Electric vehicle low-power-consumption standby power supply method and system for intersection

Abstract

The invention discloses an electric vehicle low-power-consumption standby power supply method and system for an intersection, and relates to the field of electric vehicle power supply, and the method comprises the steps: determining a to-be-driven path of a target vehicle, constructing a congestion vector sequence of the target vehicle, anchoring a target road section unit, and according to the target road section unit, constructing a congestion vector sequence of the target vehicle; constructing a neighborhood weight sequence aligned with the congestion vector sequence, determining a congestion coefficient of the target vehicle at the current moment according to the neighborhood weight sequence aligned with the congestion vector sequence, and determining a starting time point of a low-power-consumption standby mode according to the congestion coefficient of the target vehicle at the current moment, according to the low-power-consumption standby closed-loop control method, the mode length extraction and the normalization fusion are carried out on the weighted congestion vector, the congestion coefficient with the overall strength and the local sudden change response capability is generated, the arrival time interval is directly calculated through the congestion coefficient, the starting time point of the low-power-consumption standby mode is determined, and low-power-consumption standby closed-loop control is formed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric vehicle power supply, in particular to a low-power standby power supply method and system for electric vehicles at intersections. BACKGROUND

[0002] With the popularity of electric vehicles in urban traffic, how to optimize energy utilization efficiency in typical scenarios such as intersections has become an important research direction to improve the vehicle's endurance. Some vehicles still maintain full system power supply during waiting for red lights, resulting in unnecessary power consumption.

[0003] In the prior art, there are schemes that attempt to determine whether to enter a low-power mode based on the vehicle's position or the surrounding traffic flow state. For example, by GPS positioning to determine whether the vehicle enters a congested area, or according to the state of the signal ahead to control the power supply mode switching. For example, the prior art disclosed in patent publication No. CN112332664B discloses a low-power standby circuit method for a pure electric vehicle power battery monitoring power supply, which can greatly reduce standby power consumption and reduce the power loss of the power storage battery due to excessive standby power consumption.

[0004] However, in the field, there is still a lack of effective technical improvement for the power supply state control of electric vehicles at intersections, and the energy-saving management in related scenarios still has obvious deficiencies: Firstly, it cannot accurately reflect the local traffic state changes along the driving path of the vehicle, and is easy to misjudge short-time slow driving as continuous congestion, or ignore the sudden congestion ahead, resulting in delayed control decisions or false triggering; secondly, it usually uses fixed thresholds or empirical rules for judgment, without considering the influence weight differences of different road sections on the current driving situation, making it difficult to adapt to complex scenarios under different road lengths and traffic densities; thirdly, the congestion information is mostly in the form of qualitative labels, lacking quantitative expression, and cannot be converted into time control instructions that can directly drive the power management system, resulting in delayed energy-saving strategy response. Therefore, the prior art cannot accurately and reliably start the low-power standby mode at intersections while ensuring driving safety and comfort. SUMMARY

[0005] To overcome the deficiencies of the prior art, the present application provides a low-power standby power supply method and system for electric vehicles at intersections, which introduces a congestion coefficient based on double sequences, and calculates the starting time point of the low-power standby mode based on the congestion coefficient to solve the technical problems raised in the background art.

[0006] To achieve the above purpose, the present application is implemented by the following technical solutions: In a first aspect, the present application provides a low-power standby power supply method for electric vehicles at intersections, comprising: S1, determining a to-be-traveled path of the target vehicle on an electronic map; S2, cutting the to-be-traveled path into M road segment units in equal length, and constructing a congestion vector sequence of the target vehicle on the to-be-traveled path along a traveling direction based on the road segment units; S3, anchoring a road segment unit where the target vehicle is located, denoted as a target road segment unit; S4, constructing a neighborhood weight sequence aligned with the congestion vector sequence according to the target road segment unit; S5, determining a congestion coefficient of the target vehicle at a current time according to the neighborhood weight sequence aligned with the congestion vector sequence; S6, determining a starting time point of a low-power standby mode according to the congestion coefficient of the target vehicle at the current time.

