9231 results about "Power battery" patented technology

The invention relates to the technical field of new energy automobile thermal management, in particular to a direct cooling and heating management system and method in an automobile power battery. The method comprises the following steps: acquiring vehicle real-time operation condition data and battery pack state monitoring data; working condition mode recognition processing is conducted according to the vehicle real-time operation working condition data, and vehicle working condition mode feature data are generated; performing thermal load demand analysis on the battery pack state monitoring data based on the vehicle working condition mode characteristic data to obtain dynamic thermal load demand data of the battery pack; and according to the dynamic thermal load demand data of the battery pack, refrigerant demand quantity calculation and analysis are carried out, the refrigerant supply capacity of the current direct cooling system is evaluated, and refrigerant supply and demand matching feature data are obtained. The specific requirements of three heat sources of the battery, the motor and the cockpit for the refrigerant flow are quantified, and a refrigerant distribution strategy is formed according to the proportional relation, so that the safety of the battery is improved, and the driving comfort and the working stability of the electric driving system are also guaranteed.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

The invention discloses spherical aluminum oxide and a silicone rubber heat conduction composite material thereof, and belongs to the technical field of functional polymer materials. The spherical aluminum oxide is subjected to surface modification through an amphiphilic fluorosilicone heat conduction interface synergist; comprising the following raw materials in parts by weight: 100-200 parts of vinyl silicone oil, 1-6 parts of side hydrogen-containing silicone oil, 1-6 parts of end hydrogen-containing silicone oil, 0.2-2 parts of a platinum catalyst, 0.01-0.1 part of an inhibitor and 2500-6000 parts of amphiphilic fluorine-silicon heat-conducting interface synergistic spherical aluminum oxide powder. Through surface modification of spherical aluminum oxide, the prepared silicone rubber heat-conducting composite material realizes synergistic gain of heat conduction, insulation and flexibility, and can meet the requirements of multiple fields such as power battery pack heat dissipation gaskets, high-frequency circuit packaging and wearable equipment.

The invention discloses a lithium ion power battery thermal management online regulation and control method comprising the following steps: obtaining battery pack temperature data, and carrying out statistical analysis on the battery pack temperature data to obtain temperature fluctuation distribution characteristics; according to the temperature fluctuation distribution characteristics, cooling power output information is obtained through a feedback control method; obtaining a power allocation weight according to the cooling power output information, and carrying out prediction simulation on the temperature field according to the power allocation weight to obtain predicted temperature field distribution; the heat conductivity coefficient of a variable thermal interface material in the cooling system is adjusted according to the predicted temperature field distribution, a thermal diffusion path optimization scheme is obtained, a dynamic thermal channel network is constructed according to the thermal diffusion path optimization scheme, and a cooling strategy is obtained through matching according to the dynamic thermal channel network in combination with the running state of the battery; hierarchical control parameters are extracted from the cooling strategy, and an energy distribution optimization scheme is obtained; and according to the energy distribution optimization scheme, controlling a cooling system of the lithium ion power battery to realize thermal management regulation and control.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The invention discloses a power battery health scoring method and system based on multi-feature fusion. The method comprises the following steps: preprocessing and classifying obtained power battery operation data; constructing a consistency evaluation index system; determining a consistency evaluation index weight based on a combination weighting method of the game theory; calculating a total evaluation score of the power battery by combining each consistency evaluation index and the weight and the threshold value thereof; obtaining each data set and carrying out feature extraction, screening and construction to obtain an optimal feature set of each power battery; the optimal feature set is divided into a training set, a verification set and an evaluation set to be used for training a BP neural network model and a LightGRM model respectively, then evaluation weights of the two models are calculated, and finally a battery health evaluation score is calculated. According to the hybrid modeling method based on the lightweight space-time diagram convolutional network, the precision and robustness of battery health state estimation are remarkably improved by fusing physical mechanism constraints and data driving advantages.

