Electric vehicle environment control method based on digital twin multi-physical cooperation

Abstract

The invention discloses an electric vehicle environment control method based on digital twin multi-physical cooperation, which comprises the following steps: S1, monitoring driving environment parameters in real time by a sensor network, and transmitting the driving environment parameters to a digital twin system and an intelligent control unit; s2, the digital twin system processes and analyzes the data acquired by the sensor, generates a simulation result, transmits the simulation result to the multi-physical coordination system, and receives feedback data from the multi-physical coordination system; s3, the multi-physical coordination system receives a simulation result of the digital twin system and sends an instruction through the intelligent control unit; s4, the intelligent control unit generates a control instruction through the vehicle environment sensing model and sends the control instruction to the multi-physical coordination system; the vehicle environment perception model ensures the optimal performance of the vehicle in various environments through an improved DWA algorithm. The problems of adjustment lag, low energy efficiency, insufficient comfort and the like in a traditional automobile environment control technology are solved, and the driving experience and the whole automobile performance are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of automobile environmental control and energy efficiency optimization, and in particular to an electric vehicle environmental control method based on digital twin multi-physics collaboration. Background Art

[0002] In today's era, with the booming new energy vehicle industry and the continuous advancement of intelligent technology, electric vehicle environmental control technology is undergoing profound technological changes. Traditional electric vehicle environmental control technology mainly relies on single sensor monitoring and fixed control logic, resulting in problems such as adjustment lag, low energy efficiency, and insufficient comfort. These issues not only hinder further improvement of the driving experience but also, to a certain extent, affect the market competitiveness of electric vehicles.

[0003] Traditional environmental control technologies often struggle to achieve precise adjustments in complex and ever-changing driving environments. For example, the air conditioning system cannot dynamically optimize based on real-time road conditions, vehicle speed, and the actual heat load in the passenger compartment, resulting in energy waste and uneven temperature distribution. Battery temperature control systems face similar challenges, unable to precisely manage the battery's real-time status and external ambient temperature, impacting battery life and overall vehicle performance. Furthermore, there is a lack of effective coordination between subsystems. The air conditioning system, battery temperature control system, and the vehicle's powertrain and braking systems operate independently, failing to form an integrated whole and hindering maximum system efficiency. Summary of the Invention

[0004] Purpose of the invention: The present invention provides an electric vehicle environmental control technology based on digital twin multi-physics collaboration, aiming to solve the problems existing in traditional electric vehicle environmental control technology, such as adjustment lag, low energy efficiency, insufficient comfort, and lack of effective collaboration between subsystems, so as to comprehensively improve the driving experience and vehicle performance.

[0005] Technical solution: A method for electric vehicle environmental control based on digital twin multi-physics collaboration, including the following steps:

[0006] S1: The sensor network monitors driving environment parameters such as temperature, speed, humidity, and battery status in real time, and transmits the collected data to the digital twin system and intelligent control unit;

[0007] S2: The digital twin system is the data processing and simulation core of the entire system. It is responsible for processing and analyzing the data collected by sensors, generating simulation results, transmitting the simulation results to the multi-physics coordination system, and receiving feedback data from the multi-physics coordination system.

[0008] S3: The multi-physics coordination system receives simulation results from the digital twin system and sends instructions through the intelligent control unit to coordinate the operation of each subsystem. The multi-physics coordination system is the coordination and management module of the system, responsible for integrating and optimizing the various subsystems of the vehicle.

[0009] S4: The intelligent control unit is the core of the entire system, responsible for receiving and processing data from sensors and the multi-physics coordination system. It generates control instructions based on the vehicle's environmental perception model and sends these instructions to the multi-physics coordination system. The vehicle's environmental perception model adjusts the vehicle's operating state in real time to adapt to different environmental conditions based on sensor data and feedback from the multi-physics coordination system. An improved DWA algorithm ensures optimal vehicle performance in various environments.

[0010] Furthermore, the real-time monitoring of driving environment parameters by the sensor network in S1 includes:

[0011] S1.1: Temperature sensors are used to monitor ambient temperature and vehicle interior temperature;

[0012] S1.2: Speed ​​sensor is used to monitor the vehicle's speed;

[0013] S1.3: Humidity sensors are used to monitor ambient humidity and provide data support for the vehicle's air conditioning and aerodynamic systems.

[0014] S1.4: The battery status detection sensor is used to monitor abnormal battery status and provide data support for the battery management system and thermal management system.

[0015] Furthermore, in step S1.1, the temperature calculation formula is as follows:

[0016] Ambient temperature monitoring calculation formula:

[0017] T amb =a·T sen amb +b

[0018] Where, T amb is the actual ambient temperature; a is the proportional coefficient, which reflects the linear proportional relationship between the sensor measurement value and the actual value; T sen amb is the ambient temperature measured by the sensor; b is the offset, which is used to compensate for the fixed deviation between the sensor measurement value and the actual value;

[0019] Vehicle internal temperature monitoring calculation formula:

[0020] T int =a·T sen int +b

[0021] Where, T int is the actual temperature inside the car; Tsen int The temperature value measured by the temperature sensor in the car;

[0022] In step S1.2, the vehicle's speed v(t) is calculated using the following formula:

[0023] v(t)=k·f sen (t)

[0024] Where k is the proportional coefficient, which indicates the linear proportional relationship between the speed change and the sensor measurement frequency; f sen (t) is the frequency measured by the sensor as a function of time;