[0007] In some embodiments, the determining the to-be-traveled path of the target vehicle on the electronic map comprises: S1-1, obtaining a navigation path of the target vehicle on the electronic map; S1-2, locating a dynamic vehicle point of the target vehicle on the navigation path, and a static road point closest to the dynamic vehicle point; S1-3, determining the to-be-traveled path of the target vehicle on the navigation path according to the dynamic vehicle point and the closest static road point of the target vehicle; In some embodiments, the constructing the congestion vector sequence of the target vehicle along the traveling direction at the current time according to the M road segment units comprises: S2-1, obtaining road segment parameters in each road segment unit; the road segment parameters comprise: a number of lanes, a total number of vehicles along the line, and an average traveling speed; S2-2, performing parameter normalization on the road segment parameters in each road segment unit to generate road segment parameter features; S2-3, performing feature splicing on the road segment parameter features in a preset order to generate road congestion vectors of the road segment units; S2-4, assigning monotonically increasing road segment numbers to the M road segment units based on the traveling direction of the to-be-traveled path; S2-5, arranging the road congestion vectors of the M road segment units in sequence according to the road segment numbers to generate the congestion vector sequence along the traveling direction at the current time; In some embodiments, the constructing the neighborhood weight sequence aligned with the congestion vector sequence according to the target road segment unit comprises: S4-1, calculating along-road traveling distances between the target road segment unit and other M-1 road segment units; S4-2, calculating M-1 neighborhood influence weights based on the along-road traveling distances of the M-1 road segment units; S4-3, taking the neighborhood influence weight of the target road section unit as 1, adding it to the M-1 neighborhood influence weights to obtain M neighborhood influence weights of road section units; S4-4, according to the road section number, sequentially arranging the M neighborhood influence weights of road section units to form a neighborhood weight sequence aligned with the congestion vector sequence.

[0008] In some embodiments, determining the congestion coefficient of the target vehicle at the current time according to the neighborhood weight sequence aligned with the congestion vector sequence comprises: S5-1, constructing a weighted congestion vector sequence based on the neighborhood weight sequence and the congestion vector sequence; S5-2, extracting the congestion coefficient of the target vehicle at the current time in the weighted congestion vector sequence.

[0009] In some embodiments, constructing a weighted congestion vector sequence based on the neighborhood weight sequence and the congestion vector sequence comprises: S5-1-1, anchoring the neighborhood influence weight and the road congestion vector of the same road section number in the neighborhood weight sequence and the congestion vector sequence; S5-1-2, multiplying the neighborhood influence weight and the road congestion vector of the same road section number item by item to obtain the weighted congestion vector corresponding to each road section unit; S5-1-3, according to the road section number, sequentially arranging the M weighted congestion vectors of road section units to generate a weighted congestion vector sequence in the driving direction at the current time.

[0010] In some embodiments, extracting the congestion coefficient of the target vehicle at the current time in the weighted congestion vector sequence comprises: S5-2-1, calculating the M vector magnitudes of the weighted congestion vectors in the weighted congestion vector sequence; S5-2-2, calculating the normalized magnitude and the magnitude variance of the M vector magnitudes; S5-2-3, determining the congestion coefficient of the target vehicle at the current time according to the normalized magnitude and the magnitude variance.

[0011] In some embodiments, determining the starting time point of the low-power standby mode according to the congestion coefficient of the target vehicle at the current time comprises: S6-1, calculating the time interval for the target vehicle to reach the static road point according to the congestion coefficient of the target vehicle, the path length of the to-be-traveled path, and the average speed of the to-be-traveled path; S6-2, obtaining the most adjacent red light time interval and determining the overlapping time interval thereof with the time interval; S6-3, determine the starting time point of the low-power standby mode according to the starting time stamp of the overlapping time interval and the preset response preparation duration, and issue a starting instruction of the low-power standby mode at the starting time point.

[0012] The application provides a low-power standby power supply method for electric vehicles at intersections. The application divides the to-be-traveled path into multiple road section units, constructs a congestion vector sequence arranged in sequence along the traveling direction, and can continuously reflect the traffic states of each road section in front of the vehicle. Based on the road section unit where the target vehicle is located, the along-road traveling distances of other road section units are calculated, and a neighborhood influence weight of a square decay structure is constructed; through road section number alignment, it is ensured that the weight corresponds to the congestion vector one by one, so that the road sections closer in distance have a greater influence on the current congestion judgment. Further, the neighborhood influence weight is calculated based on the relative distance proportion instead of the absolute distance, so that the same proportion corresponds to the same weight, and the weight distribution can be automatically adjusted under different lengths of the to-be-traveled path. Further, the congestion coefficient with overall strength and local mutation response capability is generated by length modulus extraction and normalization fusion of the weighted congestion vector; the congestion coefficient is directly calculated to reach the time interval, and the starting time point of the low-power standby mode is determined, forming a closed-loop control of the low-power standby.

[0013] Finally, the low-power standby mode is triggered only when there is an overlap between the vehicle's estimated arrival time and the red light time interval, and the congestion coefficient of the path in front reaches the starting condition, and according to the response preparation duration, the instruction is issued in advance at the starting time point, ensuring that the vehicle completes the power supply switching before the red light is on, avoiding unnecessary energy consumption caused by entering standby too early, or affecting driving safety due to switching delay.

