An optimization method for the overall vehicle control of electric vehicles based on big data

Abstract

The present invention discloses an optimization method for the overall vehicle control of electric vehicles based on big data, which relates to the field of big data. The present invention constructs a cloud database containing the historical and real-time data of vehicles in the same batch, obtains multi-source data through the acquisition terminals of motors, batteries, environments and auxiliary equipment, and calculates the battery health degree fluctuation coefficient K after Min-Max normalization processing b , the motor efficiency fluctuation coefficient K m and the environmental factor fluctuation coefficient K e , and imports them into the dynamic energy consumption model to generate the real-time energy consumption rationality index E s . The vehicle control unit VCU dynamically adjusts the air-conditioning power, the motor output torque and the battery charge and discharge mode according to E s , sends the optimization instruction to the ECU controller and updates the historical database. This method breaks through the limitation of traditional fixed-parameter control, accurately evaluates the impacts of the battery, motor and environment, reduces the abnormal fluctuation of energy consumption, improves the energy efficiency management level of the whole life cycle, extends the service life of the battery pack, and ensures data security and compliance

Description

Technical Field

[0001] The present invention relates to the technical field of big data, and particularly to an optimization method for the overall vehicle control of an electric vehicle 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 research hotspot in the industry. The fixed-parameter control strategies commonly adopted by current electric vehicles are difficult to adapt to dynamic working conditions such as battery performance degradation, motor efficiency fluctuations, and environmental factor changes, resulting in insufficient accuracy of energy consumption control and increased equipment losses.

[0003] The existing control methods have the following technical defects: First, the traditional control strategies rely on preset empirical parameters and cannot quantify in real time the comprehensive influence of battery health, motor efficiency, and environmental factors on energy consumption. As a result, when in extreme temperatures, battery aging, or motor inefficient operation, the system cannot make an effective response, causing energy waste and shortening of equipment life. Second, the existing energy consumption optimization methods lack the ability of multi-dimensional data fusion analysis and are difficult to establish an accurate dynamic energy consumption model, resulting in a lag in the adjustment of control strategies and the inability to achieve optimal energy efficiency management under all working conditions. Third, the changes in environmental parameters (such as temperature, humidity, and wind speed) will significantly affect battery performance and motor efficiency, but the existing systems do not incorporate environmental factors into the control decision-making system, resulting in abnormal fluctuations in vehicle energy consumption in harsh environments.

[0004] In addition, when the existing control strategies execute optimization instructions, they do not consider the synergistic effects among different control parameters. For example, the independent control of air-conditioning power adjustment and motor torque limitation may cause system oscillation and reduce control stability. At the same time, the lack of a historical data feedback mechanism results in the inability of the control strategy to achieve continuous optimization through the accumulation of long-term operation data.

[0005] Therefore, there is an urgent need to provide an optimization method for the overall vehicle control of an electric vehicle 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 optimization method for the overall vehicle control of an electric vehicle based on big data to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions: An optimization method for the overall vehicle control of an electric vehicle based on big data, including 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. Among them, the motor acquisition terminal and the battery acquisition terminal are connected to the electronic control unit ECU through a CAN bus, the electronic control unit ECU, the vehicle control unit VCU, and the vehicle terminal are connected through a CAN bus, and the vehicle terminal is connected to the cloud database through a wireless network. The specific steps are as follows:

[0008] S1. Cloud database construction: Construct a cloud database, and the database content includes the historical data of vehicles in the same batch and the real-time uploaded and updated data;

[0009] S2. Multi-source data acquisition: Collect a single vehicle through the motor acquisition terminal, the battery acquisition terminal, the environment acquisition terminal, and the accessory equipment acquisition terminal to obtain battery parameters, motor parameters, environment parameters, and accessory equipment parameters;

[0010] S3. Data processing and analysis: Based on the battery parameters, motor parameters, environment parameters, and auxiliary system parameters, use Min-Max normalization in machine learning for processing, and calculate the battery health degree fluctuation coefficient K b , motor efficiency fluctuation coefficient K m and environmental factor fluctuation coefficient K e ;

[0011] S4. Energy consumption rationality calculation: Based on the battery health degree fluctuation coefficient K b , motor efficiency fluctuation coefficient K m and environmental factor fluctuation coefficient K e import into the dynamic energy consumption model to generate the real-time energy consumption rationality index E s ;

[0012] S5. Adjustment and optimization of control strategy: The vehicle control unit VCU dynamically adjusts the air conditioner power, motor output torque, and battery charge and discharge mode based on the energy consumption rationality index E s ;

[0013] S6. Feedback execution: Send the optimization instruction to the vehicle ECU controller and update the historical database.