The invention relates to the technical field of extended-range electric vehicle control, and discloses an engine starting control method and system for an extended-range electric vehicle, and the method comprises the following steps: obtaining the operation information of the extended-range electric vehicle, the operation information comprises the actual temperature of an engine, the actual rotating speed of a range extender, the state of charge (SOC) of a power battery and the maximum dischargeable power; when the starting request mark position of the engine is set, the generator is controlled to enter a torque control mode, and the engine enters a starting mode; and based on the actual temperature of the engine and the actual rotating speed of the range extender, the pre-control torque of the generator is activated through the enabling flag bit. By accurately controlling the rotating speed and the torque of the generator, smooth starting of the engine is achieved, the requirement for starting the engine in various vehicle using scenes is met, meanwhile, the actual temperature of the engine, the SOC of a power battery and the like are considered, the target rotating speed of the started engine and the starting torque of the generator are flexibly controlled, and the starting efficiency is improved. The starting performance of the engine is improved.

The invention provides a power generation control method and device for an extended-range hybrid power loader. According to the scheme, the current working mode of the range extending type hybrid power loader is determined according to the real-time operation parameters and the driving operation information; the current working mode of the range extender is determined according to the current working mode of the range-extended hybrid power loader and the charge state of the power battery, and the current required power generation power of the range extender is determined based on the current working mode of the range extender, the required power of the range-extended hybrid power loader and the charge state of the power battery; according to the current required power generation power of the range extender, a preset range extender output electric power curve and a preset engine fuel efficiency characteristic curve, the target rotating speed of a generator and the target torque of the engine are determined, and according to the current working mode of the range-extended hybrid power loader, the target rotating speed of the generator and the target torque of the engine; and controlling the operation states of the engine and the generator. According to the scheme, the problem of low energy efficiency of an extended-range hybrid power loader is solved.

The invention relates to the field of power battery energy management, in particular to a power battery pack charging and discharging efficiency dynamic optimization system and method, and the system comprises a central control unit, a multi-dimensional sensing module, a dynamic charging and discharging control module, a working condition-heat dissipation cooperation module, a parallel cell equalization module and an aging adaptation optimization module. The multi-dimensional sensing module is used for collecting basic parameters, polarization characteristics, parallel state, aging degree and environmental parameters of a battery pack, the dynamic charging and discharging control module is used for collecting data, the working condition-heat dissipation coordination module adopts a three-stage heat dissipation framework, and the parallel battery cell balancing module is used for balancing the battery pack through a dynamic current distribution and voltage active balancing double-coordination mechanism. The aging adaptation optimization module is used for grading and differentially optimizing charge and discharge parameters and heat dissipation strategies based on the aging degree of the battery cell, so that the problems of low charge and discharge efficiency, poor wide temperature range adaptability, weak parallel consistency, fast aging attenuation and multi-factor coupling deterioration of the power battery pack are solved.

The invention relates to a thermal runaway monitoring method and related equipment for a new energy automobile power battery, and the method comprises the following steps: carrying out the internal temperature monitoring of the new energy automobile power battery, and obtaining the three-dimensional thermal field distribution data; thermal characteristics are analyzed, thermal runaway precursor characteristics are identified, the thermal runaway probability is calculated, and a probability distribution diagram is generated; in combination with the heat spreading prediction model, obtaining heat spreading path prediction data; and determining the out-of-control risk level of the battery according to the data, and generating a corresponding early warning signal, thereby solving the technical problems that the existing monitoring method mostly depends on threshold judgment of a single parameter, and complex thermal field change in the power battery of the new energy automobile is difficult to comprehensively capture.

The invention relates to the technical field of power batteries, in particular to a power battery pack health state detection method and system. The power battery pack health state detection method comprises the following steps: acquiring open-circuit voltage change data and terminal voltage fluctuation data of a battery under micro current pulse excitation, recording a change curve of voltage along with time through high-precision time sequence acquisition equipment, and obtaining an initial data set of voltage dynamic response; acquiring magnetic field intensity distribution and magnetic field gradient change data of the surface of the battery in a weak alternating magnetic field, and acquiring spatial magnetic field information through a magnetic sensor array to obtain a three-dimensional distribution diagram reflecting magnetic field nonuniformity; through a three-dimensional distribution diagram of magnetic field intensity distribution and magnetic field gradient change, a defect detection algorithm is adopted to analyze a mapping relation between magnetic field non-uniformity and internal micro cracks, and correlation distribution of defect positions and internal resistance growth rates is determined.

