A power battery thermal management method and system based on driver intention recognition

Abstract

The present invention discloses a power battery thermal management method based on driver intention recognition, which includes the following steps: 100: collecting characteristic signals for driver intention recognition; 200: using a fuzzy control method to establish a membership function of the characteristic signal and a fuzzy inference rule to output a quantified driver intention; 300: obtaining a correction coefficient of the vehicle's torque and the motor's power based on the recognized driver's intention, and predicting the vehicle's torque demand and the motor's power demand based on the correction coefficient and the vehicle's steady-state basic torque and the motor's basic power; 400: predicting the future power demand of the power battery based on the motor's power demand; 500: predicting the power demand of the power battery based on the future power demand of the power battery, and calculating the maximum temperature T of the power battery at a certain moment in the future based on the power demand of the power battery. max ;600: If T max If the temperature is greater than the set threshold, the power battery is cooled in advance so that the power battery always operates within the set optimal temperature range.

Description

Technical Field

[0001] The present invention relates to a battery management method and system, and in particular to a battery thermal management method and system. Background Art

[0002] In recent years, as the country vigorously promotes new energy vehicles, the development of new energy electric vehicles has gradually attracted widespread attention. As the heart of electric vehicles, power batteries are the focus of electric vehicle research.

[0003] In electric vehicles, the environment and temperature of the power battery system directly affect its normal operation, cycle life, charge acceptability, output power, available energy, safety, and reliability. Therefore, to ensure the optimal performance and life of the power battery system in electric vehicles, a thermal management system is required to heat the batteries at low temperatures and dissipate heat at high temperatures. This achieves temperature uniformity in the battery pack, ensures that the batteries operate within an appropriate temperature range, reduces the rate of battery performance degradation, and eliminates related potential safety risks.

[0004] By regulating and controlling the temperature of the power battery through the thermal management system, the power battery can always be kept in the appropriate temperature range during operation (generally controlled at 20-30°C for best), which is of great practical significance in improving the performance and efficiency of the power battery system, extending its service life, reducing vehicle costs, and ensuring the safety of electric vehicles.

[0005] In the current existing technology, a power battery thermal management system has been designed. The control strategy of the existing power battery thermal management system can be referred to as follows: Figure 1 It generally adopts a step-by-step control method, which adjusts the temperature of the power battery and performs thermal management of the battery by comparing the collected battery temperature and temperature difference with the pre-set temperature threshold and temperature difference threshold.

[0006] like Figure 1 As shown, the control strategy of this existing power battery thermal management system may specifically include processes S201-S212, and its specific implementation process is as follows:

[0007] The BMS is used to detect the temperatures T1, T2, ..., Tn and the temperature difference ΔT = Tmax-Tmin of each collection point inside the battery system in real time, where Tmax is the maximum temperature collected and Tmin is the minimum temperature collected.

[0008] When it is determined that Tmax > the set temperature threshold Ta (generally set to 35°C) and / or max△T > the set temperature difference threshold △Ta (generally set to 5°C), the electronic water pump is controlled to start gear I. At this time, the cooling water flow rate is generally 10L / min. After the electronic water pump starts gear I, it continues to monitor the maximum temperature Tmax and temperature difference △T inside the battery system. If it is determined that Max△T < △Ta and Tmax ≤ Ta, the electronic water pump is turned off, and the power battery thermal management control strategy process ends.

[0009] When it is determined that Tmax is greater than the set temperature threshold Tb (generally set to 40°C) and / or that max△T is greater than the set temperature difference threshold △Tb (generally set to 8°C), the electronic water pump is controlled to start gear II. At this time, the cooling water flow rate is generally 20L / min. After the electronic water pump starts gear II, it continues to monitor the maximum temperature and temperature difference inside the battery system. If it is further determined that △Ta is less than max△T<△Tb and Ta is less than Tmax<Tb, the electronic water pump is controlled to start gear I. After the electronic water pump starts gear I, it continues to monitor the maximum Tmax and temperature difference △T inside the battery system. If it is determined that Max△T is less than △Ta and Tmax≤Ta, the electronic water pump is turned off, and the power battery thermal management control strategy process ends.

[0010] In existing power battery thermal management systems, when heating is required, the PTC water heater receives heating requests from the BMS battery system to heat the water and control the water inlet temperature entering the battery system. Simultaneously, an electronic water pump controls the water flow entering the battery system, with the high-temperature water passing through the aluminum runner plate to exchange heat with the battery module until the set temperature is reached. When cooling is required, the vehicle's air conditioning and electronic water pump are turned on, controlling the water flow at the battery system's inlet through the electronic water pump. Cooling water also passes through the aluminum runner plate to exchange heat with the battery module until the battery temperature reaches the set temperature.

[0011] However, research has found that the currently used power battery thermal management systems still have the following defects:

[0012] (1) The current power battery thermal management strategy has a lag. When the battery system is cooled, the temperature signal and temperature difference signal collected by the BMS battery system are usually compared with the temperature threshold and temperature difference threshold set in advance in the BMS software. The BMS software feeds the comparison results back to the electronic pump, and then the electronic pump outputs a specific flow rate to cool the battery system. In this process, the lag from the temperature and temperature difference signals collected by the BMS battery system to the coolant entering the battery system and starting to work is not taken into account. If the vehicle continues to operate in very harsh driving conditions after reaching the cooling temperature threshold, the temperature and temperature difference of the battery cells inside the battery system will continue to increase, and the initial cooling effect will be greatly reduced.

[0013] (2) The current power thermal management strategy does not take into account the impact of external factors such as the driver's driving habits, road conditions, and current vehicle speed on the battery system. Its thermal management strategy is set based on a single battery's internal temperature and temperature difference, and cannot predict the vehicle's cooling power requirements in advance. Under certain operating conditions, the battery system is prone to overheating and power limitation according to the current thermal management strategy, affecting the driving experience. When the battery temperature is too high, it will not only accelerate the attenuation of the battery life, but also lead to an increase in the demand for cooling power, increasing the energy consumption of the vehicle.

