An online and offline combined REV intelligent energy management system and method

Abstract

This invention discloses an online and offline combined REV intelligent energy management system, which includes an information processing module that obtains route sequence label codes and mileage based on vehicle navigation information; a real-time location information identification module that obtains congestion information and average vehicle speed based on the sequence label code corresponding to the current driving segment; a map information update module that determines whether to update the navigation information of the corresponding driving segment based on whether the information corresponding to the current driving segment is consistent with the corresponding information recorded in the navigation map and whether the selected route distance has reached a set value; a working condition generation module that determines whether to generate working condition segments based on congestion conditions, derives the working condition time-speed curve from the working condition segments, and calculates the energy consumption of the working condition segments; and a SOC adjustment module that dynamically adjusts the SOC based on the adjusted target SOC value, vehicle navigation information, and energy consumption. This invention can achieve future road prediction through online mapping and achieve vehicle optimization control through offline calibration.

Description

Technical Field

[0001] This invention relates to the field of energy-saving technology for passenger vehicles, specifically to an online and offline combined REV intelligent energy management system and method. Background Technology

[0002] Since the development of hybrid vehicles, decarbonization has always been the primary goal, and intelligent energy management strategies are one of the important means for hybrid vehicles to achieve decarbonization in the future.

[0003] Currently, there are two main energy management strategies in the industry. One is a rule-based energy management strategy, which generally adopts an offline calibration-based strategy. Its advantages are stability and controllability, and low computing power requirements. However, its energy-saving effect mainly depends on the calibration conditions, calibration experience, and the vehicle's status during user operation, making it difficult to achieve energy-saving effects across all scenarios. The other is an intelligent energy management strategy, which generally adopts an algorithm-based predictive energy management strategy. Its principle is to predict possible operating conditions by calculating future vehicle speed or acceleration using algorithms, and to achieve fuel saving by changing the engine's operating state, speed, and torque. However, this method requires high computing power, and the fuel-saving effect and control stability depend entirely on the algorithm, so it has not yet been mass-produced on a large scale.

[0004] Therefore, how to create an intelligent energy management strategy that can simultaneously achieve online map-based future road prediction and offline calibration for optimized vehicle control, thereby improving energy management efficiency, has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide an online and offline integrated REV intelligent energy management system. This invention can predict future roads using online maps and optimize vehicle control through offline calibration. This method ensures the accuracy of prediction and the stability of vehicle control.

[0006] To achieve this objective, the present invention provides an online and offline combined REV intelligent energy management system, which includes:

[0007] The information processing module performs road segment planning processing on the vehicle navigation information to obtain the route sequence label code of all the road segments to be passed through generated by the navigation map and the driving mileage of the selected speed range in the trip;

[0008] The real-time location information recognition module identifies the sequence label code corresponding to the current driving segment from the sequence label code of all road segments to be passed through in the vehicle navigation information based on the latitude and longitude. It then obtains the congestion information and average speed of the current driving segment from the sequence label code corresponding to the current driving segment.

[0009] The map information update module sets the distance of the selected route to an initial value based on the route selected in the navigation. If the sequence label code corresponding to the current driving segment is inconsistent with the corresponding sequence label code recorded in the navigation map and the distance of the selected route reaches the set value, the navigation information of the corresponding driving segment in the vehicle navigation information is updated using the navigation information of the current driving segment.

[0010] The vehicle condition generation module determines whether to generate a vehicle condition segment based on the current road congestion information, obtains the vehicle condition time-speed curve based on the generated vehicle condition segment, and calculates the energy consumption information of the vehicle condition segment based on the vehicle condition time-speed curve.

[0011] The SOC adjustment module adjusts the target SOC value based on the average vehicle speed of the current driving segment, energy consumption information of the operating segment, and the driving distance of the selected speed range during the trip. Based on the adjusted target SOC value, the updated vehicle navigation information, and the energy consumption of the electric vehicle, the target SOC is dynamically adjusted, thereby controlling the vehicle power source distribution ratio.

[0012] Preferably, the specific method for determining whether the working condition generation module generates a working condition segment is as follows: if the current road segment is congested and the next road segment is congested, then no working condition segment is generated; if the current road segment is congested and the next road segment is not congested, then a working condition segment is generated; if the current road segment is not congested, then a working condition segment is generated.