[0014] Preferably, the battery parameters include: the temperature T of the single battery b , charge and discharge rate R cd , current value I c and battery health state SOH; the motor parameters include: the real-time power consumption P of the motor m , motor speed Nm, motor output torque and the motor efficiency fluctuation value η m； The environmental parameters include: the external temperature T e , humidity H e and wind speed W e ; The auxiliary system parameters include: the air-conditioning power consumption P ac and the power consumption P of other electronic devices e .

[0015] Preferably, the calculation process of the battery health degree fluctuation coefficient 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 state, is the change amount of the highest single cell temperature of the battery pack within the unit time , where The unit time is 1 minute.

[0016] Preferably, the calculation formula of the motor efficiency fluctuation coefficient K m is: , where, η m is the motor efficiency fluctuation value, η m_op is the efficiency value at the best efficiency point of the motor, 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.

[0017] Preferably, the calculation formula of the environmental factor fluctuation coefficient K e is: , where , and , T e is the external temperature, T op is the preset ideal external temperature value, H e is the humidity, H op is the preset ideal humidity value, W e is the wind speed, W op is the preset ideal wind speed value.

[0018] Preferably, the calculation formula of the energy consumption rationality index E s is: , where , where P ac is the air-conditioning power consumption, P e is the power consumption of other electronic devices, P m is the real-time power consumption of the motor, Kb 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

[0019] Preferably, the correction factor and the environmental compensation coefficient are calculated as follows: , where , is the absolute deviation of the current battery discharge current from the historical moving average SMA is the historical mean value of the motor temperature is the basic distribution factor and ; , where is the moving average of the environmental temperature in the same time period in the past 7 days historical environmental temperature standard deviation is the difference between the highest single-cell temperature and the average temperature of the battery pack is obtained by placing the battery in a thermostat and operating it at different charge and discharge rates from 0.2C to 1C, monitoring the battery temperature rise , by adjusting , making , takes the maximum value that meets the conditions

[0020] Preferably, the adjustment and optimization include: when the energy consumption rationality index exceeds the preset threshold, execute reducing the air conditioner power P ac to , restricting the motor output torque to , or switching the battery charge and discharge mode to at least one of the segmented balanced charging modes, otherwise maintaining the historical optimal control strategy

[0021] Preferably, the segmented balanced charging mode is specifically: the battery pack is divided into three groups of high, medium, and low according to the state of health SOH, and the low-health battery pack is preferentially charged with a trickle charge, and the medium-high health battery packs are charged with a dynamic pulse charge

[0022] Preferably, the update period of the optimization instruction is ≤5s, and the moving average corrected is synchronized when the historical database is updated

[0023] The technical effects and advantages of the present invention are:

[0024] 1. The present invention integrates historical energy consumption and real-time operation data of multiple vehicle models by constructing a cloud database, and combines the synchronous acquisition of battery system parameters, motor parameters, environmental parameters, and auxiliary system parameters to break through the limitations of traditional fixed-parameter control strategies. By quantifying the battery health fluctuation coefficient K b , the motor efficiency fluctuation coefficient K m , and the environmental factor fluctuation coefficient K e , a comprehensive assessment of battery performance degradation, motor operating status, and environmental impact is realized, significantly improving the accuracy of energy consumption management under complex working conditions;