[0014] In a second aspect, the application discloses a low-power standby power supply system for electric vehicles at intersections, which is used to execute the low-power standby power supply method in the first aspect, and the system comprises: A path determination unit is configured to determine a to-be-traveled path of a target vehicle on an electronic map. A congestion sequence construction unit is configured to cut the to-be-traveled path into M road section units with the same length, and construct a congestion vector sequence of the target vehicle along the traveling direction on the to-be-traveled path based on the road section units. A target anchoring unit is configured to anchor the road section unit where the target vehicle is located, which is recorded as a target road section unit. A weight sequence construction unit is configured to construct a neighborhood weight sequence aligned with the congestion vector sequence according to the target road section unit. a congestion coefficient determination unit configured to determine a congestion coefficient of the target vehicle at the current time according to the neighborhood weight sequence aligned with the congestion vector sequence; a time point determination unit configured to determine a starting time point of the low-power standby mode according to the congestion coefficient of the target vehicle at the current time.

[0015] Compared with the prior art, the electric vehicle low-power standby power supply system for an intersection has the same beneficial effects as the electric vehicle low-power standby power supply method for an intersection, and thus will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 FIG. 1 is a flowchart of the electric vehicle low-power standby power supply method for an intersection according to the present application; Figure 2 FIG. 2 is a flowchart of the generation of the congestion vector sequence according to the present application; Figure 3 FIG. 3 is a flowchart of the construction of the neighborhood weight sequence according to the present application; Figure 4 FIG. 4 is a flowchart of the determination of the congestion coefficient according to the present application; Figure 5 FIG. 5 is a structural block diagram of the electric vehicle low-power standby power supply for an intersection according to the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0018] Embodiment 1: Please refer to Figures 1 to 4 The present application provides an electric vehicle low-power standby power supply method for an intersection, comprising the following steps: S1, determining a to-be-traveled path of a target vehicle on an electronic map; S2, cutting the to-be-traveled path into M road segment units at equal lengths, and constructing a congestion vector sequence of the target vehicle along the to-be-traveled path in the travel direction based on the road segment units; wherein the road segment unit is a continuous line segment divided at equal lengths on the to-be-traveled path.

[0019] S3, anchoring the road segment unit where the target vehicle is located, denoted as a target road segment unit; S4, constructing a neighborhood weight sequence aligned with the congestion vector sequence according to the target road segment unit; S5, determining a congestion coefficient of the target vehicle at the current time according to the neighborhood weight sequence aligned with the congestion vector sequence; S6, determining a starting time point of the low-power standby mode according to the congestion coefficient of the target vehicle at the current time.

[0020] The embodiment determines a to-be-traveled path and divides the to-be-traveled path into road segment units to construct a congestion vector sequence, generates a neighborhood weight sequence aligned with the target road segment unit after anchoring the target road segment unit, determines a congestion coefficient of the target vehicle at the current time based on the sequence, and further determines a starting time point of the low-power standby mode in combination with the coefficient, thereby realizing a coherent control process from path segmentation to power supply triggering and providing a quantifiable and executable control strategy for energy-saving operation of the vehicle at an intersection.

[0021] As a preferred embodiment of the embodiment, the step S1 specifically comprises: S1-1, obtaining a navigation path of the target vehicle on an electronic map; The navigation path is a complete travel route obtained from a map service platform, including a starting point position, an ending point position, and an intersection passed along the way.

[0022] S1-2, positioning a dynamic vehicle point of the target vehicle on the navigation path and a static road point most adjacent to the dynamic vehicle point; The dynamic vehicle point is a real-time geographic coordinate of the target vehicle on the navigation path, and the static road point is a geographic coordinate of the intersection preset in the map service platform, which is fixed in position and associated with a traffic signal control system.

[0023] S1-3, determining a to-be-traveled path of the target vehicle on the navigation path according to the dynamic vehicle point of the target vehicle and the most adjacent static road point; The to-be-traveled path represents a continuous road segment between the dynamic vehicle point of the target vehicle and the most adjacent static road point along the navigation path.

[0024] The embodiment realizes accurate interception of the to-be-traveled path through positioning of the navigation path, the dynamic vehicle point, and the static road point, and ensures that congestion perception and power supply control are based on the current actual travel direction of the target vehicle and the traffic state of the adjacent intersection.

