A path planning-based emergency energy management strategy for pure electric vehicle charging

Abstract

The application discloses a pure electric vehicle charging emergency energy management strategy based on path planning. The electric vehicle firstly locates a target destination. If the displayed cruising range of the electric vehicle is greater than the destination travel, no charging station tracing planning is performed. If the displayed cruising range of the electric vehicle is smaller than the destination travel, an energy management module finds all charging stations within the remaining cruising range of the electric vehicle during the travel to the destination according to the remaining cruising range of the electric vehicle, then optimizes route planning, finds the best route from the current location of the electric vehicle to all charging stations, and determines the best charging station.

Description

Technical Field

[0001] This invention relates to an emergency energy management strategy for pure electric vehicle charging based on path planning. Background Technology

[0002] With the emergence of pure electric vehicles, the number of supporting charging stations has gradually increased, alleviating consumers' range anxiety to some extent, especially in large and medium-sized cities such as Northeast China, North China, and South China, where charging convenience is already very high. However, the lack of an energy management method that can locate charging stations in real time means consumers still worry about the battery consumption of their electric vehicles. Therefore, to further alleviate consumers' range anxiety, electric vehicles need to adopt energy management strategies that can monitor mileage in real time, plan routes, compare and optimize the range of electric vehicles in real time, and enable electric vehicles to start with timely charging emergencies, so that consumers no longer have to worry about range issues. Summary of the Invention

[0003] This invention provides a path planning-based emergency energy management strategy for pure electric vehicle charging to address the problems existing in the prior art.

[0004] The technical solutions adopted in this invention are as follows:

[0005] An emergency energy management strategy for pure electric vehicle charging based on path planning.

[0006] The electric vehicle first locates the target destination. If the displayed range is greater than the distance to the destination, then no charging station tracking is performed.

[0007] If the displayed driving range is less than the destination distance, the energy management module will use the remaining driving range on the display to find all charging stations within the remaining driving range range of the electric vehicle on its journey to the destination. Then, it will optimize the route planning, find the best route from the current location of the electric vehicle to all charging stations, and determine the best charging station.

[0008] Furthermore, within the remaining driving range, the process of route planning for the optimal charging station is as follows:

[0009] 1) Let the current driving point of the electric vehicle be point A, the destination be point B, the path length from point A to point B be d, and the current battery level of the electric vehicle be SoC. Assume that during the journey towards point B, the electric vehicle passes through charging station areas C and E, both of which have several charging stations. In the optimal route planning, first calculate the farthest distance S that the electric vehicle can reach with its current battery level. max Then, the shortest path from point A to charging station area C or charging station area E is found using Dijkstra's algorithm.

[0010] 2) Assume the path is A->C->E->B, if d max If the electric vehicle can travel directly along this path to point B, then it needs to find a point whose distance from point B is less than S. max The charging station area ensures that after the electric vehicle is charged, it travels along the path between point B and point B and arrives at point B exactly.

[0011] The formula is as follows: Calculate the farthest distance S that the electric vehicle can reach with its current battery level. max :

[0012] S max =E max *SoC / 100*eff / (M*g*R)

[0013] Wherein: S max The farthest distance an electric vehicle can travel with its current battery level, measured in meters (m).

[0014] E max The total capacity of the electric vehicle's onboard battery, expressed in Wh.

[0015] SoC: Current battery level of the electric vehicle, in percentage (%).