[0025] 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 deviation degree between the actual energy consumption and the theoretical benchmark. By introducing the correction factor δ and the environmental compensation coefficient λ, non-linear compensation for temperature changes, battery aging, and environmental fluctuations is realized, breaking through the defect of the traditional control strategy's lag in responding to environmental factors. Combining with a three-level joint control mechanism, the air-conditioning power, motor torque, and charging mode are dynamically adjusted, significantly reducing the abnormal energy consumption fluctuations under extreme working conditions;

[0026] 3. The present invention realizes the hierarchical response of control instructions through a logic judgment tree, preferentially executes the collaborative optimization of air-conditioning power regulation and motor torque limitation, and avoids system oscillations caused by independent control. The segmented balanced charging mode manages the battery health in groups, realizes different charging strategies for high, medium, and low health battery packs, and extends the overall life of the battery pack. The optimization instruction update period ≤ 5 seconds, combined with the real-time correction of the historical database, constructs a closed-loop intelligent system of monitoring - analysis - regulation, significantly improving the energy efficiency management level of electric vehicles throughout their life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic diagram of the device connection of the present invention.

[0028] Figure 2 is a flowchart of the method implementation steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] The present invention provides as Figure 1The schematic diagram of the device connection shown includes: a motor acquisition terminal, a battery acquisition terminal, an environment acquisition terminal, an accessory device acquisition terminal, an electronic control unit ECU, a vehicle control unit VCU, a vehicle terminal, and a cloud database. The motor acquisition terminal and the battery acquisition terminal are connected to the electronic control unit ECU through a CAN bus. The electronic control unit ECU, the vehicle control unit VCU, and the vehicle terminal are connected through a CAN bus. The vehicle terminal is connected to the cloud database through a wireless network.

[0031] The present invention provides a method for optimizing the overall vehicle control of an electric vehicle based on big data as shown in Figure 2 and the specific implementation includes the following steps:

[0032] S1. Cloud database construction: Construct a cloud database, and the database content includes the historical data of vehicles in the same batch and the real-time uploaded and updated data;

[0033] In the preferred implementation of the above solution, the cloud database construction adopts a hybrid architecture of InfluxDB time series database and PostgreSQL relational database, combines 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 device parameters collected by the motor acquisition terminal, battery acquisition terminal, environment acquisition terminal, and accessory device acquisition terminal to the cloud through a TLS encrypted tunnel. Flink real-time completes data cleaning, Min-Max normalization, and K b 、K m 、K e coefficient calculation to support the dynamic energy consumption regulation of the VCU. The historical data is partitioned and stored according to the production batch. The InfluxDB cluster stores the hot data of the recent 30 days, and the HDFS archives the cold data. The hot and cold separation strategy reduces the storage cost by more than 40%. The system ensures the 5-second update of millions of vehicle data through Protobuf compression transmission, RocksDB state backend optimization, and batch writing technology. 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 the energy efficiency optimization of the entire life cycle of electric vehicles.

[0034] S2. Multi-source data acquisition: Collect a single vehicle through the motor acquisition terminal, battery acquisition terminal, and accessory device acquisition terminal to obtain battery parameters, motor parameters, environment parameters, and accessory device parameters;

[0035] The battery acquisition terminal consists of an NTC thermistor and a battery management system (BMS). Its main operating principle is based on the negative temperature coefficient characteristic of the material. When the temperature rises, the number of carriers inside it increases, resulting in a decrease in the resistance value. This characteristic enables the NTC thermistor to sensitively detect temperature changes and monitor the temperature by measuring the resistance value, obtaining the temperature T of the single cell battery. b The battery management system (BMS) monitors parameters such as the voltage, current, and temperature of the battery, and uses a hierarchical architecture (slave control, master control, and total control units) to obtain the charge and discharge rate R. cd 、current I c and the state of health (SOH) of the battery. The BMS monitors the voltage and current changes of each battery cell in real time, calculates the charge and discharge rate R. cd and accurately measures the current I through a current sensor. c The state of health (SOH) of the battery is estimated by monitoring parameters such as battery capacity, internal resistance, number of cycles, and self-discharge rate, and by using historical and aging models in combination, finally obtaining the battery parameters.