[0013] Preferably, the specific method for deriving the vehicle time-speed curve based on the generated vehicle operating condition segments is as follows: Based on the starting point and ending point of the generated operating condition segments, using the distance between the starting point and ending point of the corresponding generated operating condition segments, the estimated travel time between the starting point and ending point of the generated operating condition segments, the number of traffic lights, the location of traffic lights, the cycle of traffic lights, the road type, the road speed limit, road condition information, the average travel speed of the operating condition segments, and the stopping time segments, combined with a preset Markov segment library, feature value matching is performed to obtain the operating condition time-speed curve.

[0014] Preferably, the method for obtaining the start and end points of the generated work condition segment is as follows: Based on the work condition generation status flag, determine whether to retrieve the start and end points of the generated work condition segment. If the current road segment is congested and the next road segment is congested, then there is no need to retrieve the start and end points of the generated work condition segment. If the current road segment is congested and the next road segment is not congested, then the start and end points of the generated work condition segment are both the current road segment sequence label code. If the current road segment is not congested, then the start point of the generated work condition segment is the sequence label code of the most congested road segment closest to the current road segment among all the road segments that need to be passed through, and this sequence label code is recorded as the start point of the generated work condition segment. The last road segment sequence label code among the adjacent and consecutive congested road segments among all the road segments that need to be passed through is used as the end point of the generated work condition segment.

[0015] The beneficial effects of this invention are:

[0016] This invention proposes an online and offline combined REV intelligent energy management system. It fully utilizes the big data prediction capabilities of navigation, retains the original vehicle control stability, and combines existing development experience and systems. It enables parallel development of offline calibration and online map prediction, and can simultaneously realize online map prediction for future roads and offline calibration for optimized vehicle control, effectively shortening the serial cycle. It can be directly applied to the intelligent energy management design and development of REV models. The technical principle of this method is simple and the technical cost is low, which helps to realize the decarbonization of hybrid vehicles and improve the efficiency of intelligent energy management and development. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the present invention;

[0018] Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0021] Example 1

[0022] A hybrid online and offline REV smart energy management system, such as Figure 1 As shown, it includes:

[0023] The information processing module performs road segment planning processing on the vehicle navigation information to obtain the route sequence label code of all the road segments to be passed through generated by the navigation map and the driving mileage of the selected speed range in the trip;

[0024] The real-time location information recognition module identifies the sequence label code corresponding to the current driving segment from the sequence label code of all road segments to be passed through in the vehicle navigation information based on the latitude and longitude. It then obtains the congestion information and average speed of the current driving segment from the sequence label code corresponding to the current driving segment.

[0025] The map information update module sets the distance of the selected route to an initial value based on the route selected in the navigation. If the sequence label code corresponding to the current driving segment is inconsistent with the corresponding sequence label code recorded in the navigation map and the distance of the selected route reaches the set value, the navigation information of the corresponding driving segment in the vehicle navigation information is updated using the navigation information of the current driving segment.

[0026] The vehicle condition generation module determines whether to generate a vehicle condition segment based on the current road congestion information, obtains the vehicle condition time-speed curve based on the generated vehicle condition segment, and calculates the energy consumption information of the vehicle condition segment based on the vehicle condition time-speed curve.

[0027] The SOC adjustment module adjusts the target SOC value based on the average vehicle speed of the current driving segment, energy consumption information of the operating segment, and the driving distance of the selected speed range during the trip. Based on the adjusted target SOC value, the updated vehicle navigation information, and the energy consumption of the electric vehicle, the target SOC is dynamically adjusted, thereby controlling the vehicle's power source distribution ratio and thus performing intelligent energy management of the vehicle.

[0028] In the above technical solution, considering user travel habits, online map navigation is not an essential operation. To ensure the normal operation of the vehicle's functional logic, the intelligent energy management strategy of the REV model is integrated into a certain driving mode (the naming may vary depending on the company, commonly 'pure energy priority', 'hybrid', etc.). The activation conditions of this intelligent mode are as follows:

[0029] Users need to select the intelligent mode to enter this policy. If the user selects another mode, the control logic of that other mode will be executed. Users need to activate navigation and select a driving route for the policy to take effect. If the user does not activate navigation, or activates navigation but does not select a route, or activates a route but exits midway, the policy will not take effect and the default mode policy, such as pure electric priority mode, will be executed.

