Energy pre-estimation planning energy-saving control system and method for new energy automobile

Abstract

The invention discloses an energy-saving control system for energy pre-estimation planning of a new energy automobile. The energy-saving control system comprises an information acquisition module for acquiring a vehicle type flag bit, a road section identifier and real-time road condition data; the information screening and judging module dynamically generates an energy consumption calculation flag bit based on the vehicle type; the information updating module determines a distance threshold value according to the road section average vehicle speed interpolation and triggers data updating; the energy consumption calculation module combines the working condition characteristic parameters and a preset reference coefficient to calculate driving energy consumption, and obtains high-low voltage accessory energy consumption through table look-up to generate total estimated energy consumption; the energy-saving control module plans a lowest-energy-consumption navigation route for a pure electric vehicle model, and dynamically adjusts a battery SOC target value for a hybrid vehicle model to realize an energy distribution strategy. According to the method, the problems that in the prior art, route recommendation is not energy-saving, hybrid power energy consumption estimation is inaccurate, and a system is not universal are solved, cross-vehicle accurate energy consumption modeling is achieved through a universal architecture, gradient mileage updating and a lightweight algorithm are combined, and energy-saving control real-time performance and engineering applicability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy saving for passenger cars, and specifically to an energy consumption estimation and energy saving control system and method for new energy vehicles. Background Art

[0002] At present, the technical solutions for energy estimation and energy saving control in the field of new energy vehicles are mainly divided into two categories: based on the big data statistical method, by analyzing the user's historical driving data and the average energy consumption of the vehicle, predicting the remaining cruising range and recommending the reachable charging pile locations; relying on the user's recent historical travel data (such as average vehicle speed, travel distance) to establish an energy consumption estimation model, and adjusting the energy management strategy (such as the working mode switching between the engine and the motor) based on fixed rules.

[0003] The core goal of the big data statistical method is to provide low battery warning and charging path planning for users. However, the route recommendation takes "arriving at the charging pile" as the primary goal, rather than energy saving optimization. The data statistical method does not consider real-time road conditions (such as congestion status, red light waiting time) and dynamic driving behaviors (such as sudden acceleration / deceleration), resulting in the estimated energy consumption deviating from the actual demand. Redundant design (such as increasing the safety margin) is required to ensure that the vehicle can reach the destination, sacrificing the energy saving potential. The existing technology takes the reachability of the charging pile as the priority goal, and does not perform the path planning with the lowest energy consumption for real-time road conditions, and cannot achieve the energy saving route recommendation in the true sense; when the user's driving conditions (such as sudden congestion, route change) or environmental conditions (such as extreme temperature) change, the prediction accuracy of the energy consumption estimation model relying on historical data drops significantly, resulting in the failure of the energy saving control strategy. Moreover, if two independent algorithms are adopted for pure electric and hybrid models, the development and maintenance costs are high, and the energy saving function expansion (such as carbon emission reduction calculation, intelligent vehicle speed planning) cannot be achieved through a unified architecture.

[0004] Therefore, how to create an energy estimation planning and energy saving control system that can improve the accuracy of dynamic energy consumption modeling, perform energy saving control strategies for different vehicle models, and have a general system architecture to achieve cross-vehicle energy saving control functions has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of the present invention is to provide an energy estimation planning and energy saving control system for new energy vehicles. The present invention can achieve cross-vehicle energy saving control functions through high-precision modeling and lightweight algorithms driven by real-time data, and improve the cruising ability and user experience of new energy vehicles.

[0006] To achieve this purpose, a new energy vehicle energy estimation planning and energy saving control system designed by the present invention includes:

[0007] The information acquisition module is used to acquire the vehicle type flag bit of the current vehicle, the road segment identifier of the route segment, the average vehicle speed of each road segment identifier, and the road congestion status of each road segment identifier;

[0008] The information screening and judgment module is used to judge the vehicle type according to the vehicle type flag bit of the current vehicle; for pure electric vehicle models, the energy consumption calculation flag bit is directly generated; for hybrid vehicle models, the energy consumption calculation flag bit is dynamically generated based on the road segment identifier of the current driving and the road congestion status of the current driving;

[0009] The information update module is used to determine the distance threshold for updating the map information and vehicle status information based on the interpolation method according to the average vehicle speed of the current driving road segment. When the driving distance of the vehicle in the route segment reaches the distance threshold, the map information and vehicle status information are updated;

[0010] The energy consumption calculation module is used to generate the working condition characteristic parameters based on the energy consumption calculation flag bit, calculate the driving energy consumption by combining the generated working condition characteristic parameters with the preset reference working condition coefficient, obtain the high-voltage accessory energy consumption and low-voltage accessory energy consumption of the vehicle according to the experimental test table, and obtain the total estimated energy consumption of the vehicle according to the driving energy consumption, high-voltage accessory energy consumption and low-voltage accessory energy consumption;

[0011] The energy-saving control module is used to plan the navigation route with the lowest energy consumption based on the total estimated energy consumption of the vehicle for pure electric vehicle models, and perform energy-saving control according to the navigation route with the lowest planned energy consumption; for hybrid vehicle models, obtain the target adjustment change amount of the battery charge state of the hybrid vehicle model based on the total estimated energy consumption of the vehicle, dynamically control the energy distribution strategy according to the target adjustment change amount, and perform energy-saving control according to the energy distribution strategy.

