A new energy automobile power system intelligent energy consumption optimization method and system

Abstract

The application is suitable for the field of new energy automobile power system energy management technology, and provides a new energy automobile power system intelligent energy consumption optimization method and system.The method comprises the following steps: calculating required pre-cooling energy based on a pre-cooling power adjustment amount, and directly distributing part of brake recovery energy generated during the process of starting deceleration to entering a sheltered following vehicle working condition to the compressor through a direct current bus to realize the pre-cooling power adjustment amount during the process of starting deceleration to entering the sheltered following vehicle working condition.The application realizes active optimization of power system energy flow, effectively improves vehicle energy utilization efficiency, reduces battery energy consumption and prolongs endurance, and improves stability and reliability of the power system under cold chain transportation working condition.

Description

Technical Field

[0001] This invention belongs to the field of energy management technology for new energy vehicle power systems, and particularly relates to an intelligent energy consumption optimization method and system for new energy vehicle power systems. Background Technology

[0002] With the widespread application of new energy cold chain transport vehicles, the vehicle power system typically consists of a power battery, motor, and energy management system, simultaneously supplying power to the drive system and the front-mounted refrigeration compressor through a unified high-voltage power platform. To improve the overall energy utilization efficiency of the vehicle, new energy vehicles are generally equipped with regenerative braking systems, which recover kinetic energy during deceleration and restore it to the battery or supply it to other onboard loads. Existing cold chain vehicle refrigeration systems rely on condenser heat dissipation and compressor circulation to maintain the cargo compartment temperature. Their cooling capacity is estimated and adjusted in real time based on parameters such as ambient temperature and vehicle speed. However, existing efficiency models are mainly used for conventional open road conditions and do not adequately consider heat dissipation changes under complex traffic conditions, making the power system's energy management strategies prone to lag under certain special operating conditions.

[0003] In real-world road environments, refrigerated trucks often need to closely follow large vehicles in scenarios such as urban congestion, logistics park access, or highway ramps. When a vehicle enters a situation where it is obstructed while following another vehicle, the obstruction of the windward airflow by the large vehicle in front significantly weakens the condenser's heat dissipation capacity, forcing the compressor to suddenly increase its operating power to maintain the target temperature. Since the compressor and drive motor share the same power battery, this sudden additional load directly leads to an increase in the instantaneous discharge current of the power battery, causing a sharp rise in the energy consumption of the power system and adversely affecting battery life, the energy efficiency control of the drive system, and the vehicle's range. Although existing technologies can adjust the compressor power in real time, they lack the ability to identify and predict the load in advance for this special operating condition of "obstruction leading to reduced heat dissipation." Therefore, they cannot perform advance energy planning at the power system level, resulting in the power system passively responding to load spikes.

[0004] Furthermore, although existing new energy vehicles generally possess regenerative braking systems, the recovered energy is typically prioritized for recharging the battery, and then supplied to onboard equipment via battery discharge, resulting in multiple energy conversions and significant energy losses. When a vehicle is about to enter a following-enhanced situation, the reduction in speed often generates considerable regenerative braking energy. However, current technologies fail to utilize this energy to provide pre-cooling compensation for the cooling system, nor do they establish a coordinated adjustment mechanism between regenerative braking energy and the impending cooling load. This causes the powertrain to still bear unnecessary additional energy consumption under obstructed conditions. Therefore, existing technologies generally cannot solve the adverse effects of the sudden increase in compressor load caused by obstruction on powertrain energy consumption, nor can they effectively improve the overall energy utilization efficiency of the powertrain. This invention addresses the aforementioned energy consumption optimization needs of powertrain systems. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent energy consumption optimization method and system for the power system of new energy vehicles, aiming to solve the problems mentioned in the background art.

[0006] This invention is implemented as follows: a method for intelligent energy consumption optimization of a new energy vehicle power system, the method comprising:

[0007] When it is predicted that the target vehicle is about to enter a following condition caused by a large vehicle in front, the operating condition parameters corresponding to the following condition are determined and the database is retrieved.

[0008] Extract reference samples from the database. The reference vehicle corresponding to the reference sample has the same operating parameters under its historical occlusion following conditions as its current operating parameters.

[0009] Analyze the reference sample to determine the additional compressor load caused by the aerodynamic obstruction of the large vehicle in front, resulting in reduced heat dissipation of the condenser, in the historical obstructed following conditions of the reference vehicle.

[0010] Obtain the preset precooling prediction model, input the additional load and current operating parameters into the precooling prediction model to obtain the precooling power adjustment amount used to avoid additional compressor load after entering the obstructed following condition;

[0011] The required precooling energy is calculated based on the precooling power adjustment amount. During the period from the start of deceleration of the target vehicle to entering the obstructed following condition, the portion of the regenerative braking energy generated in this process that is used to meet the precooling energy is directly distributed to the compressor via the DC bus to achieve the precooling power adjustment amount.

[0012] As a further limitation of the technical solution of the present invention, both the target vehicle and the reference vehicle are new energy cold chain vehicles that include a front-mounted refrigeration compressor. Both the target vehicle and the reference vehicle have a battery system with braking energy recovery function, and the battery system is used to supply power to the vehicle power system and the refrigeration compressor.

[0013] As a further limitation of the technical solution of this embodiment of the invention, when it is predicted that the target vehicle is about to enter a following condition caused by a large vehicle in front, the step of determining the working condition parameters corresponding to the following condition and retrieving the database includes:

[0014] Real-time acquisition of road information and information of vehicles ahead of the target vehicle, and determination of whether the target vehicle is about to enter a following condition caused by obstruction caused by a large vehicle ahead based on the road information and information of vehicles ahead;

[0015] When it is determined that the target vehicle is about to enter the obstructed following condition, the operating condition parameters corresponding to the obstructed following condition are determined;

[0016] The system retrieves a shared database stored in the cloud based on the aforementioned operating parameters.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the operating parameters include driving environment condition parameters, vehicle operating status parameters, compressor operating status parameters, large vehicle ahead status parameters, predicted following distance, predicted following speed, predicted start-stop frequency, and predicted total following time.

