Method and device for optimizing low-temperature performance of heavy truck start-stop lithium battery

Abstract

The present application relates to the technical field of lithium battery performance optimization, and discloses a heavy truck start-stop lithium battery low-temperature performance optimization method and device. The method: collects the transient impact current of the heavy truck lithium battery during the start-stop process and performs frequency spectrum analysis to obtain frequency components and harmonic characteristics; determines the L1 spacing parameters of the positive and negative electrode sheet end temperature control points using the frequency components and harmonic characteristics, and divides the L2 width parameters of the temperature gradient regulation area corresponding to the thickness direction of the electrode sheet; performs transient pulse prediction based on the L1 spacing parameters and L2 width parameters to generate a temperature gradient distribution field; and implements directional driving of the lithium ion concentration front of the electrolyte in the heavy truck lithium battery with the help of the temperature gradient distribution field, so as to realize dynamic coordination control of the temperature gradient and ion transmission of the heavy truck lithium battery under the start-stop working condition. The present application constructs a three-level temperature gradient regulation structure along the thickness direction of the electrode sheet, effectively solving the problems of local overheating of the electrode sheet end and ion transmission imbalance.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery performance optimization, and in particular to a method and device for optimizing the low-temperature performance of a start-stop lithium battery for a heavy truck. Background Art

[0002] Heavy-duty truck start-stop systems require batteries to deliver a high-rate discharge current of 10C-50C in a short period of time to drive the starter motor, while also maintaining normal operation of onboard electrical equipment during engine shutdown. This operating mode places extremely stringent technical demands on the start-stop battery's transient power output capability, cycle life, and low-temperature performance. In particular, low temperatures increase the battery's internal resistance and reduce ion transmission rates, severely impacting the reliability of the start-stop system.

[0003] Existing heavy-duty truck start-stop batteries mostly use lead-acid battery technology. Although the cost is low, it has inherent defects such as low energy density, short cycle life, and poor low-temperature performance. It is difficult to meet the high-frequency, high-power, and long-life usage requirements of modern heavy-duty trucks for the start-stop system. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method and device for optimizing the low-temperature performance of heavy-duty truck start-stop lithium batteries. The present invention constructs a three-level temperature gradient control structure along the thickness direction of the electrode, which effectively solves the problems of local overheating at the electrode end and imbalance in ion transmission.

[0005] To achieve the above objectives, the present invention provides a method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery, comprising the following steps:

[0006] Collect the transient impact current of the heavy-duty truck lithium battery during the start-stop process and perform spectrum analysis to obtain the frequency components and harmonic characteristics;

[0007] Determine the L1 spacing parameter of the temperature control points at the positive and negative electrode ends by using the frequency components and the harmonic characteristics, and divide the L2 width parameter of the temperature gradient control area corresponding to the thickness direction of the electrode;

[0008] Perform transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field;

[0009] By means of the temperature gradient distribution field, the lithium ion concentration front of the electrolyte in the heavy-duty truck lithium battery is directionally driven, thereby realizing dynamic coordinated control of the temperature gradient and ion transport of the heavy-duty truck lithium battery under start-stop conditions.

[0010] Optionally, in a first implementation of the first aspect of the present invention, collecting the transient impact current of the heavy-duty truck lithium battery during the start-stop process and performing spectrum analysis to obtain frequency components and harmonic characteristics includes:

[0011] Collect current data during the start-stop process of heavy-duty truck lithium batteries to obtain transient impact current;

[0012] Classifying the transient impact current according to current amplitude and duration to obtain a current classification result, wherein the current classification result includes: a startup large current impact type, a shutdown reverse pulse type, and a start-stop transition pulse type;

[0013] Based on the current classification result, an FFT fast Fourier transform is performed on the transient impulse current to obtain start-stop current spectrum data, and harmonic component extraction and frequency component analysis are performed on the start-stop current spectrum data to obtain frequency components and harmonic characteristics.

[0014] Optionally, in a second implementation of the first aspect of the present invention, the determining of the L1 spacing parameter of the temperature control points at the positive and negative electrode ends by using the frequency component and the harmonic characteristics, and dividing the L2 width parameter of the temperature gradient control area corresponding to the electrode thickness direction, includes:

[0015] Calculating the coordinated response strength of the temperature control points based on the frequency components and the harmonic characteristics to obtain the temperature control point spacing ranges corresponding to different frequency ranges;

[0016] Performing a non-uniform layout design on the positive and negative electrode terminals according to the temperature control point spacing range to obtain an L1 spacing parameter;

[0017] Based on the L1 spacing parameter, the temperature gradient control area is spatially divided along the thickness direction of the electrode to obtain a three-level control structure of the surface enhanced heat transfer area, the middle buffer control area and the deep stable control area;

[0018] Based on the three-level control structure, temperature gradient heat transfer path analysis and L2 width optimization are performed to obtain the L2 width parameter.

[0019] Optionally, in a third implementation of the first aspect of the present invention, performing a non-uniform layout design on the positive and negative electrode terminals according to the temperature control point spacing range to obtain the L1 spacing parameter includes:

[0020] Based on the temperature control point spacing range, the temperature response characteristics of the startup large current impact type, the shutdown reverse pulse type, and the start-stop transition pulse type are analyzed to obtain a coordinated response time;

[0021] Calculate the temperature control point spacing values ​​according to the coordinated response time to obtain a first spacing value required for the startup high current impact type, a second spacing value required for the shutdown reverse pulse type, and a third spacing value required for the start-stop transition pulse type, and use the first spacing value, the second spacing value, and the third spacing value as spacing calculation results;

[0022] Performing a non-uniform distribution analysis of the spatial layout of the positive and negative electrode terminals based on the spacing calculation results to obtain a spatial coordinate distribution and a temperature control point arrangement scheme at the positive and negative electrode terminals;

[0023] Based on the spatial coordinate distribution and the temperature control point arrangement scheme, L1 spacing parameter calibration and area boundary division are performed to obtain L1 spacing parameters.