[0036] The motor acquisition terminal consists 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 calculates the input power and real-time power P of the motor according to the power formula (P = U a ×I a ×cosφa + U b ×I b ×cosφb + U c ×I c ×cosφc). m For the measurement of the output power P n , the power analyzer can be connected to the output shaft of the motor, and the output power is calculated by measuring the torque and rotational speed N m of the motor. 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 rotational speed Nm measured by the rotational speed sensor, and calculates the output power P of the motor through the formula: P = 2×π×N m × . n ; Through the formula: η = (P n / P m ) × 100%, the motor efficiency η is obtained, and then the motor efficiency fluctuation value η is obtained through the standard deviation method.m , and the specific formula is η = (P n / P m ) × 100%, to obtain the motor efficiency η, and then the motor efficiency fluctuation value η m is obtained through the standard deviation method. The specific formula is , and finally the motor parameters are obtained;

[0037] The environmental acquisition terminal is composed of a thermistor, a capacitive humidity sensor, and a mechanical wind speed sensor. Its main operating principle is that the thermistor measures temperature by using the principle that the resistance changes with temperature changes. The capacitive humidity sensor measures humidity by changing the capacitance value due to the change of the dielectric constant caused by the humidity change. The mechanical wind speed sensor drives the rotation of mechanical components (such as blades) through wind force to generate an electrical signal proportional to the wind speed. These sensors convert physical quantities into electrical signals, and the environmental acquisition terminal device collects and processes these signals to finally obtain environmental parameters;

[0038] The accessory equipment acquisition terminal is composed of a vehicle control unit VCU and a battery management system BMS. Its main operating principle is to finally obtain accessory equipment parameters through built-in sensors and algorithms;

[0039] The battery parameters include: the temperature T of a single battery b , the charge and discharge rate R cd , the current value I c and the state of health of the battery SOH; the motor parameters include: the real-time power consumption Pm of the motor, the motor speed Nm, the motor output torque and the motor efficiency fluctuation value η m ; the environmental parameters include: the external temperature T e , the humidity H e and the wind speed W e ; the auxiliary system parameters include: the air-conditioning power consumption P ac and the power consumption P of other electronic devices e ;

[0040] The above is an optimal example for a single feasible case of this embodiment, and only the function is limited to achieve the above data acquisition effect, without making specific limitations on specific devices.

[0041] S3. Data processing and analysis: Based on the battery parameters, motor parameters, environmental parameters, and auxiliary system parameters, Min-Max normalization in machine learning is used for processing to calculate the battery health degree fluctuation coefficient K b , the motor efficiency fluctuation coefficient K m and the environmental factor fluctuation coefficient K e . The calculation formula of the battery health degree fluctuation coefficient K b is: , where is the historical average charge-discharge rate, is the unit time and is the maximum change in the temperature of the highest single cell in the battery pack within the unit time.

[0042] It should be noted that K b is proportional to the temperature change rate and the absolute value of the charge-discharge rate difference and is inversely proportional to the state of health of the battery SOH. In a specific embodiment, assume that the normalized data is R cd = 0, R cd_avg = 0, , SOH = 0.8, indicating that the battery is aged by 20%. Substitute into the Kb formula for calculation: , indicating that the current charge-discharge rate is the same as the historical average, the temperature change is large, but due to the high state of health of the battery SOH, the fluctuation coefficient is small.

[0043] The calculation formula for the motor efficiency fluctuation coefficient K m is as follows: , where η m is the motor efficiency fluctuation value, η m_op is the efficiency value at the best efficiency point of the motor, 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, and is the unit time of 1 minute.

[0044] It should be noted that K m is used to measure the deviation degree between the actual operating state of the motor and the state of the best efficiency point. The larger the K m value, the farther the motor operating state deviates from the best efficiency point, and the worse the performance of the motor may be. The smaller the K m value, the closer the motor is to the best operating state. In a specific embodiment, assume that the normalized data is , substitute into the K e formula for calculation: , indicating that the current motor efficiency deviates from the best efficiency point, but due to the unchanged speed and torque, the fluctuation coefficient is 0.

