Reverse simulation method and platform for dynamic operation of power system for electric vehicle

Abstract

The invention discloses a reverse simulation method and a reverse simulation platform for dynamic operation of a power system of an electric vehicle. The method comprises the following steps: building an electric vehicle power system physical simulation model of the electric vehicle, wherein the model comprises a charging model, a temperature model, a battery model, a vehicle dynamics model and an energy transfer model; acquired real driving data are injected into the model, and the energy transfer, consumption and battery decline conditions in the real driving process of the electric vehicle under the long time span can be quickly and reversely simulated in a short time. Therefore, electric automobile consumers and manufacturers can simulate and evaluate the automobile energy transfer process and the influence of automobile use factors on energy consumption and battery life in a short time.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy electric vehicle power system modeling and performance evaluation, and in particular to a reverse simulation method for dynamic operation of a power system of an electric vehicle and a simulation platform thereof. Background Art

[0002] Most of the existing methods for evaluating the capacity degradation of electric vehicle onboard power batteries are based on accelerated lithium battery degradation experiments, including charge and discharge cycle tests, electrochemical modeling, and data-driven methods. However, few methods can quantify the impact of actual driver operation factors on battery degradation in daily life, and cannot accurately guide electric vehicle users to extend the life of power batteries by optimizing the use of electric vehicles.

[0003] There are many current automobile simulation methods, but there is a lack of simulation methods specifically for electric vehicle power systems, especially a lack of methods that can simulate and predict the energy transfer and battery degradation of different electric vehicles over a long period of time in a short period of time. Summary of the invention

[0004] In order to at least solve one of the deficiencies in the prior art, the present invention provides a reverse simulation method for the dynamic operation of a power system of an electric vehicle and a simulation platform thereof, which can quantify the impact of driver operating factors on the energy consumption and battery degradation of the electric vehicle over a long period of time in a short period of time. Specifically, a physical model of the electric vehicle power system is established to simulate the transfer of electrical energy, thermal energy and mechanical energy during vehicle driving, thereby calculating the current and battery temperature information in real time, thereby calculating the battery calendar aging rate and thermal aging rate, and thus calculating the battery capacity degradation and other conditions.

[0005] In order to achieve the purpose of the present invention, the present invention provides a reverse simulation method for dynamic operation of a power system of an electric vehicle, comprising the following steps:

[0006] A physical simulation model of an electric vehicle power system is constructed to reflect the process of energy flow and conversion of electric vehicles. The physical simulation model of an electric vehicle power system includes:

[0007] Vehicle dynamics model, used to calculate the total force on the vehicle;

[0008] The energy transfer model includes an electric vehicle braking energy recovery module, a driving current calculation module, and a driving power calculation model. The electric vehicle braking energy recovery module is used to start and stop braking energy recovery, the driving current calculation module is used to calculate the driving current of the electric vehicle, and the driving power calculation model is used to calculate the driving power of the electric vehicle;

[0009] Temperature model, including electric vehicle heating, ventilation and air conditioning system model, battery thermal management model, cab temperature model and battery temperature model. The electric vehicle heating, ventilation and air conditioning system model is used to calculate the power consumed by the heating, ventilation and air conditioning system. The battery thermal management model is used to calculate the power required for battery heating or cooling. The cab temperature model is used to calculate the cab temperature. The battery temperature model is used to calculate the battery temperature.

[0010] The battery model includes a battery capacity decay model and a battery real-time SOC calculation model. The battery capacity decay model is used to calculate the battery calendar aging rate and thermal aging rate, thereby calculating the battery capacity decay. The battery real-time SOC calculation model is used to calculate the battery real-time SOC;

[0011] The charging model includes a charging judgment module, a charger selection model, and a charging power and charging current model. The charging judgment module is used to calculate the dwell time based on driving data to determine whether there is enough time for charging during the day's trip. The charger selection model is used to select car chargers with different powers. The charging power and charging current model is used to calculate the charging current based on the charging power and charging efficiency.

[0012] Based on the driving data, the energy transfer of the electric vehicle power system is simulated by a physical simulation model of the electric vehicle power system to obtain simulation results, which include but are not limited to changes in cockpit temperature, battery pack temperature, SOC, and battery capacity degradation.

[0013] Furthermore, in the vehicle dynamics model, the formula for calculating the total force on the vehicle is:

[0014] F pt =F d +F r +F g +F a

[0015]

[0016] Among them, F pt The total force required to drive an electric vehicle to move, F d is the air resistance, F r is the rolling resistance, F g is the slope resistance, F a is the acceleration resistance, W pt , P pt Divided into the energy required to drive the electric vehicle and the total power, v c (t) is the speed of the electric vehicle, d is the drag, and t is the time.