[0024] Optionally, in a fourth implementation of the first aspect of the present invention, performing temperature gradient heat transfer path analysis and L2 width optimization based on the three-level control structure to obtain the L2 width parameter includes:

[0025] The heat transfer characteristics of the surface enhanced heat transfer zone, the intermediate buffer control zone, and the deep stable control zone in the three-level control structure are analyzed to obtain the corresponding heat transfer coefficient and thermal resistance distribution characteristics of each zone;

[0026] The temperature gradient strength is calculated based on the heat transfer coefficient and the thermal resistance distribution characteristics, and the temperature gradient requirement of the surface enhanced heat transfer zone is a first gradient strength, the temperature gradient requirement of the intermediate buffer control zone is a second gradient strength, and the temperature gradient requirement of the deep stable control zone is a third gradient strength;

[0027] Calculate the width of each region according to the temperature gradient requirement to obtain a width calculation result;

[0028] According to the width calculation results, the spatial configuration and parameter calibration of the three-level control structure are performed, and the L2 width parameter of the surface enhanced heat transfer zone is obtained as the first width value, the L2 width parameter of the intermediate buffer control zone is obtained as the second width value, and the L2 width parameter of the deep stable control zone is obtained as the third width value.

[0029] Optionally, in a fifth implementation of the first aspect of the present invention, performing transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field includes:

[0030] Perform pulse current prediction based on the L1 spacing parameter and the L2 width parameter to obtain pulse current change data;

[0031] Calculate the target temperature value of each temperature control point based on the pulse current change data, and obtain the temperature setting value and response timing corresponding to different areas of the positive and negative electrode ends;

[0032] Based on the temperature setting value and the response time sequence, the temperature gradient intensity is distributed to the three-level control structure to obtain specific temperature gradient values ​​of the surface enhanced heat transfer zone, the intermediate buffer control zone and the deep stable control zone;

[0033] The spatial temperature field distribution is calculated based on the specific temperature gradient value to obtain a temperature gradient distribution field covering the entire positive and negative electrode ends and the thickness direction of the electrode.

[0034] Optionally, in a sixth implementation of the first aspect of the present invention, performing pulse current prediction based on the L1 spacing parameter and the L2 width parameter to obtain pulse current change data includes:

[0035] Performing parameter analysis and historical pulse data matching on the L1 spacing parameter and the L2 width parameter to obtain current historical characteristic data;

[0036] Perform pulse prediction based on the current historical characteristic data to obtain pulse current amplitude and duration prediction results;

[0037] Transmitting the pulse current amplitude and the duration prediction results to the temperature response module to calculate the temperature control demand, and obtain the response intensity and response timing requirements corresponding to each temperature control point;

[0038] Performing gradient optimization according to the response intensity and the response timing requirements to obtain pulse current prediction accuracy and prediction time window;

[0039] Pulse current variation data including current amplitude variation trend, frequency distribution characteristics and duration sequence is generated based on the pulse current prediction accuracy and the prediction time window.

[0040] Optionally, in a seventh implementation of the first aspect of the present invention, the method of implementing a directionally driven lithium ion concentration front of the electrolyte in the heavy-duty truck lithium battery by means of the temperature gradient distribution field to achieve dynamic coordinated control of the temperature gradient and ion transport of the heavy-duty truck lithium battery under start-stop conditions includes:

[0041] The lithium ion transport of the electrolyte in the heavy-duty lithium battery is directed and guided by the temperature gradient distribution field, thereby obtaining an ion transport guidance scheme of a surface enhanced heat transfer zone, an intermediate buffer control zone, and a deep stable control zone;

[0042] The ion transmission guidance scheme is used to control different current impact stages during the start-stop process to obtain a temperature gradient control mode;

[0043] Tracking the lithium ion concentration front change in the temperature gradient control mode in real time to obtain the ion concentration front position coordinates and the propulsion speed change curve;

[0044] Based on the ion concentration front position coordinates and the propulsion speed change curve and the theoretical transmission model, a deviation analysis is performed to obtain a control accuracy deviation value and a response delay time;

[0045] The temperature gradient distribution field is corrected in real time according to the control accuracy deviation value and the response delay time, thereby realizing dynamic coordinated control of the temperature gradient and ion transmission of the heavy-duty truck lithium battery under start-stop conditions.

[0046] Optionally, in an eighth implementation of the first aspect of the present invention, performing deviation analysis based on the ion concentration front position coordinates and the propulsion speed change curve with a theoretical transmission model to obtain a control accuracy deviation value and a response delay time includes:

[0047] The theoretical ion concentration frontier position benchmark values ​​corresponding to the surface enhanced heat transfer zone, the intermediate buffer control zone and the deep stable control zone are calculated based on the theoretical transmission model;

[0048] Performing a numerical difference analysis based on the theoretical ion concentration front position reference value and the ion concentration front position coordinates to obtain position deviation data and a propulsion direction offset;

[0049] A time series comparison analysis is performed based on the propulsion speed variation curve and the speed reference curve in the theoretical transmission model to obtain the speed deviation amplitude and response time difference in the startup phase, shutdown phase and transition phase;

[0050] A comprehensive deviation evaluation is performed based on the position deviation data, the propulsion direction offset, the speed deviation amplitude and the response time difference to obtain a control accuracy deviation value and a response delay time.

[0051] The present invention also provides a device for optimizing the low-temperature performance of a heavy truck start-stop lithium battery, comprising:

[0052] The acquisition module is used to collect the transient impact current of the heavy-duty truck lithium battery during the start-stop process and perform spectrum analysis to obtain the frequency components and harmonic characteristics;

[0053] a division module, configured to determine an L1 spacing parameter of the temperature control points at the positive and negative electrode ends by using the frequency components and the harmonic characteristics, and to divide an L2 width parameter of a temperature gradient control area corresponding to the thickness direction of the electrode;

[0054] A generating module, configured to perform transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field;

[0055] The driving module is used to implement directionally driven lithium ion concentration front of the electrolyte in the heavy-duty truck lithium battery with the help of the temperature gradient distribution field, so as to realize dynamic coordinated control of temperature gradient and ion transmission of the heavy-duty truck lithium battery under start-stop conditions.

