Battery pack endurance improving method and device

Abstract

The invention belongs to the technical field of charging protection, and discloses a battery pack endurance improving method and device which are applied to an electric vehicle. The electric vehicle comprises a map module, a first sensor, a second sensor and an intelligent driving controller. The method comprises the steps that a map module obtains a road scene and sends the road scene to an intelligent driving controller; the first sensor acquires road information in front of the vehicle and sends the information to the intelligent driving controller; the second sensor obtains current state information of the driver and sends the current state information to the intelligent driving controller; the intelligent driving controller receives and analyzes the road scene and the road information in front of the vehicle, and generates first information and second information; adjusting an adaptive prediction energy distribution strategy according to the first information to obtain an energy distribution strategy; according to the second information, adjusting an environment perception driving pre-judgment temperature control strategy to obtain a battery pack temperature strategy; generating a driver portrait according to the current state information of the driver; obtaining a battery pack charging and discharging model according to the driver portrait and the charging and discharging strategy; the intelligent driving controller controls a battery pack of the electric vehicle to be charged or discharged according to the energy distribution strategy and the battery pack charging and discharging model; and heating or cooling the battery pack according to the battery pack temperature strategy control. The safety of the battery pack can be remarkably improved, and the endurance of the battery pack is improved.

Description

Technical Field

[0001] This application relates to the technical field of charging protection, and particularly to a method and device for improving the battery pack endurance. Background Art

[0002] Most existing battery management systems rely on the real-time feedback of a single dimension of in-vehicle sensors, lacking the ability to actively predict road scenarios and driver behavior, resulting in significant energy distribution problems; traditional methods cannot identify uphill and downhill or sharp turn sections in advance, and can only passively adjust the output power according to the current vehicle speed and battery state, easily resulting in a sudden drop in battery power during uphill driving or insufficient energy recovery during downhill driving, with limited optimization effect on the endurance mileage. Moreover, the thermal management system usually triggers cooling or heating using fixed thresholds, making it difficult to cope with the instantaneous temperature rise caused by sudden high-load working conditions, easily causing local overheating or delayed cooling response, increasing the risk of thermal runaway. At the same time, existing technologies ignore the individual differences of drivers, adopt a unified charge and discharge strategy, lack adaptability to the high-power demands of aggressive drivers or the frequent braking scenarios of novice users, and long-term use may exacerbate the battery aging problem. The insufficient integration of high-precision maps and various data further limits the system's global optimization ability for complex road conditions, making it difficult to achieve true intelligent prediction and dynamic collaborative control. Summary of the Invention

[0003] This application provides a method and device for improving the battery pack endurance, which can integrate the data collected by the map module, various sensors and driver status data, and realize the dynamic collaborative optimization of the battery pack energy and thermal management through the intelligent driving controller; also adaptively adjust the charge and discharge strategy according to the road scenario prediction to improve the endurance efficiency; adjust the battery pack temperature in advance in combination with sudden working conditions to reduce the risk of thermal runaway; match personalized charge and discharge schemes through driver portraits to balance the performance requirements and the battery pack life, solve the problems of the traditional battery pack relying on the lagging feedback of the main control system and a single strategy, significantly improve the safety of the battery pack, and improve the battery pack endurance.

[0004] In the first aspect, an embodiment of this application provides a method for improving the battery pack endurance, which is applied to an electric vehicle; the electric vehicle includes a map module, a first sensor, a second sensor and an intelligent driving controller; the method includes:

[0005] The map module obtains the road scenario and sends it to the intelligent driving controller;

[0006] The first sensor obtains the road information in front of the vehicle and sends it to the intelligent driving controller;

[0007] The second sensor obtains the current status information of the driver and sends it to the intelligent driving controller;

[0008] The intelligent driving controller receives and analyzes road scenarios and road information in front of the vehicle, and generates first information and second information; adjusts the adaptive prediction performance energy distribution strategy according to the first information to obtain the energy distribution strategy; adjusts the environmental perception-driven pre-judgment temperature control strategy according to the second information to obtain the battery pack temperature strategy; generates a driver portrait according to the current driver state information; obtains the battery pack charging and discharging model according to the driver portrait and the charging and discharging strategy;

[0009] The intelligent driving controller controls the battery pack of the electric vehicle to charge or discharge according to the energy distribution strategy and the battery pack charging and discharging model; controls the heating or cooling of the battery pack according to the battery pack temperature strategy.