[0025] In step S1.3, the humidity calculation formula is as follows:

[0026] Ambient humidity monitoring calculation formula:

[0027] H amb =a·H sen amb +b

[0028] Where H amb is the actual humidity of the environment, H sen amb The ambient humidity value measured by the sensor;

[0029] Humidity regulation formula for air conditioning system:

[0030]

[0031] Where K AC is the adjustment coefficient of the air conditioning system; H target is the target humidity in the car; H max is the maximum range of the humidity sensor;

[0032] The adjustment formula of humidity on aerodynamic system is:

[0033]

[0034] Where K Aero is the adjustment coefficient of the aerodynamic system; H standard is the standard ambient humidity;

[0035] In step S1.4, the battery status evaluation calculation formula is as follows:

[0036]

[0037] Where SOH is the health status indicator of the battery; C current is the current capacity of the battery; C nominal is the nominal capacity of the battery;

[0038] Thermal management system's regulation formula for battery temperature:

[0039] Q=m·c·(T target -T)

[0040] Where Q is the cooling or heating requirement calculated by the thermal management system based on the battery temperature; m is the mass of the battery pack; c is the specific heat capacity of the battery material; T target is the target battery temperature; T is the actual battery temperature.

[0041] Furthermore, the S1 transmitting the collected data to the digital twin system and the intelligent control unit includes:

[0042] (1) Calculation formula for raw data collected by sensor network:

[0043] D raw =[T sen amb ,T sen int ,f sen (t),H sen amb ]

[0044] Where D raw is the original data vector;

[0045] (2) For the original data vector D raw Perform preprocessing, including filtering and calibration, to obtain the processed data vector D proc :

[0046] D proc =[Filter(T sen amb ),Filter(T sen int ),Filter(f sen (t)),Filter(H sen amb )]

[0047] Where Filter is a filter function used to remove noise from sensor data;

[0048] (3) The processed data vector D proc Transmitted to the digital twin system and intelligent control unit, the transmission process calculation formula is:

[0049] D digital twin =Transmit(D proc )

[0050] D control unit =Transmit(D proc )

[0051] Where D digital twin Data transmitted to the digital twin system; D control unitData transmitted to the intelligent control unit; Transmit is the data transmission process.

[0052] Furthermore, the S2 includes:

[0053] S2.1: The electric vehicle air conditioning system simulates and optimizes the operating status of the vehicle's air conditioning system based on sensor data to ensure a comfortable temperature inside the vehicle;

[0054] S2.2: The battery temperature control system simulates and optimizes the battery temperature control based on sensor data to ensure that the battery operates within the optimal temperature range.

[0055] Furthermore, in step S2.1, the air conditioning system operating status calculation formula is as follows:

[0056] Air conditioning cooling capacity Calculation formula:

[0057]

[0058] Where k cool is the cooling gain coefficient of the air conditioning system; T1 is the target temperature in the car; η cool is the cooling efficiency coefficient of the air conditioning system, and its value range is (0~1);

[0059] Air conditioning heating capacity Calculation formula:

[0060]

[0061] Where k heat is the heating gain coefficient of the air conditioning system; η heat It is the heating efficiency coefficient of the air conditioning system, and its value range is (0~1).

[0062] In step 2.2, in order to optimize the operation of the battery temperature control system, the battery temperature control system adjustment coefficient is introduced

[0063]

[0064] Where, The optimal operating temperature of the battery; C bat is the thermal capacity of the battery; The maximum temperature allowed for the battery; The lowest temperature allowed for the battery; T amb is the actual ambient temperature;

[0065] The digital twin system passes the processed simulation results to the multi-physics coordination system, and the calculation formula is:

[0066] R sim =[Tbat opt ,C bat ,T bat max ,T bat min ,K bat temp ]

[0067] Where R sim The processed simulation results are then processed into the digital twin system;

[0068] The digital twin system receives feedback data from the multi-physics coordination system D feedback , the calculation formula is:

[0069] D feedback =[T bat act ,P cool act ,P heat act ]

[0070] Where, T bat act is the actual battery temperature; P cool act is the actual cooling power of the battery temperature control system; P heat act The actual heating power of the battery temperature control system;

[0071] In order to ensure the integrity and accuracy of data transmission, data verification is adopted. The calculation formula is:

[0072] Checksum(R sim )=Checksum(R sim_received )

[0073] Checksum(D feedback )=Checksum(D feedback_received )

[0074] Where R sim_received Simulation results received for multi-physics coordinated systems; D feedback_received It is the feedback data received by the digital twin system; Checksum is a data verification function used to verify the integrity of the data during transmission.

[0075] Furthermore, the S3 includes:

[0076] S3.1: The aerodynamic system optimizes the vehicle's aerodynamic design based on environmental conditions (such as wind speed and temperature) to reduce wind resistance and improve energy efficiency.

[0077] S3.2: The thermal management system optimizes the operation of the thermal management system based on the ambient temperature and the vehicle's internal temperature to ensure a comfortable temperature inside the vehicle.

[0078] S3.3: The battery management system optimizes the battery charge and discharge current control strategy based on the battery status data to extend battery life and improve performance.