[0025] As a preferred embodiment of the embodiment, the step S2 specifically comprises: S2-1, obtaining road segment parameters in each road segment unit; the road segment parameters include: number of lanes, total number of vehicles along the line, and average travel speed; S2-2, performing parameter normalization on the road segment parameters in each road segment unit to generate road segment parameter features; S2-3, perform feature splicing on the parameter features of the plurality of road section units in a preset order to generate a road congestion vector of each road section unit; Specifically, the parameter normalization adopts a minimum-maximum normalization method. The values of the same type of road section parameters in the M road section units are linearly mapped to the interval [0, 1] based on the global maximum value and the minimum value of the parameter at the current time to obtain the road section parameter feature, thereby eliminating the dimensional differences between different parameters. Wherein, each parameter is independently normalized, that is, the number of lanes, the total number of vehicles along the line and the average driving speed are independently normalized according to their respective maximum and minimum values.

[0026] S2-4, based on the driving direction of the to-be-traveled path, assigning a monotonically increasing road section number to the M road section units; Wherein, the road section number is a continuous integer number assigned to the M road section units in sequence according to the driving direction of the vehicle, the starting road section unit number is 0, and the subsequent road section units are sequentially increased, which is used to identify the relative position sequence of each road section unit in the to-be-traveled path.

[0027] S2-5, according to the road section number, arranging the road congestion vectors of the M road section units in sequence to generate a congestion vector sequence along the driving direction at the current time; Wherein, the congestion vector sequence is an ordered set of road congestion vectors arranged in order of road section number from small to large, representing a multi-dimensional traffic state sequence of each road section unit in front of the target vehicle along the driving direction, and used to represent the congestion distribution characteristics on the path.

[0028] In this embodiment, the normalized road congestion vectors are arranged in order through the road section number, forming a congestion vector sequence that is continuously distributed along the driving direction. This sequence not only retains the multi-dimensional traffic state of each road section unit, but also expresses the congestion spatial distribution of the front path through the position sequence, providing an ordered input for congestion analysis.

[0029] As a preferred embodiment of the present embodiment, the step S4 specifically comprises: S4-1, calculating the along-road driving distance between the target road section unit and the other M-1 road section units; S4-2, based on the along-road driving distances of the M-1 road section units, calculating M-1 neighborhood influence weights; The calculation formula of the neighborhood influence weight is: ; Wherein, represents the neighborhood influence weight of the i-th non-target road section unit; represents the along-road travel distance between the target road segment unit and the i-th non-target road segment unit, i represents the index of the road segment unit, and i>1, used to represent that the i-th road segment unit only selects the front road segment unit of the target road segment unit; represents the total length of the to-be-traveled path.

[0030] Specifically, the neighborhood influence weight is based on the relative distance proportion between the road segment unit and the target vehicle, and a calculation formula of a square attenuation structure is constructed, so that the farther the road segment unit, the smaller the congestion influence on the target road segment unit, and the influence decreases at an accelerated rate with the increase of distance, reflecting the adaptive adjustment of the neighborhood weight of the traffic state.

[0031] Exemplarily, the adaptive adjustment is reflected in: If the path is very long (such as 500m), even if the along-road travel distance =100m, the neighborhood influence weight is 0.64; If the path is very short (such as 50m), even if the along-road travel distance =10m, the neighborhood influence weight is also 0.64; It can be seen that the same relative distance proportion corresponds to the same weight, realizing adaptive weight distribution under different path lengths.

[0032] S4-3, regarding the neighborhood influence weight of the target road segment unit as 1, adding it to the M-1 neighborhood influence weights to obtain the neighborhood influence weights of the M road segment units; It should be noted that the neighborhood influence weight of the target road segment unit is 1, which means that its own traffic state is taken as a reference value in congestion perception and is not attenuated, so as to ensure that the vehicle has the highest response weight for the real-time congestion characteristics of the location.

[0033] S4-4, according to the road segment number, arranging the neighborhood influence weights of the M road segment units in sequence to form a neighborhood weight sequence aligned with the congestion vector sequence; The neighborhood weight sequence is an ordered set of neighborhood influence weights arranged in ascending order of road segment number, and each sequence element corresponds to a neighborhood influence weight of a road segment unit, which is used to spatially weight the congestion vector sequence and reflect the traffic state representation of "near heavy and far light".

[0034] The embodiment constructs the neighborhood influence weight of the square attenuation structure by the relative proportion of the distance along the road and the total length of the path, and generates a neighborhood weight sequence aligned with the congestion vector sequence based on the road segment number, so that the weight distribution of each road segment unit not only reflects its relative position in space with the target road segment unit, but also adapts to the traffic state representation under different path lengths, ensuring that the closer the distance, the greater the influence on the current congestion state, and the target road segment itself has the highest weight, realizing the adaptive quantization and ordered matching of the spatial neighborhood influence.

[0035] As a preferred embodiment of the embodiment, the step S5 specifically comprises: S5-1, constructing a weighted congestion vector sequence based on the neighborhood weight sequence and the congestion vector sequence; S5-2, extracting the congestion coefficient of the target vehicle at the current time in the weighted congestion vector sequence.