[0012] Preferably, the specific method for dynamically generating the calculation flag bit for hybrid vehicle models based on the road segment identifier of the current driving and the road congestion status of the current driving is as follows:

[0013] Sort the road congestion conditions of the required route segments according to the required route segment numbers, find the road congestion conditions through the current road segment number and the required route segment numbers. If the road congestion condition of the current road segment is congested and the road congestion condition of the next road segment is congested, the calculation flag bit is the continuous congested road segment flag bit; if the road congestion condition of the current road segment is congested and the road congestion condition of the next road segment is unobstructed, the calculation flag bit is the congested to unobstructed road segment flag bit; if the road congestion condition of the current road segment is unobstructed, the calculation flag bit is the unobstructed to congested road segment flag bit.

[0014] Preferably, the specific method for dynamically controlling the energy distribution strategy according to the target adjustment change amount is as follows: give priority to using electricity in congested road segments: reduce the target adjustment change amount and lower the target SOC value in congested road segments; switch to fuel in unobstructed road segments: increase the target adjustment change amount and raise the target SOC value in unobstructed road segments, and use fuel drive or charging to dynamically control the energy distribution.

[0015] Preferably, the specific update method in the information update module is as follows:

[0016] Set the sampling step size of the average vehicle speed to A, then the value range of the vehicle speed V is V A 、V 2A 、V 3A 、……、V NA , where N is the total number of sampling points;

[0017] Determine the distance threshold for different vehicle speeds through on-road vehicle tests, and the specific method is as follows:

[0018] (Vehicle speed & Distance) = (DIS A 、DIS 2A 、DIS 3A 、……、DIS NA )

[0019] where Vehicle speed & Distance represents the distance threshold for different vehicle speeds, and DIS NA represents the distance threshold when the vehicle speed is NA;

[0020] Advantages of the present invention: The present invention proposes an energy prediction and planning energy-saving control system for new energy vehicles. By constructing a unified system architecture, it realizes the integrated development of energy-saving functions for pure electric and hybrid vehicle models for the first time. Through the vehicle type flag bit, it dynamically switches the energy consumption calculation mode and energy-saving strategy, abandons the traditional independent development mode for different vehicle models, and reduces the repetitive algorithm development and verification costs; through segmented driving condition characteristic parameters, it dynamically reconstructs the energy consumption model, and reduces the calculation time-consuming through the off-line look-up table technology, meeting the real-time requirements of in-vehicle systems; designs a gradient mileage update algorithm, dynamically interpolates and generates update thresholds according to the average vehicle speed of the road section, and realizes an adaptive update cycle of 30 seconds to 3 minutes. This mechanism avoids the risk of signal lag caused by regular updates, improves the data timeliness in the vehicle speed fluctuation scenario, and at the same time accurately tracks the driving mileage through segmented integration to ensure the continuous calibration of the energy consumption model; for pure electric vehicle models, it generates the lowest energy consumption navigation path based on the route-level energy consumption comparison, and recommends avoiding congested road sections and smooth driving curves; for hybrid vehicle models, it creates an SOC dynamic adjustment algorithm, preferentially consumes electricity in congested road sections, switches to fuel drive and charges reversely in smooth road sections, and realizes the maximization of energy utilization rate. The present invention creates energy-saving control strategies for different vehicle models through a unified system architecture, improves the accuracy of dynamic energy consumption modeling, and realizes cross-vehicle energy prediction and planning and energy-saving control. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic structural diagram of the present invention;

[0022] Figure 2 is a schematic working process diagram of the present invention. Specific Embodiments

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. 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 represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0024] The following provides a further detailed description of the present invention in conjunction with the accompanying drawings and specific embodiments:

[0025] Embodiment 1

[0026] A new energy vehicle energy prediction and planning energy-saving control system, as Figure 1 shown, includes:

[0027] The information acquisition module is used to acquire the vehicle type flag of the current vehicle, the road section identifier of the route section, the average vehicle speed of each road section identifier, and the traffic congestion status of each road section identifier;