[0016] eff: Energy conversion efficiency of electric vehicles;

[0017] M: The mass of the electric vehicle;

[0018] g: Gravitational acceleration constant;

[0019] R: Rolling resistance coefficient of electric vehicle in motion;

[0020] 3) Calculate the shortest distance from point A to all charging stations within the charging station area of ​​charging station C, and calculate the energy consumption of electric vehicle A at each charging station within the charging station area of ​​charging station C: Plan the optimal route for all charging stations within the driving range along the route of electric vehicle A from point A to charging station C, and calculate the energy consumption of electric vehicle A at each charging station within the charging station area of ​​charging station C:

[0021] E c =G*(dis(A—>C)*0.5*rho*C*Af / 1000+M*g*sin(θc)*dis(A—>C) / 1000)

[0022] in:

[0023] E c Energy consumption of electric vehicles arriving at charging station C, in kWh;

[0024] G: Energy increment coefficient of electric vehicle during driving;

[0025] ​dis(A—>C): The distance from point A to the charging station area C, in meters;

[0026] rho: air density;

[0027] C: Air drag coefficient of electric vehicles;

[0028] Af: The body area of ​​the electric vehicle;

[0029] M: The mass of the electric vehicle;

[0030] sin(θc): The resultant force coefficient in the slope direction of the electric vehicle traveling within the C charging station area;

[0031] 4) Calculate the charging stations within the area of ​​charging station C, perform charging station scoring optimization, and select the optimal charging station.

[0032] Furthermore, after the electric vehicle enters the charging station C area, the system calculates whether the electric vehicle can reach its destination point B after completing charging at the optimal charging station within the charging station C area.

[0033] Calculate the energy consumption of the electric vehicle to reach point B from the optimal charging station within the area of ​​charging station C:

[0034] E b =G*(dis(C—>B)*0.5*rho*C*Af / 1000+M*g*sin(θb)*dis(C—>B) / 1000)

[0035] in:

[0036] E b The energy consumption to reach destination B is measured in kWh.

[0037] G: Energy consumption coefficient of electric vehicle during operation;

[0038] dis(C 优 —>B): C charging station area optimal charging station C 优 The distance to point B, in meters;

[0039] rho: air density;

[0040] C: Air drag coefficient of electric vehicles;

[0041] Af: The body area of ​​the electric vehicle;

[0042] M: The mass of the electric vehicle;

[0043] sin(θb): The coefficient of the resultant force of the electric vehicle in the direction of the slope at point B;

[0044] When E b <Emax At that time, the electric vehicle can reach point B, such as E. b >E max If the electric vehicle cannot reach point B, repeat the above calculation to find the next charging station area E.

[0045] Furthermore, the process of calculating the charging stations within the C charging station area, performing charging station scoring optimization, and selecting the optimal charging station is as follows:

[0046] By using charging station data provided by charging station operators or map service providers, priority is given to the charging station's ability to provide charging. Then, all charging stations that meet the requirements are compared, and the charging station with the fastest charging speed and the best price is selected to reduce charging time and cost.

[0047] Assume there are four charging stations in the current charging station C area, labeled C1, C2, C3, and C4. The nearest charging station is C1, and its distance from point A is dis(A—>C1). The electricity price at C1 is P, the charging speed at C1 is q, the electric vehicle's initial charge at C1 is SoC1, and the charge required to reach full capacity is target. Then, the cost and time required to charge at C1 can be calculated using the following formula:

[0048] Charging cost:

[0049] cost_c=P*(E max *(target-SoC1) / 100) / (q*eff)

[0050] Charging time:

[0051] t c =(E max *(target-SoC1) / 100) / q

[0052] in:

[0053] cost_c: The cost of charging at station C1; E max The total capacity of the electric vehicle's onboard battery;

[0054] The adaptive evaluation and matching of information for all charging stations within the C charging station area is performed using the following formula:

[0055] Charging station rating F(C)

[0056]

[0057] in:

[0058] W(k): The weight of the k-th factor;

[0059] Score(k,C): The score of the charging station corresponding to the k-th factor;

[0060] n: The number of factors (considering voltage, current, charging speed, price, location, historical records, etc.);

[0061] Charging station comparison: Set evaluation factors for all charging stations within the area of ​​charging station C, and then substitute them into the formula. Calculate the overall score F(C) for each charging station.