[0036] The step S5-1 further comprises the following specific sub-steps: S5-1-1, anchoring the neighborhood influence weight and the road segment congestion vector of the same road segment number in the neighborhood weight sequence and the congestion vector sequence; Specifically, the anchoring refers to extracting the neighborhood influence weight at the kth position from the neighborhood weight sequence and extracting the road segment congestion vector at the kth position from the congestion vector sequence according to the corresponding relationship of the road segment number, ensuring that they come from the same geographical road segment unit, and realizing the spatial position alignment.

[0037] S5-1-2, multiplying the neighborhood influence weight and the road segment congestion vector of the same road segment number item by item to obtain the corresponding weighted congestion vector of each road segment unit; Wherein, the item-by-item multiplication means that the neighborhood influence weight is multiplied with each feature component in the road segment congestion vector respectively to obtain the weighted components of each feature component, and then the weighted components are vector spliced in the same preset order to generate the weighted congestion vector of the corresponding road segment unit, which is used to represent the multi-dimensional traffic state adjusted by the spatial neighborhood weight.

[0038] S5-1-3, arranging the weighted congestion vectors of the M road segment units in sequence according to the road segment number to generate a weighted congestion vector sequence in the driving direction at the current time.

[0039] Specifically, the weighted congestion feature sequence is an ordered set of weighted congestion feature vectors arranged in ascending order of road segment number, each element corresponds to a road segment unit, and the feature dimension is consistent with the original road segment congestion vector. The sequence not only retains the multi-dimensional traffic state of each road segment, but also realizes the differential weighting of spatial influence through neighborhood weight, effectively representing the local congestion distribution of the target vehicle at the current time.

[0040] In the embodiment, by multiplying the neighborhood weight sequence and the congestion vector sequence on the basis of the alignment of the road segment numbers, the differentiated weighting of the congestion features of each road segment is realized, ensuring that the farther the road segment is from the target road segment, the smaller the influence of its traffic state on the overall state, and the weighted congestion vector still maintains the original dimension and spatial order, forming a weighted congestion vector sequence with consistent structure, thereby providing a data basis with reasonable spatial weighting and clear position relationship for extracting the congestion intensity of the area where the target vehicle is located.

[0041] Further, the step S5-2 further includes the following specific sub-steps: S5-2-1, in the weighted congestion vector sequence, the M vector moduli of the weighted congestion vectors are calculated; For example, the vector modulus is obtained by calculating the Euclidean norm of the weighted congestion feature vector corresponding to each road segment unit, which is used to represent the measurement of the overall congestion intensity of the corresponding road segment unit. The greater the value, the higher the degree of deviation of the multi-dimensional traffic state of the road segment from the "smooth" benchmark, that is, the more serious the congestion.

[0042] S5-2-2, the normalized modulus and the modulus variance of the M vector moduli are calculated; Specifically, the normalized modulus represents that the vector moduli of the M road segment units are mapped to the [0, 1] interval through the minimum-maximum normalization method, which is used to eliminate the dimensional difference of the absolute values of the moduli in different traffic scenarios. The M normalized moduli can reflect the relative intensity level of each road segment unit relative to the most serious congestion segment in the current path.

[0043] Further, the calculation formula of the modulus variance is: ; Wherein, represents the vector modulus of the kth road segment unit, represents the mean of the M vector moduli, represents the modulus variance.

[0044] The modulus variance represents the dispersion degree of the M vector moduli around the mean, which is used to represent the spatial distribution non-uniformity of the congestion state on the path to be traveled. The greater the modulus variance, the more significant the local congestion area (such as sudden congestion or slow driving in front) in the path, that is, the traffic flow changes dramatically. The smaller the modulus variance, the closer the congestion degree of each road segment, and the overall tends to be stable or consistent smooth / congestion.

[0045] S5-2-3, according to the normalized modulus and the modulus variance, the congestion coefficient of the target vehicle at the current time is determined.

[0046] The calculation formula of the congestion coefficient is: ; wherein, represents the mean value of M normalized vector lengths, representing the overall congestion intensity of the to-be-traveled path, and can represent "how much is the average congestion", if the whole path is smooth → close to 0; if the whole path is severely congested → close to 1; represents the mean value of M vector lengths, represents the mutation term, when the current side suddenly changes from smooth to congestion, the variance is large → the term tends to 1, when the state of each section approaches, the variance is small → the term tends to 0, is used for adaptive scaling of the denominator to avoid false triggering of small congestion; represents that the "overall congestion intensity" and the "congestion mutation degree" are fused with equal weight, without favoring either side, and embodies the "intensity + change" dual judgment; represents the congestion coefficient of the target vehicle at the current time.