[0028] The information screening and judgment module is used to judge the vehicle type according to the vehicle type flag of the current vehicle; for pure electric vehicle models, an energy consumption calculation flag is directly generated; for hybrid vehicle models, an energy consumption calculation flag is dynamically generated based on the road section identifier of the current driving and the traffic congestion status of the current driving road section;

[0029] The information update module is used to determine the distance threshold for updating the map information and vehicle status information based on the average vehicle speed of the current driving road section by interpolation method. When the driving distance of the vehicle in the route section reaches the distance threshold, the map information and vehicle status information are updated;

[0030] The energy consumption calculation module is used to generate working condition characteristic parameters based on the energy consumption calculation flag, calculate the driving energy consumption by combining the generated working condition characteristic parameters with the preset reference working condition coefficient, obtain the high-voltage accessory energy consumption and low-voltage accessory energy consumption of the vehicle according to the experimental test table, and obtain the total estimated energy consumption of the vehicle according to the driving energy consumption, high-voltage accessory energy consumption, and low-voltage accessory energy consumption;

[0031] The energy-saving control module is used to plan the navigation route with the lowest energy consumption for pure electric vehicle models based on the total estimated energy consumption of the vehicle, and perform energy-saving control according to the navigation route with the lowest planned energy consumption; for hybrid vehicle models, the target adjustment change amount of the battery state of charge of the hybrid vehicle model is obtained based on the total estimated energy consumption of the vehicle, the energy distribution strategy is dynamically controlled according to the target adjustment change amount, and energy-saving control is performed according to the energy distribution strategy.

[0032] In the above technical solution, for pure electric vehicle models, their energy consumption mainly depends on user driving and actual road conditions. The actual road condition energy saving is achieved through the total estimated energy consumption of the vehicle in this system. Its main function is to calculate the energy consumption of the navigation recommended route before the user travels with the help of map navigation, so as to realize the recommendation of the route with the lowest energy consumption, and update it in real time to ensure the accuracy of the prediction value and achieve the estimated planning energy saving of pure electric vehicle models.

[0033] In the above technical solution, since it is difficult to control pure electric vehicle models from the aspect of energy distribution, pure electric vehicle models achieve energy-saving control by recommending energy-saving routes. Hybrid vehicle models are based on the adjustment of the SOC target value, and through the optimal energy distribution (the energy distribution between the engine and the battery), the efficiency of the engine and the motor is improved to achieve energy-saving control.

[0034] In the above technical solution, it also includes an energy-saving display module, which is used to compare the user's historical energy consumption with the actual energy consumption after the system is applied, and display the energy-saving effect.

[0035] In the above technical solution, before judging the vehicle type according to the vehicle type flag bit of the current vehicle, it is necessary to sort the road congestion status Link_status corresponding to each road identifier according to the road identifier of the Link_ID of the passing section to obtain the Link_status_array corresponding to Link_ID_array, where Link_ID_array = [1, 2, 3,..., n] is all the road identifiers of the passing section, and Link_status_array = [1, 0, 0,..., 1] n Is the road congestion status corresponding to all the road identifiers of the passing section.

[0036] In the above technical solution, the road identifier of the passing section is the basis of global energy consumption planning, and the road identifier of the currently traveling section is the anchor point of real-time control. The dynamic association of the two realizes: data timeliness, continuity of energy consumption prediction, and forward-looking of control strategy. This design breaks through the limitations of "historical data dependence" and "fixed cycle update" in traditional technologies, and significantly improves the accuracy and real-time performance of energy-saving control.

[0037] In the above technical solution, each road identifier in the passing section corresponds to a section average vehicle speed and also corresponds to a road congestion status.

[0038] In the above technical solution, a unified system architecture across vehicle models is constructed. Through dynamically generating energy consumption flag bits and an adaptive update mechanism, accurate energy consumption prediction and energy-saving control for pure electric / hybrid vehicle models are achieved.

[0039] In the above technical solution, the specific method for dynamically controlling the energy distribution strategy according to the target adjustment variation is as follows:

[0040] Give priority to using electricity in congested sections: Reduce (negative value) the target adjustment variation in congested sections, lower the target SOC value, and force the vehicle to consume more battery energy during congestion to avoid inefficient fuel combustion.

[0041] Switch to fuel in smooth sections: Increase (positive value) the target adjustment variation in smooth sections, raise the target SOC value, and use fuel drive or charging to dynamically control energy distribution.

[0042] In the above technical solution, the dynamic control energy distribution strategy will switch to fuel in smooth sections, but for driving behaviors such as sudden acceleration, electricity will still be used to assist to ensure power performance.