[0010] Furthermore, the road scenarios include uphill scenarios, downhill scenarios, emergency stop scenarios, lane change scenarios, and sharp turn scenarios; the method further includes:

[0011] The intelligent driving controller generates first information according to the uphill scenario, downhill scenario, and road information in front of the vehicle; the first information includes the wind resistance coefficient, the frontal area, the air density, the rolling resistance coefficient, and the angle; generates second information according to the emergency stop scenario, lane change scenario, sharp turn scenario, and road information in front of the vehicle; the second information includes the environmental temperature.

[0012] Furthermore, adjusting the adaptive prediction performance energy distribution strategy according to the first information to obtain the energy distribution strategy includes:

[0013] The intelligent driving controller calculates the total power energy according to the first information;

[0014] Calculates the window optimization model according to the total power energy;

[0015] Obtains the energy distribution strategy according to the total power energy and the window optimization model.

[0016] Furthermore, the intelligent driving controller calculates the total power energy P total,i The formula for is:

[0017]

[0018] where C d is the wind resistance coefficient, A f is the frontal area, V i is the speed limit of the i-th section, m is the total mass of the vehicle, g = 9.81 m / s 2 , ρ air is the air density, C r is the rolling resistance coefficient, θ i is the slope angle of the i-th section;

[0019] The formula for the window optimization model L is:

[0020]

[0021] Among them, η discharge (SOC i ) is the battery discharge efficiency, and SOC i is the state of charge of the i-th battery pack, Δt is the step size of the control time, and η regen (SOC i ) is the efficiency of the battery to recover energy from regenerative braking when the battery charge is SOC i .

[0022] Furthermore, adjust the environmental perception-driven pre-judgment temperature control strategy according to the second information to obtain the battery pack temperature strategy, including:

[0023] The intelligent driving controller calculates the temperature prediction model ΔT pred and the scenario temperature control weight W action ;

[0024] The calculation formula of the temperature prediction model ΔT pred is:

[0025]

[0026] Among them, ΔT pred is the predicted temperature change value within the road section time window Δt, P heat is the battery heat generation power, P cool is the cooling system power, C batt is the battery heat capacity, α is the environmental heat exchange coefficient, R thermal is the thermal resistance of the battery pack insulation material, T env is the environmental temperature, and T batt is the current battery temperature;

[0027] The calculation formula of the scenario temperature control weight W action is:

[0028]

[0029] Among them, p i is the probability value of different scenarios, S i is the temperature rise sensitivity coefficient of different scenarios, and n is the total number of different working conditions;

[0030] Calculate the thermal management drive temperature control instruction according to the temperature prediction model and the scenario temperature control weight;

[0031] The calculation formula of the thermal management drive temperature control instruction u(t) is:

[0032]

[0033] Among them, T safe is the upper limit of the battery pack safety temperature, T min is the low-temperature endurance protection threshold of the battery pack, and ΔT hyst is the buffer threshold;

[0034] Obtain the battery pack temperature strategy according to the thermal management driving temperature control instruction.

[0035] Furthermore, generate a driver portrait according to the driver's current status information, including:

[0036] According to the driver's current status information, obtain the eye closure degree and the rotation angle of the head around the y-axis of the driver;

[0037] Create a driver portrait model according to the eye closure degree, rotation angle, driver's historical average driving acceleration, and driver's braking frequency, and perform threshold division on the driver portrait model to obtain the driver status type;

[0038] Calculate the battery pack charge and discharge model according to the driver status type.

[0039] Furthermore, the calculation formula for the eye closure degree EAR is:

[0040]

[0041] Among them, P1 to P6 are the coordinates of the key points of the driver's eyes. P1 is the right outer corner of the eye, P2 is the right vertex of the upper eyelid, P3 is the point on the upper eyelid to the right of the middle, P4 is the left outer corner of the eye, P5 is the point on the lower eyelid to the left of the middle, and P6 is the right vertex of the lower eyelid;

[0042] The rotation angle θ yaw The calculation formula is:

[0043]

[0044] Among them, q x is the component of the driver's head tilt around the x-axis, q y is the component of the driver's head tilt around the y-axis, q z is the component of the driver's head tilt around the z-axis, and q w is the vehicle calibration value;

[0045] The formula for the driver portrait model F is:

[0046] F = [EAR, θ yaw , a avg , f brake

[0047] Among them, a avg is the driver's historical average driving acceleration, and f brake is the driver's braking frequency during driving.​

[0048] Further, a charge-discharge model of the battery pack is calculated according to the driver state type, including:

[0049]

[0050] wherein, DoD is the depth of discharge, C rate is the discharge rate, D actual is the actual mileage traveled by the vehicle, D req is the cruising range required by the driver; the values of α and β are determined according to the driver state type.