[0014] Based on this, in order to overcome the above problems, the inventors hope to obtain a power battery thermal management method that can predict the cooling power requirements of the battery system in advance and can adaptively and self-adjust the cooling power requirements according to external environments such as driver behavior habits, road condition analysis, and current vehicle speed. Summary of the Invention

[0015] One of the objectives of the present invention is to provide a power battery thermal management method based on driver intention recognition. The power battery thermal management method can recognize the driver's intention and predict the battery system cooling power demand in advance, and then cool the power battery in advance so that the power battery always operates within the set optimal temperature range.

[0016] Unlike existing technologies, this power battery thermal management method can not only predict the battery system cooling power requirements in advance, but also adaptively adjust the cooling power requirements based on external environments such as driver behavior habits, road condition analysis, and current vehicle speed.

[0017] In order to achieve the above object, the present invention proposes a power battery thermal management method based on driver intention recognition, which includes the following steps:

[0018] 100: collecting characteristic signals for driver intention recognition;

[0019] 200: Using fuzzy control methods to establish the membership function of characteristic signals and fuzzy inference rules to output the quantified driver intention;

[0020] 300: Obtain the correction coefficient K of the vehicle torque and the motor power according to the recognized driver intention, and predict the vehicle torque demand T according to the correction coefficient K, the vehicle steady-state basic torque T0 and the motor basic power P0. req =KT0 and motor power demand P req =KP0;

[0021] 400: Based on the power demand of the motor, predict the future power demand P of the power battery Battery ;

[0022] 500: Based on the future power demand of the power battery, predict the heat power of the power battery, and calculate the maximum temperature T of the power battery at a certain moment in the future based on the heat power max ;

[0023] 600: If T max If the temperature is greater than the set threshold, the power battery is cooled in advance so that the power battery always operates within the set optimal temperature range.

[0024] In the above technical solution of the present invention, the present invention designs a power battery thermal management method based on driver intention recognition, which can realize the recognition of the driver's intention by adopting a fuzzy control method, and predict the future heating power of the power battery based on the recognized driver's intention, and then cool the power battery in advance so that the power battery always operates within the set optimal temperature range.

[0025] Furthermore, in the power battery thermal management method based on driver intention recognition described in the present invention, the characteristic signals include: accelerator pedal change rate, accelerator pedal opening, current vehicle speed, road conditions and external ambient temperature, brake pedal opening, and brake pedal change rate.

[0026] Furthermore, in the power battery thermal management method based on driver intention recognition described in the present invention, the driver intention includes acceleration intention w and braking intention u.

[0027] Furthermore, in the power battery thermal management method based on driver intention recognition described in the present invention, the acceleration intention w ranges from [1, 1.3], and its fuzzy subsets are {smooth acceleration, relatively smooth acceleration, general acceleration, relatively urgent acceleration, and urgent acceleration}; and / or the braking intention u ranges from [1, 1.2], and its fuzzy subsets are {smooth deceleration, relatively smooth deceleration, general deceleration, relatively urgent deceleration, and urgent deceleration}.

[0028] Furthermore, in the power battery thermal management method based on driver intention recognition of the present invention, in step 300 , the correction coefficient K is obtained by defuzzification and quantization.

[0029] Furthermore, in the power battery thermal management method based on driver intention recognition according to the present invention, in step 300 , a center of gravity method is used to perform defuzzification quantization.

[0030] Furthermore, in the power battery thermal management method based on driver intention recognition of the present invention, in step 400, the future power demand P of the power battery is predicted based on the following formula: Battery :

[0031] P Battery =P req / η

[0032] η=f(Tc,C,soc)

[0033]

[0034] Where η represents the charge and discharge efficiency, Tc represents the ambient temperature, C represents the power battery discharge rate, soc represents the state of charge of the power battery, η1 represents the charging efficiency of the whole vehicle, and η2 represents the discharge efficiency of the whole vehicle.

[0035] Furthermore, in the power battery thermal management method based on driver intention recognition according to the present invention, in step 500, the heat generation power Q of the power battery is predicted based on the following formula: Battery :

[0036] Q Battery =I 2 R-ITδ

[0037] I=P Battery / U_soc

[0038] R=f(soc,T)

[0039] δ=g(soc,T)=dU soc / dT

[0040] Where, I is the charge and discharge current; R is the internal resistance of the power battery; T is the power battery temperature; δ is the entropy thermal coefficient of the power battery, and U_soc represents the open circuit voltage;

[0041] Calculate the maximum temperature T of the power battery at a certain moment in the future based on the following formula max :

[0042] Q Battery *t n =C Battery m Battery (T n -T)

[0043]

[0044] Among them, C Battery is the specific heat capacity of the power battery; m Battery is the mass of the power battery; K1 is the revision coefficient formulated based on the external ambient temperature and road conditions; T n is the predicted power battery temperature at a certain moment in the future; t n It is the continuous charging and discharging time of the power battery.

[0045] Furthermore, in the power battery thermal management method based on driver intention recognition according to the present invention, step 600 includes: if T maxIf the temperature is greater than the set threshold, then:

[0046] 601: According to the heat power Q of the power battery Battery , estimate the required coolant flow q cooling :

[0047] Q Cooling =λQ Battery =C cooling ρ cooling q cooling (T outlet -T inlet )

[0048] Among them, Q Cooling is the total heat dissipation power of the cooling system; λ is the heat loss coefficient, which ranges from 1.1 to 1.3; C cooling is the specific heat capacity of the cooling liquid; ρ cooling is the density of the coolant; T outlet is the coolant outlet temperature; T inlet is the coolant inlet temperature; T outlet -T inlet is the temperature rise of the coolant;

[0049] 602: Turn on the cooling electronic water pump and adjust the electronic water pump PWM duty cycle to achieve the required coolant flow q cooling ;

[0050] 603: Real-time monitoring of the power battery temperature T and real-time update of T max .

