Electric vehicle battery thermal management method and system under intelligent network connection

Abstract

The invention discloses an electric vehicle battery thermal management method and system under intelligent network connection. The method comprises the steps that the road traffic flow state, the environment temperature, the real-time temperature of a power battery and vehicle driving state data are obtained; establishing a vehicle driving state prediction model, and predicting the vehicle driving state in the next time period by using the historical driving state data of the vehicle; establishing a thermal management model prediction controller, and predicting the temperature state of the power battery in the next time period; determining the optimal working temperature range of the battery according to the type of the power battery used by the automobile; performing power battery temperature state division according to the power battery type and the predicted power battery temperature state; a corresponding battery thermal management prediction control strategy is adopted according to the temperature state of the power battery; and a power battery emergency heat management strategy is adopted. According to the battery temperature pre-management strategy, the battery temperature control accuracy is high, and the safety and the cruising ability of the battery are guaranteed.

Description

Technical Field

[0001] The present invention relates to a thermal management method and system for an electric vehicle battery under intelligent networking, and belongs to the technical field of power batteries. Background Art

[0002] The power source of electric vehicles is the power battery. The power battery will generate a lot of heat during use. Excessive battery operating temperature will directly affect its performance and life. In order to avoid excessive heat accumulation, a battery thermal management system is needed to dissipate heat for the power battery. A reasonable thermal management system can control the battery temperature to avoid overheating or overcooling, which helps to improve the safety and reliability of the battery, extend the battery life, and ensure battery performance. Against the background of the rapid development of power battery technology, society is paying more and more attention to the performance and safety of power batteries. Considering the complex thermal environment of electric vehicles under different operating conditions, the traditional single thermal management strategy has shown its inadequacy in coping with the thermal management needs of power batteries. Therefore, researching and developing a multi-mode thermal management coupling system to achieve better thermal management effects has become a research hotspot in the field of power battery thermal management.

[0003] With the help of technologies such as artificial intelligence, big data and 5G, intelligent connected vehicles are equipped with advanced sensors, controllers and actuators to achieve all-round connections between vehicles, between vehicles and roads, between vehicles and people, and between vehicles and service platforms. Despite the continuous advancement of technology, intelligent connected vehicles face the problems of lagging construction of smart transportation infrastructure and high manufacturing costs. In addition, the existing autonomous driving technology still faces technical challenges in complex environments and is not yet fully mature. The promotion of intelligent connected vehicles also needs to adapt to existing laws and regulations. Many countries and regions have not yet established a complete legal framework in terms of liability identification and insurance, and there are network security risks in vehicle systems and network communications. Consumers still have low trust and acceptance of autonomous vehicles, and are worried about safety and reliability. Summary of the invention

[0004] Purpose of the invention: In view of the deficiencies in the prior art, the present invention provides a method and system for thermal management of electric vehicle batteries under intelligent networking. The present invention classifies the power battery temperature state according to the predicted power battery temperature state and the power battery type, and adopts corresponding battery thermal management prediction and control strategies according to different power battery temperature states to achieve battery thermal management.

[0005] Technical solution: A method for thermal management of electric vehicle batteries under intelligent networking, comprising the following steps:

[0006] Step 1: Obtain road traffic flow status, ambient temperature, real-time power battery temperature, and vehicle driving status data;

[0007] Step 2: Establish a vehicle driving state prediction model and use the vehicle's historical driving state data to predict the vehicle's driving state in the next time period;

[0008] Step 3: Establish a thermal management model prediction controller, and use the vehicle's current driving data and the vehicle's driving state data predicted in step 2, as well as the current ambient temperature, to predict the power battery temperature state in the next time period;

[0009] Step 4: Determine the optimal operating temperature range of the battery according to the type of power battery used in the car;

[0010] Step 5: classifying the power battery temperature state according to the power battery type in step 4 and the power battery temperature state predicted in step 3;

[0011] Step 6: Adopt corresponding battery thermal management prediction control strategy according to the temperature state of the power battery;

[0012] Step 7: After adopting the battery thermal management predictive control strategy, if the real-time temperature of the power battery still exceeds the limit threshold range, immediately adopt the power battery emergency thermal management strategy.

[0013] Preferably, in step one, the road traffic flow status data includes traffic flow information and road grade, and the vehicle driving status data includes speed, acceleration, deceleration, average vehicle speed, and parking time.