[0051] In a second aspect, an embodiment of the present application provides a device for improving the cruising range of a battery pack, the device includes:

[0052] A map module, configured to obtain a road scene and send it to the intelligent driving controller;

[0053] A first sensor, configured to obtain road information in front of the vehicle and send it to the intelligent driving controller;

[0054] A second sensor, configured to obtain the current state information of the driver and send it to the intelligent driving controller;

[0055] An intelligent driving controller, configured to receive and analyze the road scene and the road information in front of the vehicle, and generate a first piece of information and a second piece of information; adjust the adaptive prediction performance energy distribution strategy according to the first piece of information to obtain an energy distribution strategy; adjust the environmental perception-driven pre-judgment temperature control strategy according to the second piece of information to obtain a battery pack temperature strategy; generate a driver portrait according to the current state information of the driver; obtain a charge-discharge model of the battery pack according to the driver portrait and the charge-discharge strategy; control the battery pack of the electric vehicle to charge or discharge according to the energy distribution strategy and the charge-discharge model of the battery pack; control the heating or cooling of the battery pack according to the battery pack temperature strategy.

[0056] Further, the intelligent driving controller is further configured to:

[0057] Calculate the total power energy according to the first piece of information;

[0058] Calculate a window optimization model according to the total power energy;

[0059] Obtain an energy distribution strategy according to the total power energy and the window optimization model.

[0060] In summary, compared with the prior art, the beneficial effects brought by the technical solution provided by the embodiment of the present application at least include:

[0061] A method for improving the battery life of a battery pack provided by an embodiment of the present application. The above method can integrate the data collected by the map module, various sensors, and driver status data, and achieve dynamic collaborative optimization of battery pack energy and thermal management through an intelligent driving controller; it can also adaptively adjust the charging and discharging strategy according to road scene prediction to improve the endurance efficiency; adjust the battery pack temperature in advance in combination with sudden working conditions to reduce the risk of thermal runaway; match personalized charging and discharging solutions through driver portraits to balance performance requirements and battery pack life, solve the problems of the traditional battery pack relying on the lagging feedback of the main control system and a single strategy, significantly improve the safety of the battery pack, and improve the battery life of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flowchart of a method for improving the battery life of a battery pack provided by an exemplary embodiment of the present application.

[0063] Figure 2 It is a structural diagram of a device for improving the battery life of a battery pack provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0065] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0066] Please refer to Figure 1 , an embodiment of the present application provides a method for improving the battery life of a battery pack, and the method specifically includes the following steps:

[0067] Step S1, the map module obtains the road scene and sends it to the intelligent driving controller.

[0068] Among them, obtaining road scene information in advance through a high-precision map module provides a global prediction basis for subsequent energy distribution, avoids the lag of relying only on real-time sensor data, improves the response efficiency of the system to complex road conditions, and reduces energy waste caused by sudden road conditions.

[0069] Step S2, the first sensor obtains the road information in front of the vehicle and sends it to the intelligent driving controller.

[0070] Among them, the first sensor captures the road information in front of the vehicle in real time, such as obstacles and traffic flow status, makes up for the deficiency of the static data of the map module, ensures the real-time and accuracy of environmental perception, provides refined data support for subsequent strategy adjustments, and reduces the risk of misjudgment.

[0071] Step S3: The second sensor acquires the current status information of the driver and sends it to the intelligent driving controller.

[0072] Among them, the second sensor is used to acquire the driver status information, such as fatigue degree, operation habits and other information, and generate a driver portrait by combining driving behavior characteristics, enabling the system to identify the personalized needs of the driver, such as aggressive or conservative driving styles, providing a basis for the subsequent adaptation of the charge and discharge strategy, and enhancing the balance ability between user experience and battery life.

[0073] Step S4: The intelligent driving controller receives and analyzes the road scene and the road information in front of the vehicle, generates the first information and the second information; adjusts the adaptive prediction performance energy distribution strategy according to the first information to obtain the energy distribution strategy; adjusts the environmental perception-driven pre-judgment temperature control strategy according to the second information to obtain the battery pack temperature strategy; generates a driver portrait according to the current status information of the driver; and obtains the battery pack charge and discharge model according to the driver portrait and the charge and discharge strategy.