[0079] Furthermore, in step S3.1, the power P required to overcome the wind resistance when the vehicle is traveling is opt The calculation formula is:

[0080]

[0081] Where ρ is the air density; v is the ambient wind speed; C dopt is the drag coefficient after optimization of the aerodynamic system; A is the frontal area of ​​the vehicle;

[0082] In step S3.2, the thermal management system power adjustment P adjusted The calculation formula is:

[0083] P adjusted =P heat / cool ·η(T env )

[0084] Where, P heat / cool is the heating or heating power; η(T env ) is the ambient temperature correction factor;

[0085] In step S3.3, the charge and discharge current control strategy I bat (t) is calculated as follows:

[0086] I bat (t) = I base (1+k SOC ·(SOC target -SOC(t)))·η temp (T bat (t))

[0087] Where, I base is the baseline charge and discharge current; k SOC is the SOC adjustment coefficient; SOC target is the target state of charge; SOC(t) is the state of charge at time t; η temp (T bat (t)) is the temperature correction coefficient; T bat (t) is the battery temperature at time t;

[0088] The temperature correction coefficient calculation formula is:

[0089]

[0090] Where η0 is the efficiency at the optimal temperature; T bat is the battery temperature, T opt is the optimal operating temperature of the battery; σ T is the standard deviation of temperature effect;

[0091] In step S3.3, the control instruction calculation formula is as follows:

[0092] Aerodynamic system control command C air Calculation formula:

[0093] C thermal =k thermal ·(P heat / cool -P adjusted )

[0094] Where k air is the aerodynamic system adjustment coefficient; C dbase is the base drag coefficient;

[0095] Thermal management system control instructions C thermal Calculation formula:

[0096] C thermal =k thermal ·(P heat / cool -P adjusted )

[0097] Where k thermal is the thermal management system adjustment coefficient;

[0098] Battery management system control instructions C battery Calculation formula:

[0099] C battery =k battery ·(I base -I bat (t))

[0100] Where k battery It is the adjustment coefficient of the battery management system;

[0101] Comprehensive control instructions C total Calculation formula:

[0102] C total =w1·C air +w2·C thermal +w3·C battery

[0103] Where w1, w2, and w3 are weight coefficients, which are used to balance the priority of each subsystem in the overall optimization.

[0104] Furthermore, in step S4, the vehicle environment perception model is constructed as follows:

[0105] a) Build a vehicle environment perception model and define the environmental parameter vector E(t):

[0106] E(t)=[μ(t),v w(t),θ r (t),T(t)] T

[0107] Where μ(t) is the road friction coefficient, v w (t) is the wind speed vector, θ r (t) is the road inclination angle, T(t) is the ambient temperature;

[0108] b) Define the vehicle state vector x(t):

[0109] x(t)=[x(t),y(t),ψ(t)] T

[0110] Where (x, y) is the vehicle coordinate and ψ(t) is the heading angle.

[0111] c) The objective function Γ(t) is defined as the comprehensive vehicle environmental impact index, calculated as follows:

[0112]

[0113] Where, v thr is the wind speed threshold, β is the temperature sensitivity coefficient, △T(t)=|T(t)-T ref | is the temperature deviation, T ref is the reference temperature;

[0114] The DWA algorithm can adjust the vehicle's operating state in real time through the dynamic window method, optimize the objective function Γ(t), and combine it with the environmental perception model for control. The specific steps of the DWA algorithm are as follows:

[0115] d) Control input variable u(t)

[0116] u(t)=[v1(t),ω1(t)] T

[0117] Where v1(t) is the vehicle linear velocity; ω1(t) is the vehicle angular velocity;

[0118] e) Vehicle dynamic model constraints

[0119] The vehicle dynamic model constraints are:

[0120]

[0121] f) Dynamic window definition

[0122] The dynamic window is the set of feasible control inputs for the vehicle in the time interval [t, t+△t]

[0123]

[0124] Because the performance of the DWA algorithm is very sensitive to the selection of parameters, improper parameter selection may lead to algorithm performance degradation or even malfunction. Therefore, the DWA algorithm needs to be optimized. The optimization process is as follows:

[0125] g) Optimization goals

[0126] Maximize vehicle performance index J(t):

[0127] J(t)=α1·v(t)+α2·(T ref -T(t))+α3·μ(t)

[0128] Where α1, α2, and α3 are performance index weights, corresponding to speed, temperature regulation, and friction coefficient optimization, respectively;

[0129] h) Input optimal control instructions

[0130] Update the vehicle state and get the optimal control input u * (t):

[0131]

[0132] x(t+△t)=f(x(t),u * (t))

[0133] Where argmin is the parameter minimization operator; λ is the weight coefficient, which balances the comprehensive impact index and performance index; x(t+△t) is the state of the vehicle at the next moment; f is the vehicle dynamic model function, which is used to predict the vehicle state change;

[0134] The improved DWA algorithm has greatly improved performance and can effectively plan the electric vehicle environment, ultimately obtaining the optimal vehicle environmental comprehensive impact index Γ(t). best .

[0135] Furthermore, in step S4, the control instruction is sent to the multi-physics coordination system, and the instruction sending calculation formula is as follows:

[0136]

[0137] Where T' is the data transmission process, which ensures the integrity and accuracy of the instructions.

[0138] The instruction update formula is as follows:

[0139] C(t+Δt)=U(D proc (t+△t))

[0140] Where, C(t+△t) is the control instruction at time t+△t; U is the control instruction update function; Dproc (t+△t) is the processed data at time t+△t.