[0017] Furthermore, in the energy transfer model, the electric vehicle braking energy recovery module is used to start braking energy recovery when the vehicle speed is higher than a preset value for deceleration, and the braking energy recovery efficiency is constant. rbs Recover powertrain energy into the battery system;

[0018] The formula for calculating the total power of an electric vehicle when it is running is:

[0019] P bat =P pt +P hvac +P btms +P rbs

[0020] Among them, F pt The total force required to drive an electric vehicle to move, P hvac The power consumption of the heating, ventilation and air conditioning system for electric vehicles, P btms is the power consumed by the battery heating system, P rbs It is the braking energy recovery power;

[0021] The formula for calculating the driving current is:

[0022] P bat =(OCV+I c ·R bat )·I c

[0023] Where OCV is the open circuit voltage, R bat is the internal resistance, I c Input / output current for the battery pack.

[0024] Furthermore, in the temperature model, when the electric vehicle is running, the temperature of the battery is:

[0025]

[0026] Among them C bat is the battery thermal capacity, T bat is the battery temperature, T a is the ambient temperature, T c is the cab temperature, K ab , K bc , K btms is the heat transfer coefficient, Q btms is the heat dissipation / heating energy of the battery pack thermal management system, Q bat is the heat energy of the battery pack, T b,up , T b,low For battery management, the highest and lowest temperatures, min(T bat -T b，up , 10) refers to T bat -Tb，up or T bat -10, the smallest value, min(T b，low -T bat , 10) refers to T b，low -T bat or T b，low -The smallest value among 10;

[0027]

[0028] Among them C c is the specific heat capacity of the cockpit, K ac is the heat transfer coefficient, Q hvac Energy consumed by heating, ventilation and air conditioning systems in electric vehicles, COP cooling , COP heating It is the heat dissipation and heating energy coefficient.

[0029] Furthermore, in the battery model, the real-time SOC of the electric vehicle is:

[0030]

[0031] Among them, SOC0 is the initial SOC value of the battery pack, C bat,r The remaining rated capacity of the battery pack;

[0032] The remaining capacity of the battery pack, i.e. the battery pack capacity life calculation formula is:

[0033] C bat,r =C baa,ini τ a τ c

[0034]

[0035] Among them, C baa,ini is the initial capacity of the battery pack, τ a is the thermal aging rate of the battery pack, τ c is the calendar aging rate of the battery pack, σ fcn Battery aging degradation severity factors, α, β, η, is the battery aging coefficient, E a is the activation energy, R g is the universal gas constant, Q acc It is the accumulated ampere-hour.

[0036] Furthermore, in the charger selection model, the types of chargers include: primary charging, secondary charging, DC fast charging, and ultra-fast charging.

[0037] The present invention provides a reverse simulation platform for the dynamic operation of a power system for electric vehicles. The simulation platform collects or simulates real daily driving data and uses the above-mentioned method to perform reverse simulation. The real daily driving data includes but is not limited to: data collection time and its corresponding vehicle speed, road slope, ambient temperature and vehicle running / stop status. The platform can perform cyclic long-term span simulation based on the above driving data.

[0038] Obtain the parameters and information required for modeling and simulation; build a physical simulation model of the electric vehicle power system based on the modeling parameters. During the simulation, configure the charging and driving modes, mainly the selection of the charger type and the switching of some functions; then inject the collected real driving data into the model, and quickly reverse simulate the energy transfer, consumption and battery degradation of the real driving process of the electric vehicle over a long span in a short time.

[0039] Furthermore, the platform can be used to customize vehicle performance and appearance parameters, battery parameters, and charger parameters to perform simulations of different sizes and models.

[0040] Furthermore, a driving mode configuration can be selected, including: battery thermal management system on / off, heating, ventilation and air conditioning system on / off, and brake energy recovery system on / off.

[0041] Furthermore, the platform simulation period can be customized to simulate the degradation of electric vehicle power batteries over a long period of time or even the entire life cycle in a shorter period of time.