[0051] Accordingly, another object of the present invention is to provide a new power battery thermal management system based on driver intention recognition, which can be used to implement the power battery thermal management method mentioned above in the present invention.

[0052] To achieve the above objectives, the present invention proposes a power battery thermal management system based on driver intention recognition, which includes:

[0053] A collection device that collects characteristic signals for driver intention recognition, wherein the characteristic signals include: accelerator pedal change rate, accelerator pedal opening, current vehicle speed, external ambient temperature and road conditions, brake pedal opening, and brake pedal change rate;

[0054] The acceleration fuzzy intention controller uses fuzzy control methods to establish the membership function of the characteristic signal and fuzzy inference rules to output the quantized acceleration intention based on the input accelerator pedal change rate, accelerator pedal opening, and current vehicle speed;

[0055] The brake fuzzy intention controller adopts a fuzzy control method to establish a membership function of a characteristic signal and a fuzzy inference rule, so as to output a quantized brake intention based on an input current vehicle speed, a brake pedal opening degree, and a brake pedal change rate;

[0056] The power battery and the battery management system, the battery management system acquires the state of the power battery;

[0057] Cooling electronic water pump

[0058] The vehicle control unit is configured to:

[0059] According to the acceleration intention and the brake intention, a correction coefficient K of torque of the vehicle and power of the motor is obtained, according to the correction coefficient K and the steady-state basic torque T0 of the vehicle and the basic power P0 of the motor, the torque demand T of the vehicle is predicted req = KT0 and the power demand P of the motor req = KP0;

[0060] According to the power demand of the motor, the future power demand P of the power battery is predicted Battery ;

[0061] Based on the future power demand of the power battery, the heat generation power of the power battery is predicted, and the highest temperature T of the power battery at a future time is calculated according to the heat generation power max ;

[0062] If T max is greater than a set threshold temperature, the power battery is cooled in advance by controlling the cooling electronic water pump, so that the power battery always works in a set optimal temperature range;

[0063] The battery management system monitors the temperature T of the power battery in real time, and updates T max in real time.

[0064] Compared with the prior art, the power battery thermal management method and system based on driver intention recognition have the following advantages and beneficial effects:

[0065] (1) The power battery thermal management method of the present application uses a fuzzy recognition algorithm to identify the driver's intention in real time, which not only provides a basis for judging the subsequent vehicle torque and power demand, but also further provides a basis for formulating and implementing the subsequent cooling strategy.

[0066] (2) Due to the increasing intelligence and networking of electric vehicles, the power battery thermal management method of the present invention not only considers the impact of the driver's intention on the cooling strategy, but also takes into account the application of real-time navigation technology and big data technology. In some preferred embodiments, based on the prediction of the vehicle's posture and driving conditions within a certain time period, the optimal thermal management control strategy can be formulated according to the battery heating model and the battery system cooling model.

[0067] (3) The power battery thermal management method and system based on driver intention recognition designed by the present invention can cool the power battery in advance, so that the power battery can always operate in the optimal operating temperature range, thereby greatly improving the service life of the power battery.

[0068] (4) The power battery thermal management method and system based on driver intention recognition designed by the present invention can effectively improve the safety of the battery and avoid the thermal runaway problem of the battery system caused by excessive power battery temperature.

[0069] (5) The power battery thermal management method and system based on driver intention recognition designed by the present invention can further improve the driving performance and driving flexibility of the entire vehicle, avoid the occurrence of vehicle power limitation due to excessive battery temperature, and improve the driving experience and driving pleasure.

[0070] (6) The power battery thermal management method and system based on driver intention recognition designed by the present invention can further reduce the energy consumption of battery thermal management. Since the overheating demand is recognized in advance, it can be cooled with a relatively small flow of cooling water and cooling power, and the cooling power and cooling flow used can be fed back in real time for adaptive adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 Schematic diagram showing the control strategy flow chart of the current existing power battery thermal management system

[0072] Figure 2 The figure schematically shows the architecture diagram of the power battery thermal management system according to the present invention, which is based on the recognition of the vehicle control strategy based on the driver's intention in one embodiment.

[0073] Figure 3 The figure is a flow chart of the control strategy of the power battery thermal management method according to one embodiment of the present invention.

[0074] Figure 4 for Figure 3 The flowchart shown is a power battery thermal management method for identifying driver intention.

[0075] Figure 5The membership function of the vehicle speed v created in the acceleration intention recognition in one embodiment is schematically shown.

[0076] Figure 6 The membership function established for the accelerator pedal opening α in one embodiment is schematically shown.

[0077] Figure 7 The membership function of the accelerator pedal opening change rate dα / dt established in one embodiment is schematically shown.

[0078] Figure 8 The membership function of the driver's acceleration intention w established in one embodiment is schematically shown.

[0079] Figure 9 The membership function of the vehicle speed v generated in the braking intention detection in one embodiment is schematically shown.

[0080] Figure 10 The membership function of the brake pedal opening β established in one embodiment is schematically shown.

[0081] Figure 11 The membership function of the brake pedal opening change rate dβ / dt established in one embodiment is schematically shown.

[0082] Figure 12 The membership function of the driver's braking intention u established in one embodiment is schematically shown. DETAILED DESCRIPTION

[0083] The power battery thermal management method and system based on driver intention recognition described in the present invention will be further explained and illustrated below in conjunction with the accompanying drawings and specific embodiments of the specification. However, such explanation and illustration do not constitute an undue limitation to the technical solution of the present invention.

[0084] Figure 2 The figure schematically shows the architecture diagram of the power battery thermal management system according to the present invention, which is based on the recognition of the vehicle control strategy based on the driver's intention in one embodiment.

[0085] The present invention designs a new power battery thermal management system, which specifically includes: a collection device, a vehicle controller, an acceleration fuzzy intention controller, a braking fuzzy intention controller, a power battery, a battery management system and a cooling electronic water pump.

