Electric vehicle control optimization method based on big data

Abstract

The invention discloses an electric vehicle control optimization method based on big data, and relates to the field of big data, and the method comprises the steps: building a cloud database containing the historical and real-time data of vehicles in the same batch, obtaining multi-source data through a motor, a battery, an environment and an accessory equipment collection terminal, and carrying out the optimization of the multi-source data; and calculating a battery health degree fluctuation coefficient Kb, a motor efficiency fluctuation coefficient Km and an environmental factor fluctuation coefficient Ke after Min-Max normalization processing, and importing a dynamic energy consumption model to generate a real-time energy consumption rationality index Es. And the vehicle control unit VCU dynamically adjusts the air conditioner power, the motor output torque and the battery charging and discharging mode according to the Es, an optimization instruction is sent to the ECU controller, and a historical database is updated. The method breaks through the limitation of traditional fixed parameter control, accurately evaluates the influence of the battery, the motor and the environment, reduces the abnormal fluctuation of energy consumption, improves the energy efficiency management level of the whole life cycle, prolongs the service life of the battery pack, and guarantees the safety and compliance of data.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a whole vehicle control optimization method for electric vehicles based on big data. Background Art

[0002] With the rapid development of new energy vehicle technology, the energy efficiency optimization of electric vehicles under complex working conditions has become a hot topic in the industry. The fixed parameter control strategy currently used in electric vehicles is difficult to adapt to dynamic working conditions such as battery performance degradation, motor efficiency fluctuations, and changes in environmental factors, resulting in insufficient energy consumption control accuracy and increased equipment loss.

[0003] The existing control methods have the following technical defects: First, traditional control strategies rely on preset empirical parameters and cannot quantify the combined impact of battery health, motor efficiency and environmental factors on energy consumption in real time, resulting in the system being unable to respond effectively under extreme temperatures, battery aging or inefficient motor operation, causing energy waste and shortened equipment life. Second, existing energy consumption optimization methods lack multi-dimensional data fusion and analysis capabilities, making it difficult to establish an accurate dynamic energy consumption model, resulting in delayed control strategy adjustments and the inability to achieve optimal energy efficiency management under all operating conditions. Third, changes in environmental parameters (such as temperature, humidity, and wind speed) can significantly affect battery performance and motor efficiency, but the existing system does not incorporate environmental factors into the control decision-making system, resulting in abnormal fluctuations in vehicle energy consumption in harsh environments.

[0004] In addition, the existing control strategy does not consider the synergistic effect between different control parameters when executing optimization instructions. For example, the independent control of air conditioning power adjustment and motor torque limit may cause system oscillation and reduce control stability. At the same time, the lack of historical data feedback mechanism makes it impossible for the control strategy to achieve continuous optimization through long-term operation data accumulation.

[0005] Therefore, there is an urgent need to provide an electric vehicle control optimization method based on big data to solve the problem of electric vehicle energy consumption. Summary of the invention

[0006] The purpose of the present invention is to provide an electric vehicle whole vehicle control optimization method based on big data to solve the problems raised in the above background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for optimizing the control of an electric vehicle based on big data, comprising a motor acquisition terminal, a battery acquisition terminal, an environment acquisition terminal, an accessory equipment acquisition terminal, an electronic control unit ECU, a vehicle control unit VCU, a vehicle terminal and a cloud database, wherein the motor acquisition terminal, the battery acquisition terminal and the electronic control unit ECU are connected via a CAN bus, the electronic control unit ECU, the vehicle control unit VCU and the vehicle terminal are connected via a CAN bus, and the vehicle terminal and the cloud database are connected via a wireless network, specifically comprising the following steps: S1. Cloud database construction: Build a cloud database, which includes historical data of the same batch of vehicles and real-time uploaded update data; S2. Multi-source data collection: collect data from a single vehicle through the motor collection terminal, battery collection terminal, environment collection terminal, and accessory equipment collection terminal to obtain battery parameters, motor parameters, environment parameters, and accessory equipment parameters; S3. Data processing and analysis: Based on the battery parameters, motor parameters, environmental parameters and auxiliary system parameters, the Min-Max normalization in machine learning is used to calculate the battery health fluctuation coefficient K b , Motor efficiency fluctuation coefficient K m And environmental factor fluctuation coefficient K e ; S4. Calculation of reasonable energy consumption: based on battery health fluctuation coefficient K b , Motor efficiency fluctuation coefficient K m And environmental factor fluctuation coefficient K e Import dynamic energy consumption model to generate real-time energy consumption rationality index E s ; S5. Adjustment and optimization of control strategy: The vehicle control unit VCU is based on the energy consumption rationality index E s Dynamically adjust air conditioning power, motor output torque and battery charging and discharging mode; S6. Feedback execution: Send optimization instructions to the vehicle ECU controller and update the historical database.