[0062] Furthermore, the evaluation factors for charging stations include: charging speed, electricity cost, and platform ratings provided by the charging station operator or map service provider.

[0063] The present invention has the following beneficial effects:

[0064] This invention optimizes charging stations in real time based on vehicle range. The scoring of charging stations is relatively realistic and helpful for emergency charging of pure electric vehicles. Attached Figure Description

[0065] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0066] The invention will now be further described with reference to the accompanying drawings.

[0067] like Figure 1 This invention discloses an emergency energy management strategy for pure electric vehicles based on path planning. The electric vehicle first locates its target destination. If the displayed driving range is greater than the destination distance, no charging station tracing is performed. If the displayed driving range is less than the destination distance, the energy management module finds all charging stations within the remaining driving range range of the electric vehicle during its journey to the destination, based on the remaining driving range displayed on the vehicle. Then, it optimizes the route planning, finds the best route from the current location of the electric vehicle to all charging stations, and determines the best charging station.

[0068] Within the remaining driving range, the route planning process for the optimal charging station is as follows:

[0069] First, a path A -> B is planned to reach the target point. Then, by predicting all charging stations within the electric vehicle's driving range, factors such as charging speed, electricity cost, and platform rating for each charging station are determined and recorded in a list. All charging stations within the electric vehicle's driving range are added or removed from the path in real time, and the number of charging stations within the range is calculated and evaluated in real time. The number of charging stations within the range must be greater than or equal to one. The specific calculation formula is as follows.

[0070] The specific calculation process and formula are as follows.

[0071] 1) Let the current driving point of the electric vehicle be point A, the destination be point B, the path length from point A to point B be d, and the current battery level of the electric vehicle be SoC. Assume that during the journey towards point B, the electric vehicle passes through charging station areas C and E, both of which have several charging stations. In the optimal route planning, first calculate the farthest distance S that the electric vehicle can reach with its current battery level. max Then, the shortest path from point A to charging station area C or charging station area E is found using Dijkstra's algorithm.

[0072] 2) Assume the path is A->C->E->B, if d max If the electric vehicle can travel directly along this path to point B, then it needs to find a point whose distance from point B is less than S. max The charging station area ensures that after the electric vehicle is charged, it travels along the path between point B and point B and arrives at point B exactly.

[0073] The formula is as follows: Calculate the farthest distance S that the electric vehicle can reach with its current battery level. max :

[0074] S max =E max *SoC / 100*eff / (M*g*R)

[0075] in:

[0076] S max The farthest distance an electric vehicle can travel with its current battery level, measured in meters (m).

[0077] E max The total capacity of the electric vehicle's onboard battery, expressed in Wh.

[0078] SoC: Current battery level of the electric vehicle, in percentage (%).

[0079] eff: Energy conversion efficiency of electric vehicles;

[0080] M: The mass of the electric vehicle;

[0081] g: Gravitational acceleration constant;

[0082] R: The rolling resistance coefficient of an electric vehicle during motion.

[0083] 3) Calculate the shortest distance from point A to all charging stations within the charging station area of ​​charging station C, and calculate the energy consumption of electric vehicle A at each charging station within the charging station area of ​​charging station C: Plan the optimal route for all charging stations within the driving range along the route of electric vehicle A from point A to charging station C, and calculate the energy consumption of electric vehicle A at each charging station within the charging station area of ​​charging station C:

[0084] ​E c =G*(dis(A—>C)*0.5*rho*C*Af / 1000+M*g*sin(θc)*dis(A—>C) / 1000)

[0085] in:

[0086] E c Energy consumption of electric vehicles arriving at charging station C, in kWh;

[0087] G: Energy increment coefficient of electric vehicle during driving;

[0088] dis(A—>C): The distance from point A to the charging station area C, in meters;

[0089] rho: air density;

[0090] C: Air drag coefficient of electric vehicles;

[0091] Af: The body area of ​​the electric vehicle;

[0092] M: The mass of the electric vehicle;

[0093] sin(θc): The resultant force coefficient in the slope direction when the electric vehicle is traveling within the area of ​​charging station C.