[0086] like Figure 2As shown, in this embodiment, this power battery thermal management system can specifically include three major processes during its specific implementation: driver intention recognition process, vehicle control strategy implementation process, and control of actuators such as motors, power batteries, cooling electronic water pumps and reducers to achieve power battery thermal management.

[0087] In the power battery thermal management system of the present invention, the battery management system can obtain the current status of the power battery in real time, such as SOC, temperature, and internal resistance. The acquisition device can collect characteristic signals such as accelerator pedal change rate, accelerator pedal opening, current vehicle speed, ambient temperature and road conditions, brake pedal opening, and brake pedal change rate.

[0088] During the driver intention recognition process, the present invention divides the driver intention into two categories according to different operating states of the vehicle during travel, namely, acceleration intention and braking intention.

[0089] In the power battery thermal management system designed by the present invention, the acceleration fuzzy intention controller can use fuzzy control methods to establish the membership function of the characteristic signal and fuzzy inference rules to output quantified acceleration intention based on the collected input accelerator pedal change rate, accelerator pedal opening, and current vehicle speed.

[0090] The braking fuzzy intention controller can use fuzzy control methods to establish the membership function of the characteristic signal and fuzzy inference rules to output the quantified braking intention based on the input current vehicle speed, brake pedal opening, and brake pedal change rate.

[0091] After completing the above driver intention recognition process, the vehicle controller can implement different vehicle control strategies according to different driver intentions. In this process, the vehicle controller can predict the motor torque demand T in advance based on the recognized driver intention. req and the vehicle's power demand P req Then, based on the power matching relationship between the power battery and the motor, the power demand P of the future power battery is calculated. Battery .

[0092] Based on the obtained future power battery power demand P Battery , referring to the current state of the power battery obtained in real time by the battery management system, the heat generation power of the battery system can be further calculated. Moreover, based on the heat generation power, the temperature rise of the power battery system and the maximum temperature T at a certain moment in the future can be calculated. max .

[0093] If T maxIf the temperature is greater than the set threshold, the vehicle controller outputs the corresponding vehicle control strategy to enable the battery thermal management system to start the cooling electronic water pump in advance to cool the power battery in advance, ensuring that the power battery always operates in the optimal temperature range, greatly improving the cycle life of the power battery.

[0094] It should be noted that, in the present invention, when calculating T max When the inventors took into account the influence of road conditions and external ambient temperature on the heat generation of the power battery, they can formulate the corresponding correction coefficient K1 based on the collected road environment (including road conditions and external ambient temperature) and use it in the prediction of T max When , the correction coefficient K1 is introduced for correction.

[0095] Therefore, the power battery thermal management system of the present invention can not only control the power battery temperature, but also ensure continuous power output of the entire vehicle, avoid power limitation caused by excessive power battery system temperature, and extend the service life of the power battery.

[0096] For example, taking a pure electric ternary battery cell as an example, the test results show that its cycle life at room temperature of 25°C can reach 2,000 cycles, 100% charge and discharge deep cycle, and decay to 80% SOH; while at a high temperature of 45°C, the cycle life is only 1,000 cycles, 100% charge and discharge deep cycle, and decay to 80% SOH.

[0097] In the present invention, the Figure 2 The specific process of the power battery thermal management system shown in the figure to perform thermal management control on the temperature of the power battery is designed by the inventor as a new power battery thermal management method. The power battery thermal management is implemented based on the above system. The specific control strategy process can be found in the following Figure 3 .

[0098] Figure 3 The figure is a flow chart of the control strategy of the power battery thermal management method according to one embodiment of the present invention.

[0099] like Figure 3 As shown, in this embodiment, the control process of the power battery thermal management method of the present invention for the power battery may specifically include the following S101-S116:

[0100] S101: The power battery thermal management control strategy process based on driver intention recognition is started.

[0101] S102: The vehicle controller detects the vehicle speed v; the accelerator pedal opening α, the accelerator pedal opening change rate dα / dt; the brake pedal opening β, the brake pedal opening change rate dβ / dt in real time, these characteristic signals used to identify the driver's intention, and feeds the signals back to S103.

[0102] S103: Calculate the steady-state basic torque and basic power according to the characteristic signal of S102 to obtain the basic torque T0 and basic power P0 of the whole vehicle.

[0103] The basic torque T0 of the vehicle is related to the accelerator pedal opening α, the vehicle speed v, and the collected external ambient temperature Tc. It can be calculated by formulating the basic torque MAP table:

[0104] T0=f(α,v,Tc)

[0105] P0=f(T0,v,Tc)

[0106] This method of calculating T0 and P0 is well known in the prior art and will not be described in detail here.

[0107] S104: Based on the characteristic signal of S102, a fuzzy control method is used to establish a membership function of the characteristic signal and a fuzzy inference rule to output a quantified driver intention (i.e., acceleration intention w and deceleration intention u):

[0108] w = fuzzy(α, dα / dt, v)

[0109] u=fuzzy(β,dβ / dt,v)

[0110] In the present invention, acceleration intention w can be identified using accelerator pedal opening α, accelerator pedal change rate dα / dt, and vehicle speed v as identification parameters, while braking intention u can be identified using brake pedal opening β, brake pedal change rate dβ / dt, and vehicle speed v as identification parameters.

[0111] S105: Based on the collected road environment, the external ambient temperature of the vehicle and the real-time road conditions are predicted.

[0112] In the actual application of power batteries, the inventors have found that different external environments and real-time continuous vehicle operating conditions have great differences in the size and urgency of power battery cooling requirements.

[0113] For example: under highway conditions, when the vehicle is continuously driving on high-temperature and high-speed roads, the power battery output power is large and the heat generated is large. The battery system can easily reach the preset temperature threshold. In order to ensure the safety of the battery and the continuity of power output and avoid limiting the power output due to overheating, and also to better maintain the service life of the power battery, the power battery needs to be cooled quickly.

[0114] In the suburban working condition, when the whole vehicle continuously runs at low and medium speed at normal temperature, the output power of the battery system is relatively small, and the cooling demand of the power battery is not very urgent, and the power battery cooling instruction can be appropriately delayed.