[0074] Among them, the intelligent driving controller dynamically generates the energy distribution strategy and the battery pack temperature strategy by fusing a variety of data, and optimizes the battery pack charge and discharge model based on the driver portrait to achieve the collaborative optimization among various strategies. For example, in a sharp turn scenario, synchronously adjust the energy output and the battery pack temperature control to avoid the limitations of a single-dimensional strategy and enhance the global adaptability of the intelligent driving controller.

[0075] Step S5: The intelligent driving controller controls the battery pack of the electric vehicle to charge or discharge according to the energy distribution strategy and the battery pack charge and discharge model; controls the heating or cooling of the battery pack according to the battery pack temperature strategy.

[0076] Among them, the intelligent driving controller directly acts on the charge and discharge and thermal management systems of the battery pack by real-time executing the energy distribution strategy and the battery pack temperature strategy to ensure the rapid implementation of each strategy. For example, actively increase the battery output power and pre-heat the battery before going uphill, or cool it in time after sudden stop to avoid heat accumulation, so as to balance the endurance performance and safety and extend the battery cycle life.

[0077] A method for improving the endurance of a battery pack provided in the above embodiments can fuse the data collected by the map module, various sensors and the driver status data, and realize the dynamic collaborative optimization of the battery pack energy and thermal management through the intelligent driving controller; also adaptively adjust the charge and discharge strategy according to the prediction of the road scene to improve the endurance efficiency; adjust the battery pack temperature in advance in combination with sudden working conditions to reduce the risk of thermal runaway; and match a personalized charge and discharge scheme through the driver portrait to balance the performance requirements and the battery pack life, solving the problems of the lagging feedback of the traditional battery pack relying on the main control system and the single strategy, significantly improving the safety of the battery pack and enhancing the endurance of the battery pack.

[0078] In some embodiments, the road scenarios include uphill scenarios, downhill scenarios, sudden stop scenarios, lane change scenarios, and sharp turn scenarios; the method further includes:

[0079] The intelligent driving controller generates first information based on the uphill scenario, downhill scenario, and the road information in front of the vehicle; the first information may include the drag coefficient, frontal area, air density, rolling resistance coefficient, and angle; generates second information based on the sudden stop scenario, lane change scenario, sharp turn scenario, and the road information in front of the vehicle; the second information may include the ambient temperature.

[0080] By refining the road scenario classification and associating specific physical parameters, the intelligent driving controller significantly improves the accuracy of each strategy. For uphill and downhill scenarios, based on dynamic parameters such as the drag coefficient and frontal area, a vehicle force model is calculated to enable the energy distribution strategy to precisely match the load requirements brought about by the slope change. For example, when going uphill, the output power is optimized by combining the air density and the angle of the slope to avoid power redundancy or insufficiency caused by the traditional fixed parameter model; for sudden stop scenarios, lane change scenarios, and sharp turn scenarios, by introducing ambient temperature data, the impact of the superimposed effect of intense driving actions and the external temperature difference on the thermal stability of the battery pack is predicted, and thus the temperature control threshold is dynamically adjusted. For example, in a high-temperature environment, the cooling intensity is enhanced in advance to prevent the battery pack from overheating. By transforming the abstract scenarios into quantifiable physical parameters, the intelligent driving controller can achieve closed-loop control, solving the problem of rigid strategies caused by relying on empirical thresholds or static models in traditional methods, and also taking into account the endurance efficiency and battery safety under complex working conditions.

[0081] In some embodiments, adjusting the adaptive predictive performance energy distribution strategy according to the first information to obtain the energy distribution strategy includes:

[0082] Step S41, the intelligent driving controller calculates the total power energy according to the first information.

[0083] Step S42, a window optimization model is calculated based on the total power energy.

[0084] Step S43, the energy distribution strategy is obtained based on the total power energy and the window optimization model.

[0085] Among them, by first integrating multi-dimensional physical parameters, the calculation of the total power energy can accurately reflect the actual energy consumption requirements in different road scenarios, avoiding the power estimation deviation caused by traditional methods ignoring dynamic environmental parameters. Secondly, the window optimization model generated based on the total power energy can dynamically delimit the power threshold and time window for the charging and discharging of the battery pack. For example, when going downhill, the energy recovery window is expanded to improve the feedback efficiency, or before going uphill, a high-power output window is pre-allocated to ensure the continuity of power. Finally, the energy distribution strategy generated by combining the total power energy and the window optimization model can adaptively match the real-time road conditions and vehicle load changes, solve the problems of redundant power or insufficient supply that are prone to occur in traditional fixed charging and discharging strategies in complex scenarios, significantly improve the cruising range and reduce the risk of battery pack overload.