[0043] In the above technical solution, for the operating conditions (congested / smooth) of hybrid vehicle models, the SOC target value is dynamically adjusted to optimize energy distribution, achieving the goal of giving priority to using electricity in congested sections to avoid inefficient fuel combustion, reducing fuel consumption, using fuel drive for reverse charging in smooth sections, and improving battery utilization and fuel economy.

[0044] In the above technical solution, the information acquisition module includes a navigation information sending module and a vehicle information sending module. The navigation information sending module is used to send map information signals including road section serial number, average road section speed, road section distance, road section congestion condition, traffic light position, and traffic light status to the CAN (Controller Area Network) network of vehicle communication through an in-vehicle chip; the vehicle information sending module sends vehicle information signals including vehicle type flag bit, ambient temperature, set temperature of in-vehicle air conditioner, in-vehicle temperature, actual speed, vehicle test mass, vehicle resistance coefficient, and battery pack power to the CAN network of vehicle communication through a vehicle controller.

[0045] In the above technical solution, the sources and transmission methods of navigation and vehicle data are clarified, covering all-dimensional data of map road conditions (such as traffic lights) and vehicle states (such as temperature, resistance coefficient), ensuring the input integrity of the energy consumption model and providing a data basis for dynamic control.

[0046] In the above technical solution, the specific method for a hybrid vehicle model to dynamically generate a calculation flag bit based on the current driving road section identifier and the current driving road section congestion status is as follows:

[0047] The congestion conditions of the sections of the required route will be sorted according to the section numbers of the required route. The congestion conditions of the sections will be found by the current section number and the section numbers of the required route. If the congestion condition of the current section is congestion and the congestion condition of the next section is congestion, the calculation flag bit will be the continuous congestion section flag bit; if the congestion condition of the current section is congestion and the congestion condition of the next section is smooth, the calculation flag bit will be the congestion-to-smooth section flag bit; if the congestion condition of the current section is smooth, the calculation flag bit will be the smooth-to-congestion section flag bit.

[0048] In the above technical solution, the specific method for the hybrid vehicle model to dynamically generate the calculation flag bit based on the current driving section identifier and the current driving section congestion status is as follows: Search for Link_status_array through the current Link_ID and Link_ID_array. If the current section Link_status = 1 and the next section Link_status_next = 1, the energy consumption calculation flag bit b_compute = 0; if the current section Link_status = 1 and the next section Link_status_next = 0, the energy consumption calculation flag bit b_compute = 2; if the current section Link_status = 0, the energy consumption calculation flag bit b_compute = 3.

[0049] In the above technical solution, the flag bit is dynamically generated based on the continuity of the section congestion status (such as continuous congestion, congestion to smooth), accurately identifying the section characteristics, distinguishing continuous congestion from transition sections, avoiding model failure caused by sudden changes in working conditions, and optimizing the response logic of the hybrid strategy.

[0050] In the above technical solution, the specific update method in the information update module is as follows:

[0051] Set the sampling step size of the average vehicle speed to 10 km / h, then the vehicle speed V value range is V 10 、V 20 、V 30 、……、V 130 、V 140 , 140 km / h can meet all user travel scenarios under the speed limit in China, and N is the total number of sampling points;

[0052] Determine the distance threshold for different vehicle speeds through on-road vehicle tests to ensure that updates will be made within 30 s - 3 min. The higher the speed, the longer the update distance and update time can be. The specific method is as follows:

[0053] (Vehicle speed & distance) = (DIS 10 、DIS 20 、DIS 30 、……、DIS 130, DIS 140 )

[0054] Among them, vehicle speed & distance represent the distance thresholds for different vehicle speeds, and DIS 140 represents the distance threshold when the vehicle speed is 140;

[0055] Interpolate according to the average vehicle speed of the section to determine the distance threshold required for updating;

[0056] Perform an update judgment. If the current driving distance is greater than or equal to the required driving distance threshold, return to the information acquisition module to update the acquired signal and resend the map information signal and vehicle information signal; if the current driving distance is less than the required driving distance threshold, do not update the acquired signal and enter the energy consumption calculation module to calculate the current driving distance. The specific formula for calculating the current driving distance is;

[0057] Distance_drive = ∫v_speed

[0058] Among them, Distance_drive is the driving distance of the vehicle after update, with the unit of km; v_speed is the actual driving speed of the vehicle, with the unit of km / h.

[0059] In the above technical solution, this distance is the mileage threshold that the vehicle needs to drive at different average vehicle speeds determined in advance through actual vehicle tests. For example, when the vehicle speed is 10 km / h, the corresponding distance is DIS 10 , and when the vehicle speed is 20 km / h, the corresponding is DIS 20 etc. When the vehicle driving mileage reaches this threshold, the system automatically updates the real-time road conditions and vehicle status information to ensure that the data update cycle is controlled within the range of 30 seconds to 3 minutes (the higher the vehicle speed, the longer the threshold distance). This design reduces the computing power consumption while ensuring real-time performance by dynamically adjusting the update frequency.