[0141] Beneficial Effects: 1. Compared with traditional electric vehicle environmental control technologies, this invention achieves precise monitoring and dynamic control of the vehicle environment by integrating a high-precision sensor network, a digital twin system, a multi-physics coordination system, and an intelligent control unit. This technology can collect key parameters such as temperature, humidity, vehicle speed, and battery status in real time, and perform high-precision modeling and simulation through the digital twin system. Based on the simulation results, the intelligent control unit combines feedback from the multi-physics coordination system to accurately control the air conditioning system and battery temperature control system, ensuring a comfortable interior environment at all times. At the same time, it optimizes the battery operating temperature, significantly improving the driving experience and overall vehicle performance.

[0142] 2. This invention achieves efficient coordination among the vehicle's subsystems through deep integration and optimization of a multi-physics coordination system. The aerodynamic system optimizes vehicle design based on environmental conditions to reduce wind resistance; the thermal management system dynamically adjusts to the ambient and in-vehicle temperature to ensure comfort; and the battery management system optimizes charging and discharging strategies based on battery status data to extend battery life and improve performance. Under the management of the multi-physics coordination system, the subsystems operate collaboratively, breaking the limitations of traditional technologies where each system operates independently, optimizing vehicle energy distribution, improving energy efficiency, reducing energy consumption, and enhancing the economic efficiency and market competitiveness of electric vehicles.

[0143] 3. This invention utilizes an improved DWA algorithm to optimize vehicle operating conditions in real time, ensuring the vehicle remains in its optimal operating range under various environmental conditions. Simultaneously, the digital twin system simulates and predicts the vehicle's environment in real time, providing the control system with a forward-looking basis for decision-making and enhancing the vehicle's adaptability to environmental changes. This intelligent control model significantly improves the response speed and adjustment accuracy of the vehicle's environmental control system, ensuring vehicle stability and safety in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0144] Figure 1 It is a structural schematic diagram of the present invention;

[0145] Figure 2 This is a flow chart of the DWA algorithm of the present invention;

[0146] Figure 3 This is the DWA algorithm optimization flow chart of the present invention. DETAILED DESCRIPTION

[0147] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0148] like Figure 1 As shown, the present invention provides a technical solution:

[0149] A method for controlling the environment of an electric vehicle based on digital twin multi-physics collaboration includes the following steps:

[0150] S1: The sensor network monitors driving environment parameters such as temperature, speed, humidity, and battery status in real time, and transmits the collected data to the digital twin system and intelligent control unit;

[0151] S2: The digital twin system is the data processing and simulation core of the entire system. It is responsible for processing and analyzing the data collected by sensors, generating simulation results, transmitting the simulation results to the multi-physics coordination system, and receiving feedback data from the multi-physics coordination system.

[0152] S3: The multi-physics coordination system receives simulation results from the digital twin system and sends instructions through the intelligent control unit to coordinate the operation of each subsystem. The multi-physics coordination system is the coordination and management module of the system, responsible for integrating and optimizing the various subsystems of the vehicle.

[0153] S4: The intelligent control unit is the core of the entire system, responsible for receiving and processing data from sensors and the multi-physics coordination system. It generates control instructions based on the vehicle's environmental perception model and sends these instructions to the multi-physics coordination system. The vehicle's environmental perception model adjusts the vehicle's operating state in real time to adapt to different environmental conditions based on sensor data and feedback from the multi-physics coordination system. An improved DWA algorithm ensures optimal vehicle performance in various environments.

[0154] Furthermore, the real-time monitoring of driving environment parameters by the sensor network in S1 includes:

[0155] S1.1: Temperature sensors are used to monitor ambient temperature and vehicle interior temperature;

[0156] S1.2: Speed ​​sensor is used to monitor the vehicle's speed;

[0157] S1.3: Humidity sensors are used to monitor ambient humidity and provide data support for the vehicle's air conditioning and aerodynamic systems.

[0158] S1.4: The battery status detection sensor is used to monitor abnormal battery status and provide data support for the battery management system and thermal management system.

[0159] Furthermore, in step S1.1, the temperature calculation formula is as follows:

[0160] Ambient temperature monitoring calculation formula:

[0161] T amb =a·T sen amb +b

[0162] Where, Tamb is the actual ambient temperature; a is the proportional coefficient, which reflects the linear proportional relationship between the sensor measurement value and the actual value; T sen amb is the ambient temperature measured by the sensor; b is the offset, which is used to compensate for the fixed deviation between the sensor measurement value and the actual value;

[0163] Vehicle internal temperature monitoring calculation formula:

[0164] T int =a·T sen int +b

[0165] Where, T int is the actual temperature inside the car; T sen int The temperature value measured by the temperature sensor in the car;

[0166] In step S1.2, the vehicle's speed v(t) is calculated using the following formula:

[0167] v(t)=k·f sen (t)

[0168] Where k is the proportional coefficient, which indicates the linear proportional relationship between the speed change and the sensor measurement frequency; f sen (t) is the frequency measured by the sensor as a function of time;

[0169] In step S1.3, the humidity calculation formula is as follows:

[0170] Ambient humidity monitoring calculation formula:

[0171] H amb =a·H sen amb +b

[0172] Where H amb is the actual humidity of the environment, H sen amb The ambient humidity value measured by the sensor;

[0173] Humidity regulation formula for air conditioning system:

[0174]

[0175] Where K AC is the adjustment coefficient of the air conditioning system; H target is the target humidity in the car; H max is the maximum range of the humidity sensor;

[0176] The adjustment formula of humidity on aerodynamic system is:

[0177]

[0178] Where KAero is the adjustment coefficient of the aerodynamic system; H standard is the standard ambient humidity;

[0179] In step S1.4, the battery status evaluation calculation formula is as follows:

[0180]

[0181] Where SOH is the health status indicator of the battery; C current is the current capacity of the battery; C nominal is the nominal capacity of the battery;

[0182] Thermal management system's regulation formula for battery temperature:

[0183] Q=m·c·(T target -T)

[0184] Where Q is the cooling or heating requirement calculated by the thermal management system based on the battery temperature; m is the mass of the battery pack; c is the specific heat capacity of the battery material; T target is the target battery temperature; T is the actual battery temperature.