[0045] The calculation formula for the environmental factor fluctuation coefficient Ke is: , where and , T e is the external temperature, T op is the preset ideal external temperature value, H e is the humidity, Hop is the preset ideal humidity value, W e is the wind speed, W op is the preset ideal wind speed value.

[0046] It should be noted that, T op [20°C, 25°C], H op [40%, 60%], W op [0 m / s, 5 m / 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 K value, the greater the deviation of the current environmental condition from the preset ideal environment, and the greater the possible adverse impact of the environment on the system or equipment. The smaller the K e value, the closer the current environment is to the ideal environment, and the system or equipment may have better operating performance or status in this environment. In a specific embodiment, assume that the normalized data is T op = 0.5, H op = 0.5, W op = 0.5, T e = 0, H e = 0, , substitute into K e formula for calculation: , indicating that the environmental temperature and humidity deviate from the ideal values and the wind speed is also high, and the comprehensive fluctuation coefficient is 0.25.

[0047] S4, Calculation of energy consumption rationality: Based on the battery health fluctuation coefficient K b , motor efficiency fluctuation coefficient K m and environmental factor fluctuation coefficient K e import into the dynamic energy consumption model to generate the real-time energy consumption rationality index E s ,

[0048] The calculation formula of the energy consumption rationality index E s is: , where , where P ac is the air conditioner power consumption, 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, and

[0049] b is 0, m is 0, e is 0.25 as an example. Assuming the normalized data is , ac + e + m = 0.8, avg = 0.7, substitute into the formula for calculation: ,

[0050] Substitute into the formula for calculation again: .

[0051] The formula for calculating the correction factor is: , where , 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 distribution factor and ; The formula for calculating the environmental compensation coefficient is: , where, is the sliding average of the environmental temperature in the same period in the past 7 days, is the standard deviation of the historical environmental temperature, is the difference between the highest single-cell temperature and the average temperature of the battery pack, is obtained by placing the battery in an incubator and operating it at different charge and discharge rates from 0.2C to 1C, monitoring the battery temperature rise , by adjusting , so that , takes the maximum value that meets the conditions.

[0052] In a specific embodiment, taking the above s = 0.68 as an example, and at the same time according to industry experience and the formula logic of the document, the preset threshold after normalization is , s < 0.8, then the energy consumption is lower than the reference value, and the system does not need to be adjusted.

[0053] S5. Adjustment and optimization of the control strategy: The vehicle control unit VCU is based on the energy consumption rationality index sDynamically adjust the air conditioner power, motor output torque, and battery charge and discharge mode. The specific adjustment and optimization are as follows: when the energy consumption rationality index exceeds the preset threshold, reduce the air conditioner power P ac to , limit the motor output torque to , or perform at least one of the operations of switching the battery charge and discharge mode to the segmented balanced charging mode. Otherwise, maintain the historical optimal control strategy. The segmented balanced charging mode is specifically to divide the battery pack into three groups: high, medium, and low according to the state of health (SOH) of the battery. First, trickle charge the low-health battery group, and the medium- and high-health battery groups use dynamic pulse charging.

[0054] S6. Feedback execution: Send the optimization instruction to the vehicle ECU controller and update the historical database. The update period of the optimization instruction is ≤5s, and the sliding average value corrected during the update of the historical database is synchronized.

[0055] In this embodiment, the present invention integrates the historical energy consumption and real-time operation data of multiple vehicle models by constructing a cloud database, and combines the synchronous acquisition of battery system parameters, motor parameters, environmental parameters, and auxiliary system parameters to break through the limitations of traditional fixed-parameter control strategies. By quantifying the battery health fluctuation coefficient K b , motor efficiency fluctuation coefficient K m , and environmental factor fluctuation coefficient K e , a comprehensive evaluation of battery performance degradation, motor operating status, and environmental impact is realized, significantly improving the accuracy of energy consumption management under complex working conditions. Combining the dynamic energy consumption model and correction factors, the system can adjust the control strategy in real time, optimize energy consumption, extend the equipment life, and achieve continuous optimization through the historical data feedback mechanism.

[0056] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within 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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