[0018] As a further limitation of the technical solution of the present invention, the preset pre-cooling prediction model is a model built based on experimental data. The model learns from the compressor operation experimental data obtained under different operating conditions and establishes a mapping relationship between the compressor's additional load and the amount of pre-cooling power adjustment required to avoid the additional compressor load in the case of the target vehicle following the vehicle in an obstructed manner.

[0019] As a further limitation of the technical solution of this invention embodiment, the step of calculating the required precooling energy based on the precooling power adjustment amount, and during the period from the start of deceleration of the target vehicle to entering the obstructed following condition, directly distributing the portion of the regenerative braking energy generated during this process to meet the precooling energy requirement to the compressor via the DC bus to achieve the precooling power adjustment amount includes:

[0020] Based on real-time acquired road information, the time required for the target vehicle to enter the obstructed following condition is predicted, and the historical operation record of the compressor of the target vehicle is called. Based on the precooling power adjustment amount and the time, the average precooling energy required for the compressor to increase the precooling power adjustment amount from the current power within this time is calculated.

[0021] During the period from the start of deceleration of the target vehicle to the entry into the obstructed following condition, a portion of the regenerative braking energy generated in this stage that is used to meet the average pre-cooling energy is directly distributed to the compressor via the DC bus, so that the compressor gradually increases the pre-cooling power adjustment during the deceleration stage.

[0022] A smart energy consumption optimization system for a new energy vehicle powertrain, the system comprising:

[0023] The working condition parameter determination module is used to determine the working condition parameters corresponding to the obstructed following condition when it is predicted that the target vehicle is about to enter the obstructed following condition caused by the large vehicle in front, and to retrieve the database.

[0024] The reference sample extraction module is used to extract reference samples from the database. The reference sample corresponds to the reference vehicle whose operating parameters under its historical occlusion following conditions are consistent with the current operating parameters.

[0025] The additional load determination module is used to analyze the reference sample and determine the additional load of the compressor of the reference vehicle caused by the aerodynamic obstruction caused by the large vehicle in front, resulting in reduced heat dissipation of the condenser, in the historical obstruction following conditions.

[0026] The precooling power adjustment acquisition module is used to acquire the preset precooling prediction model. The additional load and current operating parameters are input into the precooling prediction model to obtain the precooling power adjustment amount used to avoid additional load on the compressor after entering the blocking following condition.

[0027] The precooling energy calculation and distribution module is used to calculate the required precooling energy based on the precooling power adjustment amount, and during the period from the start of deceleration of the target vehicle to entering the obstructed following condition, the portion of the braking recovery energy generated in the process that is used to meet the precooling energy is directly distributed to the compressor via the DC bus to realize the precooling power adjustment amount.

[0028] As a further limitation of the technical solution of the present invention, both the target vehicle and the reference vehicle are new energy cold chain vehicles that include a front-mounted refrigeration compressor. Both the target vehicle and the reference vehicle have a battery system with braking energy recovery function, and the battery system is used to supply power to the vehicle power system and the refrigeration compressor.

[0029] As a further limitation of the technical solution of this embodiment of the invention, the working condition parameter determination module specifically includes:

[0030] The occlusion prediction unit is used to acquire road information and vehicle information ahead of the target vehicle in real time, and to determine whether the target vehicle is about to enter an occlusion following condition caused by a large vehicle ahead based on the road information and vehicle information ahead.

[0031] The working condition parameter generation unit is used to determine the working condition parameters corresponding to the obstructed following condition when it is determined that the target vehicle is about to enter the obstructed following condition.

[0032] The database retrieval unit is used to retrieve a shared database stored in the cloud based on the operating condition parameters.

[0033] As a further limitation of the technical solution of the embodiment of the present invention, the operating parameters include driving environment condition parameters, vehicle operating status parameters, compressor operating status parameters, large vehicle ahead status parameters, predicted following distance, predicted following speed, predicted start-stop frequency, and predicted total following time.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] This invention predicts the obstruction conditions of the target vehicle in advance and, combined with historical big data and experimentally constructed pre-cooling prediction models, calculates the required pre-cooling power adjustment for the compressor before entering unfavorable heat dissipation conditions. This allows the vehicle to complete cooling preparation before it enters high-load conditions. Furthermore, this invention deeply couples the pre-cooling process with the vehicle's powertrain's regenerative braking system. During deceleration, the regenerative braking energy that would otherwise be recharged to the battery is directly supplied to the compressor via the DC bus. This eliminates the reliance on additional battery discharge for pre-cooling energy acquisition, reducing the instantaneous output demand of the power battery and preventing compressor load spikes caused by obstruction from impacting the powertrain.

[0036] Through the above technical approach, this invention achieves active optimization of the energy flow of the power system, effectively improving the energy utilization efficiency of the whole vehicle, reducing battery energy consumption and extending the driving range, while improving the stability and reliability of the power system under cold chain transportation conditions. Attached Figure Description

[0037] Figure 1 A flowchart of the method provided in the embodiments of the present invention;

[0038] Figure 2 This is a flowchart illustrating the determination of operating parameters in the method provided in this embodiment of the invention;

[0039] Figure 3 This is a flowchart illustrating the allocation of regenerative braking energy in the method provided in this embodiment of the invention;

[0040] Figure 4 Application architecture diagram of the system provided in the embodiments of the present invention;

[0041] Figure 5 This is a structural block diagram of the operating condition parameter determination module in the system provided in the embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0044] Specifically, a method for intelligent energy consumption optimization of a new energy vehicle power system includes the following steps:

[0045] Step S100: When it is predicted that the target vehicle is about to enter a following condition caused by obstruction from a large vehicle ahead, the operating condition parameters corresponding to this obstruction following condition are determined and the database is retrieved. The target vehicle is a new energy cold chain vehicle containing a front-mounted refrigeration compressor. The target vehicle has a battery system with regenerative braking function, and the battery system is used to supply power to the vehicle's power system and the refrigeration compressor.