[0056] In summary, the technical solution provided by the present invention realizes the accurate identification and classification of 10C-50C transient impact currents during the start-stop process of heavy trucks through FFT fast Fourier transform technology, establishes a non-uniform temperature control point layout based on frequency components and harmonic characteristics, and constructs a three-level temperature gradient control structure along the thickness direction of the pole piece. Through the coordinated optimization of L1 spacing parameters and L2 width parameters, combined with the integrated application of transient pulse prediction algorithms, millisecond-level dynamic response control is achieved. The present invention changes the traditional passive response mode, uses the temperature gradient distribution field to actively guide the lithium ion transmission front in the electrolyte, and establishes a dynamic coupling coordination mechanism of temperature gradient and ion transmission under the start-stop conditions of heavy trucks. Through a multi-level closed-loop optimization control architecture, the problems of local overheating at the pole piece end and imbalance in ion transmission are effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the steps of a method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery in one embodiment of the present invention;

[0058] Figure 2 This is a structural block diagram of a device for optimizing the low-temperature performance of a start-stop lithium battery for heavy trucks in one embodiment of the present invention.

[0059] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] Reference Figure 1 This embodiment provides a method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery, comprising the following steps:

[0062] S1, collects the transient impact current of the heavy truck lithium battery during the start-stop process and performs spectrum analysis to obtain the frequency components and harmonic characteristics;

[0063] The transient current signals of lithium batteries are collected during the start-stop phases of heavy-duty trucks. This acquisition process is based on a current sensor array deployed at the positive and negative terminals of the lithium battery. This array has a high sampling frequency of 10kHz and real-time response capability for surge currents ranging from 10C to 50C. This captures the millisecond-level surge currents generated during startup and shutdown, forming a raw transient current data stream. The transient surge current sequences are quantitatively analyzed and classified according to two key characteristics: current amplitude and duration. By setting specific amplitude intervals and time thresholds, all collected transient currents are divided into three types: high-amplitude, short-duration current surges generated during startup, with a typical amplitude of 40C to 50C and a duration between 50 and 100ms; reverse low-amplitude pulses generated during shutdown, with amplitudes ranging from 15C to 25C and a duration between 30 and 80ms; and fluctuating transition pulses generated during the start-stop transition, with amplitudes ranging from 10C to 20C and a duration between 100 and 200ms. Based on the above current classification results, a Fast Fourier Transform (FFT) operation was performed on each type of transient impulse current. By converting the time-domain current signal into frequency-domain information, the corresponding start-stop current spectrum data was obtained. The main frequency component and higher-order harmonic energy distribution in the spectrum were then analyzed. The main frequency was concentrated between 8Hz and 12Hz, accompanied by several significant harmonic peaks. Through statistical analysis of the spectrum energy center, frequency intensity envelope, and harmonic sequence, the frequency components and harmonic characteristics corresponding to different types of pulse current were extracted.

[0064] S2, using the frequency components and harmonic characteristics to determine the L1 spacing parameters of the temperature control points at the positive and negative electrode ends, and divide the L2 width parameters of the temperature gradient control area corresponding to the thickness direction of the electrode;

[0065] Specifically, based on frequency components and harmonic characteristics, the current impulse response intensity within different frequency ranges is quantitatively modeled. A frequency-thermal response mapping function is used to establish the coupling relationship between current spectrum characteristics and thermal response rate. The required coordinated response speed and heat diffusion time constant of the temperature control points in each frequency range are then evaluated, thereby calculating the appropriate temperature control point spacing range for each frequency range. In this process, the dynamic weights of parameters such as pulse energy density, response delay tolerance, and thermal inertia are considered, ensuring that the calculated temperature control point spacing is adaptable to operating conditions and spatially accurate. Based on this spacing range, a non-uniform layout design is implemented at the positive and negative electrode ends. A higher density of temperature control points is placed in the high-frequency, high-intensity current impulse area, with a spacing L1 set to 0.8mm to ensure millisecond-level thermal response capability under strong impulses of 40°C to 50°C. In the low-frequency transition region and reverse pulse region, the spacing is appropriately relaxed to 1.0mm or 1.2mm to balance the relationship between control response and energy consumption, forming an L1 parameter distribution spectrum along the electrode surface. A three-dimensional temperature field control structure along the electrode thickness is constructed based on the L1 spacing parameter. A spatial mapping algorithm is used to extend the surface thermal effects of the points into the electrode interior. Three control regions are then divided according to the temperature attenuation function and the heat conduction path gradient: a surface enhanced heat transfer zone, an intermediate buffer zone, and a deep stable control zone. Each zone is constructed with gradient control zones of varying thicknesses, tailored to its heat conduction role and the required control capabilities for the ion migration front. A temperature gradient path analysis is performed on this three-layer structure. Heat flux guidance simulations are performed based on the material thermal diffusivity, temperature control point coverage density, heat loss boundary conditions, and internal electrolyte heat capacity. The L2 widths of each layer are optimized accordingly. The surface enhanced zone (L2) is set between 30 and 50 μm to enhance transient thermal flow response, the intermediate buffer zone (L2) is set between 60 and 100 μm to moderate the surface-to-internal temperature gradient, and the deep stable zone (L2) is extended to 80 to 120 μm to maintain a temperature equilibrium along the long-term ion migration channel.

[0066] S3, performing transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field;

[0067] It should be noted that the two-dimensional and three-dimensional thermal response structures represented by the L1 spacing parameters and the L2 width parameters are linked and modeled, and a pulse prediction mechanism is introduced to capture the current variation trend during the start-stop process. This mechanism combines frequency feature extraction with machine learning model construction. After the temperature control system receives the initial current feature change signal, a neural network algorithm predicts the impending transient surge current 50 to 100 milliseconds in advance, generating a continuous stream of pulse current change data. Using this predicted data as the input variable, combined with the spatial coordinates of each temperature control point and its corresponding L1 distribution logic, the thermal load response required by the point is calculated within a multidimensional mapping framework, from which its target temperature setpoint is derived. At the same time, the response inertia of each control zone and the heat transfer coupling delay are considered to determine the specific response sequence, so that the temperature control point has temporal coordination and regional synergy in its thermal regulation behavior. Within the established three-level control structure, the target temperature values ​​of all temperature control points are layered and partitioned according to the high-frequency and high-heat response characteristics of the surface enhanced heat transfer zone, the transition equilibrium requirements of the intermediate buffer control zone, and the steady-state conduction characteristics of the deep stable control zone. The temperature gradient strength within each zone is calculated. The surface zone gradient strength is set to 15 to 25°C / mm to quickly respond to ion concentration perturbations caused by pulse impact. The intermediate zone gradient is maintained at 8 to 15°C / mm to achieve slow heat release and ion front transition control, and the deep zone is controlled at 3 to 8°C / mm to maintain consistency and stability in the transmission depth. After obtaining the partition gradient values, the thermal-spatial modeling module is called to integrate the electrode structure parameters, electrolyte thermophysical properties, electrode thickness information, and external boundary conditions to construct a three-dimensional finite difference temperature field model. Real-time spatial temperature field distribution calculations are performed to simulate the heat conduction behavior formed at each moment and in each level of the control zone when subjected to pulse current perturbations. The final result is a dynamic temperature gradient distribution field that spans the entire surface area of ​​the positive and negative electrode ends and extends deep along the electrode thickness direction.