[0115] In the urban working condition, although the vehicle speed is not very high, the battery continuously outputs small power, but the urban working condition is complex, and the battery system needs to be accelerated and decelerated constantly, and the instantaneous power output of the battery system is large, and the cooling demand is also urgent.

[0116] Therefore, in order to make the power battery thermal management method of the present application adaptively adjust the cooling power demand of the power battery according to the external environment of the whole vehicle and the real-time road working condition. According to the collected road environment, the inventor further sets a correction coefficient K1 so as to predict the maximum temperature of the power battery at a future time, and further predict the heat generation and temperature rise of the power battery.

[0117] It should be noted that in the present application, the collected road environment feature signal specifically includes road working condition and external environment temperature. In the present embodiment, the inventor divides the road working condition into high-speed working condition, suburban working condition and urban working condition, and divides the external environment temperature into low temperature, normal temperature and high temperature.

[0118] Meanwhile, the inventor formulates different correction coefficients K1 according to different road working conditions and external environment temperatures as shown in Table 1.

[0119] Table 1 lists the correction coefficients K1 under different road working conditions and external environment temperatures.

[0120] Table 1.

[0121]

[0122] S106: According to the quantized acceleration intention w or deceleration intention u output by S104, the quantized driver intention is used as a correction coefficient K required by the basic torque and basic power of the whole vehicle, that is, the correction coefficient K of the torque of the vehicle and the power of the motor:

[0123]

[0124] S107: According to the correction coefficient K and the steady-state basic torque T0 of the vehicle and the basic power P0 of the motor, the torque demand T of the whole vehicle and the power demand P of the motor are predicted: req req That is:

[0125] T req = KT0

[0126] P req = KP0​

[0127] Wherein, K is the correction coefficient K obtained in the above S106, T0 is the steady-state basic torque of the vehicle obtained in the above S103, and P0 is the basic power of the motor obtained in the above S103.

[0128] S108: The power requirement P of the motor obtained in S107 req , predict the power demand P of future power batteries Battery .

[0129] It should be noted that the power demand P of future power batteries Battery It is linearly related to the power of the motor, but there is a certain power loss in the power transmission process from the power battery to the motor end. Therefore, when predicting the power demand P of the power battery in the future, Battery When calculating the charging and discharging efficiency of the entire vehicle, it is necessary to consider the charging and discharging efficiency of the entire vehicle.

[0130] P Battery =P req / η

[0131] η=f(Tc,C,soc)

[0132]

[0133] Among them, η represents the charge and discharge efficiency; Tc represents the ambient temperature; C represents the power battery discharge rate; soc represents the state of charge of the power battery; η1 represents the charging efficiency of the whole vehicle, and η2 represents the discharge efficiency of the whole vehicle, which can all be obtained by referring to the existing parameter table. For example, the η1 whole vehicle charging efficiency MAP table consists of three parameters. The ambient temperature can be set to -30℃; -25℃;...; 0℃;...; 25℃;...; 60℃, the discharge rate can be set to 1 / 3C; 1 / 2C; 1C; 2C; 3C;...; 10C; the battery SOC can be set to 5%;...; 100%; any combination of the three groups of parameters will have a charging efficiency point. These points constitute the charging efficiency MAP table, and the whole vehicle discharge efficiency is also obtained by a similar method.

[0134] S109: Based on the power demand P of future power batteries Battery , predict the heat generation power Q of the power battery Battery , and further predict the maximum temperature T of the power battery at a certain moment in the future max .

[0135] In the present invention, the heat generation power Q of the power battery Battery Referring to the battery heat generation model developed by D.Bernardi based on the working principle of the battery itself and taking into account the reversible reaction, phase change process, mixing effect and Joule heat generation, the calculation process is as follows:

[0136] QBattery =I 2 R-ITδ

[0137] I=P Battery / U_soc

[0138] R=f(soc,T)

[0139] δ=g(soc,T)=dU soc / dT

[0140] Where I is the charge and discharge current; R is the battery internal resistance; δ is the entropy thermal coefficient of the power battery; T is the power battery temperature; and U_soc represents the open-circuit voltage. The battery internal resistance R, which includes both ohmic and polarization resistance, is related to the power battery's state of charge (SOC) and temperature (T). The entropy thermal coefficient δ is related to the open-circuit voltage (U_soc) and the power battery temperature (T).

[0141] Accordingly, in step S109, according to the predicted heating power Q of the power battery Battery , can further predict the temperature rise of the power battery and the possible maximum temperature T at a certain time in the future max .

[0142] In this embodiment, considering the influence of road conditions and external ambient temperature on the heat generation of the power battery, the predicted T max When the correction coefficient K1 is obtained based on the collected road environment in the above step S105, it is introduced.

[0143] Q Battery *t n =C Battery m Battery (T n -T)

[0144]

[0145] Among them, C Battery is the specific heat capacity of the battery; m Battery is the mass of the battery; K1 is the revision coefficient based on the external ambient temperature and road conditions; T n is the predicted battery temperature at a certain moment in the future; t n It is the continuous charging and discharging time of the power battery.

[0146] It should be noted that the continuous charge and discharge time of the power battery is t n It can be set to a fixed value, such as 5 minutes, 10 minutes, or 30 minutes, or it can be calculated in real time using big data analysis based on the driving conditions, road environment, and vehicle posture provided by the vehicle navigation.

[0147] S110: The maximum temperature T of the power battery at a certain moment in the future predicted by S109 max ; Judge T max Is it greater than the preset temperature threshold T1? max >T1, then start the following step S111; if it is judged that T max <T1, then return to the above step S102.

[0148] It should be noted that the preset temperature threshold T1 needs to comprehensively consider the trade-offs of battery life, vehicle energy consumption, and cruising range. In this embodiment, it can be set to 32°C to 35°C based on project experience and project requirements.

[0149] S111: According to the heat power Q of the power battery Battery , estimate the coolant flow rate q cooling The requirements are sent to S112.