[0042] Furthermore, the parameters required by the platform are obtained: according to the model simulated by the user, the specific model appearance parameters and performance parameters of the model are obtained, and the user can input the above parameters into the platform; at the same time, the user can collect driving data through the actual vehicle, or generate driving condition data through typical working conditions (the driving data collected by the actual vehicle is in the same format as the driving condition data. The former can be understood as the vehicle speed and other data collected through the platform every day, and the latter can be understood as the simulated driving speed and other data artificially generated through typical driving conditions such as NEDC and WLTC. The two are just different in the acquisition channels and contents, but the purpose and format are the same). The driving data required by the platform include: data collection time, vehicle speed, road slope, ambient temperature and vehicle running / stop status; the acquired driving data is organized into an Excel driving document. The Excel example of the driving document is as follows Figure 2 As shown, the driving document data is stored in the algorithm file unified DayTrips file directory, and the driving data will be automatically retrieved when the platform is running.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention proposes a reverse simulation method for the dynamic operation of the power system of electric vehicles and a simulation platform thereof. Compared with the existing electric vehicle simulation platform, the simulation platform designed by the present invention can quantify the influence of different driver usage factors on battery capacity and driving energy consumption (the present invention can quantify the influence of different usage factors by controlling variables, such as the charger always selects Level_1 in the first simulation and always selects Level_2 in the second simulation, and by comparing the final result difference), so as to guide electric vehicle users to optimize the use of electric vehicles.

[0045] 2. The electric vehicle simulation platform proposed in the present invention can quantify the impact of different usage factors on electric vehicle energy transfer and battery degradation over a long span or even over the entire life cycle in a short period of time by importing real driving data and user-defined simulation years. (For example, if one year of driving data is repeatedly run, such as 10 years or longer, the computer can quickly complete the calculation and quantify the impact over a long span)

[0046] 3. The present invention can quantify the impact of different chargers on battery capacity degradation by integrating the charging model (calculating the cumulative ampere-hour data required for the battery capacity life model through the vehicle dynamics model and the energy transfer model, and calculating the temperature required for the battery capacity life model through the temperature model, that is, calculating the input of the battery capacity life model through each sub-model of the power system, thereby calculating the battery capacity / life and quantifying the degradation);

[0047] 4. The present invention can quantify the impact of the above systems on the energy consumption and battery degradation of electric vehicles by allowing users to customize whether the electric vehicle brake energy recovery module is turned on, whether the battery thermal management model in the temperature model is turned on, and whether the electric vehicle heating, ventilation and air-conditioning system model is turned on (on: calculate the heat dissipation and heating amount according to the above formula to adjust the temperature; off: both the heat dissipation and heating amount are 0). BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Shown is a framework diagram of a reverse simulation method for dynamic operation of a power system and its software platform in an embodiment of the present invention.

[0049] Figure 2 Shown is an example diagram of a driving document in an embodiment of the present invention.

[0050] Figure 3 Shown is a flowchart of specific implementation steps of the reverse simulation method for dynamic operation of a power system and its software platform in an embodiment of the present invention.

[0051] Figure 4 The figure shows a comparison of the effects of level 1 charging and DC fast charging on battery capacity over a ten-year period.

[0052] Figure 5 Shown is a comparison of the impact of brake energy recovery / battery pack thermal management system / heating, ventilation and air conditioning system on battery capacity within one year.

[0053] Figure 6 Schematic diagram of the charger types and their parameters set for the platform.

[0054] Figure 7 Schematic diagram of the vehicle appearance and performance parameters set by the platform by default. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0056] The present invention provides a reverse simulation method for dynamic operation of a power system of an electric vehicle, comprising the following steps:

[0057] Build a physical simulation model of the electric vehicle power system, the physical simulation model of the electric vehicle power system includes:

[0058] Vehicle dynamics model, used to calculate the total force on the vehicle;

[0059] The energy transfer model includes an electric vehicle braking energy recovery module, a driving current calculation module, and a driving power calculation model. The electric vehicle braking energy recovery module is used to start and stop braking energy recovery, the driving current calculation module is used to calculate the driving current of the electric vehicle, and the driving power calculation model is used to calculate the driving power of the electric vehicle;

[0060] Temperature model, including electric vehicle heating, ventilation and air conditioning system model, battery thermal management model, cab temperature model and battery temperature model. The electric vehicle heating, ventilation and air conditioning system model is used to calculate the power consumed by the heating, ventilation and air conditioning system. The battery thermal management model is used to calculate the power required for battery heating or cooling. The cab temperature model is used to calculate the cab temperature. The battery temperature model is used to calculate the battery temperature.

[0061] The battery model includes a battery capacity decay model and a battery real-time SOC calculation model. The battery capacity decay model is used to calculate the battery calendar aging rate and thermal aging rate, thereby calculating the battery capacity decay. The battery real-time SOC calculation model is used to calculate the battery real-time SOC;

[0062] The charging model includes a charging judgment module, a charger selection model, and a charging power and charging current model. The charging judgment module is used to calculate the dwell time based on driving data to determine whether there is enough time for charging during the day's trip. The charger selection model is used to select car chargers with different powers. The charging power and charging current model is used to calculate the charging current based on the charging power and charging efficiency.