[0086] In some embodiments, the intelligent driving controller calculates the total power energy P according to the first information total,i The formula is:

[0087]

[0088] where C d is the drag coefficient, A f is the frontal area, V i is the speed limit of the i-th section, m is the total mass of the vehicle, g = 9.81 m / s 2 , ρ air is the air density, C r is the rolling resistance coefficient, θ i is the slope angle of the i-th section.

[0089] The calculation formula of the window optimization model L is:

[0090]

[0091] where η discharge (SOC i ) is the battery discharge efficiency, SOC i is the state of charge of the i-th battery pack, Δt is the time step of control, η regen (SOC i ) is the efficiency of the battery to recover energy from regenerative braking when the battery charge is SOC i .

[0092] Among them, represents the wind resistance, mgsinθ i V i represents the slope, mgC r cosθ i V i represents the rolling resistance, and the total power energy of the i-th section is comprehensively evaluated through the wind resistance, slope, and rolling resistance.

[0093] Among them, represents the energy required for the vehicle to travel driven by the battery pack SOC, and P total,i ·η regen (SOC i )·Δt represents the energy recovery of the vehicle; η discharge represents the battery discharge efficiency, which is a function of the battery pack SOC and temperature. That is, when the battery pack SOC is too low, the battery pack discharge efficiency decreases; η regen represents the efficiency of the battery to recover energy from regenerative braking when the battery charge is SOC i . It is a function of the battery pack SOC and temperature. That is, when the battery pack SOC is too high, the efficiency of the battery to recover energy from regenerative braking when the battery charge is SOC i decreases; Δt represents the time step of control, which is related to the update frequency between different road sections. By optimizing the window model L, the total vehicle energy under different road sections can be judged. By selecting the minimum total vehicle energy loss, it guides the battery pack to dynamically and adaptively adjust the battery charging and discharging usage to obtain the optimal energy distribution strategy.

[0094] For example, when predicting a long uphill section, more power is reserved, and the energy recovery intensity is adjusted when going downhill. When there is a long and steep slope ahead of the vehicle, at this time P total,i is extremely large. In this scenario, the battery discharge will be restricted first to ensure that the battery can support the uphill in the subsequent road section; when there is a continuous downhill road section ahead of the vehicle, at this time P total,i is continuously negative. In this scenario, the upper limit of SOC will be temporarily raised, and the feedback intensity coefficient will be adjusted to obtain the maximum benefit.

[0095] P total,i > 0 represents going uphill, and P total,i < 0 represents going downhill. When the slope is 10%, at this time P total,i value is about 150KW. It can be considered that when P total,i > 150KW, it can be considered a long and steep slope. Raising the upper limit of SOC can relieve the overcharging risk and enhance the energy recovery efficiency. For the continuous downhill scenario, the larger the absolute value of P total,i , the greater the recoverable power for going downhill, and the recovery intensity coefficient increases.

[0096] In some embodiments, according to the second information, the environmental perception-driven pre-judgment temperature control strategy is adjusted to obtain the battery pack temperature strategy, including:

[0097] The intelligent driving controller calculates the temperature prediction model ΔT pred and the scenario temperature control weight W action .

[0098] The temperature prediction model ΔTpred The calculation formula is as follows:

[0099]

[0100] Among them, ΔT pred is the predicted temperature change value within the road section time window Δt, P heat is the battery heat generation power, P cool is the cooling system power, C batt is the battery heat capacity, α is the environmental heat exchange coefficient, R thermal is the thermal resistance of the battery pack insulation material, T env is the environmental temperature, T batt is the current battery temperature.

[0101] The scene temperature control weight W action The calculation formula is as follows:

[0102]

[0103] Among them, p i is the probability value of different scenarios, such as sharp turn p turn , sudden stop p brake represents the probability value corresponding to different scenarios; S i is the temperature rise sensitivity coefficient of different scenarios, such as sharp turn p turn scenario, S i corresponds to the increase of the temperature rise sensitivity coefficient in this scenario; n is the total number of different working conditions.

[0104] Calculate the thermal management-driven temperature control instruction according to the temperature prediction model and the scene temperature control weight.