[0150] Q Cooling =λQ Battery =C cooling ρ cooling q cooling (T outlet -T inlet )

[0151] Among them, Q Cooling is the total heat dissipation power of the cooling system; λ is the heat loss coefficient caused by the influence of the heat conducting medium and the external environment during the heat conduction process of the entire cooling system. Its value range is 1.1 to 1.3 according to the specific project; C cooling is the specific heat capacity of the cooling liquid; q cooling is the coolant flow rate; T outlet is the coolant outlet temperature; T inlet is the coolant inlet temperature; T outlet -T inlet It is the temperature rise of the coolant, which is generally taken as 5℃~10℃ according to the specific project requirements.

[0152] S112: Coolant flow rate q according to S111 feedback cooling According to the demand, the cooling electronic water pump is turned on and the PWM duty cycle of the electronic water pump is adjusted to output the required flow demand q cooling .

[0153] S113: The BMS battery management system monitors the temperature T of the power battery in real time and updates the predicted maximum temperature T in real time. max .

[0154] S114: Update the predicted maximum temperature T according to S113 max , judge the updated T maxIs it greater than the preset temperature threshold T1? max >T1, then return to step S111 and re-estimate the coolant flow rate q cooling Demand; if T max <T1, then start the following S115.

[0155] S115: Turn off the cooling electronic water pump.

[0156] S116: The process of the power battery thermal management control strategy based on driver intention recognition ends.

[0157] From the foregoing, it can be seen that the power battery thermal management method designed based on the present invention can identify the driver's intention and predict the battery system cooling power demand in advance, thereby cooling the power battery in advance so that the power battery always operates within the set optimal temperature range.

[0158] At the same time, this power battery thermal management method can not only predict the cooling power requirements of the battery system in advance, but also adaptively adjust the cooling power requirements according to external environments such as road conditions and current vehicle speed.

[0159] It should be noted that, in the present invention, the recognition of the driver's intention is very important, which is directly related to the subsequent temperature control of the power battery. Figure 4 Detailed description Figure 3 The specific process of identifying the driver's intention in this power battery thermal management method is shown.

[0160] Figure 4 for Figure 3 The flowchart shown is a power battery thermal management method for identifying driver intention.

[0161] In this invention, driver intention is categorized into two types based on the vehicle's operating state: acceleration intention w and braking intention u. For acceleration or overtaking, the driver primarily operates the accelerator, which can be classified as acceleration intention w; while an intention accompanied by brake pedal operation can be classified as braking intention u.

[0162] In addition, when the vehicle is coasting at a constant speed, the driver does not operate the accelerator pedal and the brake pedal. At this time, it is recognized as a constant speed coasting intention, which is independent of the acceleration intention w and the braking intention u. Figure 3 The power battery thermal management method shown remains at step S103 .

[0163] like Figure 4As shown, in the present invention, the process of using the vehicle controller, the acceleration fuzzy intention controller and the braking fuzzy intention controller to identify the driver's intention is shown in the following S301-S314:

[0164] S301: The driver intention recognition process starts.

[0165] S302: The vehicle controller detects a characteristic signal of the accelerator pedal opening α and feeds the signal back to S305 and S310.

[0166] S303: The vehicle controller detects a characteristic signal of the vehicle speed v and feeds the signal back to S306, S310 and S311.

[0167] S304: The vehicle controller detects the characteristic signal of the brake pedal opening β and feeds the signal back to S307 and S311.

[0168] S305: Determine whether the accelerator pedal opening α is greater than 0; if α>0, it means that the vehicle is running in the acceleration state, and the signal is fed back to S308.

[0169] S306: If v>0, and it is determined that the accelerator pedal opening signal α=0 and the brake pedal opening signal β=0, the signal is fed back to S314.

[0170] S307: Determine whether the accelerator pedal opening signal β is greater than 0; if β>0, it means that the vehicle is running in a deceleration state, and the signal is fed back to S309.

[0171] S308: The vehicle controller detects the characteristic signal of the accelerator pedal opening change rate dα / dt and feeds the signal back to the acceleration fuzzy intention controller in S310.

[0172] S309: The vehicle controller detects the characteristic signal of the accelerator pedal opening change rate dβ / dt and feeds the signal back to the braking fuzzy intention controller in S311.

[0173] S310: Accelerate the fuzzy intention controller.

[0174] In the present invention, the driver's acceleration intention is a relatively vague concept, and its identification process is difficult to achieve by establishing an accurate mathematical model. The fuzzy recognition method is widely used in engineering technology because it does not rely on the precise mathematical model of the controlled object. It has the advantages of being easy to be accepted by operators, easy to implement with computer software, and good robustness and adaptability. It is very suitable for identifying driver intentions.

[0175] It should be noted that the general steps of fuzzy recognition are: first, feature parameters must be selected and fuzzified. The fuzzification process is actually the process of formulating the membership function of the feature parameters. Next, a fuzzy inference rule base must be established. This establishment of a fuzzy inference rule base is the core of fuzzy recognition and the foundation of fuzzy reasoning. However, the result of fuzzy reasoning is still a fuzzy quantity, so it must be defuzzified to obtain the final quantitative fuzzy recognition result.

[0176] Therefore, in the present invention, an acceleration fuzzy intention controller is used to identify the driver's acceleration intention using a fuzzy recognition method.

[0177] Acceleration intention is recognized through three characteristic signals: vehicle speed v, accelerator pedal opening α, and accelerator pedal opening change rate dα / dt. Among them, vehicle speed v reflects the vehicle driving state, α reflects the driver's torque and power requirements, and dα / dt reflects the driver's acceleration urgency.

[0178] In the present invention, an acceleration intention fuzzy controller is constructed based on the above three characteristic signals, and a three-input single-output fuzzy inference model is selected to establish a corresponding characteristic signal membership function.

[0179] Figure 5 The membership function of the vehicle speed v created in the acceleration intention recognition in one embodiment is schematically shown.