[0046] Specifically, Figure 2 A flowchart for determining operating parameters is shown.

[0047] Specifically, when predicting that a target vehicle is about to enter a following condition caused by obstruction from a large vehicle ahead, determining the operating parameters corresponding to this obstruction following condition and retrieving them from the database includes the following steps:

[0048] Step S101: Obtain the road information and the information of the vehicle in front of the target vehicle in real time, and determine whether the target vehicle is about to enter a following condition caused by the obstruction caused by the large vehicle in front based on the road information and the information of the vehicle in front.

[0049] Step S102: When it is determined that the target vehicle is about to enter the obstructed following condition, determine the condition parameters corresponding to the obstructed following condition.

[0050] Step S103: Retrieve the shared database stored in the cloud based on the operating condition parameters.

[0051] The operating parameters include driving environment conditions, vehicle operating status, compressor operating status, large vehicle ahead status, predicted following distance, predicted following speed, predicted start-stop frequency, and predicted total following time.

[0052] In this embodiment of the invention, step S100 first requires a description of the type and basic structure of the target vehicle involved in this case. The target vehicle is a new energy cold chain vehicle containing a front-mounted refrigeration compressor. Such vehicles typically employ pure electric or hybrid powertrains. Their powertrain is driven by a motor and its controller. The battery system on the vehicle provides power to both the powertrain and the front-mounted refrigeration compressor. In the prior art, such new energy cold chain vehicles widely employ battery systems with regenerative braking functionality. When the target vehicle decelerates or brakes, the drive motor switches to generator mode, recharging the battery system with the regenerated braking energy via a DC bus to improve the overall energy efficiency of the vehicle.

[0053] A large vehicle in front can generally be understood as a type of vehicle that is significantly larger in size, higher in body height than the target vehicle, and significantly blocks the airflow and line of sight in front of the target vehicle during driving, such as a large truck, semi-trailer tractor, heavy truck, large bus, bus, etc. In an actual road traffic scenario, when the target vehicle follows any of the above large vehicles at a small following distance, a typical blocked following driving condition is likely to form.

[0054] The so-called blocked following driving condition refers to the target vehicle continuously following the large vehicle in front within a certain following distance range. Due to the body of the large vehicle in front blocking, the airflow in front of the target vehicle is significantly disturbed or weakened, which in turn affects the windward heat dissipation effect of the condenser arranged at the front of the vehicle. For the sake of easy understanding, in common traffic scenarios, this driving condition often corresponds to the state of slow driving in a queue at a low speed, such as approaching a congested section, a ramp merging area, or a slow-moving traffic flow with a large number of large vehicles. In such scenarios, the following distance of the target vehicle is usually short, and the natural wind volume available in front of it is significantly insufficient. The blocked following driving condition can be defined as: the following distance of the target vehicle is less than or equal to a preset distance threshold, such as less than 20 m, or adaptively defined as a speed-related time interval according to the vehicle speed (such as a headway time less than 2 s), and this state lasts for a certain period of time, such as lasting for more than a certain number of seconds or a certain number of tens of seconds, so as to have a substantial impact on the condenser heat dissipation.

[0055] In the prior art, for a cold-chain new energy vehicle with a front-mounted cooling compressor, in order to ensure the normal operation of the refrigeration system, the heat dissipation of the condenser is generally achieved through the oncoming airflow generated by the vehicle speed and the cooling fan supporting the condenser. When the vehicle speed is high and there is no block in front, the air flow in front of the condenser is unobstructed and the heat dissipation condition is good. Even under the condition that the vehicle speed is relatively low, it can rely on the forced air supply of the fan and a certain amount of natural oncoming wind to obtain a basically stable heat dissipation capacity. There are indeed calculation and evaluation methods for the cooling effect in the prior art. Generally, based on the ambient temperature, vehicle driving speed, and operating parameters of the condenser and compressor, the cooling effect is estimated. For example, the heat exchange capacity of the condenser and the load power required by the compressor are estimated through the ambient temperature, vehicle speed, and fan speed, and accordingly, the operating frequency or duty cycle of the compressor is adjusted.

[0056] In this embodiment of the invention, those skilled in the art have discovered that in normal driving scenarios where the vehicle is not obstructed by a large vehicle ahead, the area where the cooling compressor is located typically has a relatively open space in front, allowing the condenser to directly receive oncoming airflow. Even if the target vehicle's speed is not high, its heat dissipation effect can still meet the current cold chain temperature control requirements for most of the time, and the compressor load remains within the expected range. However, once the target vehicle enters an obstructed following condition, the disturbance and obstruction of airflow by the large vehicle ahead will significantly weaken the airflow flow and velocity in front of the condenser, causing a significant decrease in the condenser's actual heat dissipation capacity. In this situation, if the target vehicle continues to follow the large vehicle ahead at a relatively short following distance, in order to maintain the cold chain temperature inside the cargo compartment, the compressor needs to increase its operating frequency or load power for a long time, thus generating an additional compressor load compared to the normal heat dissipation condition during the entire obstructed following condition. This additional load not only intensifies the workload of the refrigerant circulation system and the compressor itself, shortening component lifespan, but also causes the battery system to need to output more electrical energy to the compressor, increasing the overall vehicle energy consumption and adversely affecting the driving range and overall energy utilization efficiency.