[0063] Based on the driving data, the energy transfer of the electric vehicle power system is simulated by a physical simulation model of the electric vehicle power system to obtain simulation results, which include but are not limited to changes in cockpit temperature, battery pack temperature, SOC, and battery capacity degradation.

[0064] Among them, the vehicle dynamics model is used to calculate the total force required for the vehicle to run on the basis of considering the rolling resistance, climbing resistance, acceleration resistance and air resistance during the vehicle's driving process. The specific calculation formula is:

[0065] F pt =F d +F r +F g +F a

[0066]

[0067] Among them, F pt The total force required to drive an electric vehicle to move, F d is the air resistance, F r is the rolling resistance, F g is the slope resistance, F a is the acceleration resistance, W pt , P pt Divided into the energy required to drive the electric vehicle and the total power, v c (t) is the speed of the electric vehicle, d is the drag in the air drag force, d is the drag, and t is the time.

[0068] In the energy transfer model, for the braking energy recovery strategy, it is set that when the vehicle speed is higher than 5 km / h and deceleration is in progress, braking energy recovery is turned on and the braking energy recovery efficiency is constant. rbs Recover the powertrain energy into the battery system, and the braking energy recovery power P rbs for:

[0069] P rbs =P pt ·f rbs

[0070] The total power of an electric vehicle when running is:

[0071] Pbat =P bt +P hvac +P btms +P rbs

[0072] Where P hvac The power consumption of the heating, ventilation and air conditioning system for electric vehicles, P btms Power consumed by the battery heating system.

[0073] The relationship between the current and power of an electric vehicle when it is running is:

[0074] P bat =(OCV+I c ·R bat )·I c

[0075] Where OCV is the open circuit voltage, R bat is the internal resistance, I c Input / output current for the battery pack.

[0076] The temperature model is mainly used to simulate the temperature changes and energy transfer process of the electric vehicle cab and battery pack.

[0077] In the temperature model, the battery temperature is calculated as follows:

[0078]

[0079] Among them C bat is the battery thermal capacity, T bat is the battery temperature, T a is the ambient temperature, T c is the cab temperature, K ab , K bc , K btms is the heat transfer coefficient, Q btms is the heat dissipation / heating energy of the battery pack thermal management system, Q bat is the heat energy of the battery pack, T b,up , T b,low The maximum and minimum temperatures for battery management set by the platform. Users can customize their values ​​in the platform. min(T bat -T b，up , 10) refers to T bat -T b，up or T bat -10, the smallest value, min(T b，low -Tbat,10 refers to the minimum value of Tb,low-Tbat or Tb,low-10.

[0080]

[0081] Among them C c is the specific heat capacity of the cockpit, K ac is the heat transfer coefficient, Q hvac The heating, ventilation and air conditioning system of electric vehicles consumes energy. The heating, ventilation and air conditioning system of electric vehicles consumes power P hvac In some embodiments of the present invention, the maximum cooling power is set to 4 kW and the maximum heating power is set to 4.5 kW; COP cooling , COP heating It is the heat dissipation and heating energy coefficient.

[0082] The battery model includes a battery capacity decay model, i.e., a battery capacity life model, and a battery real-time SOC calculation model. The battery capacity decay model is used to calculate the battery calendar aging rate and thermal aging rate, thereby calculating the battery capacity decay, i.e., the battery life. The real-time SOC of the electric vehicle is:

[0083]

[0084] Where SOC0 is the initial SOC value of the battery pack, C bat,r The remaining rated capacity of the battery pack.

[0085] The present invention takes into account both the calendar aging and thermal aging of the power battery. The remaining capacity of the battery (pack), i.e., the capacity life of the battery (pack), is calculated as follows:

[0086] C bat,r =C baa,ini τ a τ c

[0087]

[0088] Among them, C baa,ini is the initial capacity of the battery pack, τ a is the thermal aging rate of the battery pack, τ c is the calendar aging rate of the battery pack, and its value can be obtained through the battery calendar aging coefficient table input by the user or embedded in the platform, σ fcn Battery aging degradation severity factors, α, β, η, is the battery aging coefficient set by the platform, E a is the activation energy, R g is the universal gas constant, Q acc It is the accumulated ampere-hour.

[0089] The charging model can simulate the energy transfer and battery aging of electric vehicles when charging. Users can use this charging model to simulate and evaluate the impact of different chargers on electric vehicles.