[0185] Furthermore, the S1 transmitting the collected data to the digital twin system and the intelligent control unit includes:

[0186] (1) Calculation formula for raw data collected by sensor network:

[0187] D raw =[T sen amb ,T sen int ,f sen (t),H sen amb ]

[0188] Where D raw is the original data vector;

[0189] (2) For the original data vector D raw Perform preprocessing, including filtering and calibration, to obtain the processed data vector D proc :

[0190] D proc =[Filter(T sen amb ),Filter(T sen int ),Filter(f sen (t)),Filter(H sen amb )]

[0191] Where Filter is a filter function used to remove noise from sensor data;

[0192] (3) The processed data vector D proc Transmitted to the digital twin system and intelligent control unit, the transmission process calculation formula is:

[0193] D digital twin =Transmit(D proc )

[0194] D control unit =Transmit(D proc )

[0195] Where D digital twin Data transmitted to the digital twin system; D control unit Data transmitted to the intelligent control unit; Transmit is the data transmission process.

[0196] Furthermore, the S2 includes:

[0197] S2.1: The electric vehicle air conditioning system simulates and optimizes the operating status of the vehicle's air conditioning system based on sensor data to ensure a comfortable temperature inside the vehicle;

[0198] S2.2: The battery temperature control system simulates and optimizes the battery temperature control based on sensor data to ensure that the battery operates within the optimal temperature range.

[0199] Furthermore, in step S2.1, the air conditioning system operating status calculation formula is as follows:

[0200] Air conditioning cooling capacity Calculation formula:

[0201]

[0202] Where k cool is the cooling gain coefficient of the air conditioning system; T1 is the target temperature in the car; η cool is the cooling efficiency coefficient of the air conditioning system, and its value range is (0~1);

[0203] Air conditioning heating capacity Calculation formula:

[0204]

[0205] Where k heat is the heating gain coefficient of the air conditioning system; η heat It is the heating efficiency coefficient of the air conditioning system, and its value range is (0~1).

[0206] In step 2.2, in order to optimize the operation of the battery temperature control system, the battery temperature control system adjustment coefficient is introduced

[0207]

[0208] Where, The optimal operating temperature of the battery; C bat is the thermal capacity of the battery; The maximum temperature allowed for the battery; The lowest temperature allowed for the battery; T amb is the actual ambient temperature;

[0209] The digital twin system passes the processed simulation results to the multi-physics coordination system, and the calculation formula is:

[0210] R sim =[T bat opt ,C bat ,T bat max ,T bat min ,K bat temp ]

[0211] Where R sim The processed simulation results are then processed into the digital twin system;

[0212] The digital twin system receives feedback data from the multi-physics coordination system D feedback , the calculation formula is:

[0213] D feedback =[T bat act ,P cool act ,P heat act ]

[0214] Where, T bat act is the actual battery temperature; P cool act is the actual cooling power of the battery temperature control system; P heat act The actual heating power of the battery temperature control system;

[0215] In order to ensure the integrity and accuracy of data transmission, data verification is adopted. The calculation formula is:

[0216] Checksum(R sim )=Checksum(R sim_received )

[0217] Checksum(D feedback )=Checksum(D feedback_received )

[0218] Where R sim_received Simulation results received for multi-physics coordinated systems; D feedback_received It is the feedback data received by the digital twin system; Checksum is a data verification function used to verify the integrity of the data during transmission.

[0219] Furthermore, the S3 includes:

[0220] S3.1: The aerodynamic system optimizes the vehicle's aerodynamic design based on environmental conditions (such as wind speed and temperature) to reduce wind resistance and improve energy efficiency.

[0221] S3.2: The thermal management system optimizes the operation of the thermal management system based on the ambient temperature and the vehicle's internal temperature to ensure a comfortable temperature inside the vehicle.

[0222] S3.3: The battery management system optimizes the battery charge and discharge current control strategy based on the battery status data to extend battery life and improve performance.