[0030] In the above technical solution, the map information update module includes a navigation update flag, used to determine whether the navigation information of the current driving segment needs to be updated. If the sequence label code corresponding to the current driving segment is inconsistent with the corresponding sequence label code recorded in the navigation map and the distance of the selected route reaches a set value, the navigation update flag is 1, indicating that an update is needed, and the navigation information of the corresponding driving segment in the vehicle navigation information is updated using the navigation information of the current driving segment. If the sequence label code corresponding to the current driving segment is inconsistent with the corresponding sequence label code recorded in the navigation map and the distance of the selected route does not reach the set value, the navigation update flag is 0, indicating that an update is not needed. If the sequence label code corresponding to the current driving segment is consistent with the corresponding sequence label code recorded in the navigation map, the navigation update flag is 0, indicating that an update is not needed.

[0031] In the above technical solution, the road segment planning process is based on the planned route returned by the vehicle navigation. Initially, there are three paths. The navigation will automatically classify and segment the road segments. The navigation map generates the passing sequence label code of all road segments that need to be passed, and the driving sequence is marked according to the classification and segmentation of the road segments.

[0032] In the above technical solution, the road conditions in the map information update module are dynamically changing, so it is not possible to control the entire process based solely on the initial navigation information. Real-time updates are required, but excessively frequent updates place high demands on hardware computing power and storage. Therefore, it is necessary to limit the update frequency by generating status flags and distances based on operating conditions.

[0033] In the above technical solution, the dynamic adjustment is based on the original SOC target value plus the estimated △SOC to achieve the adjustment of the target value; the dynamic allocation of energy is mainly based on the SOC target value. If the vehicle SOC is higher than the SOC target value, the control logic and control parameters will use more electricity and less fuel. If the vehicle SOC is lower than the SOC target value, the control logic and control parameters will use more fuel and less electricity.

[0034] In the above technical solution, the average vehicle speed and congestion status are obtained directly from the navigation APP (such as Gaode).

[0035] In the above technical solution, the information processing module is responsible for processing the vehicle navigation information for route planning, generating the sequence label code of the route segments and the driving mileage of the selected speed range during the trip. By planning the sequence of all the route segments to be passed in advance, the vehicle can drive in an orderly manner according to the predetermined route, reducing hesitation and errors during driving.

[0036] In the above technical solution, the real-time location information recognition module identifies the sequence label code corresponding to the current driving segment based on the latitude and longitude in the vehicle navigation information, and obtains the congestion information and average speed of the current driving segment. By identifying the sequence label code of the road segment where the vehicle is located in real time, the real-time location of the vehicle can be accurately grasped, providing an accurate basis for subsequent navigation information updates and operating condition generation; obtaining the congestion information and average speed of the current driving segment helps to understand road condition changes in a timely manner, providing real-time data support for adjusting the vehicle's driving strategy.

[0037] In the above technical solution, the map information update module updates the corresponding navigation information of the vehicle navigation information based on the selected route in the navigation. If the sequence label code of the current driving segment is inconsistent with the navigation map record and a set distance is reached, the module updates the navigation information of the corresponding driving segment in the vehicle navigation information using the navigation information of the current driving segment. This allows for dynamic updates of the vehicle navigation information, ensuring the accuracy and timeliness of the navigation information and avoiding driving deviations or errors caused by outdated navigation information. When the route changes or the navigation map is updated, the module can adjust the navigation information in a timely manner, enabling the vehicle to travel smoothly along the new route.

[0038] In the above technical solution, the operating condition generation module determines whether to generate vehicle operating condition segments based on the current road congestion information. After generation, it obtains the operating condition time-speed curve and calculates the energy consumption information of the vehicle operating condition segments. It can generate vehicle operating condition segments based on actual road conditions, providing detailed and accurate operating condition data for vehicle energy consumption analysis and power source allocation. By calculating the energy consumption of operating condition segments, it is possible to more accurately grasp the energy consumption performance of vehicles under different road conditions, providing a reliable basis for subsequent SOC adjustments.

[0039] In the above technical solution, the SOC adjustment module dynamically adjusts the target SOC value based on the average vehicle speed of the current driving segment, energy consumption information of the operating segment, and the mileage of the selected speed range during the trip, and controls the power source distribution ratio of the vehicle. It can dynamically adjust the target SOC value based on real-time data, making the vehicle's energy use more reasonable and improving energy efficiency. By adjusting the power source distribution ratio, the vehicle's power output can be optimized according to actual driving needs and energy consumption, thereby improving driving performance and driving range.