[0060] In the above technical solution, determining the distance threshold for updating the map information and vehicle status information based on the average vehicle speed of the current driving section by interpolation means updating the distance threshold according to the average vehicle speed of the current driving section, calculating using the interpolation method, and updating the map information and vehicle status information when the vehicle travels to the distance threshold.

[0061] In the above technical solution, dynamically determining the update threshold according to vehicle speed interpolation, and adaptively updating the cycle to balance data timeliness and system load, avoiding the lag or redundant calculation caused by fixed-cycle update when the vehicle speed fluctuates.

[0062] In the above technical solution, based on the calculation flag bit, select whether to generate the working condition characteristic parameters. The specific method for calculating the drive energy consumption by combining the generated working condition characteristic parameters with the preset reference working condition coefficient is:

[0063] The calculated flag bits are divided into a continuous congestion section flag bit, a pure electric vehicle type flag bit, a congestion to smooth section flag bit, and a smooth to congestion section flag bit;

[0064] Among them, if the calculated flag bit is the continuous congestion section flag bit b_compute = 0, no working condition characteristic parameters are generated, and the energy consumption is 0;

[0065] If the calculated flag bit is the pure electric vehicle type flag bit b_compute = 1, the working condition characteristic parameters of all subsequent section information including the current section are found through the current section identifier, and the energy consumption is calculated;

[0066] If the calculated flag bit is the congestion to smooth section flag bit b_compute = 2, only the working condition characteristic parameters of the current section identifier information are sent, and the energy consumption is calculated;

[0067] If the calculated flag bit is the smooth to congestion section flag bit b_compute = 3, the working condition characteristic parameters of all subsequent congestion section information including the current section are found through the current section identifier, and the energy consumption is calculated;

[0068] According to different map information signal input data, working condition characteristic parameters I vel 、I hi_brk 、I lw_brk are generated, and the driving energy consumption ECR is calculated according to the K vel_base 、K hi_brk_base 、K lw_brk_base reference working condition characteristic parameter coefficients measured in the test. The specific calculation formula for calculating the driving energy consumption is:

[0069] ECR = I vel ×K vel_base +I hi_brk ×K hi_brk_base +I lw_brk ×K lw_brk_base

[0070] Among them, K vel_base 、K hi_brk_base 、K lw_brk_base are the reference working condition characteristic parameter coefficients of speed intensity, high-speed braking intensity, and low-speed braking intensity respectively, and I vel 、I hi_brk 、I lw_brk are the working condition characteristic parameters of speed intensity, high-speed braking intensity, and low-speed braking intensity respectively. ECR is the estimated working condition driving energy consumption.

[0071] In the above technical solution, the function of the speed intensity is a parameter that quantifies the influence intensity of vehicle speed change on driving energy consumption under specific working conditions.

[0072] In the above technical solution, parameters are selectively generated according to flag bits (for example, for pure electric vehicle models, the whole road section is calculated, and only the current road section is calculated when congestion turns to smooth flow), reducing ineffective calculations and improving the algorithm efficiency; the combination of the benchmark coefficient and real-time parameters ensures the balance between model lightweight and accuracy.

[0073] In the above technical solution, the specific calculation method for generating the total estimated energy consumption is as follows:

[0074] The high-voltage energy consumption mainly depends on the temperature difference during heating or cooling. Therefore, taking the temperature difference between the in-vehicle temperature and the set temperature of the vehicle air conditioner as the target, considering the range of the Chinese environmental temperature range and the set temperature range of the vehicle air conditioner, it is recommended to set -50 to 20 °C, and the recommended step size for taking points is 5 °C, that is, the temperature difference is T -50℃ 、T -45℃ 、T -40℃ 、……、T 15℃ 、T 20℃ , since the greater the temperature difference, the greater the power demand, different power demands are obtained through a large number of simulation calculations. The specific calculation formula for the energy consumption consumed by high-voltage components is:

[0075]

[0076] (Temperature difference & Power) = (P -50℃ 、P -45℃ 、P -40℃ 、……、P 15℃ 、P 20℃ )

[0077] Among them, E highvol is the energy consumption consumed by the high-voltage components of the vehicle, P i is the power consumption of the high-voltage accessories obtained by querying the experimental test table, P 20℃ is the power of the high-voltage accessories at a temperature difference of 20 °C, and t represents time;

[0078] The specific calculation formula for the energy consumption consumed by low-voltage components is:

[0079]

[0080] (Temperature & Power) = (p -20℃ 、p -15℃ 、p -10℃ 、……、p 35℃ 、p 40℃ )

[0081] Among them, E lowvol is the energy consumption consumed by the low-voltage components of the vehicle, p 40℃ is the power of the low-voltage accessories at a temperature of 40 °C, p i is the power consumption of the low-voltage accessories obtained by querying the experimental test table;

[0082] The specific calculation formula for the estimated energy consumption is as follows:

[0083] E_consume = ECR + E highvol + E lowvol

[0084] Wherein, ECR is the estimated energy consumption for the driving condition.