[0047] To verify the effectiveness of the real congestion coefficient in the actual traffic scene, the present application carries out a simulation experiment based on typical urban road traffic data. Four types of representative traffic states are selected as test scenes, and 3 section units (M=3) are set under each scene, and the vector length is calculated by historical traffic flow data and neighborhood weighted algorithm, reflecting the comprehensive congestion intensity of each section.

[0048] The indicators in the table are as follows: The scene description in the table is as follows: ① Whole path is smooth Description: Few vehicles, stable speed.

[0049] Performance: Low vector length and mutation term, congestion coefficient is 0.09.

[0050] ② Uniform slow driving Description: Overall traffic is slow but stable.

[0051] Performance: Medium vector length, no obvious mutation, congestion coefficient is 0.255.

[0052] ③ Sudden congestion Description: Local severe congestion, other sections are smooth.

[0053] Performance: The length of a certain section increases significantly, the mutation term is high, and the congestion coefficient is 0.39.

[0054] ④ Whole path is severely congested Description: Whole line traffic is dense, and traffic is difficult.

[0055] Performance: The lengths of all sections are close to the maximum value, and the congestion coefficient reaches the highest level 0.50.

[0056] In this embodiment, the data of the above four scenarios is derived from the historical GPS trajectory and signal control data of a certain city intelligent transportation platform, and the road section level weighted congestion vector is generated after feature processing. The experimental results show that the calculation method can output reasonable and interpretable congestion coefficients under different traffic modes, and has good engineering application value.

[0057] In this embodiment, the vector module length of the weighted congestion vector sequence is calculated, and the normalized module length mean and the mutation term driven by the variance are fused to generate the congestion coefficient, which realizes the joint representation of the overall congestion intensity and the local mutation characteristics of the path. The congestion coefficient can not only reflect the "average congestion degree", but also capture "whether there is a sudden congestion in front", and has reasonable discrimination in typical scenes such as smooth, slow and local congestion, providing a stable and forward-looking basis for the triggering decision of the low-power standby mode.

[0058] As a preferred embodiment of this embodiment, the step S6 specifically comprises: S6-1, calculating the time interval of the target vehicle reaching the static road point according to the congestion coefficient of the target vehicle, the path length of the to-be-traveled path, and the average speed of the to-be-traveled path; The calculation formula of the time interval is: ; Among them: : the time interval of the target vehicle reaching the static road point (such as the stop line), unit: second (s); : the path length of the to-be-traveled path, unit: meter (m); : the historical or real-time average speed of the to-be-traveled path, unit: m / s; : speed fluctuation coefficient, value range (0, 1), used to control the adjustment range of the upper and lower limits of the speed (recommended value 0.3~0.5), to avoid the denominator being zero.

[0059] Specifically, the time interval is a relative time difference relative to the current time. For example: If the current system time is 10:00:00, the arrival time interval is calculated as [15s, 25s], and the target vehicle is expected to arrive at the static road point between 10:00:15 and 10:00:25. The absolute time range can be compared with the signal light period to determine whether it will arrive during the red light period.

[0060] S6-2, obtaining the nearest red light time interval and determining the overlapping time interval thereof and the time interval; The overlapping time interval represents the start and end time of obtaining the next red light period from the traffic signal control system or V2X communication, which is time-aligned with the above-mentioned arrival time window to determine whether there is an intersection; If there is an intersection, it means that the target vehicle has a probability of arriving at the intersection during the red light period, and needs to enter the low-power standby mode; otherwise, there is no need to start S6-3, according to the start time stamp of the overlapping time interval and the preset response preparation time, determine the starting time point of the low-power standby mode, and send the starting instruction of the low-power standby mode at the starting time point.

[0061] The start time stamp of the overlapping time interval is the larger value of the red light start time and the earliest arrival time of the target vehicle, which is used to ensure that the standby mode is triggered only when the vehicle needs to stop and wait during the red light period; The response preparation time is the minimum time required for the vehicle-mounted control system to complete the power supply mode switching, which is determined by the controller hardware response delay, power management module switching time and other factors, and is usually 2-5 seconds.

[0062] Exemplarily: The red light start time is 9:30:25; The earliest arrival time of the vehicle is 9:30:22; The start time stamp of the overlapping time interval is max(9:30:25, 9:30:22)=9:30:25 The response preparation time is 3 seconds; The starting time point is 9:30:25-3s=9:30:22; Therefore, the starting instruction of the low-power standby mode needs to be sent at 9:30:22 to make the vehicle power supply system complete the switching before the red light is on, which not only avoids unnecessary energy consumption caused by entering the standby mode too early, but also ensures the timely and reliable mode switching.