[0094] 4) Calculate the charging stations within the area of ​​charging station C, perform charging station scoring optimization, and select the optimal charging station.

[0095] After the electric vehicle enters the charging station area C, calculate whether the electric vehicle can reach its destination point B after completing charging at the optimal charging station within the charging station area C, and calculate the energy consumption of the electric vehicle from the optimal charging station within the charging station area C to point B:

[0096] E b =G*(dis(C—>B)*0.5*rho*C*Af / 1000+M*g*sin(θb)*dis(C—>B) / 1000)

[0097] in:

[0098] E b The energy consumption to reach destination B is measured in kWh.

[0099] G: Energy consumption coefficient of electric vehicle during operation;

[0100] dis(C 优 —>B): C charging station area optimal charging station C 优 The distance to point B, in meters;

[0101] rho: air density;

[0102] C: Air drag coefficient of electric vehicles;

[0103] Af: The body area of ​​the electric vehicle;

[0104] M: The mass of the electric vehicle;

[0105] sin(θb): The coefficient of the resultant force of the electric vehicle in the direction of the slope at point B;

[0106] When E b <E max At that time, the electric vehicle can reach point B, such as E. b >E max If the electric vehicle cannot reach point B, repeat the above calculation to find the next charging station area E.

[0107] The process of calculating the charging stations within the area of ​​charging station C, performing charging station scoring optimization, and selecting the optimal charging station is as follows:

[0108] By utilizing data interfaces provided by charging station operators or map service providers, other platforms or applications can access charging station data. These data interfaces are typically provided in the form of Web APIs or REST APIs, accessed and retrieved by sending HTTP requests. These APIs can be used to obtain detailed information about charging stations. After obtaining this information, the charging station's charging capabilities can be prioritized. All charging stations meeting the requirements can be compared, and the station with the best overall performance (maximizing gradient descent within the allowed time) can be selected to reduce charging time and cost, and improve the user experience.

[0109] Assume there are four charging stations in the current charging station C area, labeled C1, C2, C3, and C4. The nearest charging station is C1, and its distance from point A is dis(A—>C1). The electricity price at C1 is P, the charging speed at C1 is q, the electric vehicle's initial charge at C1 is SoC1, and the charge required to reach full capacity is target. Then, the cost and time required to charge at C1 can be calculated using the following formula:

[0110] Charging cost:

[0111] cost_c=P*(E max *(target-SoC1) / 100) / (q*eff)

[0112] Charging time:

[0113] t c =(E max*(target-SoC1) / 100) / q

[0114] in:

[0115] cost_c: The cost of charging at station C1; E max The total capacity of the electric vehicle's onboard battery;

[0116] The adaptive evaluation and matching of information for all charging stations within the C charging station area is performed using the following formula:

[0117] Charging station rating F(C)

[0118]

[0119] in:

[0120] W(k): The weight of the k-th factor

[0121] Score(k,C): The score of the charging station corresponding to the k-th factor.

[0122] n: The number of factors (considering voltage, current, charging speed, price, location, historical records, etc.).

[0123] Charging station comparison: Set evaluation factors for all charging stations within the area of ​​charging station C (including: charging speed, electricity cost, and platform ratings provided by the charging station operator or map service provider), and then substitute them into the formula. Calculate the overall score F(C) for each charging station.

[0124] Charging station comparison (C1, C2), assuming the evaluation factors and weights are as follows:

[0125] Evaluation factors: charging speed, electricity cost, platform rating, and other factors.

[0126] Weighting: Charging speed: 0.3, Electricity cost: 0.2, Platform rating: 0.4, Other factors: 0.1.