[0057] Furthermore, those skilled in the art have discovered that, from a new perspective, focusing not only on the insufficient heat dissipation of the following-car-by-car condition itself, but also extending the timeline forward to the deceleration phase before entering the obstructed following-car-by ... If it can be determined that the compressor load will exceed expectations due to obstruction during subsequent following maneuvers, and the required increase in cooling power before entering these maneuvers can be calculated, and a corresponding portion of the regenerative braking energy during deceleration can be directly allocated to the compressor to achieve the required increased cooling power in this stage, then pre-cooling of the battery can be completed using the already generated regenerative braking energy without adding extra burden to the battery discharge. This not only avoids or significantly reduces the additional compressor load during subsequent following maneuvers but also improves the overall utilization efficiency of regenerative braking energy, effectively achieving intelligent energy consumption optimization of the new energy vehicle's powertrain system.

[0058] Furthermore, those skilled in the art recognize that distributing regenerative braking energy directly to the compressor or other on-board energy-consuming equipment via the DC bus without passing through the battery system's charging and discharging stages is already feasible in the prior art. Some new energy vehicle energy management systems already support the on-demand distribution of regenerative braking energy to auxiliary loads at the vehicle-side bus level to reduce the battery system's cycle count and improve energy utilization efficiency. For example, existing vehicle thermal management systems, on-board air conditioning systems, or high-voltage accessory power supply systems all have technical frameworks for directly powering equipment such as motors, compressors, or cooling pumps via the DC bus. Therefore, the method described in this invention of directly supplying regenerative braking energy to the front-mounted refrigeration compressor via the DC bus during deceleration is a reasonable application based on the existing electric drive system architecture. It is not only feasible but also avoids energy losses caused by battery charging and discharging, thereby further improving the overall vehicle's energy utilization efficiency.

[0059] In step S100, the functions of steps S101 to S103 need to be explained. Step S101 identifies whether the target vehicle is about to enter a following-obstruction situation by real-time perception and analysis of the road environment and the status of vehicles ahead. Road information may include the target vehicle's current location, driving lane, speed limit, gradient, road type, and traffic congestion information ahead. Information about vehicles ahead may include the target vehicle's vehicle type (e.g., whether it is a large truck or a large bus), relative distance, relative speed, lateral deviation, and trajectory. The road information can be obtained using existing technologies such as satellite positioning systems, vehicle navigation systems, high-definition maps, roadside infrastructure, and vehicle-to-everything (V2X) communication. Information about vehicles ahead can be obtained using sensors such as vehicle cameras, millimeter-wave radar, lidar, and ultrasonic radar, as well as algorithms for identifying vehicles ahead. In this embodiment of the invention, the type of vehicle ahead can be identified to determine whether it is a large vehicle. Combined with the relative distance and speed between the target vehicle and the vehicle ahead, as well as road speed limit information, it can be predicted whether the target vehicle will enter an obstructed following condition within a preset prediction time window. The term "about to enter" can be limited by a preset threshold. For example, if, within a certain prediction time range (such as the next 10 seconds, 20 seconds, or longer), the predicted following distance between the target vehicle and the large vehicle ahead will be less than a preset following distance threshold, and this following state is expected to continue for more than a preset duration threshold, it can be determined that the target vehicle is about to enter an obstructed following condition caused by the large vehicle ahead, thereby triggering subsequent condition parameter determination and database retrieval operations.

[0060] Step S102 generates operating condition parameters that characterize the following condition after determining that the target vehicle is about to enter an obstructed following situation. These parameters include driving environment condition parameters, vehicle operating status parameters, compressor operating status parameters, forward large vehicle status parameters, predicted following distance, predicted following speed, predicted start-stop frequency, and predicted total following time. Driving environment condition parameters may include ambient temperature, ambient humidity, ambient wind, and road adhesion conditions, which can be obtained using existing technologies such as onboard temperature sensors, humidity sensors, and road adhesion estimation algorithms. Vehicle operating status parameters may include the target vehicle's current speed, acceleration, current drive power, current braking status, and current SOC, which can be collected from the state variables of the motor controller, braking system, and battery management system by the onboard controller. Compressor operating status parameters may include current compressor power, compressor speed, current target temperature and actual temperature inside the cargo compartment, which can be obtained using existing vehicle air conditioning control units or cold chain temperature control controllers. Forward large vehicle status parameters may include the forward large vehicle's current speed, acceleration, and lane occupancy status, which can be obtained using a forward vehicle recognition and tracking algorithm combined with sensor data. Predicting following distance, following speed, start-stop frequency, and total following time can be calculated using existing short-term trajectory prediction and speed planning algorithms, based on the target vehicle's current operating status, the status parameters of large vehicles ahead, and road speed limits and congestion conditions. The acquisition of these parameters can all be achieved using mature vehicle control and perception technologies in this field, and are existing or easily implemented techniques.

[0061] Step S103 serves to retrieve historical operating data with similar operating characteristics from the cloud or vehicle side using the determined operating condition parameters. This provides a data foundation for subsequent extraction of reference samples and analysis of compressor additional load. The shared database stored in the cloud can be the operating platform to which the target vehicle belongs, the vehicle network cloud platform provided by the vehicle manufacturer, or a third-party big data service platform. The database stores operating data of multiple or a large number of similar new energy cold chain vehicles under different historical occlusion following conditions, including corresponding operating condition parameters, compressor operating data, vehicle operating data, and environmental condition data. Retrieving the database based on the operating condition parameters can specifically involve matching the current operating condition parameters with the operating condition parameter index in the database, filtering out historical sample data similar to the current operating condition parameters within a certain tolerance range, and using them as subsequent reference samples. Such cloud-based shared databases are already widely used in existing technologies, for example, in fleet operation analysis, energy consumption statistics, and operating condition analysis. This embodiment of the invention only further expands its application in energy consumption optimization under occlusion following conditions based on existing data.

[0062] Furthermore, the intelligent energy consumption optimization method for the power system of new energy vehicles also includes the following steps:

[0063] Step S200: Extract reference samples from the database. The reference vehicle corresponding to the reference sample has the same operating parameters under its historical occlusion following conditions as its current operating parameters. The reference vehicle and the target vehicle are of the same type; both are new energy cold chain vehicles containing the same front-mounted refrigeration compressor and equipped with a battery system with braking energy recovery function.