[0008] Preferably, the battery parameters include: single cell temperature T b , charge and discharge rate R cd , current value I c and battery health status SOH; the motor parameters include: motor real-time power consumption P m , motor speed Nm, motor output torque and motor efficiency fluctuation value η m； The environmental parameters include: external temperature T e 、Humidity e and wind speed W eThe auxiliary system parameters include: air conditioning power consumption P ac and other electronic equipment power consumption P e .

[0009] Preferably, the battery health fluctuation coefficient calculation process is as follows: K b The calculation formula is: , where R cd is the charge and discharge rate, R cd_avg is the historical average charge and discharge rate, SOH is the battery health status, Unit time The maximum temperature change of the battery pack, where The unit time is 1 minute.

[0010] Preferably, the motor efficiency fluctuation coefficient K m The calculation formula is: , where η m is the motor efficiency fluctuation value, η m_op is the efficiency value of the motor at the best efficiency point, N m is the motor speed, N m_op is the speed of the motor at the best efficiency point, is the output torque of the motor at the best efficiency point, The unit time is 1 minute.

[0011] Preferably, the environmental factor fluctuation coefficient K e The calculation formula is: ,in ,and , T e is the external temperature, T op is the preset ideal external temperature value, H e is humidity, H op is the preset ideal humidity value, W e is the wind speed, W op It is the preset ideal wind speed value.

[0012] Preferably, the energy consumption rationality index E s The calculation formula is: ,in , where P ac is the power consumption of air conditioner, P e is the power consumption of other electronic devices, P m is the real-time power consumption of the motor, K b is the battery health fluctuation coefficient, K m is the motor efficiency fluctuation coefficient, K e is the environmental factor fluctuation coefficient, E real is the real-time energy consumption, E avg is the historical average energy consumption, is the correction factor, is the environmental compensation coefficient.

[0013] Preferably, the correction factor and environmental compensation coefficient The calculation formula is: ,in , is the absolute deviation of the current battery discharge current from the historical sliding average SMA, is the historical average value of the motor temperature, is the basic allocation factor and ; ,in, It is the sliding average of the ambient temperature in the same period of the past 7 days. Historical ambient temperature standard deviation, is the difference between the highest cell temperature and the average temperature of the battery pack, The value is obtained by placing the battery in a constant temperature box, running it at different charge and discharge rates of 0.2C~1C, and monitoring the battery temperature rise. , by adjusting , so that , Take the maximum value that satisfies the conditions.

[0014] Preferably, the adjustment and optimization includes: when the energy consumption rationality index exceeds a preset threshold, reducing the air conditioning power P ac to , limit the motor output torque to , or switch the battery charging and discharging mode to at least one of the segmented balanced charging modes, otherwise maintain the historical optimal control strategy.

[0015] Preferably, the segmented balanced charging mode is specifically as follows: the battery groups are divided into three groups according to their SOH, namely, high, medium and low, and the low health battery groups are preferentially trickle charged, while the medium and high health battery groups are charged using dynamic pulse charging.

[0016] Preferably, the optimization instruction update cycle is ≤5s, and the sliding average value is corrected synchronously when the historical database is updated.

[0017] Technical effects and advantages of the present invention: 1. The present invention integrates the historical energy consumption and real-time operation data of multiple models by building a cloud database, and combines the synchronous collection of battery system parameters, motor parameters, environmental parameters and auxiliary system parameters to break through the limitations of traditional fixed parameter control strategies. b , Motor efficiency fluctuation coefficient K mAnd environmental factor fluctuation coefficient K e , achieving comprehensive evaluation of battery performance degradation, motor operating status and environmental impact, significantly improving the accuracy of energy consumption management under complex working conditions; 2. The present invention constructs a dynamic energy consumption model based on multi-source data to generate a real-time energy consumption rationality index E s , effectively quantifying the degree of deviation between actual energy consumption and theoretical benchmark. By introducing the correction factor δ and the environmental compensation coefficient λ, nonlinear compensation for temperature changes, battery aging and environmental fluctuations is achieved, breaking through the defect of the traditional control strategy in responding to environmental factors lagging behind. Combined with the three-level cascade control mechanism, the air conditioning power, motor torque and charging mode are dynamically adjusted to significantly reduce abnormal fluctuations in energy consumption under extreme working conditions; 3. The present invention realizes hierarchical response of control instructions through a logical decision tree, giving priority to the coordinated optimization of air conditioning power regulation and motor torque limitation, and avoiding system oscillation caused by independent control. The segmented balanced charging mode realizes differentiated charging strategies for high, medium and low health battery packs through battery health group management, thereby extending the overall life of the battery pack. The optimization instruction update cycle is ≤5 seconds, combined with the real-time correction of the historical database, to build a closed-loop intelligent system of monitoring-analysis-control, which significantly improves the energy efficiency management level of electric vehicles throughout their life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of equipment connection of the present invention.