[0063] In this embodiment, the driving speed range is dynamically adjusted by the congestion coefficient to determine the arrival time interval, and the red light time interval is combined to determine whether the vehicle needs to wait at the intersection, and then the starting instruction is sent in advance by the preset preparation time based on the start time of the overlapping time interval, which realizes the accurate triggering of the low-power standby mode, ensures the timeliness of power supply switching, and avoids the waste of energy caused by early or false triggering.

[0064] In summary, the application intercepts the to-be-traveled path of the target vehicle to the adjacent static road point on the electronic map, divides it into equal-length path units, constructs a congestion vector sequence along the driving direction, generates a neighborhood weight sequence aligned with the target path unit, realizes spatial weighting of the front traffic state, calculates and normalizes the weighted congestion vector length, extracts the congestion coefficient considering the overall congestion intensity and local mutation characteristics, and finally combines the coefficient to predict the arrival time interval, match the red light period and trigger the low-power standby mode in advance. The whole process realizes the closed-loop decision from path identification to power supply control, so that the start of the standby mode responds to the actual traffic congestion level and meets the time accuracy requirement, effectively improving the energy utilization efficiency of electric vehicles in the intersection scene.

[0065] Embodiment 2: refer to Figure 5 The technical scheme of this embodiment 2 is different from that of embodiment 1 in that a low-power standby power supply system for electric vehicles at intersections is also disclosed, which is used to implement the above-mentioned method embodiments, and has been described and will not be repeated. The terms "module", "unit", "sub-unit" and the like used below can be a combination of software and / or hardware that can realize the predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and is conceived.

[0066] As Figure 5 shown, Figure 5 is a structural block diagram of a low-power standby power supply system for electric vehicles at intersections, which includes: a path determination unit for determining the to-be-traveled path of the target vehicle on the electronic map; a congestion sequence construction unit for cutting the to-be-traveled path into M path units at equal length, and constructing a congestion vector sequence of the target vehicle along the driving direction on the to-be-traveled path based on the path units; a target anchoring unit for anchoring the path unit where the target vehicle is located, denoted as the target path unit; a weight sequence construction unit for constructing a neighborhood weight sequence aligned with the congestion vector sequence according to the target path unit; a congestion coefficient determination unit for determining the congestion coefficient of the target vehicle at the current time according to the neighborhood weight sequence aligned with the congestion vector sequence; a time point determination unit for determining the starting time point of the low-power standby mode according to the congestion coefficient of the target vehicle at the current time.

[0067] In the system, the to-be-traveled path of the target vehicle is determined by the path determination unit, the congestion vector sequence of the target vehicle on the to-be-traveled path along the travel direction is constructed by the congestion sequence construction unit, the target road section unit is anchored by the target anchoring unit, the neighborhood weight sequence is constructed by the weight sequence construction unit, the congestion coefficient of the target vehicle at the current time is determined by the congestion coefficient determination unit, and the starting time point of the low-power standby mode is determined by the time point determination unit, thereby solving the problem of untimely response of the energy-saving strategy.

[0068] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (for example, infrared, wireless, microwave, etc.) or wireless means.

[0069] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A low-power standby power supply method for an electric vehicle at an intersection, characterized by, The method comprises the following steps: S1, determining a to-be-traveled path of a target vehicle on an electronic map; S2, cutting the to-be-traveled path into M road segment units according to equal length, and constructing a congestion vector sequence of the target vehicle on the to-be-traveled path along a traveling direction based on the road segment units; S3, anchoring a road segment unit where the target vehicle is located, and marking the road segment unit as a target road segment unit; S4, constructing a neighborhood weight sequence aligned with the congestion vector sequence according to the target road segment unit; S5, determining a congestion coefficient of the target vehicle at a current time according to the neighborhood weight sequence aligned with the congestion vector sequence; S6, determining a starting time point of a low-power standby mode according to the congestion coefficient of the target vehicle at the current time.

2. The method for low-power standby power supply of electric vehicles at intersections according to claim 1, characterized in that, The step of determining the to-be-traveled path of the target vehicle on the electronic map comprises the following steps: S1-1, obtaining a navigation path of the target vehicle on the electronic map; S1-2, locating a dynamic vehicle point of the target vehicle on the navigation path, and a static road point closest to the dynamic vehicle point; S1-3, determining the to-be-traveled path of the target vehicle on the navigation path according to the dynamic vehicle point and the closest static road point of the target vehicle.