[0180] The membership function is established by using the typical membership function approximation method. Figure 5 As shown, in this embodiment, the definition range of the vehicle speed v is [0,150] km / h, and its fuzzy subset is defined as {L (low speed), M (medium speed), H (high speed)}.

[0181] Figure 6 The membership function established for the accelerator pedal opening α in one embodiment is schematically shown.

[0182] like Figure 6 As shown, in this embodiment, the accelerator pedal opening α is defined in the range of [0,1], and its fuzzy subset is defined as {VS (small), S (small), M (medium), B (large), VB (large)}.

[0183] Figure 7 The membership function of the accelerator pedal opening change rate dα / dt established in one embodiment is schematically shown.

[0184] like Figure 7 As shown, in this embodiment, the accelerator pedal opening change rate dα / dt is defined in the range of [-1,1], and its fuzzy subset is defined as {VS (small), S (small), M (medium), B (large), VB (large)}.

[0185] Figure 8 The membership function of the driver's acceleration intention w established in one embodiment is schematically shown.

[0186] like Figure 8 As shown in the embodiment, the driver's acceleration intention w is defined as the range of [1, 1.3], and its fuzzy subsets are defined as {BL (gentle acceleration), L (relatively gentle acceleration), M (normal acceleration), BH (relatively urgent acceleration), H (urgent acceleration)}, and the membership function is established as follows: Figure 8 shown.

[0187] Accordingly, the driver's intention also corresponds to the corresponding torque and power demand intentions. After defuzzification and quantification, it can be equivalent to the torque and power demand correction coefficients of the entire vehicle. Therefore, based on experience and simulation, a fuzzy inference rule table for acceleration intention w is established, as shown in Table 2 below.

[0188] Table 2 lists the fuzzy control rules for the acceleration intention w.

[0189] Table 2.

[0190]

[0191]

[0192] S311: Deceleration fuzzy intention controller.

[0193] Accordingly, the driver's deceleration intention (i.e., braking intention u) is also a relatively fuzzy concept, and its identification process must also be achieved through a fuzzy recognition mathematical model. Therefore, in the present invention, a braking fuzzy intention controller is used to identify the driver's deceleration intention using a fuzzy recognition method.

[0194] Deceleration intention is identified through three characteristic signals: vehicle speed v, brake pedal opening β, and brake pedal opening change rate dβ / dt. Among them, vehicle speed v reflects the vehicle driving state, β reflects the driver's braking torque and braking power requirements, and dβ / dt reflects the driver's acceleration urgency.

[0195] In the present invention, a deceleration intention fuzzy controller is constructed based on the above three characteristic signals, and a three-input single-output fuzzy inference model is selected to establish a characteristic parameter membership function.

[0196] Figure 9 The membership function of the vehicle speed v generated in the braking intention detection in one embodiment is schematically shown.

[0197] The membership function is established by using the typical membership function approximation method. Figure 9As shown, in this embodiment, the definition range of the vehicle speed v is [0,150] km / h, and its fuzzy subset is defined as {L (low speed), M (medium speed), H (high speed)}.

[0198] Figure 10 The membership function of the brake pedal opening β established in one embodiment is schematically shown.

[0199] like Figure 10 As shown, in this embodiment, the range of the brake pedal opening β is defined as [0,1], and its fuzzy subset is defined as {VS (small), S (small), M (medium), B (large), VB (large)}.

[0200] Figure 11 The membership function of the brake pedal opening change rate dβ / dt established in one embodiment is schematically shown.

[0201] like Figure 11 As shown, in this embodiment, the brake pedal opening change rate dβ / dt is defined as ranging from [-1, 1], and its fuzzy subset is defined as {VS (small), S (small), M (medium), B (large), VB (large)}.

[0202] Figure 12 The membership function of the driver's braking intention u established in one embodiment is schematically shown.

[0203] like Figure 12 As shown, in this implementation, the driver's braking intention u (i.e., deceleration intention) is defined as the range [1, 1.2], and its fuzzy subsets are defined as {BL (gentle deceleration), L (relatively gentle deceleration), M (normal deceleration), BH (relatively rapid deceleration), H (rapid deceleration)}. Based on this, a fuzzy inference rule table for braking intention u is established based on experience and simulations, as shown in Table 3 below.

[0204] Table 3 lists the fuzzy control rules of braking intention u.

[0205] Table 3.

[0206]

[0207] S312: The fuzzy controller outputs the quantized acceleration intention w according to the acceleration intention in S310.

[0208] The driver's acceleration intention w also corresponds to the corresponding torque and power demand intentions. After defuzzification and quantization, it can be equivalent to the correction coefficient K for the vehicle torque and motor power. There are many defuzzification methods, such as the maximum membership method, the center of gravity method, the median method, and the area bisection method. In this embodiment, the center of gravity method is used.

[0209] S313: The fuzzy controller outputs the quantized braking intention u based on the deceleration intention in S311.

[0210] The driver's deceleration intention also corresponds to the corresponding braking torque and braking power requirements. After defuzzification and quantification, it can be equivalent to the correction coefficient K for the vehicle torque and motor power. There are many defuzzification methods, such as the maximum membership method, the center of gravity method, the median method, and the area bisection method. In this embodiment, the center of gravity method is used.

[0211] S314: Outputting the vehicle's uniform speed driving state according to the determination signal in S306.

[0212] From the above, it can be seen that the power battery thermal management method and system based on driver intention recognition designed by the present invention can further reduce the energy consumption of battery thermal management. Since the overheating demand is identified in advance, it can be cooled with a relatively small flow of cooling water and cooling power, and the cooling power and cooling flow used can be fed back in real time for adaptive adjustment.

[0213] It should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features recorded in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0214] It should also be noted that the embodiments listed above are merely specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments, and similar changes or modifications made therefrom that can be directly derived from or easily conceived by those skilled in the art based on the disclosure of the present invention are intended to fall within the scope of protection of the present invention.