[0019] Figure 2 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The present invention provides Figure 1 The device connection diagram shown includes: a motor acquisition terminal, a battery acquisition terminal, an environment acquisition terminal, an accessory equipment acquisition terminal, an electronic control unit ECU, a vehicle control unit VCU, a vehicle terminal and a cloud database. The motor acquisition terminal, the battery acquisition terminal and the electronic control unit ECU are connected via a CAN bus, the electronic control unit ECU, the vehicle control unit VCU and the vehicle terminal are connected via a CAN bus, and the vehicle terminal and the cloud database are connected via a wireless network.

[0022] The present invention provides Figure 2The electric vehicle control optimization method based on big data is shown, and the specific implementation includes the following steps: S1. Cloud database construction: Build a cloud database, which includes historical data of the same batch of vehicles and real-time uploaded update data; In the preferred implementation of the above solution, the cloud database is constructed using a hybrid architecture of the InfluxDB time series database and the PostgreSQL relational database, combined with Apache Flink real-time stream processing and Apache Spark offline analysis to achieve efficient storage and intelligent analysis of multi-source data. The vehicle terminal directly uploads the battery, motor, environment and accessory equipment parameters collected by the motor collection terminal, battery collection terminal, environment collection terminal and accessory equipment collection terminal to the cloud through the TLS encrypted tunnel. Flink completes data cleaning, Min-Max normalization and K b , K m , K e Coefficient calculation supports VCU dynamic energy consumption control. Historical data is stored in partitions according to production batches. InfluxDB cluster stores hot data for the past 30 days, and HDFS archives cold data. The hot and cold separation strategy reduces storage costs by more than 40%. The system uses Protobuf compression transmission, RocksDB state backend optimization, and batch writing technology to ensure that millions of vehicle data are updated in 5 seconds. At the same time, it integrates field-level encryption, two-way authentication, and fine-grained permission control to ensure data security and compliance, providing reliable data support for energy efficiency optimization throughout the life cycle of electric vehicles.

[0023] S2. Multi-source data collection: collect data from a single vehicle through the motor collection terminal, battery collection terminal, and accessory equipment collection terminal to obtain battery parameters, motor parameters, environmental parameters, and accessory equipment parameters; The battery acquisition terminal is composed of an NTC thermistor and a battery management system BMS. Its main operating principle is based on the negative temperature coefficient characteristics of the material. When the temperature rises, the number of carriers inside it increases, resulting in a decrease in resistance. This characteristic enables the NTC thermistor to sensitively detect temperature changes and monitor the temperature by measuring the resistance value to obtain the temperature T of the single cell. b The battery management system (BMS) monitors the battery voltage, current, temperature and other parameters, and uses a hierarchical architecture (slave control, master control and master control units) to obtain the charge and discharge rate R cd 、Current I c And battery health status SOH. BMS monitors the voltage and current changes of each battery cell in real time and calculates the charge and discharge rate R cd The current sensor can accurately measure the current I cThe battery health status SOH is estimated by monitoring parameters such as battery capacity, internal resistance, number of cycles and self-discharge rate, and combining the use of history and aging models to finally obtain battery parameters; The motor acquisition terminal is composed of a power analyzer and a torque sensor. Its main operating principle is that the power analyzer can accurately measure the three-phase voltage (U a , U b , U c )、Three-phase current(I a ,I b ,I c ) and the power factor of each phase (cosφa, cosφb, cosφc), and then according to the power formula (P=U a ×I a ×cosφa+U b ×I b ×cosφb+U c ×I c × cosφc) to calculate the motor input power and motor real-time power P m ; For output power P n The power analyzer can be connected to the output shaft of the motor and measure the torque of the motor. and speed N m To calculate the output power, the torque sensor converts the torque of the motor output shaft into an electrical signal. The power analyzer receives this signal and combines it with the speed Nm measured by the speed sensor to calculate the output power through the formula: P = 2 × π × N m × Calculate the motor output power P n ; Through the formula: η=(P n / P m )×100%, and then the motor efficiency η is obtained by the standard deviation method to obtain the motor efficiency fluctuation value η m The specific formula is η=(P n / P m )×100%, and then the motor efficiency η is obtained by the standard deviation method to obtain the motor efficiency fluctuation value η m The specific formula is ,Finally get the motor parameters; The environmental collection terminal is composed of a thermistor, a capacitive humidity sensor and a mechanical wind speed sensor. Its main operating principle is that the thermistor uses the principle that temperature changes cause resistance changes to measure temperature, and the capacitive humidity sensor uses humidity changes to cause dielectric constant changes, thereby changing the capacitance value to measure humidity. The mechanical wind speed sensor drives the mechanical parts (such as blades) to rotate through wind power, generating an electrical signal proportional to the wind speed; these sensors convert physical quantities into electrical signals, and the environmental collection terminal equipment collects and processes these signals to finally obtain environmental parameters; The accessory equipment acquisition terminal is composed of a vehicle controller VCU and a battery management system BMS. Its main operating principle is to obtain the accessory equipment parameters through built-in sensors and algorithms. The battery parameters include: single cell temperature T b , charge and discharge rate R cd , current value I c and battery health status SOH; the motor parameters include: motor real-time power consumption Pm, motor speed Nm, motor output torque and motor efficiency fluctuation value η m ; The environmental parameters include: external temperature T e 、Humidity e and wind speed W e The auxiliary system parameters include: air conditioning power consumption P ac and other electronic equipment power consumption P e ; The above is the best example of a single feasible case in this embodiment, and its function is only limited to achieve the above data collection effect, without making specific limitations on specific equipment.

