Alternating current asynchronous motor driving system and intelligent control method thereof

Abstract

The invention belongs to the technical field of motor control, and particularly relates to an alternating current asynchronous motor driving system and an intelligent control method thereof. The driving system comprises a motor, a motor controller, a double-gear gearbox and an intelligent control module; the motor controller is used for accurately controlling the energy distribution of the motor; the intelligent control module comprises a plurality of pairs of magnetic pole encoders and temperature probes, collects various parameters of the motor in real time, and achieves the comprehensive monitoring of the working state of the motor. The multiple pairs of magnetic pole encoders collect the rotating speed of the motor to improve the precision, and the temperature probe is arranged in the motor to pre-analyze the temperature value. According to the invention, a plurality of key technologies, including a variable frequency speed regulation algorithm, a vector control algorithm, a power factor correction algorithm, an energy management algorithm and the like, are applied in the motor controller, so that performance improvement is realized, and the technologies cooperate together to ensure efficient and stable operation of the motor under different working conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor control, and in particular relates to an AC asynchronous motor drive system with high reliability and stability and an intelligent control method thereof. Background Art

[0002] With the increasing awareness of environmental protection and the restrictions on exhaust emissions from traditional fuel vehicles, electric vehicles have been widely developed. The motor drive system is one of the core components of electric vehicles. Compared with traditional internal combustion engines, motor drives have higher efficiency and can convert more electrical energy into mechanical energy, reducing energy loss. At the same time, the motor drive system can also achieve energy recovery. When the vehicle brakes, the motor can act as a generator to convert the vehicle's kinetic energy into electrical energy and store it in the battery, improving the efficiency of energy utilization.

[0003] AC asynchronous motors have been widely used in electric vehicle drive systems due to their simple structure, high reliability, and easy maintenance. However, the traditional AC asynchronous motor drive system still has room for improvement in energy utilization efficiency and control accuracy, and it is difficult to meet the requirements of modern electric vehicles for high performance, high efficiency, and long endurance. With the development of intelligent control technology, the AC asynchronous motor drive system has been further integrated with the intelligent control system, realizing real-time monitoring of the motor's working status and precise power distribution and energy management.

[0004] How to distribute motor energy, select the most energy-efficient operating frequency and voltage combination, make the motor operate in the optimal efficiency range, reduce unnecessary heat loss, and improve energy conservation and emission reduction while maintaining stable and efficient operation of the vehicle has become an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide an AC asynchronous motor drive system and an intelligent control method thereof. According to the characteristics of the AC asynchronous motor drive system, the motor energy is distributed through a motor controller, and a variable frequency speed regulation algorithm, a vector control algorithm, a power factor correction algorithm and an energy management algorithm are adopted to ensure the efficient and stable operation of the AC asynchronous motor under different working conditions.

[0006] In order to achieve the above objectives, the present invention provides an AC asynchronous motor drive system, including a motor, a motor controller, a two-speed gearbox, and an intelligent control module.

[0007] The motor controller is used to accurately control the energy distribution of the motor;

[0008] The intelligent control module includes multiple pairs of magnetic pole encoders and temperature probes to collect various parameters of the motor in real time and realize comprehensive monitoring of the motor working status.

[0009] Multiple pairs of magnetic pole encoders collect motor speed to improve accuracy.

[0010] The temperature probe is built into the motor to pre-analyze the temperature value;

[0011] According to another embodiment of the present invention or the intelligent control method of any of the above embodiments, the temperature probe is an NTC thermistor.

[0012] Accordingly, an intelligent control method for an AC asynchronous motor drive system is disclosed, comprising the following steps:

[0013] S1. Use sensors to collect various parameters of the motor in real time, such as speed, temperature, voltage, current, etc., to monitor the working status of the motor;

[0014] S1.1. The motor speed is collected through multiple pairs of magnetic pole encoders to improve accuracy. The signal processing adopts incremental angle encoding method and inverse tangent operation.

[0015] S1.2 measures the internal temperature of the motor through a temperature probe. If the temperature is too high, the motor output power is reduced in advance. The resistance signal is converted into a voltage signal by means of a resistor divider, and then transferred to the voltage signal received by the port through an op amp conditioning circuit. A self-learning correction formula is added to the calculation formula. The temperature value is pre-analyzed based on driving data, ambient temperature, motor temperature, motor controller temperature, motor speed, and motor torque.

[0016] S1.3 samples the voltage to achieve electrical insulation between the strong and weak currents of the controller. The bus voltage sampling circuit uses a high-precision isolation photoelectric coupler. The input side and the output side are powered by two isolated power supplies respectively, thus achieving electrical isolation.

[0017] S1.4 samples the current. The current sensor converts the current signal into a voltage signal as the input signal of the circuit. The circuit interface is filtered and then buffered by the operational amplifier circuit. The difference is converted into a voltage signal suitable for the ADC port of the DSP chip. The accuracy of the current sampling circuit will affect the motor torque control accuracy. Considering the resistance drift and the accuracy of the operational amplifier chip, the overall sampling deviation of the sampling circuit is required to be less than 1%. The self-learning adjustment parameters use the charging current, the current parameters sent by the BMS, and the peak current of the motor.

[0018] S2. Based on the real-time monitored data, the motor is precisely controlled through algorithms to ensure that the motor operates in the best condition; according to actual needs, the power output of the motor is dynamically adjusted to achieve reasonable allocation and optimal utilization of energy. The control strategies for the three power zones are as follows:

[0019] Constant torque area: ensures the vehicle's climbing grade. In this area, the motor torque remains unchanged;

[0020] Constant power zone: ensures the acceleration of the vehicle. In this zone, the output power of the motor remains unchanged, and the torque is inversely proportional to the speed;

[0021] Natural characteristics: In this area, the output power of the motor is inversely proportional to the speed, and the torque is inversely proportional to the square of the speed; at the highest speed point, the power of the motor must ensure the power required for the vehicle's highest operating speed;

[0022] S3. Motor controller distributes energy to the motor.

[0023] By adjusting the power supply frequency of the motor, the motor speed can be precisely controlled. The variable frequency speed regulation algorithm can dynamically adjust the motor speed according to actual needs to meet different working conditions.

[0024] The specific frequency conversion speed regulation method is as follows:

[0025] S3.1 Initialization parameters,

[0026] S3.1.1 Set the motor's reference frequency f base (such as 50Hz or 60Hz).