[0014] Preferably, the step 2 is specifically as follows:

[0015] The BP back propagation neural network model is used to analyze the data of the previous time period and predict the vehicle status and road status data of the next time period. That is, a total driving distance is divided into n travel segments, and the average vehicle speed and idle time ratio of each travel segment are calculated. The speed prediction working condition of each travel segment is determined, and the corresponding trained BP neural network speed prediction sub-model is selected according to the working condition category. Five speed prediction feature parameters and corresponding neighboring vehicle speed data are extracted from the travel segment data to predict the speed of the next time period. The speed prediction feature parameters of each prediction are combined, which is the output of the BP neural network model, that is, the input of the thermal management predictive control strategy.

[0016] Preferably, the BP neural network vehicle speed prediction sub-model is specifically:

[0017] The BP neural network model is divided into 6 BP neural network speed prediction sub-models, corresponding to 6 speed prediction conditions:

[0018] 1.v m <20km / h and p d <20%;

[0019] 2.v m <20km / h and p d ≥20%;

[0020] 3. 20km / h≤v m <40km / h and p d <20%;

[0021] 4. 20km / h≤v m <40km / h and p d ≥20%;

[0022] 5. 40km / h≤v m <80km / h;

[0023] VI. m ≥80km / h.

[0024] Preferably, the five vehicle speed prediction characteristic parameters are: average vehicle speed v m , speed variance f v , idling time ratio p d , positive acceleration mean a m , velocity multiplied by the variance of acceleration f va ;

[0025] Average speedv m , refers to the average vehicle speed over the sampling time, and the calculation formula is:

[0026]

[0027] Among them, v i is the vehicle speed at the i-th second in the sampling time length, and n is the sampling time length;

[0028] Speed ​​variance f v , refers to the velocity variance over the sampling time length, and the calculation formula is:

[0029]

[0030] Among them, v i is the vehicle speed at the i-th second in the sampling time length, v m is the average vehicle speed over the sampling time, and n is the sampling time;

[0031] Idle time ratio p d , refers to the percentage of idling time in the sampling time length, and the calculation formula is:

[0032]

[0033] Among them, t dis the length of idling time on the sampling time length, T is the sampling time length;

[0034] Average value of positive acceleration a m , refers to the average value of all positive accelerations over the sampling time length, and the calculation formula is:

[0035]

[0036] Among them, a i is the positive acceleration of the ith second in the sampling time length, and m is the duration of the positive acceleration in the sampling time length;

[0037] Speed ​​multiplied by acceleration variance f va , refers to the variance of the velocity multiplied by the acceleration over the sampling time length, and the calculation formula is:

[0038]

[0039] Among them, va i is the velocity multiplied by the acceleration at the i-th second of the sampling time, va m It is the average value of velocity multiplied by acceleration over the sampling time length. Since the acceleration is one value less than the velocity length, the first acceleration value is filled with 0;

[0040] The output formula of the BP neural network model is:

[0041]

[0042] Among them, H j is the hidden layer output, ω jk is the connection weight between the hidden layer and the output layer, b k (k=1,2,…,m) is the threshold value of each neuron in the output layer, and m is the number of neurons.

[0043] Preferably, the step three is specifically as follows:

[0044] The Kalman filter state estimator is used to establish a thermal management model predictive controller, and the predicted vehicle driving state data obtained in step 2 is used to The current ambient temperature and the real-time temperature of the power battery are used as inputs of the thermal management model prediction controller to predict the power battery temperature value in the next time period. This process is specifically expressed as:

[0045] State prediction equation:

[0046] State update equation:

[0047] in: is the estimated value of the predicted system state, is the estimated value of the system state at the previous moment, is the error covariance matrix, P k-1 is the prediction error covariance matrix, A and B are the state transfer matrix and input matrix respectively, u k is the control input, Q k is the process noise covariance matrix, K k represents the Kalman gain, C is the Jacobian matrix, y k is the measured value, is the updated system state estimate, I is the unit matrix, P k is the updated error covariance matrix, and R is the observation covariance matrix.

[0048] Preferably, the step five is specifically as follows:

[0049] According to the predicted power battery temperature, the power battery temperature state is divided, and the abnormal temperature that exceeds the optimal operating temperature range of the power battery is divided into four modes:

[0050] High temperature mode 1: 30℃-40℃;

[0051] High temperature mode 2: 40℃-60℃;

[0052] Low temperature mode 1: 10℃-20℃;

[0053] Low temperature mode 2: -20℃-10℃.