[0040] In the above technical solution, the acquired navigation information includes time, vehicle speed, gradient, longitude, latitude, starting point coordinates, ending point coordinates, starting point distance, ending point distance, estimated travel time from starting point to ending point, number of traffic lights, traffic light locations, traffic light cycle, road type, road speed limit, road condition information, weather and temperature information.

[0041] In the above technical solution, information such as time, vehicle speed, and gradient will affect energy consumption calculation; longitude, latitude, starting point coordinates, and ending point coordinates are used for positioning; starting point distance, ending point distance, estimated travel time from starting point to ending point, number of traffic lights, traffic light location, traffic light cycle, and road speed limit are used for operating condition generation; road type and road condition information are used to support strategy design; weather and temperature are used for energy consumption correction.

[0042] In the above technical solution, the specific method for determining whether the working condition generation module generates a working condition segment is as follows: if the current road segment is congested and the next road segment is congested, then no working condition segment is generated; if the current road segment is congested and the next road segment is not congested, then a working condition segment is generated; if the current road segment is not congested, then a working condition segment is generated.

[0043] In the above technical solution, the congestion state and the non-congestion state are automatically set to the congestion state or the non-congestion state of the road segment based on the vehicle navigation information.

[0044] In the above technical solution, the specific method for deriving the vehicle speed-time curve based on the generated vehicle operating condition segments is as follows: Based on the starting point and ending point of the generated operating condition segments, using the distance between the starting point and ending point of the corresponding generated operating condition segments, the estimated travel time between the starting point and ending point of the generated operating condition segments, the number of traffic lights, the location of traffic lights, the cycle of traffic lights, the road type, the road speed limit, road condition information, the average travel speed of the operating condition segments, and the stopping time segments, combined with a preset Markov segment library, feature value matching is performed to obtain the vehicle speed-time curve.

[0045] In the above technical solution, the method for calculating the average vehicle speed V of the driving condition segment is as follows:

[0046] V = (Distance between start and end points) / (Estimated travel time between start and end points)

[0047] The information for this road segment is derived from navigation data, and therefore includes road condition information for that segment. So, the average speed can be obtained simply by dividing the distance by the time, without needing to calculate the influence of other factors.

[0048] In the above technical solution, the step of combining a preset Markov segment library to perform feature value matching involves extracting segments with similar feature values ​​to form a road condition time-speed curve, where segments with similar feature values ​​mean that the feature value error is within a set range.

[0049] In the above technical solution, the Markov fragment library is a pre-constructed array stored in the vehicle controller. It contains time and vehicle speed information and can calculate characteristic parameters such as average vehicle speed, average running speed, maximum acceleration, maximum deceleration, average acceleration during acceleration, average deceleration during deceleration, idling speed percentage, acceleration percentage, deceleration percentage, and constant speed percentage.

[0050] In the above technical solution, the method for obtaining the starting point and ending point of the generated work condition segment is as follows: Based on the work condition generation status flag, it is determined whether to retrieve the starting point and ending point of the generated work condition segment. If the current road segment is congested and the next road segment is congested, then there is no need to retrieve the starting point and ending point of the generated work condition segment. If the current road segment is congested and the next road segment is not congested, then the starting point and ending point of the generated work condition segment are both the current road segment sequence label code. If the current road segment is not congested, then the starting point of the generated work condition segment is the sequence label code of the congested road segment closest to the current road segment among all the road segments that need to be passed through, and this sequence label code is recorded as the starting point of the generated work condition segment. The last road segment sequence label code among the adjacent and consecutive congested road segments among all the road segments that need to be passed through is used as the ending point of the generated work condition segment.

[0051] In the above technical solution, the working condition generation flag is mainly used to determine whether to generate a working condition. When generating a working condition, it is necessary to aggregate the navigation information to form the speed-time curve of the vehicle's future driving, and process, clean, slice and calculate the navigation information. Frequent calculations place high demands on hardware computing power and storage. Therefore, the working condition generation flag is used to limit the generation of working condition segments and reduce computing power requirements.

[0052] In the above technical solution, the specific calculation formula for the energy consumption of the generated operating condition segment is as follows:

[0053] The energy consumption of electric vehicles mainly comes from three parts: driving energy consumption, high-voltage accessory energy consumption, and low-voltage accessory energy consumption. The formula for calculating driving energy consumption is as follows:

[0054]

[0055] Among them, E drive Let m be the energy consumption during vehicle operation, ∝ be the semi-loaded mass of the vehicle, g be the gradient of the road during the vehicle's operation, t be the acceleration due to gravity, and C be the time taken for a single trip by the user. D δ is the vehicle's drag coefficient, A is the frontal area projected directly in front of the vehicle, v is the vehicle's real-time speed, and δ is the vehicle's rotational mass conversion factor.