[0027] S3.1.2 Set the motor's reference speed n base (Speed ​​at base frequency).

[0028] S3.1.3 sets the proportionality factor k between the speed and the frequency (determined according to the motor design and the number of pole pairs).

[0029] S3.1.4 initializes the monitoring value of the load torque T.

[0030] S3.1.5 Initialize mechanical power loss P loss And the heat dissipation power P heat The estimated value of .

[0031] S3.2 obtains the target speed,

[0032] Determine the target speed n according to actual needs (such as process flow, load changes, etc.) target .

[0033] S3.3 Calculate the power supply frequency,

[0034] S3.3.1 Preliminary calculations ignoring loads, mechanical losses, and heat dissipation:

[0035] f calc =n target / k

[0036] S3.3.2 Frequency adjustment considering correction factors (here simplified as a comprehensive correction coefficient α): f adjusted=f calc / α

[0037] Note: α needs to be determined through experiments, empirical data or real-time monitoring systems, which reflects the combined effects of load, mechanical loss and heat dissipation on the speed.

[0038] S3.4 implements frequency adjustment,

[0039] The calculated f adjusted Applied to frequency converter to adjust the power supply frequency of the motor.

[0040] S3.4 Monitoring and feedback,

[0041] S3.4.1 Real-time monitoring of the actual speed n of the motor actual .

[0042] S3.4.2 Comparison n actual With n target , if the deviation exceeds the allowable range, fine-tuning is performed.

[0043] S3.4.3 Monitor the changes in load torque T and adjust the correction factor α as needed.

[0044] S3.4.4 Monitor the temperature and heat dissipation of the motor to ensure that the motor operates within a safe range.

[0045] S3.5 loop control,

[0046] Repeat steps S3.2 to S3.5 to form a closed-loop control system, dynamically adjusting the motor speed according to actual needs.

[0047] According to another embodiment of the present invention or any of the above embodiments of the intelligent control method, in step S1.2, the specific steps are as follows:

[0048] S1.2.1 Calculate the initial temperature T through the NTC thermistor and resistor voltage divider circuit calculated .

[0049] S1.2.2 Use the self-learning correction formula to adjust the temperature T corrected =T calculated +f(.).

[0050] S1.2.3 Determine T corrected Check whether the safety threshold is exceeded and adjust the motor output power P as needed. out , where the f function and specific correction logic need to be determined based on actual data and algorithm design

[0051] According to another embodiment of the present invention or any of the above embodiments of the intelligent control method, in step S1.3, the design of the resistor also takes into account the sampling accuracy and power consumption, and a correction formula is added to the calculation formula. The steps are as follows:

[0052] The bus voltage is V bus , the sampling resistor is R sample , the transmission ratio of the isolation optocoupler is k (usually k = 1 means no attenuation transmission, but here we retain k to consider possible transmission errors or gains), and take into account the correction factor C in the correction formula (this correction factor may come from the temperature coefficient of the resistor, nonlinear effects or other factors).

[0053] Then, the calculation formula for voltage sampling can be expressed as:

[0054]

[0055] in:

[0056] V sampled is the sampled voltage.

[0057] R measure It is the equivalent resistance in the measurement circuit (which may include sampling resistors and other resistances related to the measurement).

[0058] R out It is the equivalent resistance of the output side circuit (related to the output impedance of the isolation optocoupler and the subsequent circuit).

[0059] According to another embodiment of the present invention or any of the above embodiments of the intelligent control method, in step S1.4, the current sensor conversion process is:

[0060] V sensor =K sensor ×I actual

[0061] Filtering (assuming the filter gain is 1, i.e., the voltage value is not changed):

[0062] V filtered =V sensor

[0063] Op amp circuit buffer (again assuming a gain of 1):

[0064] V buffered =V filtered

[0065] Taking into account the resistance drift and the accuracy of the op amp chip, we introduce a correction factor:

[0066] V ADC =K gain×V buffered +K offset

[0067] Combining the above formulas, we get:

[0068] V ADC =K gain ×(K sensor ×I actual )+K offset

[0069] To simplify the calculation, we can combine the coefficients:

[0070] K total =K gain ×K sensor

[0071] V ADC =K total ×I actual +K offset

[0072] In order to infer the actual motor current from the ADC voltage value, we need to perform the following calculations:

[0073] I actual =K total V ADC -K offset

[0074] in,

[0075] I actual is the actual motor current.

[0076] K sensor It is the conversion factor of the current sensor (unit: V / A), which indicates the voltage output corresponding to each ampere of current.

[0077] V sensor is the voltage output by the current sensor.

[0078] V filtered It is the voltage after filtering.

[0079] V buffered It is the voltage after being buffered by the op amp circuit.

[0080] V ADC It is the voltage that is finally sent to the ADC port of the DSP chip.

[0081] K offset and K gain It is the correction factor after considering the resistance drift and the accuracy of the op amp chip.

[0082] According to another embodiment of the present invention or the intelligent control method of any of the above embodiments, in step S2, the specific steps are as follows:

[0083] In the constant torque area, the motor torque remains unchanged to ensure the vehicle's climbing grade. Assuming the constant torque of the motor is Tconst, in this area:

[0084] Torque T = T const

[0085] Power P = T·ω (where ω is the angular velocity of the motor, which is proportional to the rotational speed n, that is, ω = 2πn / 60).

[0087] In the constant power area, the output power of the motor remains constant to ensure the acceleration of the vehicle. Assume that the constant power of the motor is P const , then in this region:

[0088] Power P = P const ,

[0089] Torque T = P const / ω,

[0090] Where ω is the angular velocity of the motor,

[0091] Since the torque is inversely proportional to the speed, it can be expressed as

[0092] In the natural characteristic area, the motor output power is inversely proportional to the speed, and the torque is inversely proportional to the square of the speed. Assume that at the highest speed point n max , the motor power is P max , then in this region:

[0093] power

[0094] Torque

[0095] In summary, the power distribution calculation formulas of the motor in three different intervals are:

[0096] Constant torque area: T = T const， P=T·ω

[0097] Constant power area: P = Pconst

[0098] Natural feature area

[0099] According to another embodiment of the present invention or any of the above embodiments, the intelligent control method further includes S4. Controlling the stator current amplitude and phase of the motor through a vector control algorithm to achieve accurate control of the motor torque and speed, as follows:

[0100] S4.1 System initialization,

[0101] S4.2 Current acquisition and conversion,

[0102] S4.3 Speed ​​estimation and feedback,

[0103] S4.4 Torque and flux control,

[0104] S4.5 current control,

[0105] S4.6 Inverse transformation and PWM modulation,

[0106] S4.7 Real-time monitoring and optimization,

[0107] S4.8 loop control.