[0127] Assume there are four charging stations within the calculation area, labeled C1, C2, C3, and C4. Below are the scores for these four charging stations on various factors (with 1 being the highest score). This example only covers charging speed, electricity cost, and platform rating; the scores for other factors are calculated similarly. Note that each factor corresponds to a weight:

[0128]

[0129] Substitute these scores into the formula The overall score F(C) for each charging station can then be calculated:

[0130] F(C1)=0.3*0.8+0.2*0.5+0.4*0.9+0.1*X1

[0131] F(C2)=0.3*0.6+0.2*0.7+0.4*0.7+0.1*X2

[0132] F(C3)=0.3*0.9+0.2*0.8+0.4*0.8+0.1*X3

[0133] F(C4)=0.3*0.7+0.2*0.6+0.4*0.6+0.1*X4

[0134] Where X1, X2, X3, and X4 represent the scores of other factors. Since the specific scores of other factors are not given here, it is assumed that their scores are random, taking values ​​of 0.5, 0.3, 0.6, and 0.4 respectively. Substituting these scores into the formula, we obtain the comprehensive score for each charging station:

[0135] F(C1) = 0.8

[0136] F(C2)=0.61

[0137] F(C3) = 0.81

[0138] F(C4) = 0.62

[0139] Based on these comprehensive scores, we can select the highest-scoring charging station as the optimal charging station, which is the C3 charging station with fast charging speed, low electricity cost, high platform score, and good performance in other factors.

[0140] Calculate the SoC (System-on-Chips) of the electric vehicle's required power to reach each point along the path. x And calculate the amount of electricity the SoC needs to consume to reach the next charging station on the path. n Until SoC n Greater than E max If *(SoC_L-target) / 100*eff is executed, the electric vehicle is notified to find a suitable charging station within this range and to update SoC1 and SoC2. x This process is repeated until the electric vehicle reaches the target point B.

[0141] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A path planning-based emergency energy management strategy for pure electric vehicle charging, characterized in that: The electric vehicle first locates the target destination. If the displayed range is greater than the distance to the destination, then no charging station tracking is performed. If the vehicle's displayed range is less than the destination distance, based on the remaining range displayed on the vehicle, find all charging stations within the remaining range range during the electric vehicle's journey to the destination, then optimize the route planning, find the best route from the electric vehicle's current location to all charging stations, and determine the best charging station. Within the remaining driving range, the route planning process for the optimal charging station is as follows: 1) Let the current driving point of the electric vehicle be point A, the destination be point B, the path length from point A to point B be d, and the current battery level of the electric vehicle be SoC. Assume that during the journey towards point B, the electric vehicle passes through charging station areas C and E, both of which have several charging stations. In the optimal route planning, first calculate the farthest distance S that the electric vehicle can reach with its current battery level. max Then, the shortest path from point A to charging station area C or charging station area E is found using Dijkstra's algorithm. 2) Assume the path from A to B is A->C->E->B, if d max If the electric vehicle can travel directly along this path to point B, then it needs to find a point whose distance from point B is less than S. max The charging station area ensures that after the electric vehicle is charged, it travels along the path between point B and point B and arrives at point B exactly.​ The formula is as follows: Calculate the farthest distance S that the electric vehicle can reach with its current battery level. max : S max =E max *SoC / 100*eff / (M*g*R), Wherein: S max The farthest distance an electric vehicle can travel with its current battery level, measured in meters (m). E max The total capacity of the electric vehicle's onboard battery, expressed in Wh. SoC: Current battery level of the electric vehicle, in percentage (%) eff: Energy conversion efficiency of electric vehicles; M: The mass of the electric vehicle; g: Gravitational acceleration constant; R: Rolling resistance coefficient of electric vehicle in motion; 3) Calculate the shortest distance from point A to all charging stations within the charging station area of ​​charging station C, and calculate the energy consumption of electric vehicle A at each charging station within the charging station area of ​​charging station C: Plan the optimal route for all charging stations within the driving range of electric vehicle A from point A to charging station C, and calculate the energy consumption of electric vehicle A at each charging station within the charging station area of ​​charging station C: E c =G*(dis(A—>C)*0.5*rho*C*Af / 1000+M*g*sin(θc)*dis(A—>C) / 1000), in: E c Energy consumption of electric vehicles arriving at charging station C, in kWh; G: Energy increment coefficient of electric vehicle during driving; dis(A—>C): The distance from point A to the charging station area C, in meters; rho: air density; C: Air drag coefficient of electric vehicles; Af: The body area of ​​the electric vehicle; M: The mass of the electric vehicle; sin(θc): The resultant force coefficient in the slope direction of the electric vehicle traveling within the area of ​​charging station C; 4) Calculate the charging stations within the area of ​​charging station C, perform charging station scoring optimization, and select the optimal charging station; After the electric vehicle enters the charging station area C, calculate whether the electric vehicle can reach the destination point B after completing charging at the optimal charging station within the charging station area C: Calculate the energy consumption E of the electric vehicle traveling from the optimal charging station to point B within the charging station area C. b ; When E b <E max At that time, the electric vehicle can reach point B, such as E. b >E max If the electric vehicle cannot reach point B, repeat the above calculation to find the next charging station area E.