Claims

1. A power battery thermal management method based on driver intention recognition, characterized in that: Including steps: 100: collecting characteristic signals for driver intention recognition; 200: Using fuzzy control methods to establish the membership function of characteristic signals and fuzzy inference rules to output the quantified driver intention; 300: Obtain the correction coefficient K of the vehicle torque and the motor power according to the recognized driver intention, and predict the vehicle torque demand T according to the correction coefficient K, the vehicle steady-state basic torque T0 and the motor basic power P0. req =KT0 and motor power demand P req =KP0; 400: Based on the power demand of the motor, predict the future power demand P of the power battery Battery ; 500: Based on the future power demand of the power battery, predict the heat power of the power battery, and calculate the maximum temperature T of the power battery at a certain moment in the future based on the heat power max ; 600: If T max If the temperature is greater than the set threshold, the power battery is cooled in advance so that the power battery always operates within the set optimal temperature range.

2. The power battery thermal management method based on driver intention recognition according to claim 1, characterized in that: The characteristic signals include: accelerator pedal change rate, accelerator pedal opening, current vehicle speed, brake pedal opening, brake pedal change rate, road conditions and external ambient temperature.

3. The power battery thermal management method based on driver intention recognition according to claim 1, characterized in that: The driver's intention includes an acceleration intention w and a braking intention u.

4. The power battery thermal management method based on driver intention recognition according to claim 3, characterized in that: The acceleration intention w ranges from [1, 1.3], and its fuzzy subsets are {smooth acceleration, relatively smooth acceleration, normal acceleration, relatively urgent acceleration, and urgent acceleration}; and / or the braking intention u ranges from [1, 1.2], and its fuzzy subsets are {smooth deceleration, relatively smooth deceleration, normal deceleration, relatively urgent deceleration, and urgent deceleration}.

5. The power battery thermal management method based on driver intention recognition according to claim 1, characterized in that: In step 300 , the correction coefficient K is obtained by defuzzification and quantization.

6. The power battery thermal management method based on driver intention recognition according to claim 5, characterized in that: In step 300, the defuzzification quantization is performed using the center of gravity method.

7. The power battery thermal management method based on driver intention recognition according to claim 1, characterized in that: In step 400, the future power demand P of the power battery is predicted based on the following formula: Battery : P.S Battery JP req / η η=f(Tc,C,soc) Where η represents the charge and discharge efficiency, Tc represents the ambient temperature, C represents the power battery discharge rate, soc represents the state of charge of the power battery, η1 represents the charging efficiency of the whole vehicle, and η2 represents the discharge efficiency of the whole vehicle.

8. The power battery thermal management method based on driver intention recognition according to claim 7, characterized in that: In step 500, the heat generation power Q of the power battery is predicted based on the following formula: Battery : Q Battery =I 2 R-ITδ I=P Battery / U_soc R=f(soc,T) δ=g(soc,T)=dU soc / dT Where, I is the charge and discharge current; R is the internal resistance of the power battery; T is the power battery temperature; δ is the entropy thermal coefficient of the power battery, and U_soc represents the open circuit voltage; Calculate the maximum temperature T of the power battery at a certain moment in the future based on the following formula max : Q Battery *t n =C Battery m Battery (T n -T) Among them, C Battery is the specific heat capacity of the power battery; m Battery is the mass of the power battery; K1 is the revision coefficient formulated based on the external ambient temperature and road conditions; T n is the predicted power battery temperature at a certain moment in the future; t n It is the continuous charging and discharging time of the power battery.

9. The power battery thermal management method based on driver intention recognition according to claim 1, characterized in that: Step 600 includes: if T max If the temperature is greater than the set threshold, then: 601: According to the heat power Q of the power battery Battery , estimate the required coolant flow q cooling : Q Cooling =λQ Battery =C cooling ρ cooling q cooling (T outlet -T inlet ) Among them, Q Cooling is the total heat dissipation power of the cooling system; λ is the heat loss coefficient, which ranges from 1.1 to 1.3; C cooling is the specific heat capacity of the cooling liquid; ρ cooling is the density of the coolant; T outlet is the coolant outlet temperature; T inlet is the coolant inlet temperature; T outlet -T inlet is the temperature rise of the coolant; 602: Turn on the cooling electronic water pump and adjust the electronic water pump PWM duty cycle to achieve the required coolant flow q cooling ; 603: Real-time monitoring of the power battery temperature T and real-time update of T max .

10. A power battery thermal management system based on driver intention recognition, characterized in that: include: a collection device for collecting characteristic signals for driver intention recognition, the characteristic signals including: accelerator pedal change rate, accelerator pedal opening, current vehicle speed, road conditions and external ambient temperature, brake pedal opening, and brake pedal change rate; The acceleration fuzzy intention controller uses fuzzy control methods to establish the membership function of the characteristic signal and fuzzy inference rules to output the quantized acceleration intention based on the input accelerator pedal change rate, accelerator pedal opening, and current vehicle speed; A braking fuzzy intention controller uses fuzzy control methods to establish a membership function of characteristic signals and fuzzy inference rules to output a quantized braking intention based on the inputs of current vehicle speed, brake pedal opening, and brake pedal change rate; Power battery and battery management system, the battery management system obtains the status of the power battery; Cooling electronic water pump; The vehicle controller is configured as follows: The correction coefficient K of the vehicle's torque and the motor's power is obtained according to the acceleration intention and the braking intention. The vehicle's torque demand T is predicted based on the correction coefficient K, the vehicle's steady-state basic torque T0, and the motor's basic power P0. req =KT0 and motor power demand P req =KP0; According to the power demand of the motor, predict the power demand P of the future power battery Battery ; Based on the power demand of the power battery in the future, the heat generation power of the power battery is predicted, and the maximum temperature T of the power battery at a certain moment in the future is calculated based on the heat generation power. max ; If T max If the temperature is greater than the set threshold, the power battery is cooled in advance by controlling the cooling electronic water pump so that the power battery always operates within the set optimal temperature range; The battery management system monitors the temperature T of the power battery in real time and updates T in real time max .

Images