expected use.

[0038] In further development, the constraint is added as a binary penalty term to the energy function to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that each charging station at a location i has a limited charging capacity is added by: U(i)≤Ci where C i∈ [0,∞] is the maximum capacity of electric vehicles that can be charged per hour at location i, for all i ∈ 1,.. N L . The constraint is added accordingly for all other charging stations.

[0039] In further development, another constraint is added as a binary penalty term of the energy function as a binary coded energy function to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where as a further constraint it is added that a location j ∈ 1, .. N L is already occupied by a charging station: xj=1, if a charging station is already installed at location j for all existing locations j ∈ 1, .. N L .

[0040] This means that existing charging stations and their locations are taken into account when determining both the required number and their locations.

[0041] Furthermore, the object is achieved by a hybrid system for determining locations for charging stations for electric vehicles, wherein the hybrid system comprises a digital computer, where the digital computer has a set of locations i, i = 1, .. N L of charging stations as a matrix graph representing the road network, with matrix nodes w i,k where the matrix nodes w i,k the utilization of a road section between a first location i and a second location k, i with i, k ∈ 1,.. N L describes, and wherein the digital computer is designed to calculate an expected usage as a usage function at each charging station taking into account the respective road load, which is determined by the matrix nodes w i,k is given, using the following binary variables: xi={1, if a charging station is installed at location i0, otherwise with i,i=1,..NL wherein the hybrid system further comprises an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer, and wherein the digital computer is designed to generate an energy function based on the usage function, wherein for each charging station the secondary condition applies that a charging station at a location i has a limited charging capacity, and wherein the digital computer is configured to generate an initial initial state for the adiabatic quantum system based on the energy function and is further configured to initialize the energy landscape based on the initial state, and wherein the adiabatic quantum system is designed to vary the initial state into an energetic base state, wherein the energetic base state indicates an optimal expected utility based on the minimized utility function, which corresponds to an optimal number of charging stations as well as an optimal location of the respective charging station, and wherein the digital computer is designed to measure the energetic base state after adiabatic variation and encode the measurement of the energetic base state, to determine the optimal location of the charging stations and the optimal number of charging stations, and wherein the digital computer is further designed to arrange the optimal number of charging stations and the respective optimal location of the charging stations on the corresponding underlying road network.

[0042] The hybrid system is particularly designed to carry out the method according to the invention. Advantages and advantageous embodiments of the method can be transferred to the hybrid system.

[0043] In particular, the digital computer is designed to determine the expected usage, as a usage function U(i) at each charging station, taking into account the respective road load, by: U(i) :=∑k∈{1,...,NL}wk,ixi−c∑k∈{1,...,NL}wk,ixk with i= 1,.. N L equal to the number of possible locations, c is a constant which, depending on the surrounding charging stations around location i, indicates a reduced demand for charging at the charging station at location i, and where xi={1, if a charging station is installed at location i0, otherwise with the matrix nodes w i,k , where w i,kthe utilization of a road section between a first location i and a second location k with i, k ∈ 1,.. N L indicates.

[0044] Furthermore, the object is achieved by a road planning module which has a hybrid system as described above and / or a method as described above for determining locations for charging stations as well as a number of charging stations for a road network for electric vehicles.

[0045] The road network can be available in digital form, making it easier to plan locations and numbers even across a large road network.

[0046] Further features and advantages of the present invention will become apparent from the following description with reference to the accompanying figures, which schematically show: Fig. 1: a method according to the invention schematically, Fig. 2: a hybrid system for generating a road network with charging stations.

[0047] Fig. 1 shows the inventive method for determining locations i, i = 1, .. N L for charging stations for electric vehicles schematically. Where N L is an integer and corresponds to the maximum number. In a first step S1, a set of locations i, i = 1,.. N L of charging stations as a matrix graph representing the road network 4, with matrix nodes w i,k ∈ [0,∞] generated or provided.

[0048] The matrix nodes w i,k the utilization of a road section between a first location i and a second location k, i with i, k ∈ 1,.. N L represents.

[0049] For example, the average number of electric cars expected per hour on this road section can be used as the capacity utilization. Alternatively, the peak number of electric cars expected per hour on this road section can be used.

[0050] To simplify the matrix, w i,k = w k,i This means that the load on a road section is considered equal in both directions.