[0223] Furthermore, in step S3.1, the power P required to overcome the wind resistance when the vehicle is traveling is opt The calculation formula is:

[0224]

[0225] Where ρ is the air density; v is the ambient wind speed; C dopt is the drag coefficient after optimization of the aerodynamic system; A is the frontal area of ​​the vehicle;

[0226] In step S3.2, the thermal management system power adjustment P adjusted The calculation formula is:

[0227] P adjusted =P heat / cool ·η(T env )

[0228] Where, P heat / cool is the heating or heating power; η(T env ) is the ambient temperature correction factor;

[0229] In step S3.3, the charge and discharge current control strategy I bat (t) is calculated as follows:

[0230] I bat (t) = I base (1+k SOC ·(SOC target -SOC(t)))·η temp (T bat (t))

[0231] Where, I base is the baseline charge and discharge current; k SOC is the SOC adjustment coefficient; SOC target is the target state of charge; SOC(t) is the state of charge at time t; η temp (Tbat (t)) is the temperature correction coefficient; T bat (t) is the battery temperature at time t;

[0232] The temperature correction coefficient calculation formula is:

[0233]

[0234] Where η0 is the efficiency at the optimal temperature; T bat is the battery temperature, T opt is the optimal operating temperature of the battery; σ T is the standard deviation of temperature effect;

[0235] In step S3.3, the control instruction calculation formula is as follows:

[0236] Aerodynamic system control command C air Calculation formula:

[0237] C thermal =k thermal ·(P heat / cool -P adjusted )

[0238] Where k air is the aerodynamic system adjustment coefficient; C dbase is the base drag coefficient;

[0239] Thermal management system control instructions C thermal Calculation formula:

[0240] C thermal =k thermal ·(P heat / cool -P adjusted )

[0241] Where k thermal is the thermal management system adjustment coefficient;

[0242] Battery management system control instructions C battery Calculation formula:

[0243] C battery =k battery ·(I base -I bat (t))

[0244] Where k battery It is the adjustment coefficient of the battery management system;

[0245] Comprehensive control instructions C total Calculation formula:

[0246] C total =w1·Cair +w2·C thermal +w3·C battery

[0247] Where w1, w2, and w3 are weight coefficients, which are used to balance the priority of each subsystem in the overall optimization.

[0248] Furthermore, in step S4, the vehicle environment perception model is constructed as follows:

[0249] a) Build a vehicle environment perception model and define the environmental parameter vector E(t):

[0250] E(t)=[μ(t),v w (t),θ r (t),T(t)] T

[0251] Where μ(t) is the road friction coefficient, v w (t) is the wind speed vector, θ r (t) is the road inclination angle, T(t) is the ambient temperature;

[0252] b) Define the vehicle state vector x(t):

[0253] x(t)=[x(t),y(t),ψ(t)] T

[0254] Where (x, y) is the vehicle coordinate and ψ(t) is the heading angle.

[0255] c) The objective function Γ(t) is defined as the comprehensive vehicle environmental impact index, calculated as follows:

[0256]

[0257] Where, v thr is the wind speed threshold, β is the temperature sensitivity coefficient, △T(t)=|T(t)-T ref | is the temperature deviation, T ref is the reference temperature;

[0258] The DWA algorithm can adjust the vehicle's operating state in real time through the dynamic window method, optimize the objective function Γ(t), and combine it with the environmental perception model for control. The specific steps of the DWA algorithm are as follows:

[0259] d) Control input variable u(t)

[0260] u(t)=[v1(t),ω1(t)] T

[0261] Where v1(t) is the vehicle linear velocity; ω1(t) is the vehicle angular velocity;

[0262] e) Vehicle dynamic model constraints

[0263] The vehicle dynamic model constraints are:

[0264]

[0265] f) Dynamic window definition

[0266] The dynamic window is the set of feasible control inputs for the vehicle in the time interval [t, t+△t]

[0267]

[0268] Because the performance of the DWA algorithm is very sensitive to the selection of parameters, improper parameter selection may lead to algorithm performance degradation or even malfunction. Therefore, the DWA algorithm needs to be optimized. The optimization process is as follows:

[0269] g) Optimization goals

[0270] Maximize vehicle performance index J(t):

[0271] J(t)=α1·v(t)+α2·(T ref -T(t))+α3·μ(t)

[0272] Where α1, α2, and α3 are performance index weights, corresponding to speed, temperature regulation, and friction coefficient optimization, respectively;

[0273] h) Input optimal control instructions

[0274] Update the vehicle state and get the optimal control input u * (t):

[0275]

[0276] x(t+△t)=f(x(t),u * (t))

[0277] Where argmin is the parameter minimization operator; λ is the weight coefficient, which balances the comprehensive impact index and performance index; x(t+△t) is the state of the vehicle at the next moment; f is the vehicle dynamic model function, which is used to predict the vehicle state change;

[0278] Figure 2 This is a flow chart of the DWA algorithm of the present invention; Figure 3 This is the DWA algorithm optimization flow chart of the present invention. The improved DWA algorithm has greatly improved performance and can effectively plan the electric vehicle environment, ultimately obtaining the optimal vehicle environmental comprehensive impact index Γ(t) best.

[0279] Furthermore, in step S4, the control instruction is sent to the multi-physics coordination system, and the instruction sending calculation formula is as follows:

[0280]

[0281] Where T' is the data transmission process, which ensures the integrity and accuracy of the instructions.

[0282] The instruction update formula is as follows:

[0283] C(t+Δt)=U(D proc (t+△t))

[0284] Where, C(t+△t) is the control instruction at time t+△t; U is the control instruction update function; D proc (t+△t) is the processed data at time t+△t.

Claims

1. An electric vehicle environment control method based on digital twin multi-physics collaboration, characterized in that: The following steps are involved: S1: The sensor network monitors driving environment parameters in real time and transmits the collected data to the digital twin system and intelligent control unit; S2: The digital twin system is responsible for processing and analyzing the data collected by the sensors, generating simulation results, transmitting the simulation results to the multi-physics coordination system, and receiving feedback data from the multi-physics coordination system; S3: The multi-physics coordination system receives the simulation results of the digital twin system and sends instructions through the intelligent control unit to coordinate the operation of each subsystem; S4: The intelligent control unit is responsible for receiving and processing data from sensors and the multi-physics coordination system, generating control instructions through the vehicle environment perception model, and sending the control instructions to the multi-physics coordination system; the vehicle environment perception model ensures the optimal performance of the vehicle in various environments through the improved DWA algorithm.

2. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 1 is characterized in that: The sensor network in S1 monitors driving environment parameters in real time, including: S1.1: Temperature sensors are used to monitor ambient temperature and vehicle interior temperature; S1.2: Speed ​​sensor is used to monitor the vehicle's speed; S1.3: Humidity sensors are used to monitor ambient humidity and provide data support for the vehicle's air conditioning and aerodynamic systems. S1.4: The battery status detection sensor is used to monitor abnormal battery status and provide data support for the battery management system and thermal management system.

3. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 2 is characterized in that: In step S1.1, the temperature calculation formula is as follows: Ambient temperature monitoring calculation formula: T amb =a·T senamb +b Where, T amb is the actual ambient temperature; a is the proportional coefficient, which reflects the linear proportional relationship between the sensor measurement value and the actual value; T senamb is the ambient temperature measured by the sensor; b is the offset, which is used to compensate for the fixed deviation between the sensor measurement value and the actual value; Vehicle internal temperature monitoring calculation formula: T int =a·T senint +b Where, T int is the actual temperature inside the car; T senint The temperature value measured by the temperature sensor in the car; In step S1.2, the vehicle's speed v(t) is calculated using the following formula: v(t)=k·f sen (t) Where k is the proportional coefficient, which indicates the linear proportional relationship between the speed change and the sensor measurement frequency; f sen (t) is the frequency measured by the sensor as a function of time; In step S1.3, the humidity calculation formula is as follows: Ambient humidity monitoring calculation formula: H amb =a·H senamb +b Where H amb is the actual humidity of the environment, H senamb The ambient humidity value measured by the sensor; Humidity regulation formula for air conditioning system: Where K AC is the adjustment coefficient of the air conditioning system; H target is the target humidity in the car; H max is the maximum range of the humidity sensor; The adjustment formula of humidity on aerodynamic system is: Where K Aero is the adjustment coefficient of the aerodynamic system; H standard is the standard ambient humidity; In step S1.4, the battery status evaluation calculation formula is as follows: Where SOH is the health status indicator of the battery; C current is the current capacity of the battery; C nominal is the nominal capacity of the battery; Thermal management system's regulation formula for battery temperature: Q=m·c·(T target -T) Where Q is the cooling or heating requirement calculated by the thermal management system based on the battery temperature; m is the mass of the battery pack; c is the specific heat capacity of the battery material; T target is the target battery temperature; T is the actual battery temperature.

4. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 3 is characterized in that: The S1 process of transmitting the collected data to the digital twin system and the intelligent control unit includes: (1) Calculation formula for raw data collected by sensor network: D raw =[T senamb ,T senint ,f sen (t),H senamb ] Where D raw is the original data vector; (2) For the original data vector D raw Perform preprocessing, including filtering and calibration, to obtain the processed data vector D proc : D proc =[Filter(T senamb ),Filter(T senint ),Filter(f sen (t)),Filter(H senamb )] Where Filter is a filter function used to remove noise from sensor data; (3) The processed data vector D proc Transmitted to the digital twin system and intelligent control unit, the transmission process calculation formula is: D digitaltwin =Transmit(D proc ) D controlunit =Transmit(D proc ) Where D digitaltwin Data transmitted to the digital twin system; D controlunit Data transmitted to the intelligent control unit; Transmit is the data transmission process.

5. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 1 is characterized in that: The S2 includes: S2.1: The electric vehicle air conditioning system simulates and optimizes the operating status of the vehicle's air conditioning system based on sensor data to ensure a comfortable temperature inside the vehicle; S2.2: The battery temperature control system simulates and optimizes the battery temperature control based on sensor data to ensure that the battery operates within the optimal temperature range.

6. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 5 is characterized in that: In step S2.1, the air conditioning system operating status calculation formula is as follows: Air conditioning cooling capacity Calculation formula: Where k cool is the cooling gain coefficient of the air conditioning system; T1 is the target temperature in the car; η cool is the cooling efficiency coefficient of the air conditioning system; Air conditioning heating capacity Calculation formula: Where k heat is the heating gain coefficient of the air conditioning system; η heat is the heating efficiency coefficient of the air conditioning system; In step 2.2, in order to optimize the operation of the battery temperature control system, the battery temperature control system adjustment coefficient is introduced Where, The optimal operating temperature of the battery; C bat is the thermal capacity of the battery; The maximum temperature allowed for the battery; The lowest temperature allowed for the battery; T amb is the actual ambient temperature; The digital twin system passes the processed simulation results to the multi-physics coordination system, and the calculation formula is: R sim =[T batopt ,C bat ,T batmax ,T batmin ,K battemp ] Where R sim The processed simulation results are then processed into the digital twin system; The digital twin system receives feedback data from the multi-physics coordination system D feedback , the calculation formula is: D feedback =[T batact ,P coolact ,P heatact ] Where, T batact is the actual battery temperature; P coolact is the actual cooling power of the battery temperature control system; P heatact The actual heating power of the battery temperature control system; In order to ensure the integrity and accuracy of data transmission, data verification is adopted. The calculation formula is: Checksum(R sim )=Checksum(R sim_received ) Checksum(D feedback )=Checksum(D feedback_received ) Where R sim_received Simulation results received for multi-physics coordinated systems; D feedback_received It is the feedback data received by the digital twin system; Checksum is a data verification function used to verify the integrity of the data during transmission.

7. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 1 is characterized in that: The S3 includes: S3.1: Aerodynamics system optimizes the vehicle's aerodynamic design to reduce wind resistance based on environmental conditions; S3.2: The thermal management system optimizes the operation of the thermal management system based on the ambient temperature and the vehicle's internal temperature to ensure a comfortable temperature inside the vehicle. S3.3: The battery management system optimizes the battery charge and discharge current control strategy based on the battery status data to extend battery life and improve performance.

8. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 7 is characterized in that: In step S3.1, the power P required to overcome the wind resistance when the vehicle is traveling opt The calculation formula is: Where, ρ is the air density; v is the ambient wind speed; is the drag coefficient after optimization of the aerodynamic system; A is the frontal area of ​​the vehicle; In step S3.2, the thermal management system power adjustment P adjusted The calculation formula is: P adjusted =P heat / cool ·η(T env ) Where, P heat / cool is the heating or heating power; η(T env ) is the ambient temperature correction factor; In step S3.3, the charge and discharge current control strategy I bat (t) is calculated as follows: I bat (t)=I base ·(1+k SOC ·(SOC target -SOC(t)))·η temp (T bat (t)) Where, I base is the baseline charge and discharge current; k SOC is the SOC adjustment coefficient; SOC target is the target state of charge; SOC(t) is the state of charge at time t; η temp (T bat (t)) is the temperature correction coefficient; T bat (t) is the battery temperature at time t; The temperature correction coefficient calculation formula is: Where η0 is the efficiency at the optimal temperature; T bat is the battery temperature, T opt is the optimal operating temperature of the battery; σ T is the standard deviation of temperature effect; In step S3.3, the control instruction calculation formula is as follows: Aerodynamic system control command C air Calculation formula: C thermal =k thermal ·(P heat / cool -P adjusted ) Where k air is the aerodynamic system adjustment coefficient; C dbase is the base drag coefficient; Thermal management system control instructions C thermal Calculation formula: C thermal =k thermal ·(P heat / cool -P adjusted ) Where k thermal is the thermal management system adjustment coefficient; Battery management system control instructions C battery Calculation formula: C battery =k battery ·(I base -I bat (t)) Where k battery It is the adjustment coefficient of the battery management system; Comprehensive control instructions C total Calculation formula: C total =w1·C air +w2·C thermal +w3·C battery Where w1, w2, and w3 are weight coefficients, which are used to balance the priority of each subsystem in the overall optimization.

9. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 1 is characterized in that: In step S4, the process of constructing the vehicle environment perception model is as follows: a) Build a vehicle environment perception model and define the environmental parameter vector E(t): E(t)=[μ(t),v w (t),θ r (t),T(t)] T Where μ(t) is the road friction coefficient, v w (t) is the wind speed vector, θ r (t) is the road inclination angle, T(t) is the ambient temperature; b) Define the vehicle state vector x(t): x(t)=[x(t),y(t),ψ(t)] T Where (x, y) is the vehicle coordinate, ψ(t) is the heading angle; c) The objective function Γ(t) is defined as the comprehensive vehicle environmental impact index, calculated as follows: Where, v thr is the wind speed threshold, β is the temperature sensitivity coefficient, △T(t)=|T(t)-T ref | is the temperature deviation, T ref is the reference temperature; The DWA algorithm uses a dynamic window method to adjust the vehicle's operating state in real time, optimizes the objective function Γ(t), and combines it with an environmental perception model for control. The specific steps of the DWA algorithm are as follows: d) Control input variable u(t) u(t)=[v1(t),ω1(t)] T Where v1(t) is the vehicle linear velocity; ω1(t) is the vehicle angular velocity; e) Vehicle dynamic model constraints The vehicle dynamic model constraints are: f) Dynamic window definition The dynamic window is the set of feasible control inputs for the vehicle in the time interval [t, t+△t] The DWA algorithm is improved as follows: g) Optimization goals Maximize vehicle performance index J(t): J(t)=α1·v(t)+α2·(T ref -T(t))+α3·μ(t) Where α1, α2, and α3 are performance index weights, corresponding to speed, temperature regulation, and friction coefficient optimization, respectively; h) Input optimal control instructions Update the vehicle state and get the optimal control input u * (t): x(t+△t)=f(x(t),u * (t)) Where argmin is the parameter minimization operator; λ is the weight coefficient, balancing the comprehensive impact index and performance index; x(t+△t) is the state of the vehicle at the next moment; f is the vehicle dynamic model function, which is used to predict the change of vehicle state. The optimal vehicle environmental comprehensive impact index Γ(t) is obtained using the improved DWA algorithm. best .

10. The electric vehicle environment control method based on digital twin multi-physics collaboration according to claim 1 is characterized in that: In step S4, the control instruction is sent to the multi-physics coordination system, and the instruction sending calculation formula is as follows: Where T' is the data transmission process, which ensures the integrity and accuracy of the instructions; The instruction update formula is as follows: C(t+△t)=U(D proc (t+△t)) Where C(t+△t) is the control command at time t+△t; U is the control command update function; D proc (t+△t) is the processed data at time t+△t.

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