[0024] S3. Data processing and analysis: Based on the battery parameters, motor parameters, environmental parameters and auxiliary system parameters, the Min-Max normalization in machine learning is used to calculate the battery health fluctuation coefficient K b , Motor efficiency fluctuation coefficient K m And environmental factor fluctuation coefficient K e , the battery health fluctuation coefficient K b The calculation formula is: ,in is the historical average charge and discharge rate, Unit time The maximum temperature change of the battery cell in the battery pack.

[0025] What you need to know is that K b Rate of change with temperature The absolute value of the difference between the charge and discharge rates In a specific embodiment, assuming that the normalized data is R cd =0, R cd_avg =0, , SOH=0.8, indicating that the battery has aged by 20%. Substitute it into the Kb formula to calculate: , indicating that the current charge and discharge rate is the same as the historical average, and the temperature changes greatly, but due to the high battery health status SOH, the fluctuation coefficient is small.

[0026] The motor efficiency fluctuation coefficient K mThe calculation formula is: , where η m is the motor efficiency fluctuation value, η m_op is the efficiency value of the motor at the best efficiency point, N m is the motor speed, N m_op is the speed of the motor at the best efficiency point, is the output torque of the motor at the best efficiency point, The unit time is 1 minute.

[0027] What you need to know is that K m It is used to measure the deviation between the actual operating state of the motor and the state of the optimal efficiency point. m The larger the value, the further the motor operating state deviates from the optimal efficiency point, and the worse the motor performance may be. m The smaller the value, the closer the motor is to the optimal operating state. In a specific embodiment, assuming that the normalized data is , substitute K e Calculation formula: , indicating that the current motor efficiency deviates from the optimal efficiency point, but because the speed and torque have not changed, the fluctuation coefficient is 0.

[0028] The calculation formula of the environmental factor fluctuation coefficient Ke is: ,in ,and , T e is the external temperature, T op is the preset ideal external temperature value, H e is humidity, H op is the preset ideal humidity value, W e is the wind speed, W op It is the preset ideal wind speed value.

[0029] What you need to know is that T op [20℃, 25℃], H op [40%, 60%], W op [0m / s, 5m / s]; add the contribution values ​​of the three environmental parameters of temperature, humidity and wind speed to the comprehensive evaluation index to obtain the final K e Value, K e The larger the value, the greater the deviation between the current environment and the preset ideal environment, and the greater the adverse impact of the environment on the system or equipment. e The smaller the value, the closer the current environment is to the ideal environment, and the system or device may have better operating performance or status in this environment. In a specific embodiment, assuming that the normalized data is T op=0.5, H op =0.5, W op =0.5, T e =0,H e =0, , substitute K e Calculation formula: , indicating that the ambient temperature and humidity deviate from the ideal values, the wind speed is also high, and the comprehensive fluctuation coefficient is 0.25.