The invention provides a gear switching method, a vehicle and a storage medium, the method is applied to the technical field of hybrid power control, and the method comprises the steps that in the process that the vehicle runs based on a direct drive mode, if it is determined that the vehicle needs to switch gears, the whole vehicle driving power corresponding to the direct drive mode and the discharging power of a power battery are determined; based on the whole vehicle driving power, the discharging power and the gear change state of the vehicle, a target torque reduction gradient is determined; based on the target torque reduction gradient, the actual torque of the engine is controlled to be reduced to the target torque; and under the condition that the actual torque of the engine is equal to the target torque, the clutch is controlled to be in an open state, so that the vehicle is shifted under the condition that the clutch is in the open state. The method can ensure that the power is not interrupted in the vehicle gear shifting process, the vehicle stability is improved, and the user driving experience is improved.

The invention discloses a new energy automobile battery health prediction method and system, and relates to the technical field of monitoring analysis, and the method comprises the steps: carrying out the real-time collection of the operation state parameters of a power battery pack corresponding to a new energy automobile, obtaining a battery state data set corresponding to the new energy automobile, carrying out the adaptive noise suppression processing of the battery state data set, and obtaining a new energy automobile battery health prediction result. Generating a de-noised battery state feature map, performing feature extraction on the de-noised battery state feature map to obtain a multi-dimensional coupling feature set, constructing a dynamic weight distribution matrix, performing feature fusion on the multi-dimensional coupling feature set, determining a battery health state comprehensive index, and triggering a grading early warning mechanism based on the battery health state comprehensive index; and constructing a battery health prediction model, carrying out long-term modeling on the battery health state comprehensive index, predicting a battery health state evolution trend in a future charging and discharging period, generating a balance maintenance strategy, and pushing the balance maintenance strategy to the vehicle-mounted terminal and the cloud management platform. The method has the effect of improving the accuracy of battery health prediction.

The invention relates to the technical field of electric vehicle power battery thermal management, and discloses a temperature change-based power battery thermal management system, which comprises a thermal simulation pre-judgment end, a sensor network end, a dynamic liquid cooling control end, a BMS decision end and a fault diagnosis module, aiming at the technical defects of out-of-control temperature difference, response hysteresis and leakage risk caused by static flow channel design of the existing liquid cooling system, breakthrough is realized through a three-dimensional technical architecture: a CFD (Computational Fluid Dynamics) prejudgment layer: automatically calibrating sensor distribution points based on temperature field standard deviation, and improving temperature difference identification precision; according to the dynamic valve control layer, an electromagnetic three-way reversing valve is adopted to execute a directional temperature control strategy, an edge battery cell is preferentially introduced under the low-temperature working condition, and heat dissipation is enhanced under the high-temperature working condition; according to the redundant safety layer, the valve body and the pipeline are integrally formed, the leakage rate is reduced, and flow maintenance under single-point failure is achieved through a double-water-inlet parallel pump source. The method has the effects of improving the convergence speed of temperature difference between modules, reducing the attenuation rate of battery life and reducing the risk probability of thermal runaway.

The invention provides a battery health state evaluation method, device and equipment and a medium, and the method comprises the steps: collecting multi-source parameters of a power battery in a charge-discharge cycle process, the multi-source parameters comprising voltage, current, temperature and electrochemical impedance spectroscopy; performing feature engineering processing on the multi-source parameters based on a battery aging mechanism, and extracting key feature parameters; the key characteristic parameters are input into a multi-physics field coupling model to simulate thermoelectric coupling behaviors of the battery under a dynamic load, lithium ion concentration distribution and temperature distribution in the battery are obtained, and the multi-physics field coupling model is used for representing a coupling relation between an electrochemical field and a thermal field of the battery; and fusing the key characteristic parameters with data output by the multi-physics field coupling model, and determining a health state evaluation result of the power battery. According to the invention, the accuracy of power battery health state evaluation is improved.