[0054] Preferably, the step six is ​​specifically as follows:

[0055] When the temperature of the coolant inside the power battery is in high temperature mode 1, the coolant is cooled in the first stage and circulated through the power battery to cool the power battery. If the battery temperature has not dropped to 30°C after 1 minute, the system will jump to the battery thermal management prediction control strategy in high temperature mode 2: the coolant is cooled in the second stage to make the battery reach the optimal temperature range;

[0056] When the coolant temperature inside the power battery is in low temperature mode 1, the coolant is heated up in the first stage and circulated through the power battery to heat up the power battery. If the battery temperature has not risen to 20°C after 1 minute, the system jumps to the battery thermal management predictive control strategy of low temperature mode 2: the coolant is heated up in the second stage to make the battery reach the optimal temperature range.

[0057] Preferably, the step seven is specifically as follows:

[0058] When the real-time temperature of the power battery exceeds the set limit threshold range of -20℃-60℃, the power battery emergency thermal management strategy is activated, and the battery thermal management is no longer performed according to the predicted temperature. When the power battery temperature stabilizes within the set temperature threshold of 20℃-30℃ for 180s, the power battery emergency thermal management strategy is exited, the battery thermal management predictive control strategy is activated, and the battery resumes normal operation.

[0059] A system for realizing a thermal management method of an electric vehicle battery under intelligent networking includes a hydraulic pump, a first temperature sensor, a second temperature sensor, a first one-way valve, a compressor, a battery low temperature management module, and a battery high temperature management module, which are respectively connected to a controller by signal. The hydraulic pump, the first temperature sensor, the power battery, the second temperature sensor, the first one-way valve, and the compressor are connected in sequence.

[0060] The battery low temperature management module includes a primary heating component and a secondary heating component, the primary heating component includes a first heating solenoid valve, a plate heat exchanger, and a second one-way valve, and the hydraulic pump, the first heating solenoid valve, the plate heat exchanger, the second one-way valve, and the compressor are connected in sequence;

[0061] The secondary temperature raising component comprises a second heating solenoid valve, a PTC heater and a third one-way valve connected in sequence; one end of the secondary temperature lowering component is connected in parallel between the hydraulic pump and the first temperature sensor, and the other end is connected in parallel between the second temperature sensor and the first one-way valve;

[0062] The battery high temperature management module includes a primary cooling component and a secondary cooling component, the primary cooling component includes a first cooling solenoid valve, an evaporator, an external condenser, and a fourth one-way valve, and the hydraulic pump, the first cooling solenoid valve, the evaporator, the external condenser, the fourth one-way valve, and the first one-way valve are connected in sequence;

[0063] The secondary cooling component includes a second cooling solenoid valve arranged between the external condenser and the compressor.

[0064] Beneficial effects: The cloud data of the intelligent network system is used to predict the driving status of the vehicle. The cloud data is updated in real time and covers a wide range of road sections, ensuring the authenticity and extensiveness of the data. The driving status is predicted within the vehicle driving section, and the battery temperature is qualitatively and quantitatively analyzed, which provides new ideas and methods for the battery thermal management control strategy. At the same time, the battery temperature status prediction is also applicable to the whole vehicle thermal management model and cabin thermal management model of intelligent networked vehicles. The battery temperature pre-management strategy of the present invention has a high battery temperature control accuracy, ensuring the safety and endurance of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0066] Figure 1 is a flow chart of the method of the present invention;

[0067] Figure 2 It is a system structure diagram of the present invention;

[0068] Figure 3 It is the principle diagram of the method of the present invention;

[0069] Figure 4 is a schematic diagram of high temperature mode 1 of the present invention;

[0070] Figure 5 is a schematic diagram of high temperature mode 2 of the present invention;

[0071] Figure 6 is a schematic diagram of low temperature mode 1 of the present invention;

[0072] Figure 7 is a schematic diagram of low temperature mode 2 of the present invention;

[0073] Figure 8 Schematic diagram of the power battery emergency thermal management strategy of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] In the description of the present invention, it is necessary to understand that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0076] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0077] like Figure 1 As shown, a method for thermal management of electric vehicle batteries under intelligent networking includes the following steps:

[0078] Step 1: Obtain road traffic flow status, ambient temperature, real-time power battery temperature, and vehicle driving status data;

[0079] In the step 1, the road traffic flow status data includes traffic flow information and road grade, and the vehicle driving status data includes speed, acceleration, deceleration, average vehicle speed, and parking time.