[0056] The formula for calculating the consumption of high-voltage accessories is:

[0057]

[0058] Among them, E highvol For the energy consumption of high-voltage components in vehicles, U PTC For PTC voltage, I PTC U is the PTC current. Comp For compressor voltage, I comp This refers to the compressor current.

[0059] The formula for calculating the consumption of low-pressure accessories is:

[0060]

[0061] Among them, E lowvol For the energy consumption of the vehicle's low-voltage components, U DCDC For the voltage of low-voltage components, I DCDC This refers to the total current of the low-voltage components;

[0062] Based on the above formula and the generated operating condition curve, the total energy consumption of the electric vehicle is calculated, i.e.:

[0063] E all =E drive +E highvol +E lowvol

[0064] Finally, the power consumption E all Converted to ΔSOC value, that is:

[0065] △SOC=E all / Bms_energy

[0066] Where △SOC represents the SOC deviation value of the battery that the vehicle needs to charge and discharge. If the current road segment is congested and the next road segment is not congested, then discharge is required, and △SOC is a negative value; if the current road segment is congested and the next road segment is congested, then charge is required, and △SOC is a positive value. all Bms_energy is the amount of energy a vehicle needs to charge and discharge to pass through congested areas, calculated as the energy required for charging and discharging. Bms_energy is the battery capacity of the vehicle's own battery pack.

[0067] In the above technical solution, the operating condition curve can obtain vehicle speed information and the relationship between vehicle speed and time, which can be used for real-time calculation in the energy consumption calculation formula.

[0068] In the above technical solution, the specific method for adjusting the target SOC value is as follows:

[0069] If the average speed of the current road segment is greater than the set speed limit, the vehicle is traveling at high speed; otherwise, the vehicle is traveling at medium to low speed. When the vehicle is traveling at medium to low speed:

[0070] When △SOC≥SOC_limit_up:

[0071] SOC_target_new=SOC_target+SOC_limit_up

[0072] Where, △SOC is the difference between the adjusted value after target value adjustment and the target value, SOC_limit_up is the upper limit value allowed for SOC target value adjustment, SOC_target_new is the adjusted SOC target value, and SOC_target is the SOC target value calibrated by the vehicle itself;

[0073] When △SOC ≥ SOC_limit_up:

[0074] SOC_target_new = SOC_target + SOC_limit_down

[0075] Where, SOC_limit_down is the lower limit value allowed for SOC target value adjustment;

[0076] When SOC_limit_down < △SOC < SOC_limit_up:

[0077] SOC_target_new = SOC_target + △SOC

[0078] When the vehicle is driving at high speed:

[0079] When the high-speed mileage in the trip is greater than the mileage set value, then this journey is a long-distance high-speed drive, otherwise it is a short-distance high-speed drive. When this journey is a long-distance high-speed drive:

[0080] SOC_target_new = SOC_target + SOC_long

[0081] Where, SOC_long is the increased SOC target value when the vehicle is driving at high speed for a long distance;

[0082] When this journey is a short-distance high-speed drive, it is considered that the user will perform a short-distance high-speed drive, and overcharging will lead to high fuel consumption, that is:

[0083] SOC_target_new = SOC_target + SOC_short

[0084] Where, SOC_short is the increased SOC target value when the vehicle is driving at high speed for a short distance, and SOC_short ≤ SOC_long.

[0085] In the above technical solution, considering the user's actual long-distance and short-distance travel situations, it is recommended that the mileage set value be 100 km - 300 km. Considering the user's actual high-speed driving situation and medium-low speed driving situation, it is recommended that the vehicle speed set value be 80 - 120 km / h.

[0086] In the above technical solution, it further includes a controller execution module. Due to the NVH problems caused by the dynamic changes of the SOC, it is necessary to make adaptive changes to the control, that is, to increase the engine start-stop conditions under low-speed conditions:

[0087] When the vehicle speed ≥ Speed_num2 and the SOC ≥ SOC_low, where Speed_num2 is a certain vehicle speed value designed artificially and SOC_low is a certain SOC value designed artificially, the engine starts;

[0088] When the vehicle speed ≤ Speed_num3 and the SOC ≥ SOC_low, where Speed_num3 is a certain vehicle speed value designed artificially and Speed_num2 > Speed_num3, the engine stops;

[0089] When the SOC < SOC_low, the engine starts and charges until the SOC ≥ SOC_high, then the engine stops. SOC_high is a certain SOC value designed artificially, and SOC_high > SOC_low;

[0090] Place control maps such as engine start-stop control and engine operating point control in the vehicle controller. Search for different control instructions through SOC_target_new, and issue the instructions to the engine controller, generator controller, drive motor controller, and battery controller through the vehicle controller. Each controller controls each component to execute the vehicle's instructions, thereby realizing the overall energy management strategy.