The invention discloses a power battery active equalization control method, which comprises the steps of extracting characteristic information of a battery pack, generating quantitative state change trend description, and obtaining potential unbalanced risk point distribution; analyzing operation constraint conditions related to load changes, generating trigger weights of unbalanced risks, screening high-priority risk points, generating a preliminary equalization path planning scheme, calculating equalization operation current intensity of each single battery in different time periods in combination with battery pack state data collected in real time, and generating an equalization operation schedule; obtaining feedback state data in real-time execution, and comparing the feedback state data with the predicted risk point to obtain a final control decision instruction; and for the final control decision instruction, acquiring executed battery pack state update data, analyzing the consistency of the battery pack state update data and a target equalization state, judging whether an equalization path or time arrangement needs to be finely adjusted, and generating a continuously optimized equalization control scheme. Accurate prediction and balance control of the state of the battery pack are realized.

The invention relates to the technical field of power batteries, and discloses a plastic uptake support CCS integrated busbar assembly, which comprises a plastic uptake support body, busbar bodies, a wiring harness assembly and a collection nickel sheet, the plastic uptake support body is provided with a plurality of positioning grooves and riveting columns, the positioning grooves are fixedly connected in series with the plurality of busbar bodies through the riveting columns, and the collection nickel sheet is connected with the wiring harness assembly. The busbar bodies comprise a series busbar, a bridging busbar, a positive leading-out electrode busbar and a negative leading-out electrode busbar, the blister support body is provided with a wire harness buckle, the wire harness buckle is located between the two groups of busbar bodies, a wire is fixed on the wire harness buckle, the acquisition nickel sheet is welded on the busbar bodies through laser, and the acquisition nickel sheet is upwarped in a non-breaking manner after being welded. According to the plastic uptake support CCS integrated busbar assembly, the wiring harness buckle can simply, conveniently and quickly fix the wiring harness and prevent the wiring harness from being separated from the plastic uptake support, a traditional mode of fixing the wiring harness through hot pressing or hot riveting is replaced, and the product cost is greatly reduced.

The invention discloses a real vehicle power battery capacity attenuation trajectory prediction method based on a de-noising diffusion model. According to the method, a diffusion generation model is applied to the field of battery capacity prediction for the first time, multi-modal features are constructed for charging and discharging data under a real vehicle working condition, feature expressions are extracted through Transform and CNN, and the features are integrated into high-dimensional embedded vectors. And then, a DDPM model based on a ContextUnet architecture is utilized to learn a dynamic change rule of capacity attenuation in a noise environment through a noise diffusion and reverse denoising process under the guidance of multi-modal features, so that high-precision capacity prediction is realized. The training process is further combined with an implicit denoising mechanism of DDIM, and the reasoning speed is increased while the prediction quality is improved. Finally, a capacity attenuation track confidence interval is obtained by performing multiple times of prediction sampling calculation on a predicted capacity attenuation result, and the capacity attenuation track confidence interval can be used for deducing key indexes such as the state of health (SOH) and the remaining service life (RUL) of the battery. The method has the characteristics of high prediction precision and strong adaptability, obviously enhances the intelligent level of the battery management system, and has important practical value for health assessment and life management of the new energy automobile battery.

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.

An electrified vehicle assembly includes an enclosure assembly of a battery pack. The enclosure assembly has a coating. An attachment point is disposed within the enclosure assembly. The enclosure assembly is secured directly to the attachment point. The assembly includes a coating clearing feature that removes some of the coating from the enclosure assembly when the enclosure assembly is secured directly to the attachment point.

The invention discloses an intelligent regulation and control method for a power battery positive electrode binder production process, and the method comprises the steps: synchronously collecting multi-dimensional process parameters such as temperature, viscosity and the like and performance indexes such as particle size distribution, bonding strength and the like, carrying out the normalization, denoising and time sequence alignment processing, and fusing hydrodynamic simulation and historical data to construct an initial parameter coupling model; identifying a parameter influence weight through an attention mechanism neural network, generating a decoupling factor matrix to reconstruct a parameter space, and establishing a virtual control channel; executing constrained gradient descent multi-objective optimization in the channel, generating a regulation and control instruction, and reversely mapping the regulation and control instruction into an equipment executable parameter; according to the method, performance indexes and optimization targets after regulation and control are continuously compared, model parameters are updated, retraining is triggered when deviation exceeds a threshold value, regulation and control precision and process stability are guaranteed, and the real-time performance of parameter regulation and control, the collaborative optimization capability and the quality consistency of the production process are remarkably improved.