[0105] The calculation formula of the thermal management-driven temperature control instruction u(t) is as follows:

[0106]

[0107] Among them, T safe is the upper limit of the battery pack safety temperature, T min is the low-temperature endurance protection threshold of the battery pack, ΔT hyst is the buffer threshold. By setting the buffer threshold, the frequent start and stop of the vehicle can be avoided.

[0108] Obtain the battery pack temperature strategy according to the thermal management-driven temperature control instruction. By controlling the battery pack temperature through thermal management, the battery thermal control system can be dynamically adjusted to heat or cool the battery pack, enhancing the safety of the battery pack.

[0109] The map module can display whether there are sharp turns, whether there are continuous uphill and downhill curves, and whether there are accident-prone areas; each sensor will provide real-time feedback on whether there are dangerous working conditions that may cause an emergency stop; if the map module identifies a road section scenario with an emergency stop or locates congestion ahead through navigation, the intelligent driving controller will lower the T safe threshold value before deceleration, which can effectively prevent the sudden rise in the battery pack temperature caused by the superposition of regenerative braking heat generation. When the map module and each sensor identify a continuous curve scenario, calculate P heat the heat generation power of the battery, and the liquid cooling can be activated in advance.

[0110] In some embodiments, a driver portrait is generated based on the driver's current status information, including:

[0111] Based on the driver's current status information, obtain the eye closure degree and the rotation angle of the head around the y-axis of the driver.

[0112] Create a driver portrait model based on the eye closure degree, rotation angle, driver's historical average driving acceleration, and driver's braking frequency, and perform threshold division on the driver portrait model to obtain the driver status type.

[0113] Calculate the charge and discharge model of the battery pack according to the driver status type.

[0114] For example, for aggressive drivers, the intelligent driving controller will limit the peak power output and enhance the braking energy recovery to balance safety and range requirements, solving the problem of policy homogenization caused by the traditional method relying on single-dimensional data. Through the fusion of multi-source data and the classification of driver status, the refined management and scenario adaptation of the battery pack are realized, extending the life of the battery pack and reducing safety risks.

[0115] In some embodiments, the calculation formula for the eye closure degree EAR is:

[0116]

[0117] where P1 to P6 are the coordinates of the key points of the driver's eyes. P1 is the right outer corner of the eye, P2 is the right vertex of the upper eyelid, P3 is the point on the upper eyelid to the right of the middle, P4 is the left outer corner of the eye, P5 is the point on the lower eyelid to the left of the middle, and P6 is the right vertex of the lower eyelid.

[0118] Among them, ‖P2 - P6‖ is used to capture the degree of upper eyelid closure in the vertical direction, ‖P3 - P5‖ is used to enhance the sensitivity to the partially closed state, and ‖P1 - P4‖ is used to eliminate individual eye shape differences, ensuring that the eye closure degree is independent of the face size. Through the detection of the eye closure degree, the fatigue state of the vehicle driver can be obtained in real time. For example, a threshold value of 0.3 can be set. When EAR < 0.3, it is considered that the driver is in a fatigue state at this time.

[0119] The rotation angle θ yaw has the following calculation formula:

[0120]

[0121] where q x is the component of the driver's head tilting around the x-axis, q y is the component of the driver's head tilting around the y-axis, q z is the component of the driver's head tilting around the z-axis, q w is the calibration value of the actual vehicle, reflecting the intensity of the overall tilt. From the above formula, the deflection angle of the driver's head around the y-axis can be obtained. For example, when θ yaw > 40°, it can be considered that the driver has a relatively obvious head-turning action at this time, and the driver is in a distracted state.

[0122] The formula for the driver portrait model F is:

[0123] F = [EAR, θ yaw , a avg , f brake

[0124] where a avg is the average acceleration of the driver's historical driving, which can reflect the aggressiveness of the driver; f brake is the braking frequency during the driver's driving, which can be the number of brakings per minute. By performing threshold screening on the driver portrait model, the classification status of the driver can be obtained. For example, the current driver belongs to aggressive driving, distracted driving, or conservative driving, etc. Different model portraits can obtain different driver states.

[0125] In some embodiments, calculating the battery pack charge and discharge model according to the driver state type includes:

[0126]

[0127] where DoD is the depth of discharge, C rate is the discharge rate, D actual is the actual mileage traveled by the vehicle, D req is the cruising range required by the driver; the values of α and β are determined according to the driver state type.