[0091] In the above technical solution, by dynamically adjusting the target SOC, the distribution ratio of the vehicle power sources (engine and battery) is controlled to achieve improvements in comprehensive efficiency, increased endurance, and optimized NVH, etc.

[0092] Embodiment 2

[0093] An online and offline combined REV intelligent energy management method, as Figure 2 shown, by selecting the mode of vehicle navigation, processing the navigation information obtained from the in-vehicle navigation; identifying the information of the current location to obtain the congestion situation information and the average vehicle speed of the current driving section; updating the navigation map according to the route selected by the vehicle navigation; judging whether to generate a working condition segment according to the congestion situation of the current section, and obtaining the working condition time-vehicle speed curve according to the generated working condition segment; calculating the energy consumption of the working condition segment according to the working condition energy consumption; adjusting the target SOC value according to the vehicle running speed, the energy consumption situation of the working condition segment, and the high-speed mileage in the journey; making adaptive changes to the control, that is, increasing the engine start-stop conditions under low-speed conditions, thereby realizing the overall energy management strategy.

[0094] The REV intelligent energy management method includes the following steps:

[0095] Based on the road segment planning processing of the vehicle navigation information, the route sequence label code of all the road segments to be passed and the driving mileage of the selected speed range in the trip are obtained by generating the navigation map.

[0096] Based on the latitude and longitude in the vehicle navigation information, identify the sequence label code corresponding to the current driving segment from all the route sequence label codes of the road segments to be passed. Obtain the congestion information and average speed of the current driving segment from the sequence label code corresponding to the current driving segment.

[0097] Based on the route selected in the navigation, the distance of the selected route is set to the initial value. If the sequence label code corresponding to the current driving segment is inconsistent with the corresponding sequence label code recorded in the navigation map and the distance of the selected route reaches the set value, the navigation information of the corresponding driving segment in the vehicle navigation information is updated using the navigation information of the current driving segment.

[0098] Based on the current road congestion information, determine whether to generate vehicle operating condition segments. Based on the generated vehicle operating condition segments, obtain the operating condition time-speed curve, and calculate the energy consumption information of the vehicle operating condition segments based on the operating condition time-speed curve.

[0099] The target SOC value is adjusted based on the average vehicle speed on the current driving segment, energy consumption information of the operating segment, and the driving distance of the selected speed range during the trip. Based on the adjusted target SOC value, the updated vehicle navigation information, and the energy consumption of the electric vehicle, the target SOC is dynamically adjusted to control the vehicle power source distribution ratio.

[0100] Example 3

[0101] A computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 2.

[0102] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A REV intelligent energy management system that combines online and offline operation, characterized in that, It includes: The information processing module performs road segment planning processing on the vehicle navigation information to obtain the route sequence label code of all the road segments to be passed through generated by the navigation map and the driving mileage of the selected speed range in the trip; The real-time location information recognition module identifies the sequence label code corresponding to the current driving segment from the sequence label code of all road segments to be passed through in the vehicle navigation information based on the latitude and longitude. It then obtains the congestion information and average speed of the current driving segment from the sequence label code corresponding to the current driving segment. The map information update module sets the distance of the selected route to an initial value based on the route selected in the navigation. If the sequence label code corresponding to the current driving segment is inconsistent with the corresponding sequence label code recorded in the navigation map and the distance of the selected route reaches the set value, the navigation information of the corresponding driving segment in the vehicle navigation information is updated using the navigation information of the current driving segment. The vehicle condition generation module determines whether to generate a vehicle condition segment based on the current road congestion information. If the current road segment is congested and the next road segment is also congested, then no vehicle condition segment will be generated. If the current road segment is congested and the next road segment is not congested, a driving condition segment is generated; if the current road segment is not congested, a driving condition segment is generated; the driving condition time-speed curve is obtained from the generated vehicle driving condition segment, and the energy consumption information of the vehicle driving condition segment is calculated based on the driving condition time-speed curve. The SOC adjustment module adjusts the target SOC value based on the average vehicle speed of the current driving segment, energy consumption information of the operating segment, and the driving distance of the selected speed range during the trip. Based on the adjusted target SOC value, the updated vehicle navigation information, and the energy consumption of the electric vehicle, the target SOC is dynamically adjusted, thereby controlling the vehicle power source distribution ratio.