[0085] In the above technical solution, the accessory energy consumption is quantified by the look-up table method (experimental test data), and is superimposed with the driving energy consumption to generate the total estimated value, covering the energy consumption of accessories such as air conditioners and low-voltage electrical appliances, improving the comprehensiveness of the estimation.

[0086] In the above technical solution, the specific calculation formula for obtaining the target adjustment change amount of the battery charge state of the hybrid vehicle based on the total estimated energy consumption of the vehicle is as follows:

[0087] △SOC = E_consume / Bat energy

[0088] Wherein, △SOC is the target adjustment change amount, and Bat energy is the energy of the battery pack, and E_consum represents the estimated energy consumption.

[0089] In the above technical solution, the SOC target value is dynamically adjusted based on the ratio of the total estimated energy consumption to the energy of the battery pack, avoiding the strategy fluctuations or instability caused by empirical parameter tuning.

[0090] In the above technical solution, for pure electric vehicles, based on the total estimated energy consumption of the vehicle, the navigation route with the lowest energy consumption is planned, and the energy consumption of the recommended route is calculated and updated in real time before the user travels with the help of map navigation, realizing the recommendation of the route with the lowest energy consumption, improving the endurance of pure electric vehicles and the user experience; for hybrid vehicles, according to the total estimated energy consumption of the vehicle, the target adjustment change amount of the battery charge state is obtained, and then the energy distribution strategy is dynamically controlled, giving priority to using electricity in congested sections and switching to fuel in smooth sections, realizing the precise improvement of the energy utilization rate, giving full play to the advantages of hybrid vehicles, improving the fuel economy and the overall performance of the vehicle, and enhancing the competitiveness of the product in the market.

[0091] Embodiment 2

[0092] An energy-saving control method for a new energy vehicle, as Figure 2As shown in the figure, Step 1: Send the high-precision map information signal to the CAN network of vehicle communication through the in-vehicle chip, and send the vehicle status information signal to the CAN network of vehicle communication through the relevant vehicle controllers; Step 2: Determine which method should be used to calculate energy consumption under different road conditions or vehicle types by judging the vehicle type flag bit; Step 3: Calculate the distance threshold according to the average vehicle speed, and then update the road condition information in real time according to the distance threshold and the traveled distance; Step 4: Calculate the driving energy consumption through the flag bit, and calculate the estimated vehicle energy consumption by combining the consumption of the vehicle's high-voltage components and the consumption of the vehicle's low-voltage components; Step 5: Plan the navigation route and energy-saving control method respectively according to the estimated energy consumption; Step 6: Compare the energy consumption of the previous user's trips with the actual energy consumption after using this system, and display the energy-saving effect.

[0093] A method for energy estimation and planning of new energy vehicles and energy-saving control, which includes the following steps:

[0094] Obtain the vehicle type flag bit of the current vehicle, the road segment identifier of the passing road segment, the average vehicle speed of each road segment identifier, and the congestion status of each road segment identifier;

[0095] Judge the vehicle type according to the vehicle type flag bit of the current vehicle; for pure electric vehicle models, directly generate the energy consumption calculation flag bit; for hybrid vehicle models, dynamically generate the energy consumption calculation flag bit based on the current road segment identifier and the congestion status of the current road segment;

[0096] Determine the distance threshold for updating the map information and vehicle information based on the average vehicle speed of the current driving road segment by interpolation method. When the driving distance of the vehicle in the passing road segment reaches the distance threshold, update the in-vehicle signal and vehicle signal;

[0097] Generate the working condition characteristic parameters based on the energy consumption calculation flag bit, calculate the driving energy consumption by combining the generated working condition characteristic parameters with the preset reference working condition coefficient, obtain the energy consumption of the vehicle's high-voltage accessories and low-voltage accessories according to the experimental test table, and obtain the total estimated vehicle energy consumption based on the driving energy consumption, high-voltage accessory energy consumption and low-voltage accessory energy consumption;

[0098] For pure electric vehicle models, plan the navigation route with the lowest energy consumption based on the total estimated vehicle energy consumption, and perform energy-saving control according to the planned navigation route with the lowest energy consumption; for hybrid vehicle models, obtain the target adjustment change amount of the state of charge of the hybrid vehicle battery based on the total estimated vehicle energy consumption, dynamically control the energy distribution strategy according to the target adjustment change amount, and perform energy-saving control according to the energy distribution strategy.