[0068] S4, with the help of the temperature gradient distribution field, implements directional driving on the lithium ion concentration front of the electrolyte in the heavy-duty truck lithium battery, and realizes the dynamic coordinated control of the temperature gradient and ion transmission of the heavy-duty truck lithium battery under the start-stop condition.

[0069] Specifically, the three-dimensional temperature gradient distribution field is coupled with the lithium ion migration mechanism inside the electrolyte. Based on the principle of temperature gradient-induced diffusion, a heat-driven migration factor is introduced into the electrochemical system as a control variable. Through the correlation between the temperature field and the ion concentration field, a directional ion transport driving potential field is formed. According to the different functional requirements of the driving field in the three structural regions of the surface enhanced heat transfer zone, the intermediate buffer control zone and the deep stable control zone, the corresponding ion transport guidance path and propulsion rate model are set respectively, thereby constructing an ion migration guidance scheme with spatiotemporal adaptability. In the actual start-stop operation process, different guidance strategies are selected according to the characteristic differences of different current impact stages. In the high current startup stage, the high-gradient heat transfer path in the surface area is activated to quickly promote the deep penetration of the concentration front. Under reverse current, the intermediate area thermal buffer regulation is implemented to stabilize the migration trend. In the start-stop fluctuation stage, the low-gradient field in the deep area is used to maintain the migration equilibrium state, thereby forming a temperature gradient control mode covering different operating conditions. An array of micro-ion sensors placed at different depths in the pole piece monitors the real-time spatial position of the ion concentration front and its rate of advance over time with high precision. Combined with data fitting and edge tracking algorithms, the system continuously outputs the three-dimensional coordinate trajectory and advance speed curve of the ion concentration front. This dynamic data is then gradually compared with the ideal diffusion path set by the theoretical transmission model to identify the spatial offset and speed response deviation between the actual migration trajectory and the theoretical model, extracting two core performance indicators: control accuracy deviation and response delay time. Based on these deviations and timing errors, the system performs real-time corrections to the currently operating temperature gradient distribution field. This includes strategies such as adjusting the heating power density of the temperature control points in the local area, modifying the gradient intensity boundary conditions, or resetting the response delay compensation parameters. This reduces the difference between the actual ion front path and the theoretical path and improves the thermal response coupling accuracy of the overall ion migration behavior, thereby achieving full coordination between the dynamic advancement process of lithium ion concentration and the multi-level temperature gradient control system under the complex start-stop conditions of heavy trucks.

[0070] In one example, the transient inrush current of a heavy-duty truck's lithium battery during start-stop operations is collected and spectral analyzed to obtain frequency components and harmonic characteristics, including:

[0071] Collect current data during the start-stop process of heavy-duty truck lithium batteries to obtain transient impact current;

[0072] The transient impact current is classified according to the current amplitude and duration to obtain the current classification results, which include: starting large current impact type, shutdown reverse pulse type and start-stop transition pulse type;

[0073] Based on the current classification results, the transient impulse current is subjected to FFT fast Fourier transform to obtain the start-stop current spectrum data. The harmonic components of the start-stop current spectrum data are then extracted and the frequency components are analyzed to obtain the frequency components and harmonic characteristics.

[0074] In this example, a high-precision current sensor array is deployed at the positive and negative terminals of the lithium battery. This array features a sampling frequency of at least 10kHz and millisecond-level signal response, capable of covering the temporal scale and amplitude variation characteristics of all types of pulse currents. The current sensor array collects current data during the start-stop cycle of a heavy-duty truck's lithium battery, generating transient surge currents. The raw current signal undergoes denoising filtering, baseline calibration, and amplitude normalization. The system categorizes each transient surge current according to a set classification rule. This classification rule is based on two key indicators: current amplitude and duration. The startup high-current surge type must simultaneously meet the conditions of a current amplitude greater than 40C and a duration less than or equal to 100ms. The shutdown reverse pulse type corresponds to an amplitude between 15C and 25C and a duration less than or equal to 80ms. The start-stop transition pulse type is limited to a fluctuation state between 10C and 20C and a duration between 100ms and 200ms. The classification algorithm automatically classifies each signal stream based on a continuous data window mechanism, thereby converting the raw current data into a physically meaningful typed current sequence and completing the structured labeling of the current type. A fast Fourier transform operation is performed on each type of current signal as input to convert the time domain signal into a frequency domain signal. The fast Fourier transform algorithm can quickly identify the periodic fluctuation structure in the current signal and extract all frequency domain components, including the main frequency, sub-frequency, and higher harmonics. During the actual conversion process, the system selects sampling windows and conversion intervals based on the duration of different current types to ensure spectral resolution and analysis accuracy. For example, for high-current startup surge signals, selecting a 100ms window and sampling at 10kHz provides sufficiently dense spectral information. After Fast Fourier Transform (FFT), a frequency domain dataset consisting of both amplitude and phase spectra is generated. Harmonic components are extracted and frequency components analyzed from the start-stop current spectrum data. The algorithm first locates the peak position of the main frequency, which occurs between 8Hz and 12Hz and represents the typical oscillation pattern and load switching rhythm during heavy-duty truck start-stop operations. Harmonic components on either side of the main frequency are identified by analyzing the spectral amplitude at integer multiples of the frequency. The system extracts and ranks the harmonic components based on energy contribution, spectral density variation, and harmonic sequence spacing. A frequency component feature vector is constructed to quantify the frequency structure characteristics of this current type. High-order spectrum analysis techniques (such as power spectral density and short-time Fourier transform) are introduced to refine the frequency non-stationarity and energy concentration of complex pulse processes. This effectively enhances the accuracy of frequency component analysis, especially when dealing with multi-frequency mixed signals during the start-stop transition phase. Through this analysis process, the system ultimately outputs a set of frequency components and corresponding harmonic signatures covering three types of pulse currents. Each type of current signal is assigned its own independent spectrum label and frequency-amplitude mapping structure.