[0090] In the charging model, the charger selection model can select car chargers with different powers according to the user's settings before simulation. The charging power and charging current model can calculate the charging current according to the charging power and charging efficiency of the charger selected by the user:

[0091] I chg =P chg *E chg / V chg

[0092] Among them, I chg is the charging current, E chg is the charging efficiency, V chg is the charging voltage.

[0093] In some embodiments of the present invention, four charging types are set, including primary charging, secondary charging, DC fast charging, and ultra-fast charging, and the user can set and change the above information in the EV_DOS file.

[0094] The present invention also provides a reverse simulation platform for the dynamic operation of a power system of an electric vehicle. The simulation platform collects or simulates real daily driving data and performs reverse simulation using any of the methods described in claims 1-6. The real daily driving data includes but is not limited to: data collection time and its corresponding vehicle speed, road slope, ambient temperature, and vehicle running / stopping status. The platform can perform cyclic long-term span simulation based on the above driving data.

[0095] The platform allows users to customize vehicle performance and appearance parameters, battery parameters, and charger parameters to simulate vehicles of different sizes and models.

[0096] The present invention can select the driving mode configuration, specifically including: battery thermal management system on / off, heating, ventilation and air conditioning system on / off, and brake energy recovery system on / off. In some embodiments of the present invention, the user can set the electric vehicle charger type and the total number of simulated vehicle driving years in the EV_DOS file, and can set whether the electric vehicle brake energy recovery is turned on, whether the battery pack thermal management system is turned on, and whether the heating, ventilation and air conditioning system model is turned on, so as to achieve the purpose of the user simulating the impact of different driving conditions on the electric vehicle.

[0097] The platform simulation period can be customized to simulate the degradation of electric vehicle power batteries over a long period of time or even the entire life cycle in a relatively short period of time.

[0098] The charging model can count the daily charging time based on the driving data input by the user. In the platform, according to the charging mode selected by the user, it simulates the energy transfer of the electric vehicle power system through the electric vehicle power system physical simulation model, and restores the changes in cockpit temperature, battery pack temperature, SOC and battery capacity degradation.

[0099] Through the above settings, the platform can generate simulation results Excel in the same folder as the program. Users can read the remaining value of the battery rated capacity, vehicle daily energy consumption, cumulative mileage, cumulative driving time, cumulative charge and discharge capacity, cumulative charging energy, cumulative charging price and cumulative charging time data in Excel.

[0100] The effectiveness of the present invention is verified by using specific examples below.

[0101] Example 1: The platform of the present invention is used to quantify the impact of using only level 1 charging (charging power set to 1.8 kW, charging voltage set to 120 V, charging efficiency set to 0.85) and only using DC fast charging (charging power set to 60 kW, charging voltage set to 480 V, charging efficiency set to 0.85) on the capacity degradation of electric vehicle batteries within 10 years.

[0102] Step 1: The user opens the folder where the software platform is located and puts the set one-year driving data into the "DayTrips" folder. This embodiment 1 uses the driving document data set by the platform by default. The driving document data example is as follows: Figure 2 As shown, the vehicle appearance and performance parameters use the default parameters set by the platform, as shown in Table 2.

[0103] Step 2: The user opens the software platform and sets the charger type chargetype to "Level_1" (i.e., first-level charging) in the file named EV-DOS, sets the electric vehicle braking energy recovery to be turned on, the battery pack thermal management system to be turned on, the heating, ventilation and air-conditioning system to be turned on, and sets the simulation period to 10 years.

[0104] Step 3: Click Run, refer to Figure 3 , the platform will automatically initialize parameters, import the driving data files placed by the user in "DayTrips" into the simulation platform, and automatically read the number of driving files / simulation days. When the number of simulation days is less than the number of days loaded with driving data, the driving data of each day will be simulated accordingly.

[0105] Step 4: Furthermore, based on the total daily travel time obtained by importing the single-day driving data, determine whether to continue the simulation. When the cumulative simulation time is less than the total daily travel time, determine whether the battery SOC is lower than 0.1. When it is lower than 0.1, the platform automatically enters the charging mode. When the SOC is not lower than 0.1, determine whether the vehicle is running. When the vehicle is not running, the dwell time is accumulated. When the vehicle is running, the power consumed by the heating, ventilation and air-conditioning system and the battery thermal management system, the open circuit voltage OCV, the internal resistance of the battery pack, the battery power and the current are calculated through the established electric vehicle power system physical simulation model. The real-time SOC, the consumed energy, the accumulated ampere-hours and the temperature of the cockpit and the battery are calculated. Finally, the remaining rated capacity of the battery pack is calculated every 60 seconds.