[0108] According to another embodiment of the present invention or any of the above embodiments, the intelligent control method further includes S5. a power factor correction step for improving the power factor of the motor and reducing the loss of reactive power. By adjusting the power supply voltage and current phase of the motor, the power factor of the motor is close to 1, thereby improving the energy utilization efficiency of the motor. The specific steps are as follows:

[0109] S5.1 Measure voltage and current:

[0110] S5.1.1 Use a voltage sensor and a current sensor to measure the motor supply voltage V(t) and current I(t) respectively.

[0111] S5.1.2 The sampling frequency should be high enough to accurately capture changes in voltage and current.

[0112] S5.2 Calculate instantaneous power:

[0113] S5.2.1 Calculate the instantaneous power Pinst(t)=V(t)·I(t).

[0114] S5.2.2 Calculate the instantaneous reactive power Qinst(t), usually by calculating the phase difference between voltage and current.

[0115] S5.3 Calculate the power factor:

[0116] S5.3.1 Use the active power P and apparent power S to calculate the power factor PF = SP.

[0117] S5.3.2 Where P is the average active power over a period of time, and S is the apparent power, which can be calculated from the effective values ​​of voltage and current: S = P2 + Q2

[0118] S5.3.3Q is the reactive power, which can be obtained by integrating the instantaneous reactive power Qinst(t).

[0119] S5.4 Adjust phase:

[0120] S5.4.1 Calculate the phase angle Δθ that needs to be adjusted based on the current power factor PF.

[0121] S5.4.2 Phase adjustment can be achieved by controlling the PWM (pulse width modulation) signal of the motor driver.

[0122] S5.5 Application Control Strategy:

[0123] S5.5.1 Use a PI (proportional-integral) controller or other advanced control algorithms (such as predictive control, fuzzy control, etc.) to adjust the duty cycle and phase of the PWM signal to gradually reduce reactive power and improve the power factor.

[0124] S5.5.2 The controller should be set to a target power factor (e.g., 0.98 or higher) and continuously monitored and adjusted until the target is achieved.

[0125] S5.6 Iteration and Feedback:

[0126] S5.6.1 Repeat steps 1 to 5 to form a closed-loop control system.

[0127] S5.6.2 Keep the motor power factor close to 1 through continuous monitoring and adjustment.

[0128] According to another embodiment of the present invention or any of the above embodiments, the intelligent control method further includes an S6 energy management step, which dynamically adjusts the power output and energy distribution of the motor according to the real-time monitored motor working state and actual demand to achieve optimal energy utilization and cruising range. The specific steps are as follows:

[0129] S6.1 Data collection and preprocessing:

[0130] S6.1.1 Collect vehicle speed, acceleration, load (such as traction), battery status (such as voltage, current, remaining power, temperature, etc.) and other information in real time.

[0131] S6.1.2 Preprocess the collected data, such as filtering and calibration, to ensure the accuracy and reliability of the data.

[0132] S6.2 Demand Analysis and Forecasting:

[0133] S6.2.1 Analyze the energy demand in the present and future periods of time based on the current vehicle status (e.g., speed, acceleration) and driving mode (e.g., normal, economy, sport, etc.).

[0134] S6.2.2 Use predictive algorithms (such as machine learning models based on historical data) to predict future changes in energy demand, such as acceleration, deceleration, and ramp climbing.

[0135] S6.3 Energy allocation strategy:

[0136] S6.3.1 Develop an energy distribution strategy based on energy demand and battery status, including power output from the motor, energy release from the battery, and recovery (such as braking energy recovery).

[0137] S6.3.2 Considering the health management of the battery, the algorithm should avoid overcharging, overdischarging, and overheating of the battery to extend the battery life.

[0138] S6.4 real-time adjustment and optimization:

[0139] S6.4.1 During vehicle operation, monitor energy consumption and battery status in real time and adjust the energy allocation strategy based on actual conditions.

[0140] S6.4.2 Use optimization algorithms (such as dynamic programming, model predictive control, etc.) to find the optimal energy allocation plan to minimize energy consumption and maximize driving range.

[0141] S6.5 Fault detection and response:

[0142] S6.5.1 Monitor the status of the vehicle and battery system in real time to detect potential failures or abnormal conditions.

[0143] S6.5.2 When a fault or abnormal condition is detected, take immediate countermeasures, such as reducing power output, switching to backup mode, etc., to ensure the safety of the vehicle and passengers.

[0144] Working principle of the present invention:

[0145] Beneficial effects of the present invention:

[0146] 1. The present invention discloses an AC asynchronous motor drive system and its intelligent control method. The system adopts intelligent control and can improve the cruising range and effectively improve the energy efficiency and operation stability of the motor compared with the traditional drive system. Compared with the previous drive system, the system has made significant progress in actual measurements: at a cruising speed of 40km / h, the cruising range has increased by 5%; and at a cruising speed of 80km / h, the cruising range has increased by 10%. This improvement is due to the introduction of an intelligent control system, which can monitor and accurately regulate the working state of the motor in real time, thereby effectively improving the energy efficiency and operation stability of the motor.

[0147] 2. The present invention uses a number of key technologies in the motor controller, including variable frequency speed regulation algorithm, vector control algorithm, power factor correction algorithm and energy management algorithm, to achieve performance improvement. These technologies work together to ensure efficient and stable operation of the motor under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0148] Figure 1 It is the external characteristic curve diagram of the motor described in the present invention;

[0149] Figure 2 This is a simulation diagram of the thermistor signal processing circuit of the present invention;

[0150] Figure 3 This is a schematic diagram of the voltage sampling circuit of the present invention;

[0151] Figure 4 This is a schematic diagram of the current sampling circuit of the present invention;

[0152] Figure 5 It is a schematic diagram of the three stages of power distribution according to the present invention. DETAILED DESCRIPTION

[0153] 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.