The invention discloses a preparation method and application of a sulfide solid electrolyte membrane based on a gradient polymer / ionic liquid composite binder, and an all-solid-state battery, and belongs to the technical field of all-solid-state batteries. The method comprises the following steps: firstly coating a polymer / ionic liquid / sulfide electrolyte composite layer through a gradient coating process, filling particle gaps under low pressure by utilizing the plasticizing effect of ionic liquid, and reducing the porosity of an interface; and then coating a polymer / sulfide electrolyte surface layer to provide mechanical support and inhibit side reaction of the electrode. According to the design, the electrolyte membrane still keeps high ionic conductivity under the working pressure of a commercial power battery module, and the interface impedance is reduced compared with that of a traditional homogeneous membrane. Through the innovative gradient structure and material combination, the ion transmission bottleneck of the sulfide electrolyte under the low-pressure condition is solved, excellent interface stability and process compatibility are achieved, and a key technical scheme is provided for all-solid-state battery industrialization.

The invention relates to the technical field of model prediction, in particular to a power battery thermal management simulation method and system based on a neural network. The method comprises the following steps: acquiring electrical performance parameters, thermal performance parameters and mechanical performance parameters corresponding to the power battery through coupling experiment measurement, and performing three-dimensional coupling simulation construction to generate a three-dimensional physical field coupling model of the power battery; battery thermal characteristic evaluation is carried out from four dimensions of a micro material corresponding to the power battery, a battery monomer, a battery module and a thermal management system through the power battery three-dimensional physical field coupling model, and a power battery thermal characteristic data set is obtained; and constructing a multi-scale deep neural network architecture based on a neural network in combination with the thermal characteristic data set of the power battery to perform thermal management prediction training and thermal management constraint optimization control, and generating an optimal thermal management control strategy of the power battery to execute corresponding thermal management control work. According to the invention, efficient simulation and intelligent prediction optimization of thermal management of the power battery can be realized.

The invention discloses a fault diagnosis and early warning method for an intelligent thermal management system of a power battery, and relates to the technical field of new energy automobile power battery safety. Comprising the following steps executed in sequence: S1) based on real-time monitoring data of a distributed temperature sensor in a battery module, constructing a dynamic space-time neural field model through an adaptive graph neural network, and generating a thermal field fingerprint representing a thermal propagation topology mode; according to the fault diagnosis and early warning method for the intelligent thermal management system of the power battery, a dynamic space-time neural field model is constructed, a sub-DEG C weak thermal abnormal signal is accurately captured, and a micro short circuit fault is recognized in advance in combination with electric heating delay analysis, so that the problem that a fault precursor signal is submerged by a complex working condition is solved; a double-channel verification mechanism depends on double mutual verification of physical law rigid constraint and intelligent residual feature recognition, the misjudgment risk caused by cooling system fluctuation and sensor noise is eliminated, and the early warning accuracy is improved.

The invention relates to the technical field of battery state-of-charge estimation, and discloses an unmanned aerial vehicle power battery state-of-charge accurate estimation method, which is characterized by comprising the steps of multi-modal dynamic data acquisition and preprocessing, quantum-classical hybrid modeling and feature extraction, meta-learning driven dynamic adaptive optimization, multi-modal dynamic state-of-charge estimation, multi-modal dynamic state-of-charge estimation, multi-modal dynamic state-of-charge estimation and multi-modal dynamic state-of-charge estimation. Uncertainty quantification and security boundary management: multi-modal dynamic data acquisition and preprocessing, quantum-classical hybrid modeling, meta-learning driven dynamic adaptive optimization and uncertainty quantification and security boundary management technologies are adopted; the method solves the problems of insufficient estimation precision and poor adaptability of a traditional method under a dynamic flight working condition due to complex coupling of multi-modal data and strong nonlinearity of a battery polarization effect and an aging characteristic in the estimation of the state of charge of the power battery of the unmanned aerial vehicle.