[0064] Step S300: Analyze the reference sample to determine the additional compressor load caused by the aerodynamic obstruction caused by the large vehicle in front, resulting in reduced heat dissipation of the condenser, in the historical obstruction following conditions of the reference vehicle.

[0065] In this embodiment of the invention, the purpose of step S200 is to use samples of real-world occlusion following conditions from a large amount of historical operational data to conduct a comparative analysis of the occlusion following conditions that the target vehicle is about to face. Since the operating parameters of the occlusion following conditions that the target vehicle may enter have already been obtained in step S100, in step S200, by querying historical operating parameters that are consistent with the current operating parameters or match within a preset similarity range in the cloud-shared database, reference samples that the reference vehicle has experienced and that are highly similar to the occlusion following conditions predicted for the target vehicle can be selected. The reference vehicle and the target vehicle are of the same type, both being new energy refrigerated vehicles containing a front-mounted refrigeration compressor and equipped with a battery system with braking energy recovery function. Therefore, their thermal management architecture, condenser heat dissipation mode, compressor working mechanism, and energy system operating characteristics are comparable. By selecting reference samples with consistent or similar operating parameters, it is possible to accurately determine whether the core issue currently of concern to the target vehicle exists in these existing historical operating conditions, namely, the situation where, under occlusion following conditions, the compressor experiences additional load due to deteriorated heat dissipation conditions in order to maintain the cooling effect.

[0066] In this embodiment of the invention, maintaining consistent operating parameters ensures the comparability of samples. If the reference sample's driving environment conditions, vehicle operating status, compressor operating status, status of the large vehicle ahead, and predicted following distance, following speed, start-stop frequency, and other parameters match the target vehicle's current predicted operating conditions well, then the compressor's operating characteristics and heat load changes in the reference sample can accurately reflect the potential load changes of the target vehicle in the upcoming occlusion following condition. The advantage of using a big data platform for screening is that it can quickly find multiple reference samples that meet the matching conditions from a large amount of historical data, thereby improving sample reliability and statistical significance. Without a big data foundation, it is impossible to obtain a large amount of occlusion following condition data from different times, different users, and different vehicles in real road scenarios, making it impossible to achieve reference sample screening based on statistical laws. This embodiment of the invention does not require every operating parameter of the sample to be completely consistent with the current operating condition, but rather to be approximately equal within a certain tolerance range. For example, all parameters should be within a preset deviation range, thereby ensuring that the screening process has a sufficient sample size and takes into account actual road fluctuations.

[0067] The purpose of step S300 is to analyze the changes in condenser heat dissipation conditions of the reference vehicle under historical obstructed following conditions based on the reference samples obtained in step S200, thereby determining the additional load on the compressor due to reduced heat dissipation. The additional load reflects the extra cooling load borne by the reference vehicle under obstructed following conditions to maintain the cargo compartment temperature within the target range. Essentially, it is the power difference or energy consumption difference between the compressor under normal heat dissipation conditions and under obstructed conditions that reduce heat dissipation. In this embodiment of the invention, the additional load can be determined based on historical compressor operating data, condenser temperature data, vehicle speed, ambient temperature, and condenser temperature difference data in the reference samples.

[0068] Specifically, the additional load can be determined as follows: First, the baseline operating power of the compressor in the unobstructed condition during the historical driving process of the reference vehicle is statistically analyzed to obtain the baseline cooling capacity and compressor power corresponding to the current operating parameters. Then, the actual operating power of the compressor in the obstructed following condition is extracted from the historical data of the reference sample. By comparing the difference between the actual compressor power and the baseline power, the additional load caused by insufficient heat dissipation of the compressor can be obtained. In addition, the additional load can also be derived based on the change in condenser cooling efficiency. For example, the additional energy consumption of the compressor can be inferred by analyzing the changes in condenser inlet air temperature, condensing pressure, evaporator load, etc. before and after obstruction. The above data are all routinely collected data in the thermal management system and refrigeration system monitoring of existing cold chain vehicles, so they can be directly obtained from the reference sample. The processing method is also a part of existing vehicle thermal management control technology and statistical analysis methods. This invention only innovatively expands the way the data is used.

[0069] Furthermore, the intelligent energy consumption optimization method for the power system of new energy vehicles also includes the following steps:

[0070] Step S400: Obtain a preset pre-cooling prediction model. Input the additional load and current operating parameters into the pre-cooling prediction model to obtain the pre-cooling power adjustment amount used to avoid additional compressor load after entering the obstructed following condition. The preset pre-cooling prediction model is a model built based on experimental data. The model learns from the compressor operation experimental data obtained under different operating parameters to establish a mapping relationship between the compressor's additional load and the pre-cooling power adjustment amount required to avoid additional compressor load on the target vehicle in the obstructed following condition.

[0071] Step S500: Calculate the required precooling energy based on the precooling power adjustment amount, and during the period from the start of deceleration of the target vehicle to entering the obstructed following condition, directly distribute the portion of the braking recovery energy generated during this process to the compressor via the DC bus to meet the precooling energy requirement, so as to realize the precooling power adjustment amount.

[0072] Specifically, Figure 3 A flowchart illustrating the distribution of regenerated braking energy is shown.

[0073] The calculation of the required precooling energy based on the precooling power adjustment, and the portion of the regenerative braking energy generated during the period from the start of deceleration to entering the obstructed following condition that is used to meet the precooling energy requirement, is directly distributed to the compressor via the DC bus to achieve the precooling power adjustment. The specific steps include:

[0074] Step S501: Based on the real-time acquired road information, predict the time required for the target vehicle to enter the obstructed following condition, and call the historical operation record of the compressor of the target vehicle. Calculate the average precooling energy required for the compressor to increase the precooling power adjustment amount from the current power to the precooling power adjustment amount within this time period based on the precooling power adjustment amount and the time.