[0080] Step 2: Establish a vehicle driving state prediction model and use the vehicle's historical driving state data to predict the vehicle's driving state in the next time period;

[0081] The BP neural network model is used to analyze the data of the previous time period and predict the vehicle status and road status data of the next 60-second time period, that is, a total driving distance is divided into n travel segments, the average vehicle speed and idle time ratio of each travel segment are calculated, the vehicle speed prediction working condition of each travel segment is determined, and the corresponding trained BP neural network speed prediction sub-model is selected according to the working condition category. The total duration of the output speed and input speed predicted by the BP neural network model is limited to 150 seconds, 60 seconds is selected as the prediction output duration, and the input speed duration is set to 90 seconds, that is, the vehicle driving status data of the same time and the same road section are selected, and the duration of 90 seconds is defined as a travel segment, and 5 vehicle speed prediction feature parameters and corresponding neighboring vehicle speed data are extracted from the 90-second travel segment data, and the vehicle speed is predicted for the next time period. The vehicle speed prediction feature parameters of each prediction are combined, which is the output of the BP neural network model, that is, as the input of the thermal management prediction control strategy.

[0082] The BP neural network model is divided into 6 BP neural network speed prediction sub-models, corresponding to 6 speed prediction conditions:

[0083] 1.v m <20km / h and p d <20%;

[0084] 2.v m <20km / h and p d ≥20%;

[0085] 3. 20km / h≤v m <40km / h and p d <20%;

[0086] 4. 20km / h≤v m <40km / h and p d ≥20%;

[0087] 5. 40km / h≤v m <80km / h;

[0088] VI. m ≥80km / h.

[0089] The five vehicle speed prediction characteristic parameters are: average vehicle speed v m , speed variance f v , idling time ratio p d , positive acceleration mean a m , velocity multiplied by the variance of acceleration f va ;

[0090] Average speedv m , refers to the average vehicle speed over the sampling time, and the calculation formula is:

[0091]

[0092] Among them, v i is the vehicle speed at the i-th second in the sampling time length, and n is the sampling time length;

[0093] Speed ​​variance f v , refers to the velocity variance over the sampling time length, and the calculation formula is:

[0094]

[0095] Among them, v i is the vehicle speed at the i-th second in the sampling time length, v m is the average vehicle speed over the sampling time, and n is the sampling time;

[0096] Idle time ratio p d , refers to the percentage of idling time in the sampling time length, and the calculation formula is:

[0097]

[0098] Among them, t dis the length of idling time on the sampling time length, T is the sampling time length;

[0099] Positive acceleration average value a m , refers to the average value of all positive accelerations over the sampling time length, and the calculation formula is:

[0100]

[0101] Among them, a i is the positive acceleration of the ith second in the sampling time length, and m is the duration of the positive acceleration in the sampling time length;

[0102] Speed ​​multiplied by acceleration variance f va , refers to the variance of the velocity multiplied by the acceleration over the sampling time length, and the calculation formula is:

[0103]

[0104] Among them, va i is the velocity multiplied by the acceleration at the i-th second of the sampling time, va m It is the average value of velocity multiplied by acceleration over the sampling time length. Since the acceleration is one value less than the velocity length, the first acceleration value is filled with 0;

[0105] The output formula of the BP neural network model is:

[0106]

[0107] Among them, H j is the hidden layer output, ω jk is the connection weight between the hidden layer and the output layer, b k (k=1,2,…,m) is the threshold value of each neuron in the output layer, and m is the number of neurons.

[0108] Step 3: Establish a thermal management model prediction controller, and use the vehicle's current driving data and the vehicle's driving state data predicted in step 2, as well as the current ambient temperature, to predict the power battery temperature state in the next time period;

[0109] The Kalman filter state estimator is used to establish a thermal management model predictive controller, and the predicted vehicle driving state data obtained in step 2 is used to The current ambient temperature and the real-time temperature of the power battery are used as inputs of the thermal management model prediction controller to predict the power battery temperature value in the next time period. This process is specifically expressed as:

[0110] State prediction equation:

[0111] State update equation:

[0112] in: is the estimated value of the predicted system state, is the estimated value of the system state at the previous moment, is the error covariance matrix, P k-1 is the prediction error covariance matrix, A and B are the state transfer matrix and input matrix respectively, u k is the control input, Q k is the process noise covariance matrix, K k represents the Kalman gain, C is the Jacobian matrix, y k is the measured value, is the updated system state estimate, I is the unit matrix, P k is the updated error covariance matrix, and R is the observation covariance matrix.