[0106] Step 5: When the cumulative simulation time is greater than the total time of a single-day trip, enter the single-day charging simulation. When the cumulative stay time is greater than 30 minutes and the SOC is less than 0.8, the power consumed by the heating, ventilation and air-conditioning system and the battery thermal management system, the open circuit voltage OCV, the battery pack internal resistance, the battery power and the current are calculated through the established electric vehicle power system physical simulation model. The real-time SOC, the consumed energy, the cumulative ampere-hours and the temperature of the cockpit and the battery are calculated. Finally, the remaining rated capacity of the battery pack is calculated every 60 seconds.

[0107] After each day's driving data is processed through steps 4 and 5 above, determine whether the cumulative simulation years have reached the set cumulative simulation years. If not, continue with steps 4 and 5 until the set years are reached. The program automatically stops and outputs the simulation result Excel data in the same file. Users can view the specific data of the electric vehicle battery capacity decline each day when the first-level charger is selected for charging in the simulation result Excel.

[0108] Step 6: The user opens the software platform, sets the charger type chargetype to "DC_Fast" in the file named EV-DOS, sets the electric vehicle braking energy recovery to be turned on, the battery pack thermal management system to be turned on, the heating, ventilation and air conditioning system to be turned on, and sets the simulation period to 10 years. Click Run, the platform will repeat steps 3 to 5, the program will stop automatically, and the simulation result Excel data will be output in the same file. The user can view the specific data of the electric vehicle battery capacity decline every day when the DC fast charger is selected for charging in the simulation result Excel.

[0109] After the above steps, a comparison chart of the effects of primary charging and DC fast charging on battery capacity degradation is obtained after 10 years. Figure 4As shown in the figure, it can be seen that through the simulation platform, the capacity decay of the primary charging battery is slower, and the capacity decay of the DC fast charging battery is 9% more than that of the primary charging method after ten years. The above simulation takes less than 8 hours in total. It can be seen from Example 1 that the platform can simulate the impact of different charger types on the battery life of electric vehicles after 10 years within one day.

[0110] Example 2: Use the platform of the present invention to quantify the impact of the braking energy recovery system, heating, ventilation and air conditioning system, and battery thermal management system on battery capacity degradation within one year:

[0111] Step 1: The user opens the folder where the software platform is located. In this embodiment 2, the driving document data (one year of driving data) set by the platform by default is used. The driving document data example is as follows: Figure 2 As shown in Table 2, the set one-year driving data is placed in the "DayTrips" folder, and the vehicle appearance and performance parameters use the default parameters set by the platform (the platform default data is set by a pure electric model by default on the platform, which can be obtained by searching the specific parameter table and references on the web page, so users can also change these parameters according to the model they need to simulate), as shown in Table 2.

[0112] Step 2: The user opens the software platform and sets the charger type chargetype to "DC_Fast" in the file named EV-DOS, sets the electric vehicle braking energy recovery to be turned on, the battery pack thermal management system to be turned off, the heating, ventilation and air conditioning system to be turned off, and sets the simulation period to 1 year.

[0113] Step 3: Click Run, refer to Figure 3 , the platform will automatically initialize parameters, import the driving data files placed by the user in "DayTrips" into the simulation platform, and automatically read the number of driving files / simulation days. When the number of simulation days is less than the number of days loaded with driving data, the driving data of each day will be simulated accordingly.

[0114] Step 4: Determine whether to continue the simulation based on the total daily travel time obtained by importing the single-day driving data. When the cumulative simulation time is less than the total daily travel time, determine whether the battery SOC is lower than 0.1. When it is lower than 0.1, the platform automatically enters the charging mode. When the SOC is not lower than 0.1, determine whether the vehicle is running. When the vehicle is not running, the dwell time is accumulated. When the vehicle is running, the power consumed by the heating, ventilation and air-conditioning system and the battery thermal management system, the open circuit voltage OCV, the internal resistance of the battery pack, the battery power and the current are calculated through the established electric vehicle power system physical simulation model. The real-time SOC, the consumed energy, the accumulated ampere-hours and the temperature of the cockpit and the battery are calculated. Finally, the remaining rated capacity of the battery pack is calculated every 60 seconds.

[0115] Step 5: When the cumulative simulation time is greater than the total time of a single-day trip, enter the single-day charging simulation. When the cumulative stay time is greater than 30 minutes and the SOC is less than 0.8, the power consumed by the heating, ventilation and air-conditioning system and the battery thermal management system, the open circuit voltage OCV, the battery pack internal resistance, the battery power and the current are calculated through the established electric vehicle power system physical simulation model. The real-time SOC, the consumed energy, the cumulative ampere-hours and the temperature of the cockpit and the battery are calculated. Finally, the remaining rated capacity of the battery pack is calculated every 60 seconds.