[0128] For example, by collecting the current state information of the driver in real time through the in-vehicle camera, and obtaining the driving behavior data in real time through each sensor, combining the current state information of each driver, performing threshold division on the driver portrait, obtaining the driver state type, and obtaining the optimal battery pack charging and discharging effects according to the driver state type.

[0129] Please refer to Figure 2 ​, Another embodiment of the present application provides a device for improving the battery life of a battery pack, the device comprising:

[0130] A map module 101, configured to obtain a road scene and send it to an intelligent driving controller.

[0131] A first sensor 102, configured to obtain road information in front of the vehicle and send it to the intelligent driving controller.

[0132] A second sensor 103, configured to obtain the current state information of the driver and send it to the intelligent driving controller.

[0133] An intelligent driving controller 104, configured to receive and analyze the road scene and the road information in front of the vehicle, generate first information and second information; adjust the adaptive prediction performance energy distribution strategy according to the first information to obtain an energy distribution strategy; adjust the environmental perception drive pre-judgment temperature control strategy according to the second information to obtain a battery pack temperature strategy; generate a driver portrait according to the current state information of the driver; obtain a battery pack charge and discharge model according to the driver portrait and the charge and discharge strategy; control the battery pack of the electric vehicle to charge or discharge according to the energy distribution strategy and the battery pack charge and discharge model; control heating or cooling of the battery pack according to the battery pack temperature strategy.

[0134] In some embodiments, the intelligent driving controller is further configured to:

[0135] Calculate the total power energy according to the first information.

[0136] Calculate a window optimization model based on the total power energy.

[0137] Obtain the energy distribution strategy according to the total power energy and the window optimization model.

[0138] The specific limitations provided in this embodiment regarding a device for improving the battery life of a battery pack can be referred to the embodiments of a method for improving the battery life of a battery pack in the above text, and will not be elaborated here. Each module in the above device for improving the battery life of a battery pack can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0139] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0140] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for improving the endurance of a battery pack, characterized in that, Applied to electric vehicles; the electric vehicle includes a map module, a first sensor, a second sensor, and an intelligent driving controller; the method includes: The map module obtains the road scene and sends it to the intelligent driving controller; The first sensor obtains the road information in front of the vehicle and sends it to the intelligent driving controller; The second sensor obtains the current state information of the driver and sends it to the intelligent driving controller; The intelligent driving controller receives and analyzes the road scene and the road information in front of the vehicle, generates first information and second information; adjusts the adaptive prediction performance energy distribution strategy according to the first information to obtain an energy distribution strategy; adjusts the environmental perception-driven pre-judgment temperature control strategy according to the second information to obtain a battery pack temperature strategy; generates a driver portrait according to the current state information of the driver; obtains a battery pack charge and discharge model according to the driver portrait and the charge and discharge strategy; The intelligent driving controller controls the battery pack of the electric vehicle to charge or discharge according to the energy distribution strategy and the battery pack charge and discharge model; controls the heating or cooling of the battery pack according to the battery pack temperature strategy.

2. The method for improving the battery life of the battery pack according to claim 1, wherein The road scene includes an uphill scene, a downhill scene, an emergency stop scene, a lane change scene, and a sharp turn scene; the method further includes: The intelligent driving controller generates the first information according to the uphill scene, the downhill scene, and the road information in front of the vehicle; the first information includes a wind resistance coefficient, a frontal area, an air density, a rolling resistance coefficient, and an angle; generates the second information according to the emergency stop scene, the lane change scene, the sharp turn scene, and the road information in front of the vehicle; the second information includes an environmental temperature.

3. The method for improving the battery life of the battery pack according to claim 2, wherein The adjusting the adaptive prediction performance energy distribution strategy according to the first information to obtain an energy distribution strategy includes: The intelligent driving controller calculates the total power energy according to the first information; Calculates a window optimization model according to the total power energy; Obtains the energy distribution strategy according to the total power energy and the window optimization model.

4. The method for improving the battery life of the battery pack according to claim 3, wherein, The intelligent driving controller calculates the total power energy P according to the first information total,i The formula is as follows: Among them, C d is the drag coefficient, A f is the frontal area, V i is the speed limit of the i-th section, m is the total mass of the vehicle, g = 9.81 m / s 2 , ρ air is the air density, C r is the rolling resistance coefficient, θ i is the slope angle of the i-th section; The calculation formula of the window optimization model L is: Among them, η discharge (SOC i ) is the battery discharge efficiency, SOC i is the state of charge of the i-th battery pack, Δt is the time step of control, η regen (SOC i ) is the efficiency of the battery to recover energy from regenerative braking when the battery power is SOC i .