[0075] In step S502, during the period from the start of deceleration of the target vehicle to entering the obstructed following condition, a portion of the regenerative braking energy generated in this stage that is used to meet the average pre-cooling energy is directly distributed to the compressor via the DC bus, so that the compressor gradually increases the pre-cooling power adjustment amount during the deceleration stage.

[0076] In this embodiment of the invention, in step S400, a preset pre-cooling prediction model is obtained. The additional load and current operating parameters are input into the pre-cooling prediction model to obtain the pre-cooling power adjustment amount used to avoid additional compressor load after entering the obstructed following condition. Step S400 can be understood as a key step in this embodiment of the invention. Its function is to transform the additional compressor load obtained by referring to the sample statistics into a specific power adjustment command that can be used for pre-cooling control before entering the obstructed following condition. This transforms the "additional load problem that has occurred in similar obstructed following conditions in the past" into a quantitative control quantity of "how much cooling power needs to be increased in advance before the current target vehicle enters the obstructed following condition". Through this step, the risk of compressor load under obstructed following conditions is perceived in advance, and a clear target value is provided for the subsequent allocation and utilization of regenerative braking energy.

[0077] The preset pre-cooling prediction model is a model built based on experimental data. This model learns from compressor operating experimental data obtained under different operating parameters to establish a mapping relationship between the compressor's additional load and the required adjustment of the pre-cooling power to avoid additional compressor load in the target vehicle's obstructed following conditions. In practice, multiple sets of controlled variable experiments can be conducted on new energy cold chain vehicles containing pre-cooling compressors under test bench or vehicle road test conditions. Under different combinations of operating parameters such as ambient temperature, vehicle speed, condenser airflow, cargo box load, and target temperature settings, data such as compressor operating power, outlet air temperature, condensing pressure, and evaporator load are recorded under normal heat dissipation and simulated obstructed heat dissipation reduction conditions. This allows for the calculation of the increased cooling power required to offset the heat dissipation reduction caused by obstruction under various operating conditions. After organizing the above experimental data into a sample set, mature machine learning techniques in this field can be used to model it. For example, a multivariate nonlinear regression model, a nonlinear fitting model based on a neural network, a regression model based on a decision tree or gradient boosting tree, or a support vector regression model can be used. By training the model on the relationship between operating parameters, compressor additional load, and precooling power adjustment, a precooling prediction model can be obtained. The above machine learning algorithms are all existing mature technologies and have been widely used in fields such as vehicle energy consumption prediction and thermal management control strategy optimization. Therefore, using them to construct the precooling prediction model in this invention is a reasonable choice for those skilled in the art based on existing technology.

[0078] In constructing the precooling prediction model, the input features can be set as current operating parameters and corresponding compressor additional load. Operating parameters include all or part of the following: driving environment conditions, vehicle operating status, compressor operating status, the status of the large vehicle ahead, predicted following distance, predicted following speed, predicted start-stop frequency, and predicted total following duration. The output is set as the pre-cooling power adjustment required to avoid additional compressor load on the target vehicle during obstructed following conditions. After training, the precooling prediction model can output the corresponding precooling power adjustment when given operating parameters and additional load related to the target vehicle's current and future obstructed following conditions. This precooling power adjustment can be understood as the power increment the compressor needs to increase based on its current operating power before the target vehicle enters the obstructed following condition. This is used to provide additional cooling capacity reserves to the cargo compartment in advance, so that even if the condenser's heat dissipation capacity decreases during subsequent obstructed following conditions, the compressor does not need to increase its load or only needs a limited increase, thereby avoiding or significantly reducing the additional compressor load. By adjusting the precooling power, this invention unifies complex environmental changes, operating parameters, and historical statistical results into a single power adjustment target that can be directly used for control, enabling the control strategy to be implemented in engineering.

[0079] In step S500, the required precooling energy is calculated based on the precooling power adjustment amount. During the period from the start of deceleration of the target vehicle to entering the following-meeting condition, the portion of the regenerative braking energy generated during this process used to meet the precooling energy requirement is directly allocated to the compressor via the DC bus to achieve the precooling power adjustment amount. The significance of step S500 is that it further transforms the precooling power adjustment amount from a "power-level control quantity" into an energy allocation plan executable in the time dimension. By utilizing the regenerative braking energy generated by the target vehicle itself during the deceleration phase, it provides an energy source for the compressor's precooling process, thereby completing precooling control without increasing the additional battery discharge burden. On the one hand, it makes full use of the regenerative braking energy that would otherwise be generated, avoiding the conversion loss caused by using it entirely for battery charging and then discharging it to supply the compressor. On the other hand, it also ensures a good match between the energy source of the precooling process and the regenerative braking process in terms of time, making it highly practical in engineering.

[0080] Specifically, the process of calculating the required precooling energy based on the precooling power adjustment and distributing the regenerative braking energy during the period from the start of deceleration of the target vehicle to entering the obstructed following condition can include steps S501 and S502. In step S501, the time required for the target vehicle to enter the obstructed following condition is predicted based on real-time acquired road information, and the historical operation record of the compressor of the target vehicle is called. The average precooling energy required for the compressor to increase the precooling power adjustment from the current power to the precooling power adjustment during this time is calculated according to the precooling power adjustment and the time. Specifically, the time interval from the current moment to the target vehicle entering the obstructed following condition can be predicted using a navigation system, high-definition map, and road traffic information ahead, for example, predicting a time window of several seconds to several tens of seconds in the future. Let the time interval be T. Based on this, combined with the precooling power adjustment ΔP output by the precooling prediction model, the total precooling energy E required to maintain the compressor power increase ΔP within time T can be estimated based on an empirical model or based on data such as the actual refrigeration efficiency, compressor energy efficiency ratio, cargo compartment heat capacity, and temperature change response recorded in the historical compressor operation record of the target vehicle. pre Furthermore, the total precooling energy E can be... pre The average precooling energy or equivalent average power that needs to be provided to the compressor from the brake recovery energy per unit time is calculated by amortizing the energy over time T, thus obtaining the value of the average precooling energy. The above calculation can be performed by integral calculation, table lookup, or approximate estimation based on existing energy models. The compressor's historical operating records and energy estimation methods used can all be implemented based on mature technologies of existing cold chain vehicle temperature control systems and energy management systems.