The invention discloses a power battery real-time heat flow field regulation and control method based on edge calculation, and the method comprises the following steps: S1, collecting and preprocessing temperature data, and constructing a time sequence data set; s2; organizing a Euclidean heat data structure according to space + time; s3, performing diffusion mapping to obtain non-Euclidean manifold features; s4, constructing a thermal field graph structure model; s5, inputting the graph structure to the graph adaptive convolutional neural network model of the edge node; s6, training the model by using historical data; s7, optimizing model training by using a sharpness perception minimization algorithm; s8, predicting future heat flow distribution in real time; s9, generating a thermal management strategy instruction set; and S10, controlling an execution module to carry out real-time regulation and control. According to the invention, the accuracy of thermal anomaly prediction and the adaptive ability of the cooling strategy are significantly improved, and the method can be widely applied to an intelligent thermal management scene.

A new energy vehicle energy intelligent management method and system, and a related device. The method comprises: determining at least one candidate energy-saving path on the basis of a starting point and an ending point of a vehicle, wherein path-based vehicle energy consumption predicted on the basis of the at least one candidate energy-saving path is lower than path-based vehicle energy consumption predicted on the basis of other paths, and the path-based vehicle energy consumption is predicted on the basis of road condition information of each path and energy consumption impact information; in response to a selection operation on the at least one candidate energy-saving path, determining a preset travel path, wherein the preset travel path comprises a plurality of road sections, and the path-based vehicle energy consumption comprises road section-based vehicle energy consumption of the plurality of road sections; and by taking the minimization of fuel consumption along the preset travel path as an objective, controlling an operation state of an engine on the basis of an initial SOC of a power battery and the road section-based vehicle energy consumption at each road section and an actual vehicle demand of the vehicle, so that the engine is in an efficient operation range during operation.

The invention provides a vehicle control method and device, a vehicle and a storage medium, the method is applied to the technical field of hybrid power control, and the method comprises the steps that based on the current electric quantity of a power battery of the vehicle and the whole vehicle demand power of the vehicle, an initial driving mode of the vehicle is firstly determined; calculating the total power consumption corresponding to each of multiple candidate power distribution proportions in the initial driving mode, determining a target power distribution proportion in the multiple candidate power distribution proportions according to the total power consumption corresponding to each of the multiple candidate power distribution proportions, and finally determining the target power distribution proportion in the multiple candidate power distribution proportions based on the target power distribution proportion. And correcting the initial driving mode to obtain a target driving mode, and controlling the vehicle to run in the target driving mode. According to the method, the energy utilization efficiency of the whole vehicle can be maximized, it is ensured that the engine and the motor operate in respective efficient intervals as much as possible, and more stable power output can be provided for the vehicle under different road conditions.

The invention relates to the technical field of power battery evaluation, and discloses a power battery health state evaluation and life early warning method. The method comprises the following steps: dividing a target power battery module into a plurality of layers of evaluation areas, and randomly selecting a plurality of measurement position points in each layer of evaluation area; then, collecting battery state original data of each measurement position point of each layer of evaluation area in real time, and comprehensively analyzing the data to obtain a health state evaluation value of the target power battery module; judging and analyzing the health state evaluation value and a preset health state threshold value; if the health state evaluation value is lower than a preset threshold value, triggering a preset life early warning measure; and if the health state evaluation value is higher than a preset threshold value, maintaining the current evaluation period. The method can improve the accuracy of battery health state evaluation, optimizes the time of life early warning, and is suitable for health management of various power battery modules.

The invention discloses a power battery thermal runaway fault detection method and system based on multi-source parameters, and belongs to the technical field of battery management, and the method comprises the steps: inputting a high-frequency signal generated by a signal generator to a positive electrode feed point of a battery cell, connecting a coaxial line to a positive electrode, and grounding a negative electrode to form a grounding loop; the collected dendritic crystal reflection signals are analyzed, a quantitative correlation model is established, the growth condition of dendritic crystals is analyzed according to the quantitative correlation model, and preliminary early warning is carried out; electrochemical parameters and auxiliary parameters of the battery cell are collected, and cross validation is carried out on the electrochemical parameters and the auxiliary parameters and preliminary early warning of dendritic crystals; and establishing a dynamic risk and grading response mechanism, dividing the risk into different grades, and setting corresponding response measures. In the implementation process of the technical scheme provided by the invention, by combining dendritic crystal growth prediction and electrochemical parameter analysis, accurate monitoring of the internal state of the power battery is realized, the timeliness and accuracy of thermal runaway early warning are effectively improved, and safe and stable operation of a battery system is ensured.