[0099] Embodiment 3

[0100] A computer program product, including a computer program, which when executed by a processor implements the steps of the method described in Embodiment 2.

[0101] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

Claims

1. A new energy vehicle energy estimation planning energy-saving control system, characterized in that: include: The information acquisition module is used to obtain the vehicle type flag of the current vehicle, the section identification of the passing section, the average vehicle speed of the section corresponding to each section identification, and the congestion status of the section corresponding to each section identification; The information screening and judgment module is used to judge the vehicle type according to the vehicle type flag of the current vehicle; for pure electric vehicles, the energy consumption calculation flag is directly generated; For hybrid vehicles, the energy consumption calculation flag is dynamically generated based on the current road segment identification and the current road segment congestion status; The information updating module is used to determine the distance threshold for updating the map information and the vehicle status information based on the interpolation method according to the average vehicle speed of the current driving section, and update the map information and the vehicle status information when the driving distance of the vehicle in the passing section reaches the distance threshold; The energy consumption calculation module is used to generate operating condition characteristic parameters based on the energy consumption calculation flag, calculate the driving energy consumption by combining the generated operating condition characteristic parameters with the preset reference operating condition coefficient, obtain the high-voltage accessory energy consumption and low-voltage accessory energy consumption of the vehicle according to the experimental test table, and obtain the total estimated energy consumption of the vehicle according to the driving energy consumption, high-voltage accessory energy consumption and low-voltage accessory energy consumption; The energy-saving control module is used to plan the navigation route with the lowest energy consumption for pure electric vehicles based on the total estimated energy consumption of the vehicle, and perform energy-saving control according to the planned navigation route with the lowest energy consumption; For hybrid vehicles, a target adjustment variation of the battery state of charge of the hybrid vehicle is obtained based on the total estimated energy consumption of the vehicle, an energy distribution strategy is dynamically controlled according to the target adjustment variation, and energy-saving control is performed according to the energy distribution strategy.

2. According to claim 1, a new energy vehicle energy estimation planning energy-saving control system is characterized in that: The specific method of energy-saving control according to the energy distribution strategy is: Prioritize electricity use in congested sections: Reduce the target adjustment change and lower the target SOC value in congested sections; Switch fuel on unobstructed roads: Increase the target adjustment change on unobstructed roads, return to the target SOC value, use fuel to drive or charge, and dynamically control energy distribution.

3. The energy estimation planning and energy-saving control system for new energy vehicles according to claim 1 is characterized in that: The information acquisition module includes a navigation information sending module and a vehicle information sending module. The navigation information sending module is used to send a map information signal including a road section number, an average road section speed, a road section distance, a road section congestion condition, a traffic light position and a traffic light status to a CAN network of vehicle communication through an on-board chip; the vehicle information sending module sends a vehicle information signal including a vehicle type flag, an ambient temperature, a vehicle air conditioning set temperature, an in-vehicle temperature, an actual vehicle speed, a vehicle test mass, a vehicle drag coefficient, and a battery pack power to a CAN network of vehicle communication through a vehicle controller.

4. The energy estimation planning and energy-saving control system for new energy vehicles according to claim 1 is characterized in that: The specific method for a hybrid vehicle to dynamically generate and calculate the flag position based on the current road section identification and the current road section congestion status is as follows: The congestion conditions of the sections of the required route will be sorted according to the section numbers of the required route, and the section congestion conditions will be found through the current section number and the section number of the required route. If the current section is congested and the next section is congested, the calculation flag will be the continuous congested section flag; if the current section is congested and the next section is unobstructed, the calculation flag will be the congested to unobstructed section flag; if the current section is unobstructed, the calculation flag will be the unobstructed to congested section flag.

5. The energy estimation planning and energy-saving control system for new energy vehicles according to claim 1 is characterized in that: The specific update method in the information update module is: The average speed point step is set to A, and the speed V value range is V A 、V 2A 、V 3A ,……,V NA , N is the total number of points; The distance thresholds for different vehicle speeds are determined through actual vehicle testing, and the specific method is as follows: (Speed ​​& Distance) = (DIS A 、DIS 2A 、DIS 3A ,……,DIS NA ) Among them, speed & distance represent the distance thresholds at different speeds, DIS NA Indicates the distance threshold when the vehicle speed is NA; Interpolate based on the average speed of the road section to determine the threshold of the distance required for updating; Perform update judgment. If the current driving distance is greater than or equal to the required driving distance threshold, return to the information acquisition module, update the acquired signal and resend the map information signal and vehicle information signal; if the current driving distance is less than the required driving distance threshold, do not update the acquired signal, enter the energy consumption calculation module, calculate the current driving distance, and the specific formula for calculating the current driving distance is: Distance_drive=∫v_speed Among them, Distance_drive is the distance traveled by the vehicle after the update; v_speed is the actual speed of the vehicle.