2. The REV intelligent energy management system combining online and offline operation according to claim 1, characterized in that: The acquired navigation information includes time, vehicle speed, gradient, longitude, latitude, starting point coordinates, ending point coordinates, starting point distance, ending point distance, estimated travel time from starting point to ending point, number of traffic lights, traffic light locations, traffic light cycle, road type, road speed limit, road condition information, weather and temperature information.

3. The REV intelligent energy management system combining online and offline operation according to claim 1, characterized in that: The specific method for deriving the vehicle speed-time curve based on the generated vehicle operating condition segments is as follows: Based on the start and end points of the generated operating condition segments, using the distance between the start and end points of the corresponding generated operating condition segments, the estimated travel time between the start and end points of the generated operating condition segments, the number of traffic lights, the location of traffic lights, the cycle of traffic lights, the road type, the road speed limit, road condition information, the average travel speed of the operating condition segments, and the stopping time segments, combined with a preset Markov segment library, feature value matching is performed to obtain the operating time-speed curve.

4. The REV intelligent energy management system combining online and offline operation according to claim 3, characterized in that: The method for obtaining the start and end points of the generated work condition segment is as follows: Based on the work condition generation status flag, determine whether to retrieve the start and end points of the generated work condition segment. If the current road segment is congested and the next road segment is congested, then there is no need to retrieve the start and end points of the generated work condition segment. If the current road section is congested and the next road section is not congested, the starting point and the ending point of the generated driving condition segment are both the sequence label code of the current road section. If the current road section is not congested, the starting point of the generated driving condition segment is found by looking backward from the sequence label code of the current road section. The sequence label code of the congested road section closest to the current road section among the congestion conditions of all the road sections to be passed through is used as the starting point, and the sequence label code of the road section is recorded as the starting point of the generated driving condition segment. The last sequence label code among the adjacent and consecutive sequence label codes of the congested road sections in the congestion conditions of all the road sections to be passed through is used as the ending point of the generated driving condition segment.

5. The REV intelligent energy management system combining online and offline operation according to claim 1, characterized in that: The specific calculation formula for generating the energy consumption of the driving condition segment is as follows: The energy consumption of an electric vehicle mainly comes from three major parts: driving consumption, high-voltage accessory consumption, and low-voltage accessory consumption. The calculation formula for driving energy consumption is as follows: Among them, E drive Let m be the energy consumption during vehicle operation, ∝ be the semi-loaded mass of the vehicle, g be the gradient of the road during the vehicle's operation, t be the acceleration due to gravity, and C be the time taken for a single trip by the user. D δ is the vehicle's drag coefficient, A is the frontal area projected directly in front of the vehicle, v is the vehicle's real-time speed, and δ is the vehicle's rotational mass conversion factor. The calculation formula for high-voltage accessory consumption is: Among them, E highvol For the energy consumption of high-voltage components in vehicles, U PTC For PTC voltage, I PTC U is the PTC current. Comp For compressor voltage, I comp This refers to the compressor current. The calculation formula for low-voltage accessory consumption is: Among them, E lowvol For the energy consumption of the vehicle's low-voltage components, U DCDC For the voltage of low-voltage components, I DCDC This refers to the total current of the low-voltage components; According to the above formulas, combined with the generated driving condition curve, the total energy consumption of the electric vehicle is calculated, that is: AND all =And drive +E highvol +E lowvol Finally, the power consumption E all Converted to ΔSOC value, that is: △SOC=E all / Bms_energy Where △SOC represents the SOC deviation value of the battery that the vehicle needs to charge and discharge. If the current road segment is congested and the next road segment is not congested, then discharge is required, and △SOC is a negative value; if the current road segment is congested and the next road segment is congested, then charge is required, and △SOC is a positive value. all Bms_energy is the amount of energy a vehicle needs to charge and discharge to pass through congested areas, calculated as the energy required for charging and discharging. Bms_energy is the battery capacity of the vehicle's own battery pack.