[0116] After each day's driving data is processed through steps 4 and 5 above, determine whether the cumulative simulation years have reached the simulation years set by the user. If not, continue with steps 4 and 5 until the set years are reached. The program automatically stops and outputs the simulation result Excel data in the same file. Users can view the specific data of the electric vehicle battery capacity decline each day when the brake energy recovery system is turned on in the simulation result Excel.

[0117] Step 6: The user opens the software platform, sets the charger type chargetype to "DC_Fast" in the file named EV-DOS, sets the electric vehicle braking energy recovery to be turned off, the battery pack thermal management system to be turned on, the heating, ventilation and air conditioning system to be turned off, and sets the simulation period to 1 year. Click Run, the platform will repeat steps 3 to 5, the program will stop automatically, and the simulation result Excel data will be output in the same file. The user can view the specific data of the electric vehicle battery capacity decline every day when the battery pack thermal management system is turned on in the simulation result Excel.

[0118] Step 7: The user opens the software platform, sets the charger type chargetype to "DC_Fast" in the file named EV-DOS, sets the electric vehicle braking energy recovery to be turned off, the battery pack thermal management system to be turned off, the heating, ventilation and air conditioning system to be turned on, and sets the simulation period to 1 year. Click Run, the platform will repeat steps 3 to 5, the program will stop automatically, and the simulation result Excel data will be output in the same file. The user can view the specific data of the electric vehicle battery capacity decline every day when the heating, ventilation and air conditioning system is turned on in the simulation result Excel.

[0119] After the above steps, a comparison chart of the effects of the braking energy recovery system, heating, ventilation and air conditioning system, and battery thermal management system on battery capacity degradation is obtained one year later. The comparison chart is as follows: Figure 5 The benchmark shown is the result of a run on a driving profile with HVAC turned off, brake energy regeneration turned off, and battery thermal management turned off.

[0120] BTMS_ON is the result of running on a driving profile with only battery thermal management turned on.

[0121] HVAC_ON is the result of running on a driving profile with only the HVAC system turned on.

[0122] RBS_ON is the result of running on a driving profile with only regenerative braking switched on.

[0123] Clearly, the results show that BTMS is beneficial in delaying battery degradation in electric vehicles. Compared to the baseline, when only the battery thermal management system is turned on, the remaining battery capacity is improved by 2.57% in one year; however, using only the HVAC system will accelerate battery degradation, degrading by 3.65% in one year, and turning on only the brake energy recovery also helps to improve the remaining battery capacity, by 1.83%. The above simulation takes less than 2 hours in total. It can be seen from Example 2 that the platform can simulate the impact of different auxiliary functions in electric vehicles on battery life in a short time.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A reverse simulation method for dynamic operation of a power system of an electric vehicle, characterized in that: The following steps are involved: Build a physical simulation model of the electric vehicle power system, the physical simulation model of the electric vehicle power system includes: Vehicle dynamics model, used to calculate the total force on the vehicle; The energy transfer model includes an electric vehicle braking energy recovery module, a driving current calculation module, and a driving power calculation model. The electric vehicle braking energy recovery module is used to start and stop braking energy recovery, the driving current calculation module is used to calculate the driving current of the electric vehicle, and the driving power calculation model is used to calculate the driving power of the electric vehicle; Temperature model, including electric vehicle heating, ventilation and air conditioning system model, battery thermal management model, cab temperature model and battery temperature model. The electric vehicle heating, ventilation and air conditioning system model is used to calculate the power consumed by the heating, ventilation and air conditioning system. The battery thermal management model is used to calculate the power required for battery heating or cooling. The cab temperature model is used to calculate the cab temperature. The battery temperature model is used to calculate the battery temperature. The battery model includes a battery capacity decay model and a battery real-time SOC calculation model. The battery capacity decay model is used to calculate the battery calendar aging rate and thermal aging rate, thereby calculating the battery capacity decay. The battery real-time SOC calculation model is used to calculate the battery real-time SOC; The charging model includes a charging judgment module, a charger selection model, and a charging power and charging current model. The charging judgment module is used to calculate the dwell time based on driving data to determine whether there is enough time for charging during the day's trip. The charger selection model is used to select car chargers with different powers. The charging power and charging current model is used to calculate the charging current based on the charging power and charging efficiency. Based on the driving data, the energy transfer of the electric vehicle power system is simulated by a physical simulation model of the electric vehicle power system to obtain simulation results, which include but are not limited to changes in cockpit temperature, battery pack temperature, SOC, and battery capacity degradation.