6. The energy estimation planning and energy-saving control system for new energy vehicles according to claim 1 is characterized in that: Based on the calculation flag, it is selected whether to generate the operating condition characteristic parameters. The specific method for calculating the driving energy consumption by combining the generated operating condition characteristic parameters with the preset reference operating condition coefficient is as follows: The calculation flags are divided into continuous congested road section flags, pure electric vehicle flags, congested to unobstructed road section flags, and unobstructed to congested road section flags; Among them, if the calculation flag is the continuous congested road section flag, no working condition characteristic parameters are generated, and the energy consumption is 0; If the calculation flag is a pure electric vehicle flag, the operating condition characteristic parameters of all subsequent road section information including the current road section are searched through the current road section mark, and the energy consumption is calculated; If the calculation flag is the congestion-to-unblocked road section flag, only the working condition characteristic parameters of the current road section identification information are sent, and the energy consumption is calculated; If the calculation flag is a flag of a smooth-to-congested road section, the operating condition characteristic parameters including all subsequent congested road section information of the current road section are searched through the current road section identifier, and the energy consumption is calculated; The specific calculation formula for calculating the drive energy consumption is: in, are the benchmark operating condition characteristic parameter coefficients of speed intensity, high-speed braking intensity, and low-speed braking intensity, respectively. vel ,I hi_brk ,I lw_brk They are the operating condition characteristic parameters of speed intensity, high-speed braking intensity and low-speed braking intensity respectively, and ECR is the estimated operating condition driving energy consumption.

7. The energy estimation planning and energy-saving control system for new energy vehicles according to claim 1 is characterized in that: The specific calculation method to generate the total estimated energy consumption is: The specific calculation formula for the energy consumption consumed by high-voltage components is: Among them, E highvol is the energy consumed by the high-voltage components of the vehicle, P i is the power consumption of the high-voltage accessories obtained by querying the experimental test table, and t represents time; The specific calculation formula for the energy consumption of low-voltage components is: Among them, E lowvol is the energy consumed by the low-voltage components of the vehicle, p i The power consumption of low voltage accessories is obtained by querying the experimental test table; The specific calculation formula for estimated energy consumption is: E_consume=ECR+E highvol +E lowvol Among them, ECR is the estimated driving energy consumption under working conditions.

8. The energy estimation planning and energy-saving control system for new energy vehicles according to claim 1 is characterized in that: The specific calculation formula for obtaining the target adjustment change of the battery state of charge of the hybrid vehicle based on the total estimated energy consumption of the vehicle is: △SOC=E_consume / Bat energy Among them, △SOC is the target adjustment change, Bat energy is the battery pack energy, and E_consum represents the estimated energy consumption.

9. A new energy vehicle energy estimation planning energy-saving control system, characterized in that: It includes the following steps: Obtain the vehicle type flag of the current vehicle, the section identifier of the passing section, the average vehicle speed of each section identifier, and the congestion status of each section identifier; The vehicle type is determined based on the vehicle type flag of the current vehicle; for pure electric vehicles, the energy consumption calculation flag is directly generated; for hybrid vehicles, the energy consumption calculation flag is dynamically generated based on the current road section logo and the current road section congestion status; Determine the distance threshold for updating the map information and vehicle information based on the interpolation method according to the average vehicle speed of the current driving section, and update the vehicle signal and vehicle signal when the driving distance of the vehicle in the passing section reaches the distance threshold; Based on the energy consumption calculation flag, the operating condition characteristic parameters are generated. The driving energy consumption is calculated by combining the generated operating condition characteristic parameters with the preset reference operating condition coefficient. The high-voltage accessory energy consumption and low-voltage accessory energy consumption of the vehicle are obtained according to the experimental test table. The total estimated energy consumption of the vehicle is obtained based on the driving energy consumption, high-voltage accessory energy consumption and low-voltage accessory energy consumption. For pure electric vehicles, the navigation route with the lowest energy consumption is planned based on the total estimated energy consumption of the vehicle, and energy-saving control is performed according to the planned navigation route with the lowest energy consumption; For hybrid vehicles, a target adjustment variation of the battery state of charge of the hybrid vehicle is obtained based on the total estimated energy consumption of the vehicle, an energy distribution strategy is dynamically controlled according to the target adjustment variation, and energy-saving control is performed according to the energy distribution strategy.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in claim 9 are implemented.

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