6. The REV intelligent energy management system combining online and offline operation according to claim 1, characterized in that: The specific method for adjusting the target SOC value is as follows: If the average speed of the current road section is greater than the set speed value, the vehicle is traveling at a high speed; otherwise, the vehicle is traveling at a medium or low speed. When the vehicle is traveling at a medium or low speed: When △SOC≥SOC_limit_up: SOC_target_new = SOC_target + SOC_limit_up Where, △SOC is the difference between the adjusted value after the target value adjustment and the target value, SOC_limit_up is the upper limit value allowed for the SOC target value adjustment, SOC_target_new is the adjusted SOC target value, and SOC_target is the SOC target value calibrated by the vehicle itself; When △SOC≥SOC_limit_up: SOC_target_new = SOC_target + SOC_limit_down Where, SOC_limit_down is the lower limit value allowed for the SOC target value adjustment; When SOC_limit_down < △SOC < SOC_limit_up: SOC_target_new = SOC_target + △SOC When the vehicle is traveling at a high speed: If the high-speed mileage in the journey is greater than the set mileage value, this journey is a long-distance high-speed drive; otherwise, it is a short-distance high-speed drive. When this journey is a long-distance high-speed drive: SOC_target_new = SOC_target + SOC_long Where, SOC_long is the increased SOC target value when the vehicle is driving at a high speed for a long distance; When this journey is a short-distance high-speed drive, it is considered that the user will perform a short-distance high-speed drive, and overcharging will cause high fuel consumption, that is: SOC_target_new = SOC_target + SOC_short Where, SOC_short is the increased SOC target value when the vehicle is driving at a high speed for a short distance, and SOC_short ≤ SOC_long.

7. The REV intelligent energy management system combining online and offline operation according to claim 1, characterized in that: It also includes a controller execution module. Due to the NVH problems caused by the dynamic changes of the SOC, the control needs to be adaptively changed, that is, the engine start-stop conditions are added under low-speed conditions: When the vehicle speed ≥ Speed_num2 and SOC ≥ SOC_low, Speed_num2 is a certain vehicle speed value designed artificially, and SOC_low is a certain SOC value designed artificially, the engine starts; When the vehicle speed ≤ Speed_num3 and SOC ≥ SOC_low, Speed_num3 is a certain vehicle speed value designed artificially and Speed_num2 > Speed_num3, the engine stops; When SOC < SOC_low, the engine starts and charges until SOC ≥ SOC_high, then the engine stops. SOC_high is a certain SOC value designed artificially, and SOC_high > SOC_low; Place the control maps such as engine start-stop control and engine operating point control in the vehicle controller. Search for different control instructions through SOC_target_new, and send the instructions to the engine controller, generator controller, drive motor controller, and battery controller through the vehicle controller. According to each controller, control each component to execute the instructions of the vehicle, so as to realize the overall energy management strategy.

8. A combined online and offline REV intelligent energy management method, characterized in that, It includes the following steps: Perform route planning processing on the vehicle navigation information to obtain the passing sequence tag codes of all the required passing sections generated from the navigation map and the driving mileage of the selected vehicle speed range during the journey; Identify the sequence tag code corresponding to the current driving section in the passing sequence tag codes of all the required passing sections according to the longitude and latitude in the vehicle navigation information, and obtain the congestion situation information of the current driving section and the average vehicle speed of the current driving section from the sequence tag code corresponding to the current driving section; Set the distance of the selected route as the initial value according to the selected route in the navigation. If the sequence tag code corresponding to the current driving section is inconsistent with the corresponding sequence tag code of this section recorded in the navigation map and the distance of the selected route reaches the set value, use the navigation information of the current driving section to update the corresponding driving section navigation information in the vehicle navigation information; Judge whether to generate a vehicle condition segment according to the congestion situation information of the current section. If the current section is congested and the next section is congested, no condition segment is generated; If the current section is congested and the next section is not congested, a condition segment is generated; if the current section is not congested, a condition segment is generated; obtain the condition time vehicle speed curve according to the generated vehicle condition segment, and calculate the energy consumption situation information of the vehicle condition segment based on the condition time vehicle speed curve; Adjust the target SOC value according to the average vehicle speed of the current driving section, the energy consumption situation information of the condition segment, and the driving mileage of the selected vehicle speed range during the journey. Realize the dynamic adjustment of the target SOC according to the adjusted target SOC value, the updated vehicle navigation information, and the electric vehicle energy consumption, so as to control the power source distribution ratio of the vehicle.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it realizes the steps of the method described in claim 8.

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