2. The emergency energy management strategy for pure electric vehicle charging based on path planning as described in claim 1, characterized in that: Energy consumption E for electric vehicles to reach point B from the optimal charging station within the C charging station area b The calculation process is as follows: E b =G*(dis(C 优 —>B)*0.5*rho*C*Af / 1000+M*g*sin(θb)*dis(C 优 —>B) / 1000), in: E b The energy consumption to reach destination B is measured in kWh. G: Energy consumption coefficient of electric vehicle during operation; dis(C 优 —>B): C is the optimal charging station in the charging station area. 优 The distance to point B, in meters; rho: air density; C: Air drag coefficient of electric vehicles; Af: The body area of ​​the electric vehicle; M: The mass of the electric vehicle; sin(θb): The resultant force coefficient of the electric vehicle in the direction of the slope at point B.

3. The emergency energy management strategy for pure electric vehicle charging based on path planning as described in claim 1, characterized in that: The process of calculating the charging stations within the area of ​​charging station C, performing charging station scoring optimization, and selecting the optimal charging station is as follows: By using charging station data provided by charging station operators or map service providers, priority is given to the charging station's ability to provide charging. Then, all charging stations that meet the requirements are compared, and the charging station with the fastest charging speed and the best price is selected to reduce charging time and cost. Assume there are four charging stations in the current charging station C area, labeled C1, C2, C3 and C4 respectively. The nearest charging station is station C1, and the distance from point A is dis(A—>C1). The electricity price of station C1 is P, the charging speed of station C1 is q, the battery level of the electric vehicle when it arrives at station C1 is SoC1, and the battery level required to reach the target full charge is target. The cost and time required to charge at station C1 can be calculated using the following formula: Charging cost: cost_c=P*(E max *(target-SoC1) / 100) / (q*eff), Charging time: t c =(E max *(target-SoC1) / 100) / q, in: cost_c: The cost of charging at station C1; E max The total capacity of the electric vehicle's onboard battery; The adaptive evaluation and matching of information for all charging stations within the C charging station area is performed using the following formula: Charging station rating F(C) ， in: W(k): The weight of the k-th factor; Score(k,C): The score of the charging station corresponding to the k-th factor; n: the number of factors; Charging station comparison: Set evaluation factors for all charging stations within the area of ​​charging station C, and then substitute them into the formula. The overall score F(C) for each charging station is calculated.

4. The emergency energy management strategy for pure electric vehicle charging based on path planning as described in claim 1, characterized in that: Factors that evaluate charging stations include: charging speed, electricity cost, and platform ratings provided by the charging station operator or map service provider.

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