[0154] An AC asynchronous motor drive system and an intelligent control method thereof are applied to a four-seater pure electric version of the MOKE vehicle. The body of the four-seater pure electric version of the MOKE adopts an all-aluminum structure, and the vehicle's curb weight is controlled below 500Kg, aiming to achieve a lightweight design. For performance requirements, the maximum speed of this model must reach 80km / h, and the maximum climbing grade must meet the 25% standard. In view of the light weight of the vehicle, in order to ensure structural stability and collision safety, the weight of the power battery system is not designed to be heavy. Specifically, the power battery's charge is maintained within 20kWh, and the vehicle's endurance is improved as much as possible while ensuring safety.

[0155] By formula According to the calculation of driving at constant speeds of 40, 60, and 80 km / h, the full-load driving condition is 120 km. When V = 40 km / h, the motor power consumption Pv is calculated to be 2.1 kW; when V = 60 km / h, the motor power consumption Pv is calculated to be 5.4 kW; when V = 80 km / h, the motor power consumption Pv is calculated to be 11.3 kW. From the above data, it can be seen that the faster the speed, the greater the power consumption, and the power consumption increases at a higher rate. Due to the light weight of the vehicle, while meeting the motor torque requirements, through calculation and analysis, we decided to use an AC asynchronous motor drive system intelligent control system, which can further optimize the energy efficiency performance on the basis of lightweight. The above control system mainly includes a two-speed gearbox and a motor controller, which uses a motor energy distribution algorithm for control.

[0156] The following is an AC asynchronous motor drive system and its intelligent control method:

[0157] First, calculate the maximum speed of the motor using the formula: n≥i*V max / 0.377*r

[0158] n—maximum motor speed;

[0159] i—main reduction ratio;

[0160] V max - Maximum vehicle speed;

[0161] r—tire rolling radius;

[0162] The peak power Pmax of the motor must also meet the power Pmax required by the electric vehicle when it is running at the highest speed. max1 , the power P required to climb a slope at the maximum gradient at a certain speed max2 And the maximum power P required during acceleration max3 ,Right now:

[0163] P max ≥max{P max1 , P max2 , P max3}

[0164]

[0165] Where: m is the fully loaded mass of the electric vehicle, g is the acceleration of gravity, f is the rolling resistance factor, between 0.01 and 0.02, CD is the air resistance factor (generally 0.3 to 0.6), A is the windward area, η T is the mechanical efficiency of the transmission system, u max Maximum speed.

[0166]

[0167] Where: u_climb is the climbing vehicle speed, and α_max is the maximum gradient angle.

[0168]

[0169] Where: δ is the conversion coefficient of rotating mass (generally 1.1 - 1.4), take 1.1; v_a is the vehicle speed at the end of acceleration, a en d is the acceleration, x is the fitting coefficient, take 0.5.

[0170] The maximum torque T_max of the drive motor needs to meet the requirements of the electric vehicle's starting acceleration and maximum climbing gradient:

[0171]

[0172] The external characteristic curve of the motor is as Figure 1 shown. Currently, the maximum speed of the mainstream 10KW AC asynchronous motor for low-speed vehicles in the market is about 7000 r / min. Through calculation, it is known that it fully meets the requirements of the maximum vehicle speed. According to the torque-speed output characteristics of this AC asynchronous motor, the rated efficiency, maximum efficiency and efficiency distribution of the drive motor, a two-speed gearbox system is decided to be selected to simultaneously meet several important parameters such as the maximum vehicle speed, maximum climbing gradient, and the highest efficiency area of the drive motor.

[0173] The AC asynchronous motor drive system and its intelligent control method, a system that combines traditional control theory and artificial intelligence technology, can achieve efficient control and optimization in complex and uncertain environments. In the AC asynchronous motor drive system, the intelligent control system is mainly used to monitor the working state of the motor in real time, including parameters such as speed, temperature, current, etc., and to achieve precise power distribution and energy management based on this information.

[0174] Collect various parameters of the motor in real time through sensors, such as speed, temperature, voltage, current, etc., to achieve comprehensive monitoring of the motor's working state.

[0175] The speed uses multiple pairs of pole encoders to improve accuracy, and the signal processing uses incremental angle encoding method and arctangent operation, etc.

[0176] The temperature probe is built into the motor, using an NTC thermistor. The resistance signal is converted into a voltage signal by the method of resistance voltage division, and then transferred to the voltage signal received at the port through an operational amplifier conditioning circuit. A self-learning correction formula is added to the calculation formula. According to the driving data, ambient temperature, motor temperature, motor controller temperature, motor speed, and motor torque, a pre-analysis is made on the temperature value. If the temperature is too high, the motor output power is reduced in advance.

[0177] ①Initialization and parameter setting:

[0178]

[0179]

[0180] ②NTC thermistor voltage signal conversion and temperature calculation

[0181] Assuming that the voltage divider resistor before the op amp conditioning circuit is Rref, the output voltage Vout can be expressed as:

[0182]

[0183] Wherein, Vcc is the power supply voltage.

[0184]

[0185] ③Self-learning correction formula:

[0186] The self-learning correction based on driving data, ambient temperature, motor temperature, motor controller temperature, motor speed, and motor torque can be expressed as a complex function f, which adjusts the temperature value according to historical data and current status:

[0187]

[0188] ④Power adjustment algorithm:

[0189]

[0190]

[0191] Combining the above steps, we can summarize it into a complete calculation formula framework:

[0192] The initial temperature Tcalculated is calculated by the NTC thermistor and the resistor voltage divider circuit.

[0193] Use the self-learning correction formula to adjust the temperature: Tcorrected = Tcalculated + f(.).

[0194] Determine whether Tcorrected exceeds the safety threshold and adjust the motor output power Pout as needed.

[0195] The f function and specific correction logic here need to be determined based on actual data and algorithm design. The simulation diagram of thermistor signal processing circuit is as follows: Figure 2 shown.

[0196] Voltage sampling: In order to achieve electrical insulation between the strong and weak currents of the controller, the bus voltage sampling circuit uses a high-precision isolation photocoupler. The input side and the output side are powered by two isolated power supplies, respectively, to achieve electrical isolation. The design of the resistor also takes into account the sampling accuracy and power consumption, and a correction formula is added to the calculation formula.

[0197] Suppose the bus voltage is Vbus, the sampling resistor is Rsample, the transmission ratio of the isolation optocoupler is k (usually k=1 means transmission without attenuation, but here we retain k to consider possible transmission errors or gains), and take into account the correction factor C in the correction formula (this correction factor may come from the temperature coefficient of the resistor, nonlinear effects or other factors).