[0113] Step 4: Determine the optimal operating temperature range of the battery according to the type of power battery used in the car;

[0114] The types of power batteries in this embodiment include: lithium-ion batteries, lithium polymer batteries, lithium iron phosphate batteries, nickel-cobalt-manganese batteries, nickel-metal hydride batteries, and solid-state batteries. The specific optimal operating temperature range is shown in Table 1:

[0115] Table 1 Optimal operating temperature range of batteries

[0116] Battery Type Optimum operating temperature range Lithium-ion battery 20℃-25℃ Lithium polymer battery 20℃-25℃ Lithium iron phosphate battery 20℃-30℃ Nickel-cobalt-manganese battery 15℃-35℃ NiMH battery 20℃-30℃ Solid-state batteries 20℃-25℃

[0117] Step 5: classifying the power battery temperature state according to the power battery type in step 4 and the power battery temperature state predicted in step 3;

[0118] According to the predicted power battery temperature, the power battery temperature state is divided, and the abnormal temperature that exceeds the optimal operating temperature range of the power battery is divided into four modes:

[0119] High temperature mode 1: 30℃-40℃;

[0120] High temperature mode 2: 40℃-60℃;

[0121] Low temperature mode 1: 10℃-20℃;

[0122] Low temperature mode 2: -20℃-10℃.

[0123] Step 6: Adopt corresponding battery thermal management prediction control strategy according to the temperature state of the power battery;

[0124] When the temperature of the coolant inside the power battery is in high temperature mode 1, the coolant is cooled in the first stage and circulated through the power battery to cool the power battery. If the battery temperature has not dropped to 30°C after 1 minute, the system will jump to the battery thermal management prediction control strategy in high temperature mode 2: the coolant is cooled in the second stage to make the battery reach the optimal temperature range;

[0125] When the coolant temperature inside the power battery is in low temperature mode 1, the coolant is heated up in the first stage and circulated through the power battery to heat up the power battery. If the battery temperature has not risen to 20°C after 1 minute, the system jumps to the battery thermal management predictive control strategy of low temperature mode 2: the coolant is heated up in the second stage to make the battery reach the optimal temperature range.

[0126] Step 7: After adopting the battery thermal management predictive control strategy, if the real-time temperature of the power battery still exceeds the limit threshold range, immediately adopt the power battery emergency thermal management strategy.

[0127] When the real-time temperature of the power battery exceeds the set limit threshold range of -20℃-60℃, the power battery emergency thermal management strategy is activated, and the battery thermal management is no longer performed according to the predicted temperature. When the power battery temperature stabilizes within the set temperature threshold of 20℃-30℃ for 180s, the power battery emergency thermal management strategy is exited, the battery thermal management predictive control strategy is activated, and the battery resumes normal operation.

[0128] like Figure 2 As shown, a system for realizing a thermal management method of an electric vehicle battery under intelligent networking includes a hydraulic pump 1, a first temperature sensor 2, a second temperature sensor 3, a first one-way valve 4, a compressor 5, a battery low temperature management module, and a battery high temperature management module, which are respectively connected to the controller by signal. The hydraulic pump 1, the first temperature sensor 2, the power battery 17, the second temperature sensor 3, the first one-way valve 4, and the compressor 5 are connected in sequence.

[0129] The battery low temperature management module includes a primary heating component and a secondary heating component, the primary heating component includes a first heating solenoid valve 6, a plate heat exchanger 7, and a second one-way valve 8, the hydraulic pump 1, the first heating solenoid valve 6, the plate heat exchanger 7, the second one-way valve 8, and the compressor 5 are connected in sequence;

[0130] The secondary temperature raising component comprises a second heating solenoid valve 9, a PTC heater 10, and a third one-way valve 11 connected in sequence; one end of the secondary temperature lowering component is connected in parallel between the hydraulic pump 1 and the first temperature sensor 2, and the other end is connected in parallel between the second temperature sensor 3 and the first one-way valve 4;

[0131] The battery high temperature management module includes a primary cooling component and a secondary cooling component, the primary cooling component includes a first cooling solenoid valve 12, an evaporator 13, an external condenser 14, and a fourth one-way valve 15, the hydraulic pump 1, the first cooling solenoid valve 12, the evaporator 13, the external condenser 14, the fourth one-way valve 15, and the first one-way valve 4 are connected in sequence;

[0132] The secondary cooling component includes a second cooling solenoid valve 16 disposed between the external condenser 14 and the compressor 5 .