[0051] In a second step S2, an expected usage, which is specified as the usage function U(i), is determined.

[0052] The usage function U(i) at each charging station i is determined taking into account the respective road load by: U(i) :=∑k∈{1,...,NL}wk,ixi−c∑k∈{1,...,NL}wk,ixk with i= 1, .. N Lequal to the number of possible locations, c is a constant which, depending on the surrounding charging stations around location i, indicates a reduced demand for charging at the charging station at location i, and where xi={1, if a charging station is installed at location i0, otherwise with the matrix nodes w i,k , where w i,k the utilization of a road section between a first location i and a second location k with i, k ∈ 1,.. N L indicates.

[0053] This means that the load factor is defined as the road load leading to location i, minus the charging performed by charging stations located in the neighborhood, i.e., minus the number of vehicles charging in the neighborhood. This is weighted by a constant c.

[0054] In a fourth step S4, an adiabatic quantum system is provided, in particular a quantum annealer 3, wherein the quantum annealer 3 is associated with a corresponding energy landscape. The quantum annealer 3 is coupled to the digital computer 2.

[0055] In a fifth step S5, an energy function H is generated based on the usage function U(i), where for each charging station the constraint applies that a charging station is located at a location i with i= 1, .. N L has a limited loading capacity.

[0056] The energy function H is given by: H=−(∑i∈{1,..,NL}U(i))

[0057] The constraint is added as a binary penalty term to the energy function H to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that each charging station at a location i has a limited charging capacity is added by: U(i)≤Ci where C i ∈ [0,∞] is the maximum capacity of electric vehicles that can be charged per hour at location i, for all i ∈ 1,.. N L .

[0058] Such a QUBO (Quadratic Unconstrained Binary Optimization) problem can be solved with the quantum computer.

[0059] Furthermore, as a further constraint, it can be added that a location j ∈ 1, .. N L is already occupied by a charging station,

[0060] This constraint is added as a binary penalty term to the energy function H as a binary coded energy function to generate a QUBO (Quadratic Unconstrained Binary Optimization) model in which xj=1, where a charging station is located at location j is set.

[0061] This means that existing charging stations with their locations i, i = 1,.. N L in determining both the required number and their locations i, i = 1,.. N L be taken into account.

[0062] In a sixth step (S6), the energy landscape of quantum annealer 3 is initialized using an initial state. The initial state of the quantum computer typically corresponds to a solvable quantum system in which the qubits are initialized to the ground state. This initial state is then transferred to the matrix of the problem, which ideally also brings the qubits to the ground state of the problem.

[0063] In the adiabatic quantum computer, the matrix is ​​translated into a coupling strength between the qubits.

[0064] In a seventh step S7, the initial state is changed into an energetic base state using the energy function H, where the energetic base state contains an optimal number of charging stations as well as the respective optimal location i, i = 1,.. N L coded for the respective charging station.

[0065] In an eighth step S8, the achieved energetic ground state after adiabatic change is measured by the digital computer 2 and the measurement is encoded. Based on the encoding, an optimal location i, i = 1, .. N L of the charging stations and the optimal number of charging stations in the investigated road network 4 are known or can be read out from the encoding.

[0066] Fig. 2 shows the hybrid system 1 according to the invention for determining locations i, i = 1, .. N L for charging stations for electric vehicles in a road network 4.

[0067] The hybrid system 1 comprises a digital computer 2. Furthermore, the hybrid system 1 comprises an adiabatic quantum system as a quantum annealer 3, wherein the quantum annealer 3 is associated with a corresponding energy landscape, wherein the quantum annealer 3 is coupled to the digital computer 2.

[0068] Furthermore, the digital computer 2 has in particular a memory unit, wherein in the memory unit a set of locations i, i = 1, .. N L of charging stations as a matrix graph representing the road network 4, with matrix nodes w i,k ∈ [0,∞] or generated by the digital computer 2 and stored there, where the matrix nodes w i,k the utilization of the road section between a first location i and a second location k, i with i, k ∈ 1,.. N L describes.