[0198] Then, the calculation formula for voltage sampling can be expressed as:

[0199]

[0200] in:

[0201] Vsampled is the sampled voltage.

[0202] Rmeasure is the equivalent resistance in the measurement circuit (which may include the sampling resistor and other resistances related to the measurement).

[0203] Rout is the equivalent resistance of the output side circuit (related to the output impedance of the isolation optocoupler and the subsequent circuit). The voltage sampling chip circuit is as follows: Figure 3 shown.

[0204] Current sampling: The current sensor converts the current signal into a voltage signal as the input signal of the circuit, performs filtering at the circuit interface, and then passes through the operational amplifier circuit buffer and difference processing to convert it into a voltage signal suitable for receiving by the ADC port of the DSP chip. The accuracy of the current sampling circuit will affect the motor torque control accuracy. Considering the resistance drift and the accuracy of the operational amplifier chip, the overall sampling deviation of the sampling circuit is required to be less than 1%. The self-learning adjustment parameters use charging current, BMS sending current parameters, motor peak current, etc.

[0205] Iactual is the actual motor current.

[0206] Ksensor is the conversion factor of the current sensor (unit: V / A), which indicates the voltage output corresponding to each ampere of current.

[0207] Vsensor is the voltage output by the current sensor.

[0208] Vfiltered is the voltage after filtering.

[0209] Vbuffered is the voltage after being buffered by the op amp circuit.

[0210] VADC is the voltage that is ultimately sent to the ADC port of the DSP chip.

[0211] Koffset and Kgain are correction coefficients that take into account the resistance drift and the accuracy of the op amp chip.

[0212] Current sensor conversion process:

[0213] Vsensor=Ksensor×Iactual

[0214] Filtering (assuming the filter gain is 1, i.e., the voltage value is not changed):

[0215] Vfiltered=Vsensor

[0216] Op amp circuit buffer (again assuming a gain of 1):

[0217] Vbuffered=Vfiltered

[0218] Taking into account the resistance drift and the accuracy of the op amp chip, we introduce a correction factor:

[0219] V ADC =K gain ×V buffered +K offset

[0220] Combining the above formulas, we get:

[0221] V ADC =K gain ×(K sensor ×I actual )+K offset

[0222] To simplify the calculation, we can combine the coefficients:

[0223] K total =K gain ×K sensor ,

[0224] VADC=K total ×Iactual+K offset

[0225] In order to infer the actual motor current from the ADC voltage value, we need to perform the following calculations:

[0226] I actual =K total V ADC -K offset

[0227] Notice:

[0228] K total and K offset This needs to be determined through a calibration process.

[0229] The overall sampling deviation of the sampling circuit should be less than 1%, which means that special attention needs to be paid to the accuracy of these coefficients during the design and calibration process.

[0230] Self-learning adjustment parameters (such as charging current, BMS sending current parameters, motor peak current, etc.) can be used to dynamically adjust these coefficients to improve the accuracy and adaptability of current sampling. However, in this formula, these parameters are mainly used as a reference during the calibration process and are not directly reflected in the formula. The current sampling circuit is Figure 4 shown.

[0231] 2. Precise control: Based on the real-time monitored data, the intelligent control system uses advanced algorithms to precisely control the motor to ensure that the motor runs in the best condition.

[0232] 3. Power distribution: The intelligent control system can dynamically adjust the power output of the motor according to actual needs to achieve reasonable distribution and optimal utilization of energy. The torque-speed characteristics of the drive motor ensure the dynamic performance of the vehicle and are important technical indicators in the design, such as Figure 5 As shown, it includes the following three stages:

[0233] Constant torque area: ensures the vehicle's climbing grade. In this area, the motor torque remains unchanged.

[0234] Constant power zone: ensures the acceleration of the vehicle. In this zone, the output power of the motor remains unchanged and the torque is inversely proportional to the speed.

[0235] Natural characteristics: In this area, the output power of the motor is inversely proportional to the speed, and the torque is inversely proportional to the square of the speed. At the highest speed point, the power of the motor must ensure the power required for the vehicle's highest operating speed.

[0236] In the constant torque area, the motor torque remains constant to ensure the vehicle's climbing grade. Assume that the constant torque of the motor is T const , then in this region:

[0237] Torque T = T const，

[0238] Power P = T·ω (where ω is the angular velocity of the motor, which is proportional to the speed n, i.e. ω = 2πn / 60)

[0239] In the constant power area, the output power of the motor remains constant to ensure the acceleration of the vehicle. Assume that the constant power of the motor is Pconst , then in this region:

[0240] Power P = P const ,

[0241] Torque T = Pconst / ω (where ω is the angular velocity of the motor),

[0242]

[0243] Assuming that at the highest speed point nmax, the motor power is Pmax, then in this area:

[0244] power

[0245] Torque

[0246] In summary, the power distribution calculation formulas of the motor in three different intervals are:

[0247] Constant torque area: T = T const , P = T·ω,

[0248] Constant power area: P = P const

[0249] Natural feature area

[0250] 3. Algorithm of motor controller for motor energy distribution

[0251] The motor controller is the core component of the AC asynchronous motor drive system, responsible for converting the input electrical energy into mechanical energy and achieving precise control of the motor. Under the framework of the intelligent control system, the motor controller achieves precise control of the motor energy distribution through a series of algorithms.

[0252] 1. Variable frequency speed regulation algorithm: By adjusting the power supply frequency of the motor, the motor speed can be accurately controlled. The variable frequency speed regulation algorithm can dynamically adjust the motor speed according to actual needs to meet different working conditions.

[0253] The frequency conversion speed regulation algorithm framework is as follows:

[0254] Initialization parameters,

[0255] Set the motor's reference frequency f base (such as 50Hz or 60Hz).

[0256] Set the motor's reference speed n base (Speed ​​at base frequency).

[0257] Set the proportionality factor k between speed and frequency (determined according to motor design and number of pole pairs).

[0258] Initialize the monitoring value of the load torque T.

[0259] Initialize mechanical power loss P loss And the heat dissipation power P heat The estimated value of .

[0260] Get the target speed:

[0261] Determine the target speed n according to actual needs (such as process flow, load changes, etc.) target .