[0133] like Figure 3 As shown, the historical traffic status of the road and the current driving status of the vehicle are input into the vehicle driving status prediction model as input ends, and the model predicted data, the current ambient temperature and the real-time temperature of the power battery 17 are input into the thermal management model prediction controller, and the optimization and correction are continuously performed to obtain the battery thermal management prediction control strategy. Among them, the compressor 5, PTC heater 10, hydraulic pump 1 and the in-vehicle condenser 14 and evaporator 13 of the heat pump air conditioner are the main temperature control devices. When the battery temperature is too high, the hydraulic pump 1 and the evaporator 13 first provide heat dissipation, and the flow rate of the coolant is controlled by adjusting the size of the valve of the hydraulic pump 1; when the temperature is still too high, the compressor 5 starts to directly cool the battery; when the air conditioner is also turned on in the cab of the vehicle, the compressor 5 cools the cockpit and the battery pack at the same time. When the battery temperature is too low, the compressor 5 starts to heat the vehicle power battery 17 to improve the endurance achievement rate of the power battery 17; when the heat pump air conditioner needs to heat the cab at the same time or the battery temperature cannot reach the best by the heat pump air conditioner alone, the PTC heater 10 on the battery pack starts to heat the battery. The temperature of the power battery 17 is fed back in real time, and the battery thermal management predictive control strategy is updated and optimized repeatedly.

[0134] like Figure 4 and Figure 5 As shown, combined with step six, specifically: when the coolant temperature inside the power battery 17 is in high temperature mode one, the first temperature sensor 2 detects that the coolant temperature is in a high temperature state, which exceeds the reasonable range of the battery temperature, the first cooling solenoid valve 12 opens, and the coolant enters the evaporator 13 and the external condenser 14 through the hydraulic pump 1 to dissipate heat, passes through the fourth one-way valve 15 and the first one-way valve 4, and is detected by the second temperature sensor 3, and then returns to the power battery 17 again to cool down the power battery 17. If the battery temperature has not dropped to 30°C after 1 minute, jump to the battery thermal management prediction control strategy of high temperature mode two: on the basis of starting the high temperature mode one, the second cooling solenoid valve 16 is opened at the same time, and the compressor 5 works to cool down the coolant together, and enters the second temperature sensor 3 through the first one-way valve 4. The second temperature sensor 3 detects that the coolant temperature at this time can make the battery reach the optimal temperature range to achieve battery cooling;

[0135] like Figure 6 and Figure 7 As shown, when the coolant temperature inside the power battery 17 is in low temperature mode 1, the first temperature sensor 2 detects that the power battery 17 is in a low temperature state at this time, the first heating solenoid valve 6 is opened, and heat is absorbed by the plate heat exchanger 7. The coolant enters the first one-way valve 4 after being heated by the second one-way valve 8 and the compressor 5. The second temperature sensor 3 detects that the coolant temperature at this time can make the battery reach the optimal operating temperature range to achieve the heating of the power battery 17. If the battery temperature still does not rise to 20°C after 1 minute, it jumps to the battery thermal management predictive control strategy of low temperature mode 2: on the basis of starting the low temperature mode 1, the second heating solenoid valve 9 is opened at the same time, the PTC heater 10 heats the power battery 17, and returns to the second temperature sensor 3 again through the third one-way valve 11. The second temperature sensor 3 detects that the coolant temperature at this time can make the battery reach the optimal temperature range to achieve the heating of the battery.

[0136] like Figure 8 As shown, combined with step seven, it is specifically as follows:

[0137] The first temperature sensor 2 detects that the coolant temperature is in an abnormally high temperature state, exceeding the battery temperature setting limit threshold of -20°C-60°C. The first cooling solenoid valve 12 and the second cooling solenoid valve 16 are opened at the same time, and the hydraulic pump 1 runs at maximum power. The coolant enters the evaporator 13 and the external condenser 14 through the hydraulic pump 1 to dissipate heat. The compressor 5 runs at the lowest temperature to the maximum frequency, and finally returns to the power battery 17 through the first one-way valve 4 and the second temperature sensor 3.