[0075] In one example, the frequency components and harmonic characteristics are used to determine the L1 spacing parameters of the temperature control points at the positive and negative electrode ends, and the L2 width parameters of the temperature gradient control area corresponding to the thickness direction of the electrode are divided, including:

[0076] The coordinated response strength of the temperature control points is calculated based on the frequency components and harmonic characteristics, and the temperature control point spacing range corresponding to different frequency ranges is obtained;

[0077] According to the temperature control point spacing range, the positive and negative electrode terminals are non-uniformly arranged to obtain the L1 spacing parameters;

[0078] Based on the L1 spacing parameter, the temperature gradient control area is spatially divided along the thickness direction of the electrode, and a three-level control structure is obtained: the surface enhanced heat transfer area, the middle buffer control area, and the deep stable control area.

[0079] Based on the three-level control structure, the temperature gradient heat transfer path analysis and L2 width optimization are performed to obtain the L2 width parameters.

[0080] In this example, the frequency components and harmonic characteristics extracted from the start-stop process of a heavy-duty truck lithium-ion battery are used as input. Combined with the periodic variation trend and amplitude modulation structure of the current waveform, the system derives temperature control response intensity requirement curves within different frequency ranges through a coupled mapping relationship between the spectral energy density function and the temperature response delay model. In this process, the system converts the energy release rate represented by each frequency component into a thermal activation time constant, thereby quantifying the speed and intensity of the local thermal field formed by a specific frequency signal on the electrode surface. A response intensity grading model is then constructed based on the heat diffusion efficiency and pulse cycle coverage of each frequency segment (e.g., low frequency <8Hz, medium frequency 8-12Hz, high frequency >12Hz). A function is then established to map the thermal response intensity of each frequency segment to the required temperature control point density. A normalized response intensity fitting method is used to determine the temperature control point spacing range for the corresponding frequency segment. High-frequency, high-energy components correspond to the smallest spacing, while low-frequency, flat components correspond to the largest spacing. This initially generates a set of control point spacing ranges that adapt to the spectral structure. Based on this spacing range, the system employs a non-uniform point placement strategy to precisely arrange temperature control points on the positive and negative electrode end surfaces. The layout process introduces a fusion calculation mechanism of frequency-space segmented mapping and physical structure constraint model, so that control points are densely deployed in areas with concentrated high-frequency current impacts, such as the 40C~50C current-dominated range during the startup phase. The minimum spacing L1 is set to 0.8mm, and gradually relaxed to 1.0mm or 1.2mm in the reverse pulse area or the start-stop buffer phase with lower frequency density, to form an adaptive response distribution network. The system uses the L1 spacing parameter as the spatial resolution basis for lateral thermal field regulation, and further constructs a vertical multi-level thermal regulation structure in the direction of the electrode thickness. The structure uses the thickness of the battery electrode (150~300μm) as the longitudinal spatial boundary, and is spatially partitioned according to the differences in temperature response speed and heat transfer stability from the surface to the inside, forming a three-layer structure: surface enhanced heat transfer area, intermediate buffer control area, and deep stable control area. The surface zone, located near the high-frequency impulse current, carries the highest heat flux and the shortest response delay, thus requiring a high-gradient, fast-response thermal field to serve as a primary thermal barrier and heat conduction channel. The intermediate zone absorbs and slowly releases heat transferred from the surface, balancing the expansion velocity of the ion migration front with the rate of change of the temperature field gradient. Its response requirements lie between rapid perturbations and deep stability. The deep stable zone primarily maintains the continuity and stability of the migration process. Its structural tolerance is high, but it requires the temperature gradient to remain consistent over time without drastic fluctuations, forming a deep thermal buffer structure. Temperature gradient heat transfer path analysis and L2 width optimization are performed based on a three-level control structure. A heat flux flow map is constructed through thermal field simulation and path-solving algorithms, spatially defining the path direction, flow velocity, and distribution concentration of the heat flux extending from the surface heating point to the interior of the electrode. Correction is also performed based on material parameters such as the electrolyte heat capacity, the thermal conductivity of the electrode material, and the thermal resistance distribution.By performing piecewise differential operations on the spatial distribution function of the thermal gradient, the stable gradient strength range required to be maintained in each level area is obtained. Then, combining the gradient requirements with the thermal conductivity behavior of the material, the geometric thickness required for each layer of the control area is inferred to form the L2 optimization objective function.

[0081] In one example, a non-uniform layout design is performed on the positive and negative electrode terminals according to the temperature control point spacing range to obtain L1 spacing parameters, including:

[0082] Based on the temperature control point spacing range, the temperature response characteristics of the startup high current impact type, shutdown reverse pulse type, and start-stop transition pulse type are analyzed to obtain the coordinated response time;

[0083] Calculate the temperature control point spacing values ​​based on the coordinated response time to obtain the first spacing value required for the startup high current impact type, the second spacing value required for the shutdown reverse pulse type, and the third spacing value required for the start-stop transition pulse type, and use the first spacing value, the second spacing value, and the third spacing value as the spacing calculation results;

[0084] Perform non-uniform distribution analysis of the spatial layout of the positive and negative electrode terminals based on the spacing calculation results to obtain the spatial coordinate distribution and temperature control point layout plan at the positive and negative electrode terminals;

[0085] Based on the spatial coordinate distribution and temperature control point layout scheme, L1 spacing parameter calibration and area boundary division are performed to obtain the L1 spacing parameter.