3. The method for low power standby power supply for electric vehicles at intersections according to claim 2, characterized in that, The step of constructing the congestion vector sequence of the target vehicle along the traveling direction at the current time according to the M road segment units comprises the following steps: S2-1, obtaining road segment parameters in each road segment unit; the road segment parameters comprise the number of lanes, the total number of vehicles along the line, and the average traveling speed; S2-2, performing parameter normalization on the road segment parameters in each road segment unit to generate road segment parameter features; S2-3, performing feature splicing on the road segment parameter features in a preset order to generate road congestion vectors of the road segment units; S2-4, assigning monotonically increasing road segment numbers to the M road segment units based on the traveling direction of the to-be-traveled path; S2-5, arranging the road congestion vectors of the M road segment units in sequence according to the road segment numbers to generate the congestion vector sequence along the traveling direction at the current time.

4. The method for low-power standby power supply of electric vehicles at intersections according to claim 3, characterized in that, The step of constructing the neighborhood weight sequence aligned with the congestion vector sequence according to the target road segment unit comprises the following steps: S4-1, calculating the along-road traveling distances between the target road segment unit and other M-1 road segment units; S4-2, calculating M-1 neighborhood influence weights based on the along-road traveling distances of the M-1 road segment units; S4-3, regarding the neighborhood influence weight of the target road segment unit as 1, adding the neighborhood influence weight to the M-1 neighborhood influence weights to obtain neighborhood influence weights of the M road segment units; S4-4, arranging the neighborhood influence weights of the M road segment units in sequence according to the road segment numbers to construct the neighborhood weight sequence aligned with the congestion vector sequence.

5. The method for low power standby power supply for electric vehicles at intersections according to claim 4, characterized in that, The step of determining the congestion coefficient of the target vehicle at the current time according to the neighborhood weight sequence aligned with the congestion vector sequence comprises the following steps: S5-1, constructing a weighted congestion vector sequence based on the neighborhood weight sequence and the congestion vector sequence; S5-2, extracting the congestion coefficient of the target vehicle at the current time in the weighted congestion vector sequence.

6. The method for low power standby power supply for electric vehicles at intersections according to claim 5, characterized in that, The step of constructing the weighted congestion vector sequence based on the neighborhood weight sequence and the congestion vector sequence comprises the following steps: S5-1-1, anchoring the neighborhood influence weight and the road congestion vector of the same road segment number in the neighborhood weight sequence and the congestion vector sequence. S5-1-2, multiply the neighborhood influence weight and the road congestion vector of the same road section number item by item to obtain a weighted congestion vector corresponding to each road section unit; S5-1-3, according to the road section number, arrange the weighted congestion vectors of the M road section units in sequence to generate a weighted congestion vector sequence in the driving direction at the current time.

7. The method for low power standby power supply for electric vehicles at intersections according to claim 5, characterized in that, In the weighted congestion vector sequence, the congestion coefficient of the target vehicle at the current time is extracted, including: S5-2-1, in the weighted congestion vector sequence, the M vector lengths of the weighted congestion vectors are calculated; S5-2-2, the normalized length and length variance of the M vector lengths are calculated; S5-2-3, according to the normalized length and length variance, the congestion coefficient of the target vehicle at the current time is determined.

8. The method for low power standby power supply for electric vehicles at intersections according to claim 5, characterized in that, According to the congestion coefficient of the target vehicle at the current time, the starting time point of the low-power standby mode is determined, including: S6-1, according to the congestion coefficient of the target vehicle, the path length of the to-be-traveled path, and the average speed of the to-be-traveled path, the time interval of the target vehicle reaching the static road point is calculated; S6-2, the nearest red light time interval is obtained, and the overlapping time interval thereof with the time interval is determined; S6-3, according to the starting time stamp of the overlapping time interval and the preset response preparation time length, the starting time point of the low-power standby mode is determined, and the starting instruction of the low-power standby mode is sent at the starting time point.

9. A low-power standby power supply system for electric vehicles at an intersection for carrying out the low-power standby power supply method according to any one of claims 1 to 8, characterized by The system comprises: a path determination unit configured to determine a to-be-traveled path of a target vehicle on an electronic map; a congestion sequence construction unit configured to cut the to-be-traveled path into M road section units according to length, and to construct a congestion vector sequence of the target vehicle in the driving direction on the to-be-traveled path based on the road section units; a target anchoring unit configured to anchor a road section unit where the target vehicle is located, denoted as a target road section unit; a weight sequence construction unit configured to construct a neighborhood weight sequence aligned with the congestion vector sequence according to the target road section unit; a congestion coefficient determination unit configured to determine a congestion coefficient of the target vehicle at a current time according to the neighborhood weight sequence aligned with the congestion vector sequence; a time point determination unit configured to determine a starting time point of a low-power standby mode according to the congestion coefficient of the target vehicle at the current time.

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