5. The method for improving the battery life of the battery pack according to claim 2, wherein The adjusting the environmental perception-driven pre-judgment temperature control strategy according to the second information to obtain a battery pack temperature strategy includes: The intelligent driving controller calculates the temperature prediction model ΔT based on the second information pred and the scenario temperature control weight W action ; The temperature prediction model ΔT pred has the following calculation formula: Among them, ΔT pred is the predicted temperature change value within the road segment time window Δt, P heat is the battery heat generation power, P cool is the cooling system power, C batt is the battery heat capacity, α is the environmental heat exchange coefficient, R thermal is the thermal resistance of the battery pack insulation material, T env is the environmental temperature, T batt is the current battery temperature; The temperature control weight W of the described scenario action has the following calculation formula: Among them, p i is the probability value of different scenarios, S i is the temperature rise sensitivity coefficient of different scenarios, and n is the total number of different working conditions; Calculates a thermal management-driven temperature control instruction according to the temperature prediction model and the scenario temperature control weight; The calculation formula of the thermal management-driven temperature control instruction u(t) is: Among them, T safe is the upper limit of the battery pack safety temperature, T min is the low-temperature endurance protection threshold of the battery pack, and ΔT hyst is the buffer threshold; Obtains a battery pack temperature strategy according to the thermal management-driven temperature control instruction.

6. The method for improving the battery life of a battery pack according to claim 1, wherein The generating a driver portrait according to the current state information of the driver includes: Obtains the eye closure degree and the rotation angle of the head around the y-axis of the driver according to the current state information of the driver; Creates a driver portrait model according to the eye closure degree, the rotation angle, the average historical driving acceleration of the driver, and the driver's braking frequency, and performs threshold division on the driver portrait model to obtain a driver state type; Calculates a battery pack charge and discharge model according to the driver state type.

7. The method for improving the battery life of a battery pack according to claim 6, wherein The calculation formula of the eye closure degree EAR is: Among them, P1 to P6 are the key point coordinates of the driver's eyes. P1 is the right outer corner of the eye, P2 is the right vertex of the upper eyelid, P3 is the point on the right side of the middle of the upper eyelid, P4 is the left outer corner of the eye, P5 is the point on the left side of the middle of the lower eyelid, and P6 is the right vertex of the lower eyelid; The rotation angle θ yaw is calculated by the following formula: Among them, q x is the component of the driver's head tilting around the x-axis, q y is the component of the driver's head tilting around the y-axis, q z is the component of the driver's head tilting around the z-axis, q w is the calibration value of the actual vehicle; The formula of the driver portrait model F is: F = [EAR, θ yaw , a avg , f brake ​ Among them, a avg is the average acceleration of the driver's historical driving, and f brake is the braking frequency during the driver's driving process.

8. The method for improving the battery life of the battery pack according to claim 6, wherein, Calculating the battery pack charge and discharge model according to the driver state type includes: Wherein, DoD is the depth of discharge, C rate is the discharge rate, D actual is the actual mileage traveled by the vehicle, D req is the cruising range required by the driver; the values of α and β are determined according to the type of driver state.

9. A device for improving the endurance of a battery pack, characterized in that, The device includes: A map module for obtaining the road scene and sending it to the intelligent driving controller; A first sensor for obtaining the road information in front of the vehicle and sending it to the intelligent driving controller; A second sensor for obtaining the current state information of the driver and sending it to the intelligent driving controller; An intelligent driving controller for receiving and analyzing the road scene and the road information in front of the vehicle to generate first information and second information; adjusting the adaptive prediction performance energy distribution strategy according to the first information to obtain the energy distribution strategy; adjusting the environment perception drive pre-judgment temperature control strategy according to the second information to obtain the battery pack temperature strategy; generating a driver portrait according to the current state information of the driver; obtaining the battery pack charge and discharge model according to the driver portrait and the charge and discharge strategy; controlling the battery pack of the electric vehicle to charge or discharge according to the energy distribution strategy and the battery pack charge and discharge model; controlling the heating or cooling of the battery pack according to the battery pack temperature strategy.

10. The battery pack endurance improvement device according to claim 8, wherein The intelligent driving controller is further used for: Calculating the total power energy according to the first information; Calculating the window optimization model according to the total power energy; Obtaining the energy distribution strategy according to the total power energy and the window optimization model.

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