[0262] Calculate the power supply frequency:

[0263] Preliminary calculation ignoring load, mechanical losses and heat dissipation:

[0264] f calc =n target / k

[0265] Frequency adjustment considering correction factors (here simplified to a comprehensive correction coefficient α): f adjusted =f calc / α,

[0266] Note: α needs to be determined through experiments, empirical data or real-time monitoring systems, which reflects the combined effects of load, mechanical loss and heat dissipation on the speed.

[0267] Implement frequency adjustment:

[0268] The calculated f adjusted Applied to frequency converter to adjust the power supply frequency of the motor.

[0269] Monitoring and Feedback:

[0270] Real-time monitoring of the actual speed n of the motor actual .

[0271] Compare actual With n target , if the deviation exceeds the allowable range, fine-tuning is performed.

[0272] Monitor the changes in load torque T and adjust the correction factor α as needed.

[0273] Monitor the temperature and heat dissipation of the motor to ensure that the motor operates within a safe range.

[0274] Loop Control:

[0275] Repeat steps 2 to 5 to form a closed-loop control system to dynamically adjust the motor speed according to actual needs.

[0276] 2. Vector control algorithm: Vector control is an advanced motor control algorithm that achieves precise control of motor torque and speed by precisely controlling the motor's stator current amplitude and phase. In the framework of intelligent control systems, the vector control algorithm can be further combined with real-time monitored data to achieve precise optimization of motor energy distribution.

[0277] Vector control algorithm re-frame schematic:

[0278] #Define the parameters of the PI controller (e.g., gain)

[0279]

[0280]

[0281]

[0282]

[0283]

[0284]

[0285] 3. Power factor correction algorithm: The power factor correction algorithm is used to improve the power factor of the motor and reduce the loss of reactive power. By adjusting the power supply voltage and current phase of the motor, the power factor of the motor is close to 1, thereby improving the energy utilization efficiency of the motor.

[0286] The algorithm steps are as follows:

[0287] Measuring voltage and current:

[0288] A voltage sensor and a current sensor are used to measure the motor supply voltage V(t) and current I(t) respectively.

[0289] The sampling frequency should be high enough to accurately capture the changes in voltage and current.

[0290] Calculate instantaneous power:

[0291] Calculate the instantaneous power P inst (t) = V(t)*I(t).

[0292] Calculate the instantaneous reactive power Q inst (t), usually by calculating the phase difference between voltage and current.

[0293] Calculate the power factor:

[0294] 1. Use the active power P and apparent power S to calculate the power factor PF = SP.

[0295] 2. Where P is the average active power over a period of time, and S is the apparent power, which can be calculated from the effective values ​​of voltage and current: S = P 2 +Q 2 ,

[0296] 3.Q is the reactive power, which can be obtained by integrating the instantaneous reactive power Q inst (t)Get.

[0297] 4. Adjust the phase:

[0298] 1. Calculate the phase angle Δ that needs to be adjusted based on the current power factor PF θ .

[0299] 2. Phase adjustment can be achieved by controlling the PWM (pulse width modulation) signal of the motor driver.

[0300] 5. Application control strategy:

[0301] 1. Use PI (proportional-integral) controller or other advanced control algorithms (such as predictive control, fuzzy control, etc.) to adjust the duty cycle and phase of the PWM signal to gradually reduce reactive power and improve the power factor.

[0302] 2. The controller should be set with a target power factor (such as 0.98 or higher) and continuously monitored and adjusted until the target is reached.

[0303] 6. Iteration and Feedback:

[0304] 1. Repeat steps 1 to 5 to form a closed-loop control system.

[0305] 2. Through continuous monitoring and adjustment, the power factor of the motor is close to 1.

[0306] Program framework example:

[0307]

[0308]

[0309] 4. Energy management algorithm: The energy management algorithm in the intelligent control system dynamically adjusts the power output and energy distribution of the motor according to the real-time monitoring of the motor working status and actual demand. For example, in electric vehicles, the energy management algorithm can adjust the power output of the motor in real time according to information such as the vehicle speed, load and battery status to achieve optimal energy utilization and driving range.

[0310] The algorithm steps are as follows:

[0311] Data collection and preprocessing:

[0312] Collect vehicle speed, acceleration, load (such as traction), battery status (such as voltage, current, remaining power, temperature, etc.) and other information in real time.

[0313] The collected data is preprocessed, such as filtering and calibration, to ensure the accuracy and reliability of the data.

[0314] Demand Analysis and Forecasting:

[0315] Analyze the energy demand in the current and future period based on the current vehicle status (such as speed, acceleration) and driving mode (such as normal, economy, sports, etc.).

[0316] Use predictive algorithms (such as machine learning models based on historical data) to predict future changes in energy demand, such as acceleration, deceleration, and ramp climbing.

[0317] Energy allocation strategy:

[0318] According to the energy demand and battery status, an energy distribution strategy is formulated, including the power output of the motor, the energy release and recovery of the battery (such as braking energy recovery).

[0319] Considering the battery health management, the algorithm should avoid battery overcharging, over-discharging and overheating to extend the battery life.

[0320] Real-time adjustment and optimization:

[0321] During vehicle operation, energy consumption and battery status are monitored in real time, and the energy allocation strategy is adjusted according to actual conditions.

[0322] Use optimization algorithms (such as dynamic programming, model predictive control, etc.) to find the optimal energy allocation plan to minimize energy consumption and maximize driving range.

[0323] Fault detection and response:

[0324] Monitor the status of the vehicle and battery system in real time to detect potential failures or abnormal conditions.

[0325] When a fault or abnormal situation is detected, immediate countermeasures are taken, such as reducing power output, switching to backup mode, etc., to ensure the safety of the vehicle and passengers.

[0326] Program framework example:

[0327]

[0328] At present, the prototype of the MOKE model has been successfully trial-produced and rolled off the production line. Its AC asynchronous motor drive system and its intelligent control system have shown excellent performance in the prototype test. Compared with the previous drive system, the system has made significant progress in actual testing: at a cruising speed of 40km / h, the cruising range has increased by 5%; and at a cruising speed of 80km / h, the cruising range has increased by 10%. This improvement is due to the introduction of an intelligent control system, which can monitor and accurately control the working status of the motor in real time, thereby effectively improving the energy utilization efficiency and operating stability of the motor.

[0329] Several key technologies in the motor controller, including variable frequency speed regulation algorithm, vector control algorithm, power factor correction algorithm and energy management algorithm, are important means to achieve this performance improvement. These technologies work together to ensure efficient and stable operation of the motor under different working conditions.