[0086] In this example, a dynamic thermal response modeling system is constructed with pulse current classification features as input and temperature response coordination time as output. This system relies on the previous classification and identification results of transient currents during the start-stop process of heavy-duty truck lithium batteries. All pulse current signals are divided into three basic types: starting high-current impact type, shutdown reverse pulse type, and start-stop transition pulse type. On this basis, the system combines multi-dimensional feature data such as frequency content, harmonic characteristics, and current duration to model the transient thermal load changes caused by each type of current pulse. By jointly simulating the response capability of the temperature control point and the heat diffusion process, the length of time from the system sensing the current signal to the thermal response taking effect is derived, and this time is defined as the temperature control coordination response time. Based on the modeling results of the coordinated response time, the system analyzes the three pulse types separately. Among them, the startup large current impact type has a high current amplitude (40C to 50C) and a short duration (only 50ms to 100ms), which causes the thermal power density to rise extremely quickly instantaneously. The temperature control system is required to complete the response within an extremely short delay of 20ms to 40ms. Therefore, a high-density temperature control point grid is configured to achieve millisecond-level control. Under the constraints of parameters such as heat conduction speed, response inertia, and heat capacity matching, the system calculates the first spacing value required for this type by inversely solving the heat diffusion path length, and controls it at around 0.8mm to ensure that the thermal response window covers the entire space of the thermal shock area. For the shutdown reverse pulse type, its current amplitude is low (15C to 25C) and its duration is between 30ms and 80ms. The thermal activation rate caused is relatively slow, and the system response time is relaxed to 40ms to 60ms. Therefore, the required temperature control point coverage range is also relatively relaxed, and the calculated second spacing value is set to 1.0m m, to maintain a balance between heat diffusion and heat absorption. For the start-stop transition pulse type, this type of pulse, with an amplitude of 10°C to 20°C and a duration of 100 to 200 ms, represents a current perturbation with slow energy release and frequent fluctuations. Its thermal response is not characterized by sudden changes, but rather by regulatory stability and frequency tracking. Therefore, the upper limit of the coordinated response time is extended to 80 to 100 ms, and the corresponding temperature control point density is appropriately reduced. The third spacing value is set to 1.2 mm, ensuring effective regulatory response while balancing system power consumption and structural complexity. By combining the three response time and heat conduction models, three key spacing values ​​are ultimately derived to guide point layout. These values ​​constitute the spacing calculation results. Using the spacing calculation results as input loads, a spatial topology model of the battery electrode surface is introduced to perform a non-uniform distribution analysis. This analysis establishes a three-dimensional spatial coordinate system based on the positive and negative electrode terminal geometry, the current cell size, the current conduction path, and the distribution of the main heat flow channels. The electrode surface is then divided into regions using a pulse response level map.The system uses a weight distribution algorithm to map each section of the pole piece surface to the main action area of ​​the startup, shutdown or transition pulse, and implements differentiated point density design within different areas. The temperature control points are loaded by area according to the three spacing values ​​mentioned above, and the density decreases step by step, eventually forming a composite layout scheme with a density gradient from the center to the edge and a response capability gradually changing from high speed to stable. The system performs geometric constraint verification and thermal equivalent coverage simulation on the scheme to ensure that at least two temperature control points are covered within the range of any pulse interference, thereby ensuring the continuity of the thermal regulation link, the blind spot coverage capability and the closed-loop controllability of the response efficiency. Based on the established spatial coordinate distribution of the temperature control points and the regional affiliation information, the system completes the refined calibration of the L1 spacing parameters and the division of the spatial area boundaries.

[0087] In one example, temperature gradient heat transfer path analysis and L2 width optimization were performed based on a three-level control structure, and the L2 width parameters were obtained, including:

[0088] The heat transfer characteristics of the surface enhanced heat transfer zone, the intermediate buffer control zone, and the deep stable control zone in the three-level control structure were analyzed to obtain the corresponding heat transfer coefficient and thermal resistance distribution characteristics of each zone.

[0089] The temperature gradient strength is calculated based on the heat transfer coefficient and thermal resistance distribution characteristics. The temperature gradient requirement for the surface enhanced heat transfer zone is the first gradient strength, the temperature gradient requirement for the intermediate buffer control zone is the second gradient strength, and the temperature gradient requirement for the deep stable control zone is the third gradient strength.

[0090] Calculate the width of each area according to the temperature gradient requirements and obtain the width calculation results;

[0091] According to the width calculation results, the spatial configuration and parameter calibration of the three-level control structure are carried out, and the L2 width parameter of the surface enhanced heat transfer zone is obtained as the first width value, the L2 width parameter of the intermediate buffer control zone is obtained as the second width value, and the L2 width parameter of the deep stable control zone is obtained as the third width value.