[0030] S4. Calculation of reasonable energy consumption: based on battery health fluctuation coefficient K b , Motor efficiency fluctuation coefficient K m And environmental factor fluctuation coefficient K e Import dynamic energy consumption model to generate real-time energy consumption rationality index E s , The energy consumption rationality index E s The calculation formula is: ,in , where P ac is the power consumption of air conditioner, P e is the power consumption of other electronic devices, P m is the real-time power consumption of the motor, K b is the battery health fluctuation coefficient, K m is the motor efficiency fluctuation coefficient, K e is the environmental factor fluctuation coefficient, E real is the real-time energy consumption, E avg is the historical average energy consumption, is the correction factor, is the environmental compensation coefficient.

[0031] In a specific embodiment, the above K b =0,K m =0,K e =0.25 as an example, assuming that the normalized data is , P ac +P e +P m =0.8, E avg =0.7, substitute into the formula to calculate: , Substitute it into the formula and calculate: .

[0032] The correction factor The calculation formula is: ,in , is the absolute deviation of the current battery discharge current from the historical sliding average SMA, is the historical average value of the motor temperature, is the basic allocation factor and ; The environmental compensation coefficient The calculation formula is: ,in, It is the sliding average of the ambient temperature in the same period of the past 7 days. Historical ambient temperature standard deviation, is the difference between the highest cell temperature and the average temperature of the battery pack, The value is obtained by placing the battery in a constant temperature box, running it at different charge and discharge rates of 0.2C~1C, and monitoring the battery temperature rise. , by adjusting , so that , Take the maximum value that satisfies the conditions.

[0033] In a specific embodiment, the above-mentioned E s =0.68 as an example, and based on industry experience and document formula logic, the normalized preset threshold is , E s <0.8, the energy consumption is lower than the baseline value and the system does not need to be adjusted.

[0034] S5. Adjustment and optimization of control strategy: The vehicle control unit VCU is based on the energy consumption rationality index E s Dynamically adjust the air conditioning power, motor output torque and battery charging and discharging mode. The adjustment and optimization are specifically to reduce the air conditioning power P when the energy consumption rationality index exceeds the preset threshold. ac to , limit the motor output torque to , or switch the battery charge and discharge mode to at least one operation in the segmented balanced charging mode, otherwise maintain the historical optimal control strategy. The segmented balanced charging mode specifically divides the battery group into three groups: high, medium and low according to the battery health state SOH, and preferentially performs trickle charging on the low health battery group, and adopts dynamic pulse charging for the medium and high health battery groups.

[0035] S6, feedback execution: send the optimization instruction to the vehicle ECU controller and update the historical database. The optimization instruction update cycle is ≤5s, and the sliding average value is corrected synchronously when the historical database is updated.

[0036] In this embodiment, the present invention integrates the historical energy consumption and real-time operation data of multiple models by building a cloud database, and combines the synchronous collection of battery system parameters, motor parameters, environmental parameters and auxiliary system parameters to break through the limitations of traditional fixed parameter control strategies. b , Motor efficiency fluctuation coefficient K mAnd environmental factor fluctuation coefficient K e , achieving a comprehensive assessment of battery performance degradation, motor operating status and environmental impact, significantly improving the accuracy of energy consumption management under complex working conditions. Combined with dynamic energy consumption models and correction factors, the system can adjust control strategies in real time, optimize energy consumption, extend equipment life, and achieve continuous optimization through historical data feedback mechanisms.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the control of an electric vehicle based on big data, characterized in that: It includes a motor acquisition terminal, a battery acquisition terminal, an environment acquisition terminal, an auxiliary equipment acquisition terminal, an electronic control unit ECU, a vehicle control unit VCU, a vehicle terminal and a cloud database, wherein the motor acquisition terminal, the battery acquisition terminal and the electronic control unit ECU are connected via a CAN bus, the electronic control unit ECU, the vehicle control unit VCU and the vehicle terminal are connected via a CAN bus, and the vehicle terminal is connected to the cloud database via a wireless network, specifically including the following steps: S1. Cloud database construction: Build a cloud database, which includes historical data of the same batch of vehicles and real-time uploaded update data; S2. Multi-source data collection: collect data from a single vehicle through the motor collection terminal, battery collection terminal, environment collection terminal, and accessory equipment collection terminal to obtain battery parameters, motor parameters, environment parameters, and accessory equipment parameters; S3. Data processing and analysis: Based on the battery parameters, motor parameters, environmental parameters and auxiliary system parameters, the Min-Max normalization in machine learning is used to calculate the battery health fluctuation coefficient K b , Motor efficiency fluctuation coefficient K m And environmental factor fluctuation coefficient K e ; S4. Calculation of reasonable energy consumption: based on battery health fluctuation coefficient Kb and motor efficiency fluctuation coefficient K m And environmental factor fluctuation coefficient K e Import dynamic energy consumption model to generate real-time energy consumption rationality index E s ; S5. Adjustment and optimization of control strategy: The vehicle control unit VCU is based on the energy consumption rationality index E s Dynamically adjust air conditioning power, motor output torque and battery charging and discharging mode; S6. Feedback execution: Send optimization instructions to the vehicle ECU controller and update the historical database.