[0138] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0139] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for thermal management of electric vehicle batteries under intelligent networking, characterized in that: The following steps are involved: Step 1: Obtain road traffic flow status, ambient temperature, real-time power battery temperature, and vehicle driving status data; Step 2: Establish a vehicle driving state prediction model and use the vehicle's historical driving state data to predict the vehicle's driving state in the next time period; Step 3: Establish a thermal management model prediction controller, and use the vehicle's current driving data and the vehicle's driving state data predicted in step 2, as well as the current ambient temperature, to predict the power battery temperature state in the next time period; Step 4: Determine the optimal operating temperature range of the battery according to the type of power battery used in the car; Step 5: classifying the power battery temperature state according to the power battery type in step 4 and the power battery temperature state predicted in step 3; Step 6: Adopt corresponding battery thermal management prediction control strategy according to the temperature state of the power battery; Step 7: After adopting the battery thermal management predictive control strategy, if the real-time temperature of the power battery still exceeds the limit threshold range, immediately adopt the power battery emergency thermal management strategy.

2. The method for thermal management of electric vehicle batteries under intelligent networking according to claim 1 is characterized in that: In the step 1, the road traffic flow status data includes traffic flow information and road grade, and the vehicle driving status data includes speed, acceleration, deceleration, average vehicle speed, and parking time.

3. The method for thermal management of electric vehicle batteries under intelligent networking according to claim 1, characterized in that: The step 2 is specifically as follows: The BP neural network model is used to analyze the data of the previous time period and predict the vehicle status and road status data of the next time period. That is, a total driving distance is divided into n travel segments, and the average vehicle speed and idle time ratio of each travel segment are calculated. The speed prediction working condition of each travel segment is determined, and the corresponding trained BP neural network speed prediction sub-model is selected according to the working condition category. Five speed prediction feature parameters and corresponding neighboring vehicle speed data are extracted from the travel segment data to predict the speed of the next time period. The speed prediction feature parameters of each prediction are combined, which is the output of the BP neural network model, that is, the input of the thermal management prediction control strategy.

4. The method for thermal management of electric vehicle batteries under intelligent networking according to claim 3 is characterized in that: The BP neural network vehicle speed prediction sub-model is specifically: The BP neural network model is divided into 6 BP neural network speed prediction sub-models, corresponding to 6 speed prediction conditions: 1.v m <20km / h and p d <20%; 2.v m <20km / h and p d ≥20%; 3. 20km / h≤v m <40km / h and p d <20%; 4. 20km / h≤v m <40km / h and p d ≥20%; 5. 40km / h≤v m <80km / h; VI. m ≥80km / h.

5. The electric vehicle battery thermal management method under intelligent networking according to claim 4 is characterized in that: The five vehicle speed prediction characteristic parameters are: average vehicle speed v m , speed variance f v , idling time ratio p d , positive acceleration mean a m , velocity multiplied by the variance of acceleration f va ; Average speedv m , refers to the average vehicle speed over the sampling time, and the calculation formula is: Among them, v i is the vehicle speed at the i-th second in the sampling time length, and n is the sampling time length; Speed ​​variance f v , refers to the velocity variance over the sampling time length, and the calculation formula is: Among them, v i is the vehicle speed at the i-th second in the sampling time length, v m is the average vehicle speed over the sampling time, and n is the sampling time; Idle time ratio p d , refers to the percentage of idling time in the sampling time length, and the calculation formula is: Among them, t d is the length of idling time on the sampling time length, T is the sampling time length; Positive acceleration average value a m , refers to the average value of all positive accelerations over the sampling time length, and the calculation formula is: Among them, a i is the positive acceleration of the ith second in the sampling time length, and m is the duration of the positive acceleration in the sampling time length; Speed ​​multiplied by acceleration variance f va , refers to the variance of the velocity multiplied by the acceleration over the sampling time length, and the calculation formula is: Among them, va i is the velocity multiplied by the acceleration at the i-th second of the sampling time, va m It is the average value of velocity multiplied by acceleration over the sampling time length. Since the acceleration is one value less than the velocity length, the first acceleration value is filled with 0; The output formula of the BP neural network model is: Among them, H j is the hidden layer output, ω jk is the connection weight between the hidden layer and the output layer, b k (k=1,2,…,m) is the threshold value of each neuron in the output layer, and m is the number of neurons.