[0092] In this example, the heat transfer mechanisms of the surface enhanced heat transfer zone, intermediate buffer control zone, and deep stable control zone are systematically deconstructed and characterized from the perspective of thermophysical properties. This process relies on material parameters such as thermal conductivity, thermal diffusivity, specific heat capacity, and contact thermal resistance, while also considering the density of temperature control point layout, heat flux distribution path, and boundary heat exchange conditions. In the surface enhanced heat transfer zone, since this area is directly exposed to the maximum thermal excitation source caused by the large startup current shock, its thermal disturbance manifests as high-frequency, high-amplitude, short-cycle heat input, resulting in extremely rapid local temperature changes and significantly higher heat flux density than other areas. Therefore, the heat conduction path in this area is short and concentrated, relying mainly on internal material heat conduction and rapid response of temperature control points to form an efficient thermal channel. Through thermal transient simulation and time-temperature derivative analysis of this area, the system obtains its average transient heat transfer coefficient in the range of 300-450 W / (m·K), and the thermal resistance exhibits a low resistance and wide distribution, showing the typical high-gradient characteristics of strong response and fast equilibrium. At the same time, the intermediate buffer control zone is located in the middle layer of the electrode. It not only receives the heat flow conducted inward from the surface area, but also needs to control the heat flow rate transferred to the deep area. Therefore, it plays an intermediary role in regulating buffering and gradient smoothing in the system heat flow path. Its heat transfer characteristics are greatly affected by the accumulation of thermal resistance and heat capacity distribution. The heat transfer coefficient is lower than that of the surface area, distributed between 180 and 260 W / (m·K). There is a local increase in thermal resistance caused by material interfaces, microscopic pores or phase change boundaries, which manifests as a medium-resistance and medium-expansion heat transfer mode. The heat flux density is stable but the response time is long. It is suitable for constructing a medium-intensity gradient to achieve heat flux buffering and energy fluctuation suppression. As for the deep stable control area, which is close to the bottom of the electrode, the main goal in actual operation is to maintain long-term temperature consistency and ion transmission channel stability. Its heat transfer behavior is mainly steady-state conduction, with weak dynamic heat flow and strong thermal response inertia. The heat transfer coefficient distribution range is 100-160W / (m·K). The thermal resistance is large and gradually increases along the thickness direction, forming a high-resistance and low-speed heat diffusion channel. It is suitable for configuring a low-intensity temperature gradient to maintain the deep thermal stability of the system and prevent risks such as deformation of the electrode structure or thermal decomposition of the electrolyte due to drastic temperature fluctuations. After completing the heat transfer characteristic parameter extraction of the above three regions, the system performs differential calculations on the temperature gradient intensity based on their respective thermal conductivity and thermal resistance distributions. The inverse relationship between the temperature change rate and thermal resistance is established through the heat flux conservation equation and the one-dimensional steady-state conduction model. The local heat flux density is introduced as a weight parameter to construct a multi-level heat flux distribution model to obtain the temperature gradient intensity that meets the heat diffusion requirements of each control area.This step determined that the surface enhanced heat transfer zone's temperature gradient must be maintained within the range of 15-25°C / mm (the first gradient strength), ensuring that this zone can achieve temperature ramping and gradient establishment within 20 milliseconds of the initial impact. The intermediate buffer control zone corresponds to the second gradient strength, set at 8-15°C / mm. This balances energy transfer between the high surface heat flux and the deep, low heat capacity region, buffering sudden changes and maintaining a smooth temperature transition in the intermediate layer. The deep stable control zone, corresponding to the third gradient strength, requires a minimum temperature gradient of only 3-8°C / mm. Its primary goal is to maintain long-term deep thermal stability and avoid thermal distribution distortion in the ion migration channel. The system incorporates a regional thickness-gradient strength matching model to inversely solve the required physical width for each control zone. This model, based on the proportional relationship between gradient strength and path length per unit heat flux, combines the thermal conductivity of the material, the density of temperature control points, and the functional requirements of the zone. Multiple simulation iterations are performed on the required width of each layer to ensure a continuous, smooth, and abrupt temperature transition from the surface to the deep layer along the thickness direction. After a series of parameter fitting and heat flux density control, the system determined that the optimal width of the surface enhanced heat transfer zone is controlled between 30 and 50 μm to achieve high-density heat flow and complete the temperature difference formation in an extremely short path; the width of the intermediate buffer control zone is set in the range of 60 to 100 μm to establish a sufficient gradient buffer layer to absorb thermal energy diffusion and balance the gradient intensity; the deep stable control zone needs to have a structural thickness of 80 to 120 μm to provide sufficient thermal resistance channels and form a wide and slow heat diffusion zone to achieve long-term temperature field stability and ion transport consistency. The system incorporates the width calculation results of the above-mentioned control zones into the structural calibration module for spatial configuration optimization and parameter archiving, ensuring that the L2 width parameters of each layer are accurately controlled during actual processing or engineering implementation. The surface enhanced heat transfer zone is calibrated to the first width value, i.e., L2=40μm; the middle buffer control zone is calibrated to the second width value, i.e., L2=80μm; and the deep stable control zone uses L2=100μm as the third width value. On this basis, the system establishes a three-level temperature control area spatial topology map and embeds the corresponding temperature control response function and control parameters of each layer in the electronic control model.

[0093] In one example, performing transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field includes:

[0094] Pulse current is predicted based on L1 spacing parameters and L2 width parameters to obtain pulse current change data;

[0095] Calculate the target temperature value of each temperature control point based on the pulse current change data, and obtain the temperature setting value and response timing corresponding to different areas of the positive and negative electrode ends;

[0096] Based on the temperature setting value and response time sequence, the temperature gradient intensity is distributed to the three-level control structure, and the specific temperature gradient values ​​of the surface enhanced heat transfer zone, the middle buffer control zone and the deep stable control zone are obtained.

[0097] The spatial temperature field distribution is calculated based on the specific temperature gradient value to obtain the temperature gradient distribution field covering the entire positive and negative electrode ends and the thickness direction of the electrode.

[0098] In this example, a dynamic computational model integrating current prediction and thermal response scheduling is constructed based on the temperature control point distribution structure and the electrode thickness-direction zoning. This model uses real-time current data sequences as its initial input. By comparing the rate of change, frequency content, and amplitude fluctuations of the current waveform over different time periods and combining historical response data for start-stop pulses, it uses a recurrent neural network or a one-dimensional convolutional time series prediction model to predict the trend of impending pulse currents. This prediction module integrates the lateral thermal response distribution density represented by the L1 spacing parameter with the thickness-direction temperature gradient channel structure constructed by the L2 width parameter to enhance the accuracy of predictions of thermal disturbance propagation speed, spatial expansion trend, and adaptability to high-frequency shocks. It then outputs a multi-point pulse current change data sequence. Based on this pulse current change data, the system calculates target temperatures for all preset temperature control points. Each control point receives the corresponding spatial and temporal thermal disturbance intensity from the current prediction data based on its L1 distribution node and L2 location within the three-level hierarchy. Using a response function, it calculates the target temperature to be achieved in the next scheduling cycle. This response function comprehensively considers the local current density, resistance heat release, heat conduction speed, heat capacity and thermal interference factors between points, and iteratively solves it in the short-term thermal response control model, so that the temperature setting value of each temperature control point can not only match the heat generated by the current disturbance in a timely manner, but also avoid excessive heating or hot spot accumulation, thereby ensuring that the temperature control point group forms a coordinated regulation situation. At the same time, response timing data is generated, which indicates the complete dynamic timetable for each temperature control point to start heating, reach the target temperature, maintain the temperature and return to normal temperature. Based on the temperature setting value and response timing information, the system distributes the temperature gradient intensity to different regions in the three-level control structure. This allocation process follows the gradient continuity principle and the thermal resistance balance principle, that is, the temperature gradient distribution in the same area should be inversely proportional to its L2 width, while ensuring that the gradient intensity transition between different areas has physical continuity. In the surface enhanced heat transfer zone, since this area receives high-frequency, large-amplitude transient current shocks, the system controls the target temperature difference generated by scheduling within the maximum range and compresses the reaction distance to the minimum thickness layer. Therefore, its temperature gradient strength is set in the range of 15-25°C / mm to ensure rapid thermal drive and ion response within 20-40ms after the pulse occurs. In the intermediate buffer control zone, this area carries medium-frequency and medium-amplitude thermal disturbances and needs to take into account the smooth transmission of heat to the deep layer and the slow release of thermal shock to the surface layer. Its gradient strength is set to 8-15°C / mm, achieving a smooth gradient transition while enhancing the stability of the thermal coupling structure. In the deep stable control zone, this area mainly responds to low-amplitude, low-frequency, long-period residual heat flow, and has the lowest requirement for the speed of gradient response. The system limits the gradient strength to 3-8°C / mm to maintain the thermal background consistency of the ion migration front and the thermal stability of the electrolyte, ultimately forming a three-layer gradient structure from outside to inside and from strong to weak.After setting the gradient strength for each region, the system inputs the target temperature values ​​and gradient direction constraints for each control point into the temperature field calculation module. Finite difference or finite element simulation is then used to solve the spatial temperature field for the entire electrode. This calculation uses the L1 spacing parameter as the horizontal node density and the L2 width parameter as the vertical spatial scale boundary to construct a three-dimensional heat conduction grid structure. The set gradient value is used as the heat flux condition for each unit boundary. The spatial temperature distribution at a given time step is solved by the relationship between the temperature difference between nodes and the thermal resistance. To improve calculation accuracy, the system incorporates a nonlinear material model to simulate the changes in the thermal conductivity of the electrolyte at different temperatures. A dynamic boundary correction strategy is used to adjust the external boundary conditions in real time based on changes in the battery case temperature, heat dissipation conditions, and ambient temperature, ensuring that the temperature field model is more accurate for actual operating conditions. The final output is a set of three-dimensional time-evolving temperature gradient distribution maps that span the entire positive and negative electrode segments horizontally and through the entire thickness of the electrode segment vertically, forming a multi-stage composite temperature control pattern with high gradients, buffer gradients, intermediate gradients, and low gradients from the surface to the deep layers.