[0069] Furthermore, the digital computer 2 is designed to determine an expected usage, as a usage function U(i) at each charging station, taking into account the respective road load, by: U(i) :=∑k∈{1,...,NL}wk,ixi−c∑k∈{1,...,NL}wk,ixk with i= 1,.. N Lequal to the number of possible locations, c is a constant which, depending on the surrounding charging stations around location i, indicates a reduced demand for charging at the charging station at location i, and where xi={1, if a charging station is installed at location i0, otherwise with the matrix nodes w i,k , where w i,k the utilization of a road section between a first location i and a second location k with i, k ∈ 1,.. N L where w i,k = w k,i set.

[0070] The digital computer 2 is configured to generate the energy function H based on the utilization function U(i), whereby the constraint that each charging station at a location i has a limited charging capacity applies to each charging station. The constraint is added as a binary penalty term to the energy function (H) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model by: U(i)≤Ci where C i ∈ [0,∞] is the maximum capacity of electric vehicles that can be charged per hour at location i, for all i ∈ 1,.. N L .

[0071] The digital computer 2 then generates an initial state for the quantum annealer 3 based on the energy function H and initializes the energy landscape based on the initial state.

[0072] The quantum annealer 3 is designed to achieve a variation of the initial state into an energetic base state, wherein the energetic base state indicates an optimal expected benefit based on the minimized utility function U(i), which corresponds to an optimal number of charging stations as well as an optimal location i, i = 1,.. N L corresponds to the respective charging station, Subsequently, the digital computer 2 measures the energetic ground state after adiabatic variation and encodes the measurement of the energetic ground state.

[0073] Based on the encoding, both the optimal number and the optimal location i, i = 1,.. N L the charging stations are recognized and arranged by the digital computer 2 in the corresponding underlying road network 4. List of reference symbols 1 hybrid system 2 digital computers 3 quantum annealers 4 Road network QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] DE 102013202059 A1

[0006]