6. The method for thermal management of electric vehicle batteries under intelligent networking according to claim 1, characterized in that: The step three is specifically as follows: The Kalman filter state estimator is used to establish a thermal management model predictive controller, and the predicted vehicle driving state data obtained in step 2 is used to The current ambient temperature and the real-time temperature of the power battery are used as inputs of the thermal management model prediction controller to predict the power battery temperature value in the next time period. This process is specifically expressed as: State prediction equation: State update equation: in: is the estimated value of the predicted system state, is the estimated value of the system state at the previous moment, is the error covariance matrix, P k-1 is the prediction error covariance matrix, A and B are the state transfer matrix and input matrix respectively, u k is the control input, Q k is the process noise covariance matrix, K k represents the Kalman gain, C is the Jacobian matrix, y k is the measured value, is the updated system state estimate, I is the unit matrix, P k is the updated error covariance matrix, and R is the observation covariance matrix.

7. The method for thermal management of electric vehicle batteries under intelligent networking according to claim 1, characterized in that: The step five is specifically as follows: According to the predicted power battery temperature, the power battery temperature state is divided, and the abnormal temperature that exceeds the optimal operating temperature range of the power battery is divided into four modes: High temperature mode 1: 30℃-40℃; High temperature mode 2: 40℃-60℃; Low temperature mode 1: 10℃-20℃; Low temperature mode 2: -20℃-10℃.

8. The method for thermal management of electric vehicle batteries under intelligent networking according to claim 7, characterized in that: The step six is ​​specifically as follows: When the temperature of the coolant inside the power battery is in high temperature mode 1, the coolant is cooled in the first stage and circulated through the power battery to cool the power battery. If the battery temperature has not dropped to 30°C after 1 minute, the system will jump to the battery thermal management prediction control strategy in high temperature mode 2: the coolant is cooled in the second stage to make the battery reach the optimal temperature range; When the coolant temperature inside the power battery is in low temperature mode 1, the coolant is heated up in the first stage and circulated through the power battery to heat up the power battery. If the battery temperature has not risen to 20°C after 1 minute, the system jumps to the battery thermal management predictive control strategy of low temperature mode 2: the coolant is heated up in the second stage to make the battery reach the optimal temperature range.

9. The method for thermal management of electric vehicle batteries under intelligent networking according to claim 8, characterized in that: The step seven is specifically as follows: When the real-time temperature of the power battery exceeds the set limit threshold range of -20℃-60℃, the power battery emergency thermal management strategy is activated, and the battery thermal management is no longer performed according to the predicted temperature. When the power battery temperature stabilizes within the set temperature threshold of 20℃-30℃ for 180s, the power battery emergency thermal management strategy is exited, the battery thermal management predictive control strategy is activated, and the battery resumes normal operation.

10. A system for implementing the electric vehicle battery thermal management method under intelligent networking as claimed in any one of claims 1 to 9, characterized in that: The invention comprises a hydraulic pump (1), a first temperature sensor (2), a second temperature sensor (3), a first one-way valve (4), a compressor (5), a battery low temperature management module, and a battery high temperature management module, each of which is connected to a controller by signals. The hydraulic pump (1), the first temperature sensor (2), the power battery (17), the second temperature sensor (3), the first one-way valve (4), and the compressor (5) are connected in sequence. The battery low temperature management module comprises a primary heating component and a secondary heating component, the primary heating component comprises a first heating solenoid valve (6), a plate heat exchanger (7), and a second one-way valve (8), the hydraulic pump (1), the first heating solenoid valve (6), the plate heat exchanger (7), the second one-way valve (8), and the compressor (5) are connected in sequence; The secondary temperature raising component comprises a second heating solenoid valve (9), a PTC heater (10), and a third one-way valve (11) which are connected in sequence; one end of the secondary temperature lowering component is connected in parallel between the hydraulic pump (1) and the first temperature sensor (2), and the other end is connected in parallel between the second temperature sensor (3) and the first one-way valve (4); The battery high temperature management module comprises a primary cooling component and a secondary cooling component, the primary cooling component comprises a first cooling solenoid valve (12), an evaporator (13), an external condenser (14), and a fourth one-way valve (15), and the hydraulic pump (1), the first cooling solenoid valve (12), the evaporator (13), the external condenser (14), the fourth one-way valve (15), and the first one-way valve (4) are connected in sequence; The secondary cooling component comprises a second cooling solenoid valve (16) arranged between an external condenser (14) and a compressor (5).

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