2. The electric vehicle control optimization method based on big data according to claim 1 is characterized in that: The battery parameters include: single cell temperature T b , charge and discharge rate R cd , current value I c and battery health status SOH; the motor parameters include: motor real-time power consumption P m , Motor speed N m , Motor output torque and motor efficiency fluctuation value η m ; The environmental parameters include: external temperature T e 、Humidity e and wind speed W e The auxiliary system parameters include: air conditioning power consumption P ac and other electronic equipment power consumption P e .

3. The electric vehicle control optimization method based on big data according to claim 1 is characterized in that: The battery health fluctuation coefficient calculation process is as follows: The calculation formula of Kb is: , where R cd is the charge and discharge rate, R cd_avg is the historical average charge and discharge rate, SOH is the battery health status, Unit time The maximum temperature change of the battery pack, where The unit time is 1 minute.

4. The electric vehicle control optimization method based on big data according to claim 1 is characterized in that: The motor efficiency fluctuation coefficient K m The calculation formula is: , where η m is the motor efficiency fluctuation value, η m_op is the efficiency value of the motor at the best efficiency point, N m is the motor speed, N m_op is the speed of the motor at the best efficiency point, is the output torque of the motor at the best efficiency point, The unit time is 1 minute.

5. The electric vehicle control optimization method based on big data according to claim 1 is characterized in that: The environmental factor fluctuation coefficient K e The calculation formula is: ,in ,and , T e is the external temperature, T op is the preset ideal external temperature value, H e is humidity, H op is the preset ideal humidity value, W e is the wind speed, W op It is the preset ideal wind speed value.

6. The electric vehicle control optimization method based on big data according to claim 1 is characterized in that: The energy consumption rationality index E s The calculation formula is: ,in , where P ac is the power consumption of air conditioner, P e is the power consumption of other electronic devices, P m is the real-time power consumption of the motor, K b is the battery health fluctuation coefficient, K m is the motor efficiency fluctuation coefficient, K e is the environmental factor fluctuation coefficient, E real is the real-time energy consumption, E avg is the historical average energy consumption, is the correction factor, is the environmental compensation coefficient.

7. The electric vehicle control optimization method based on big data according to claim 6 is characterized in that: The correction factor and environmental compensation coefficient The calculation formula is: ,in , is the absolute deviation of the current battery discharge current from the historical sliding average SMA, is the historical average value of the motor temperature, is the basic allocation factor and ; ,in, It is the sliding average of the ambient temperature in the same period of the past 7 days. Historical ambient temperature standard deviation, is the difference between the highest cell temperature and the average temperature of the battery pack, The value is obtained by placing the battery in a constant temperature box, running it at different charge and discharge rates (0.2C~1C), and monitoring the battery temperature rise. , by adjusting , so that , Take the maximum value that satisfies the conditions.

8. The electric vehicle control optimization method based on big data according to claim 1 is characterized in that: The adjustment and optimization includes: when the energy consumption rationality index exceeds a preset threshold, reducing the air conditioning power P ac to , limit the motor output torque to , or switch the battery charging and discharging mode to at least one of the segmented balanced charging modes, otherwise maintain the historical optimal control strategy.

9. The electric vehicle control optimization method based on big data according to claim 8 is characterized in that: The segmented balanced charging mode is specifically as follows: the battery groups are divided into three groups according to their SOH, namely, high, medium and low, and trickle charging is preferentially performed on the low health battery groups, while dynamic pulse charging is adopted for the medium and high health battery groups.

10. The electric vehicle control optimization method based on big data according to claim 1, characterized in that: The optimization instruction update cycle is: ≤5s, and the sliding average value is corrected synchronously when the historical database is updated.

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