[0330] Looking into the future, with the continuous progress and innovation of intelligent control technology, AC asynchronous motor drive systems and their intelligent control systems are expected to demonstrate their unique advantages in more fields and play a more important role.

[0331] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above implementation is only to illustrate the technical concept and features of the present invention, and its purpose is to enable people familiar with this technology to understand the content of the present invention and implement it, and it cannot be used to limit the protection scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. An AC asynchronous motor drive system, characterized in that: Including motor, motor controller, two-speed gearbox, intelligent control module, The motor controller is used to accurately control the energy distribution of the motor; The intelligent control module includes multiple pairs of magnetic pole encoders and temperature probes to collect various parameters of the motor in real time and realize comprehensive monitoring of the motor working status. Multiple pairs of magnetic pole encoders collect motor speed to improve accuracy. The temperature probe is built into the motor to pre-analyze the temperature value.

2. The AC asynchronous motor drive system according to claim 1, characterized in that: The temperature probe is an NTC thermistor.

3. The intelligent control method of an AC asynchronous motor drive system according to claim 1 or 2, characterized in that it comprises the following steps: S1. Use sensors to collect various parameters of the motor in real time, such as speed, temperature, voltage, and current, to monitor the working status of the motor; S1.

1. The motor speed is collected through multiple pairs of magnetic pole encoders to improve accuracy. The signal processing adopts incremental angle encoding method and inverse tangent operation. S1.2 measures the internal temperature of the motor through a temperature probe. If the temperature is too high, the motor output power is reduced in advance. The resistance signal is converted into a voltage signal by means of a resistor divider, and then transferred to the voltage signal received by the port through an op amp conditioning circuit. A self-learning correction formula is added to the calculation formula. The temperature value is pre-analyzed based on driving data, ambient temperature, motor temperature, motor controller temperature, motor speed, and motor torque. S1.3 samples the voltage to achieve electrical insulation between the strong and weak currents of the controller. The bus voltage sampling circuit uses a high-precision isolation photoelectric coupler. The input side and the output side are powered by two isolated power supplies respectively, thus achieving electrical isolation. S1.4 samples the current. The current sensor converts the current signal into a voltage signal as the input signal of the circuit. The circuit interface is filtered and then buffered by the operational amplifier circuit. The difference is converted into a voltage signal suitable for the ADC port of the DSP chip. The accuracy of the current sampling circuit will affect the motor torque control accuracy. Considering the resistance drift and the accuracy of the operational amplifier chip, the overall sampling deviation of the sampling circuit is required to be less than 1%. The self-learning adjustment parameters use the charging current, the current parameters sent by the BMS, and the peak current of the motor. S2. Based on the real-time monitored data, the motor is precisely controlled through algorithms to ensure that the motor operates in the best condition; according to actual needs, the power output of the motor is dynamically adjusted to achieve reasonable distribution and optimal utilization of energy. The control strategies for the three power zones are as follows: Constant torque area: ensures the vehicle's climbing grade. In this area, the motor torque remains unchanged; Constant power zone: ensures the acceleration of the vehicle. In this zone, the output power of the motor remains unchanged, and the torque is inversely proportional to the speed; Natural characteristics: In this area, the output power of the motor is inversely proportional to the speed, and the torque is inversely proportional to the square of the speed; at the highest speed point, the power of the motor must ensure the power required for the vehicle's highest operating speed; S3. Motor controller distributes energy to the motor. By adjusting the power supply frequency of the motor, the motor speed can be precisely controlled; the variable frequency speed regulation algorithm can dynamically adjust the motor speed according to actual needs to meet different working conditions. The specific frequency conversion speed regulation method is as follows: S3.1 Initialization parameters, S3.1.1 Set the motor reference frequency f base , S3.1.2 Set the motor's reference speed n base , S3.1.3 sets the proportional coefficient k between speed and frequency. S3.1.4 initializes the monitoring value of the load torque T. S3.1.5 Initialize mechanical power loss P loss And the heat dissipation power P heat The estimated value of . S3.2 obtains the target speed, Determine the target speed n according to actual needs target , S3.3 Calculate the power supply frequency, S3.3.1 Preliminary calculations ignoring loads, mechanical losses, and heat dissipation: f calc =n target / k, S3.3.2 Frequency adjustment considering correction factors: f adjusted =f calc / a, Note: α needs to be determined through experiments, empirical data or real-time monitoring systems. It reflects the combined effects of load, mechanical loss and heat dissipation on the speed. S3.4 implements frequency adjustment, The calculated f adjusted Applied to frequency converter to adjust the power supply frequency of the motor. S3.4 Monitoring and feedback, S3.4.1 Real-time monitoring of the actual speed n of the motor actual , S3.4.2 Comparison n actual With n target If the deviation exceeds the allowable range, fine-tuning is performed. S3.4.3 Monitor the change of load torque T and adjust the correction coefficient α as needed. S3.4.4 Monitor the temperature and heat dissipation of the motor to ensure that the motor operates within a safe range. S3.5 loop control, Repeat steps S3.2 to S3.5 to form a closed-loop control system to dynamically adjust the motor speed according to actual needs.

4. The intelligent control method of an AC asynchronous motor drive system according to claim 1 or 2, characterized in that: in the step S1.2, the specific steps are as follows: S1.2.1 Calculate the initial temperature T through the NTC thermistor and resistor voltage divider circuit calculated , S1.2.2 Use the self-learning correction formula to adjust the temperature T corrected =T calculated +f(.), S1.2.3 Determine T corrected Whether it exceeds the safety threshold, and adjust the motor output power P as needed out , The f function and specific correction logic need to be determined based on actual data and algorithm design. According to another embodiment of the present invention or an intelligent control method of any of the above embodiments, wherein: In step S1.3, the design of the resistor also takes into account the sampling accuracy and power consumption. A correction formula is added to the calculation formula. The steps are as follows: The bus voltage is V bus , the sampling resistor is R sample , the transmission ratio of the isolation optocoupler is k, and considering the correction factor C in the correction formula, Then, the calculation formula for voltage sampling can be expressed as: in: V sampled is the sampled voltage, R measure is the equivalent resistance in the measuring circuit, R out is the equivalent resistance of the output side circuit.