[0081] In step S502, during the period from the start of deceleration of the target vehicle to entering the following-meeting condition, a portion of the regenerative braking energy generated in this stage, used to meet the average pre-cooling energy requirement, is directly distributed to the compressor via the DC bus. This allows the compressor to gradually increase the pre-cooling power adjustment during the deceleration phase. "Average" means that during the time period T from the current moment to entering the following-meeting condition, the compressor power is not increased instantaneously and significantly, but rather gradually increased in a relatively smooth manner throughout the time period based on the average pre-cooling energy or equivalent average power. This allows the compressor power to transition from the current level to the target value plus the pre-cooling power adjustment. Such smooth control avoids instantaneous high-power surges to the vehicle's electrical system and also gradually reduces the internal temperature, creating a suitable cooling capacity reserve. In the energy path, using the DC bus as a common bus, a portion of the regenerative braking energy, based on the pre-calculated average pre-cooling energy and meeting both regenerative braking safety and battery charging requirements, is directly supplied to the compressor, enabling the compressor to complete the power increase corresponding to the pre-cooling power adjustment during the deceleration phase.

[0082] In actual implementation, the braking recovery energy generated during the deceleration phase may not be sufficient to fully meet the average precooling energy requirement. In this case, the available braking recovery energy can be allocated to the compressor as much as possible, so that the compressor can gradually increase the precooling power according to the available energy level, so as to achieve a precooling effect that is as close as possible to the precooling power adjustment amount.

[0083] Through the above steps, the overall beneficial effects of this invention are reflected in several aspects. On the one hand, by predicting the obstructed following conditions, conducting big data analysis on historical reference samples, and calculating the pre-cooling power adjustment amount through a pre-cooling prediction model, this invention enables the system to quantitatively assess the risk of impending additional load before the target vehicle enters the obstructed following condition, based on historical compressor additional load situations under similar conditions, and to take countermeasures in advance. This directly addresses the core research point mentioned in step S100, namely, the question of "whether additional load will occur due to poor heat dissipation in order to maintain the cooling effect under obstructed following conditions." On the other hand, by directly supplying the compressor with the brake recovery energy through the DC bus during the deceleration phase, the pre-cooling energy can be directly used. This reduces energy loss during battery charging and discharging and avoids the instantaneous current surge and additional battery energy consumption caused by the compressor needing to significantly increase its load under obstructed following conditions. This achieves a reduction in the overall energy consumption of the power system of new energy cold chain vehicles and an improvement in energy utilization efficiency. Furthermore, the precooling control and braking energy recovery coordinated distribution strategy based on the precooling prediction model proposed in this invention has good versatility and can be applied to a variety of new energy cold chain vehicles with front-mounted cooling compressors. It can also be extended to other vehicle auxiliary systems that require front-mounted energy regulation before specific adverse operating conditions, and has good engineering application prospects and industrial promotion value.

[0084] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0085] In another preferred embodiment of the present invention, a smart energy consumption optimization system for a new energy vehicle power system includes:

[0086] The working condition parameter determination module 100 is used to determine the working condition parameters corresponding to the obstructed following condition when it is predicted that the target vehicle is about to enter the obstructed following condition caused by the large vehicle in front, and to retrieve the database.

[0087] Specifically, Figure 5 The diagram shows a structural block diagram of the operating condition parameter determination module 100 in the system provided by an embodiment of the present invention.

[0088] In a preferred embodiment of the present invention, the operating condition parameter determination module 100 specifically includes:

[0089] The occlusion prediction unit 101 is used to acquire road information and vehicle information ahead of the target vehicle in real time, and determine whether the target vehicle is about to enter an occlusion following condition caused by a large vehicle ahead based on the road information and vehicle information ahead.

[0090] The working condition parameter generation unit 102 is used to determine the working condition parameters corresponding to the obstructed following condition when it is determined that the target vehicle is about to enter the obstructed following condition.

[0091] The database retrieval unit 103 is used to retrieve a shared database stored in the cloud based on the operating condition parameters.

[0092] The operating parameters include driving environment conditions, vehicle operating status, compressor operating status, large vehicle ahead status, predicted following distance, predicted following speed, predicted start-stop frequency, and predicted total following time.

[0093] Furthermore, the intelligent energy consumption optimization system for the new energy vehicle power system also includes:

[0094] The reference sample extraction module 200 is used to extract reference samples from the database. The reference parameters of the reference vehicle corresponding to the reference sample are consistent with the current operating parameters under its historical occlusion following conditions.

[0095] The additional load determination module 300 is used to analyze the reference sample and determine the additional load on the compressor of the reference vehicle caused by the aerodynamic obstruction caused by the large vehicle in front, resulting in reduced heat dissipation of the condenser, in the historical obstruction following conditions.

[0096] Both the target vehicle and the reference vehicle are new energy cold chain vehicles that include a front-mounted refrigeration compressor. Both the target vehicle and the reference vehicle have a battery system with regenerative braking function, and the battery system is used to supply power to the vehicle power system and the refrigeration compressor.

[0097] Furthermore, the intelligent energy consumption optimization system for the new energy vehicle power system also includes:

[0098] The precooling power adjustment acquisition module 400 is used to acquire a preset precooling prediction model. The additional load and current operating parameters are input into the precooling prediction model to obtain the precooling power adjustment amount used to avoid additional load on the compressor after entering the blocking following condition.

[0099] The precooling energy calculation and distribution module 500 is used to calculate the required precooling energy based on the precooling power adjustment amount, and during the period from the start of deceleration of the target vehicle to entering the obstructed following condition, the portion of the braking recovery energy generated in the process that is used to meet the precooling energy is directly distributed to the compressor via the DC bus to realize the precooling power adjustment amount.