Claims

[1] Method for determining locations for charging stations for electric vehicles comprising the steps: - Providing a digital computer (2), - Providing a set of locations (i, i = 1,.. N L ) of charging stations as a matrix graph representing the road network (4), with matrix nodes (w i,k ∈ [0,∞]) where the matrix nodes (w i,k ) the capacity utilization of a road section between a first location (i) and a second location (k with i, k ∈ 1, .. N L ) in which digital computer (2), - Determination of an expected usage, which is specified as a usage function (U(i)), at each charging station taking into account the respective road load, which is determined by the matrix nodes (w i,k ) is given, using the following binary variables: xi={1, if a charging station is installed at location i0, otherwise - providing an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer (2), - generating an energy function (H) based on the usage function (U(i)) for each charging station under the constraint that each charging station at a location (i) has a limited charging capacity, - Generating an initial state for the adiabatic quantum system based on the energy function (H) and initializing the energy landscape based on the initial state and changing the initial state using the energy function (H) in the quantum computer into an energetic ground state, wherein the energetic ground state indicates an optimal expected utility based on the minimized utility function (U(i)), which corresponds to an optimal number of charging stations as well as an optimal location of each charging station, - Measuring the basic energetic state by the digital computer (2) after adiabatic change and encoding the measurement by the digital computer (2) and determining the optimal locations of the charging stations and the optimal number of charging stations based on the encoding. [2] Method according to claim 1, characterized by that the set of locations (i, i = 1,.. N L) of charging stations as a matrix graph representing the road network (4), with matrix nodes (w i,k ∈ [0,∞]), where the matrix nodes (w i,k ) the capacity utilization of a road section between a first location (i) and a second location (k, i with i, k ∈ 1,.. N L ), and where the load of a road section is considered to be the same in both directions. [3] Method according to claim 1 or 2, characterized by that the expected usage, as a usage function (U(i)) at each charging station taking into account the respective road load, is determined by: U(i) :=∑k∈{1,...,NL}wk,ixi−c∑k∈{1,...,NL}wk,i with (i, i = 1,.. N L ) is equal to the number of possible locations, (c) a constant which, depending on the surrounding charging stations around the location (i), indicates a reduced demand for charging at the charging station at the location (i), and where xi={1, if a charging station is installed at location i0, otherwise and the matrix node (w i,k ) , where (w i,k ) the capacity utilization of a road section between a first location (i) and a second location (k with i, k ∈ 1, .. N L ) indicates. [4] Method according to claim 3, characterized by that the energy function (H) is given by: H=min−(∑i∈{1,..,HL}U(i)) [5] Method according to one of the preceding claims, characterized by that the constraint is added as a binary penalty term to the energy function (H) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that each charging station at a location (i) has a limited charging capacity is added by: U(i)≤Ci where (C i∈ [0,∞]) is the maximum capacity of electric vehicles that can be charged per hour at location (i), for all (i, i = 1, .. N L ). [6] Method according to one of the preceding claims, characterized by that a further constraint is added as a binary penalty term to the energy function (H) as a binary coded energy function, to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where as a further constraint it is added that a location (j ∈ 1, .. N L ) is already occupied by a charging station: xj=1, if a charging station is already installed at location j for all existing locations (j ∈ 1,..N L ). [7] Hybrid system (1) for determining locations for charging stations for electric vehicles, wherein the hybrid system (1) comprises a digital computer (2), wherein the digital computer (2) has a set of locations (i, i = 1,.. N L) of charging stations as a matrix graph representing the road network (4), with matrix nodes (w i,k ∈ [0,∞]), where the matrix nodes (w i,k ) the capacity utilization of a road section between a first location (i) and a second location (k, i with i, k ∈ 1,.. N L ), and wherein the digital computer (2) is designed to calculate an expected usage as a usage function (U(i)) at each charging station, taking into account the respective road load, which is determined by the matrix nodes w i,k is given, using the following binary variables: xi={1, if a charging station is installed at location i0, otherwise wherein the hybrid system (1) further comprises an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer (2), and wherein the digital computer (2) is designed to generate an energy function (H) on the basis of the usage function (U(i)), wherein for each charging station the secondary condition applies that a charging station at a location i has a limited charging capacity, and wherein the digital computer (2) is designed to generate an initial initial state for the adiabatic quantum system based on the energy function and is further designed to initialize the energy landscape based on the initial state, and wherein the adiabatic quantum system is designed to effect a variation of the initial state into an energetic base state, wherein the energetic base state indicates an optimally expected utility based on the minimized utility function (U(i)), which corresponds to an optimal number of charging stations and also to an optimal location of the respective charging station, and wherein the digital computer (2) is designed to measure the energetic base state after adiabatic variation and encode the measurement of the energetic base state, to determine the optimal location of the charging stations and the optimal number of charging stations, and wherein the digital computer (2) is further designed to arrange the optimal number of charging stations and the respective optimal location of the charging stations on the corresponding underlying road network (4). [8] Hybrid system (1) according to claim 7, characterized bythat the digital computer (2) is designed to determine the expected usage, as a usage function (U(i)) at each charging station, taking into account the respective road load, by: U(i) :=∑k∈{1,...,NL}wk,ixi−c∑k∈{1,...,NL}wk,i with (i= 1,..N L ) is equal to the number of possible locations, (c) a constant which, depending on the surrounding charging stations around the location (i), indicates a reduced demand for charging at the charging station at the location (i), and where xi={1, if a charging station is installed at location i0, otherwise and the matrix node (w i,k ), where (w i,k ) the utilization of a road section between a first location (i) and a second location (k with i, k ∈ 1,.. N L ) indicates. [9] Hybrid system (1) according to claim 8, characterized by that the energy function (H) is given by: H=min−(∑i∈{1,...,NL}U(i))2 [10] Hybrid system (1) according to claim 7 or 8, characterized by that the constraint can be added as a binary penalty term to the energy function (H) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that each charging station at a location (i) has a limited charging capacity is added by: U(i)≤Ci where (C i ∈ [0,∞]) is the maximum capacity of electric vehicles that can be charged per hour at location (i), for all (i ∈ 1,.. N L . [11] Hybrid system (1) according to claim 7, 8 or 9, characterized by that a further constraint is that a location (j ∈ 1, .. N L ) is already occupied by a charging station, so that xj=1,where a charging station is located at location j where the further constraint can be added as a binary penalty term to the energy function (H) as a binary coded energy function, to generate a QUBO (Quadratic Unconstrained Binary Optimization) model. [12] Road planning module comprising a hybrid system (1) according to one of the preceding claims 1 to 7 and / or a method according to one of the preceding claims 8 to 11, for determining locations (i, i = 1, .. N L ) for charging stations as well as a number of charging stations for a road network (4) for electric vehicles.

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