5. The intelligent control method of an AC asynchronous motor drive system according to claim 1 or 2, characterized in that: in the step S1.4, the current sensor conversion process: V sensor =K sensor ×I actual Filtering: V filtered =V sensor Op amp circuit buffer, assuming a gain of 1: V buffered =V filtered Taking into account the resistance drift and the accuracy of the op amp chip, we introduce a correction factor: V ADC =K gain ×V buffered +K offset Combining the above formulas, we get: In ADC =K gain ×(K sensor ×I actual )+K offset To simplify the calculation, we can combine the coefficients: K total =K gain ×K sensor V ADC =K total ×I actual +K offset In order to infer the actual motor current from the ADC voltage value, we need to perform the following calculations: I actual =K total V ADC -K offset in, I actual is the actual motor current, K sensor It is the conversion coefficient of the current sensor, unit: V / A, which indicates the voltage output corresponding to each ampere of current. V sensor is the voltage output by the current sensor, V filtered is the voltage after filtering. V buffered is the voltage after being buffered by the op amp circuit. V ADC It is the voltage that is finally sent to the ADC port of the DSP chip. K offset and K gain It is the correction factor after considering the resistance drift and the accuracy of the op amp chip.

6. The intelligent control method of an AC asynchronous motor drive system according to claim 1 or 2, characterized in that: in the step S2, specifically as follows: In the constant torque area, the torque of the motor remains unchanged to ensure the vehicle's climbing grade. Assuming that the constant torque of the motor is Tconst, in this area: Torque T = T const Power P = T·ω, Where ω is the angular velocity of the motor, which is proportional to the speed n, that is, ω = 2πn / 60. In the constant power area, the output power of the motor remains unchanged to ensure the acceleration of the vehicle. Assume that the constant power of the motor is P const , then in this region: Power P = P const , Torque T = P const / ω, Where ω is the angular velocity of the motor, Since the torque is inversely proportional to the speed, it can be expressed as In the natural characteristic area, the output power of the motor is inversely proportional to the speed, and the torque is inversely proportional to the square of the speed. Assuming that at the highest speed point n max , the motor power is P max , then in this region: power Torque In summary, the power distribution calculation formulas of the motor in three different intervals are: Constant torque area: T = T const， P=T·ω Constant power area: P = Pconst, Natural feature area 7. The intelligent control method of an AC asynchronous motor drive system according to claim 1 or 2, characterized in that: S4. controls the stator current amplitude and phase of the motor through a vector control algorithm to achieve precise control of the motor torque and speed, as follows: S4.1 System initialization, S4.2 Current acquisition and conversion, S4.3 Speed ​​estimation and feedback, S4.4 Torque and flux control, S4.5 current control, S4.6 Inverse transformation and PWM modulation, S4.7 Real-time monitoring and optimization, S4.8 loop control.

8. The intelligent control method of an AC asynchronous motor drive system according to claim 1 or 2, characterized in that: it also includes S5. a power factor correction step for improving the power factor of the motor and reducing the loss of reactive power, and by adjusting the power supply voltage and current phase of the motor so that the power factor of the motor is close to 1, thereby improving the energy utilization efficiency of the motor, and the specific steps are as follows: S5.1 Measure voltage and current: S5.1.1 Use a voltage sensor and a current sensor to measure the motor supply voltage V(t) and current I(t) respectively. S5.1.2 The sampling frequency should be high enough to accurately capture the changes in voltage and current. S5.2 Calculate instantaneous power: S5.2.1 Calculation of instantaneous power P inst (t) = V(t)·I(t), S5.2.2 Calculation of instantaneous reactive power Q inst (t), which is usually achieved by calculating the phase difference between voltage and current, S5.3 Calculate the power factor: S5.3.1 Use the active power P and apparent power S to calculate the power factor PF = SP. S5.3.2 Among them, P is the average active power over a period of time, and S is the apparent power, which can be calculated from the effective values ​​of voltage and current: S = P2 + Q2, S5.3.3Q is the reactive power, which can be obtained by integrating the instantaneous reactive power Q inst (t) get, S5.4 Adjust phase: S5.4.1 Calculate the phase angle Δ to be adjusted based on the current power factor PF θ , S5.4.2 Phase adjustment can be achieved by controlling the PWM signal of the motor driver. S5.5 Application Control Strategy: S5.5.1 Use a PI controller or other advanced control algorithm to adjust the duty cycle and phase of the PWM signal to gradually reduce reactive power and improve the power factor. S5.5.2 The controller shall set a target power factor and continuously monitor and adjust until the target is achieved. S5.6 Iteration and Feedback: S5.6.1 Repeat steps 1 to 5 to form a closed-loop control system. S5.6.2 Keep the motor power factor close to 1 through continuous monitoring and adjustment.

9. The intelligent control method of an AC asynchronous motor drive system according to claim 1 or 2 is characterized in that: it also includes an S6 energy management step, dynamically adjusting the power output and energy distribution of the motor according to the real-time monitored motor working state and actual demand to achieve optimal energy utilization and cruising range, and the specific steps are as follows: S6.1 Data collection and preprocessing: S6.1.1 Collect vehicle speed, acceleration, load, and battery status information in real time. S6.1.2 Preprocess the collected data, such as filtering and calibration, to ensure the accuracy and reliability of the data. S6.2 Demand Analysis and Forecasting: S6.2.1 Analyze the energy demand in the present and future period based on the current vehicle status and driving mode. S6.2.2 Use prediction algorithms to predict future energy demand changes, such as acceleration, deceleration, and ramp climbing, S6.3 Energy allocation strategy: S6.3.1 Develop energy allocation strategies based on energy demand and battery status, including motor power output, battery energy release and recovery, S6.3.2 Considering the health management of the battery, the algorithm should avoid overcharging, overdischarging, and overheating of the battery to extend the battery life. S6.4 real-time adjustment and optimization: S6.4.1 During vehicle operation, monitor energy consumption and battery status in real time and adjust energy allocation strategy according to actual conditions. S6.4.2 Use optimization algorithms to find the best energy allocation solution to minimize energy consumption and maximize driving range. S6.5 Fault detection and response: S6.5.1 Monitor the status of the vehicle and battery system in real time to detect potential faults or abnormal conditions. S6.5.2 When a fault or abnormal condition is detected, take immediate countermeasures, such as reducing power output or switching to backup mode, to ensure the safety of the vehicle and passengers.

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