[0100] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0102] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A new energy vehicle power system intelligent energy consumption optimization method, characterized in that, The method comprises: When predicting that the target vehicle is about to enter the sheltered following condition caused by the large vehicle in front, determining the condition parameters corresponding to the sheltered following condition, and calling the database; The specific steps include: Real-time acquisition of road information and front vehicle information of the target vehicle, and judgment of whether the target vehicle is about to enter the sheltered following condition caused by the large vehicle in front based on the road information and front vehicle information; When determining that the target vehicle is about to enter the sheltered following condition, determining the condition parameters corresponding to the sheltered following condition; Based on the condition parameters, the shared database stored in the cloud is called; Extracting reference samples from the database, the reference samples corresponding to the reference vehicles have consistent condition parameters in the historical sheltered following condition and the current condition parameters; Analyzing the reference samples to determine the additional load of the compressor caused by the decrease of condenser heat dissipation due to the aerodynamic shelter of the large vehicle in front in the historical sheltered following condition; Obtaining a preset pre-cooling prediction model, inputting the additional load and the current condition parameters into the pre-cooling prediction model to obtain a pre-cooling power adjustment amount for avoiding the additional load of the compressor after entering the sheltered following condition; the preset pre-cooling prediction model is a model constructed based on experimental data, and the model establishes a mapping relationship between the additional load of the compressor and the pre-cooling power adjustment amount required to avoid the additional load of the compressor of the target vehicle in the sheltered following condition by learning the experimental data of the compressor running under different condition parameters; Based on the pre-cooling power adjustment amount, the required pre-cooling energy is calculated, and during the period from the start of deceleration to the entry into the sheltered following condition of the target vehicle, the part of the brake recovery energy generated in this process for meeting the pre-cooling energy is directly distributed to the compressor through the DC bus to realize the pre-cooling power adjustment amount; The specific steps include: Based on the real-time acquired road information, the time required for the target vehicle to enter the sheltered following condition is predicted, and the historical operation record of the compressor of the target vehicle is called, and the average pre-cooling energy required for the compressor to increase the pre-cooling power adjustment amount from the current power within the time is calculated according to the pre-cooling power adjustment amount and the time; During the period from the start of deceleration to the entry into the sheltered following condition of the target vehicle, the part of the brake recovery energy generated in this stage for meeting the average pre-cooling energy is directly distributed to the compressor through the DC bus, so that the compressor gradually increases the pre-cooling power adjustment amount in the deceleration stage.

2. The intelligent energy consumption optimization method for a new energy vehicle power system according to claim 1, characterized in that, The target vehicle and the reference vehicle are both new energy cold chain vehicles containing a front refrigeration compressor, and both the target vehicle and the reference vehicle have a battery system with brake energy recovery function, and the battery system is used to power the vehicle power system and the refrigeration compressor. 3.The intelligent energy consumption optimization method for new energy vehicle power system according to claim 1, characterized in that, The condition parameters include driving environment condition parameters, vehicle running state parameters, compressor running state parameters, front large vehicle state parameters, predicted following distance, predicted following speed, predicted start-stop frequency, and predicted total following time.

4. A new energy vehicle power system intelligent energy consumption optimization system, characterized in that, The system comprises: The working condition parameter determination module is configured to determine a working condition parameter corresponding to the shelter following condition caused by the large vehicle in front of the target vehicle when the target vehicle is about to enter the shelter following condition, and to call a database; The working condition parameter determination module specifically includes: The shelter condition prediction unit is configured to acquire road information and front vehicle information of the target vehicle in real time, and to determine whether the target vehicle is about to enter the shelter following condition caused by the large vehicle in front of the target vehicle based on the road information and the front vehicle information; The working condition parameter generation unit is configured to determine the working condition parameter corresponding to the shelter following condition when it is determined that the target vehicle is about to enter the shelter following condition; The database calling unit is configured to call a shared database stored in the cloud based on the working condition parameter; The reference sample extraction module is configured to extract a reference sample from the database, the working condition parameter of a reference vehicle corresponding to the reference sample being consistent with the current working condition parameter in a historical shelter following condition of the reference vehicle; The additional load amount determination module is configured to analyze the reference sample, and to determine an additional load amount of a compressor caused by a decrease in condenser heat dissipation due to aerodynamic shelter of the large vehicle in front of the reference vehicle in the historical shelter following condition; The precooling power adjustment amount acquisition module is configured to acquire a precooling prediction model, to input the additional load amount and the current working condition parameter into the precooling prediction model, and to obtain a precooling power adjustment amount for avoiding the additional load of the compressor after entering the shelter following condition; the precooling prediction model is a model constructed based on experimental data, and the model establishes a mapping relationship between the additional load amount of the compressor and the precooling power adjustment amount required for avoiding the additional load of the compressor of the target vehicle in the shelter following condition by learning experimental data of the compressor obtained under different working condition parameters; The precooling energy calculation and distribution module is configured to calculate required precooling energy based on the precooling power adjustment amount, and to directly distribute, to the compressor via a direct current bus, a portion of brake recovery energy generated during a period from when the target vehicle starts to decelerate to when the target vehicle enters the shelter following condition, to achieve the precooling power adjustment amount; The time required for the target vehicle to enter the shelter following condition is predicted based on real-time acquired road information, and a historical operation record of a compressor of the target vehicle is called, and average precooling energy required for the compressor to increase the precooling power adjustment amount from a current power within a time is calculated based on the precooling power adjustment amount and the time; During a period from when the target vehicle starts to decelerate to when the target vehicle enters the shelter following condition, a portion of brake recovery energy generated in this period for satisfying the average precooling energy is directly distributed to the compressor via a direct current bus, so that the compressor gradually increases the precooling power adjustment amount in the deceleration period.

Images