2. A reverse simulation method for dynamic operation of a power system of an electric vehicle according to claim 1, characterized in that: In the vehicle dynamics model, the formula for calculating the total force on the vehicle is: F pt =F d +F r +F g +F a Among them, F pt The total force required to drive an electric vehicle to move, F d is the air resistance, F r is the rolling resistance, F g is the slope resistance, F a is the acceleration resistance, W pt , P pt Divided into the energy required to drive the electric vehicle and the total power, v c (t) is the speed of the electric vehicle, d is the drag, and t is the time.

3. The method for reverse simulation of dynamic operation of a power system for electric vehicles according to claim 1, characterized in that: In the energy transfer model, the electric vehicle braking energy recovery module is used to start braking energy recovery when the vehicle speed is higher than the preset value and decelerate, and the braking energy recovery efficiency is constant. rbs Recover powertrain energy into the battery system; The formula for calculating the total power of an electric vehicle when it is running is: P bat =P pt +P hvac +P btms +P rbs Among them, F pt The total force required to drive an electric vehicle to move, P hvac The power consumption of the heating, ventilation and air conditioning system for electric vehicles, P btms is the power consumed by the battery heating system, P rbs It is the braking energy recovery power; The formula for calculating the driving current is: P bat =(OCV+I c ·R bat )·I c Where OCV is the open circuit voltage, R bat is the internal resistance, I c Input / output current for the battery pack.

4. The method for reverse simulation of dynamic operation of a power system for electric vehicles according to claim 1, characterized in that: In the temperature model, when the electric vehicle is running, the battery temperature is: Among them C bat is the battery thermal capacity, T bat is the battery temperature, T a is the ambient temperature, T c is the cab temperature, K ab , K bc , K btms is the heat transfer coefficient, Q btms is the heat dissipation / heating energy of the battery pack thermal management system, Q bat is the heat energy of the battery pack, T b,up , T b,low For battery management, the highest and lowest temperatures, min(T bat -T b，up , 10) refers to T bat -T b，up or T bat -10, the smallest value, min(T b，low -T bat , 10) refers to T b，low -T bat or T b，low -The smallest value among 10; Among them C c is the specific heat capacity of the cockpit, K ac is the heat transfer coefficient, Q hvac Energy consumed by heating, ventilation and air conditioning systems in electric vehicles, COP cooling , COP heating It is the heat dissipation and heating energy coefficient.

5. The method for reverse simulation of dynamic operation of a power system for electric vehicles according to claim 1, characterized in that: In the battery model, the real-time SOC of the electric vehicle is: Among them, SOC0 is the initial SOC value of the battery pack, C bat,r The remaining rated capacity of the battery pack; The remaining capacity of the battery pack, i.e. the battery pack capacity life calculation formula is: C bat,r =C baa,ini t a t c Among them, C baa,ini is the initial capacity of the battery pack, τ a is the thermal aging rate of the battery pack, τ c is the calendar aging rate of the battery pack, σ fcn Battery aging degradation severity factors, α, β, η, is the battery aging coefficient, E a is the activation energy, R g is the universal gas constant, Q acc It is the accumulated ampere-hour.

6. A reverse simulation method for dynamic operation of a power system of an electric vehicle according to any one of claims 1 to 5, characterized in that: In the charger selection model, the types of chargers include: primary charging, secondary charging, DC fast charging, and ultra-fast charging.

7. A reverse simulation platform for dynamic operation of power system of electric vehicles, characterized in that: The simulation platform collects or simulates real daily driving data and performs reverse simulation using any method described in claims 1-6. The real daily driving data includes but is not limited to: data collection time and its corresponding vehicle speed, road slope, ambient temperature and vehicle running / stop status. The platform can perform cyclic long-term span simulation based on the above driving data.

8. The reverse simulation platform for dynamic operation of the power system of electric vehicles according to claim 7, characterized in that: The platform allows users to customize vehicle performance and appearance parameters, battery parameters, and charger parameters to simulate vehicles of different sizes and models.

9. The reverse simulation platform for dynamic operation of the power system of electric vehicles according to claim 7, characterized in that: A driving mode configuration can be selected, including: battery thermal management system on / off, heating, ventilation and air conditioning system on / off, and brake energy recovery system on / off.

10. A reverse simulation platform for dynamic operation of a power system for electric vehicles according to any one of claims 7 to 9, characterized in that: The platform simulation period can be customized to simulate the degradation of electric vehicle power batteries over a long period of time or even the entire life cycle in a relatively short period of time.

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