[0099] In one example, pulse current prediction is performed based on the L1 spacing parameter and the L2 width parameter to obtain pulse current change data, including:

[0100] Perform parameter analysis and historical pulse data matching on the L1 spacing parameter and L2 width parameter to obtain the current historical characteristic data;

[0101] Pulse prediction is performed based on the historical current characteristic data to obtain the pulse current amplitude and duration prediction results;

[0102] The pulse current amplitude and duration prediction results are transmitted to the temperature response module to calculate the temperature control demand and obtain the response intensity and response timing requirements corresponding to each temperature control point;

[0103] Perform gradient optimization according to the response intensity and response timing requirements to obtain the pulse current prediction accuracy and prediction time window;

[0104] Based on the pulse current prediction accuracy and prediction time window, pulse current change data including current amplitude change trend, frequency distribution characteristics and duration series are generated.

[0105] In this example, the system uses the lithium battery's structural distribution parameters—namely, the lateral temperature control point spacing L1 and the thickness gradient region width L2—as spatial references. Feature extraction and model fitting are performed on start-stop pulse response data recorded during past operation, achieving coupled analysis at the parameter and data levels. During this process, the system accesses a database of historical temperature control responses, using the L1 and L2 parameters as input indexes. It selects historical pulse segments that closely match the current structural configuration, extracts the pulse current intervals most sensitive to temperature response, and establishes a historical current feature set based on pulse amplitude, frequency fluctuation, duration, and thermal response delay as core metrics. Using a multivariate clustering algorithm, the system divides all qualifying data segments into several typical operating modes and constructs independent statistical feature vectors for each mode, forming a standardized pulse type profile. After completing the historical feature data matching, the system enters the pulse prediction stage. This stage uses temperature disturbance precursors and current waveform trends within the current time window as trigger conditions. Combined with previously extracted historical pulse pattern data, it uses a recurrent neural network, gated recurrent unit, or bidirectional LSTM architecture to model and predict current pulse behavior within the next cycle. The model structurally integrates the spatial thermal control capabilities represented by the L1 and L2 parameters with the time-amplitude evolution characteristics of historical data. The input layer incorporates thermal diffusion factors and gradient change rates related to the electrode structural parameters, embedding physical structural information into the prediction sequence. This enables the model to maintain adaptive prediction capabilities even when dealing with boundary perturbations and structural changes. Through multiple rounds of training and validation, the system outputs a set of pulse current amplitude and duration predictions for the next time period. The amplitude ranges from 10°C to 50°C, and the duration prediction accuracy is controlled within ±10ms. Confidence intervals and fluctuation boundary information are also output simultaneously. The pulse current amplitude and duration predictions are transmitted to the temperature response module to initiate the temperature control demand calculation process. Based on the distribution density of the L1 spacing and the control layer thickness defined by L2, this module maps the current amplitude to the heat flux input intensity per unit time. It then estimates the total heat load based on the duration and compares the maximum response capacity of each temperature control point at its structural location to perform distributed temperature control task allocation. During this allocation process, the system uses point response strength and timing response capability as constraints to generate the target temperature value, heating rate, and response start time required for each control point. This results in a response strength scalar and response timing vector for each control point. At the regional level, the system aggregates the response results of all control points into a three-level structure, completing the control structure mapping from point to surface and from surface to volume, realizing a global thermal control layout based on predicted current behavior. Gradient optimization is performed based on the response strength and response timing requirements.The temperature gradient distribution control curve is reconstructed based on response strength and response timing requirements. This minimizes temperature control delay while meeting heat flow and ion migration efficiency requirements, improving spatial consistency and temporal coordination of the thermal control distribution. The system employs a gradient dynamic adjustment model with input parameters including predicted pulse current peak, response delay target, control point activation priority, and heat diffusion rate constant. Regional temperature gradients are inversely adjusted using a nonlinear least-squares method to obtain local control curves and an overall temperature control delay function. This optimization process targets the maximum prediction accuracy and minimum response window in pulse response control, ultimately outputting two key performance indicators: pulse current prediction accuracy, which measures how well the system adapts to the upcoming current change; and prediction time window, which measures the time margin between successful pulse prediction and the start of the temperature control system's response. This directly determines whether the system can complete preheating compensation and temperature control pre-adjustment. Based on the pulse current prediction accuracy and prediction time window, the original pulse prediction results are combined for fusion and reconstruction to generate a structured pulse current change data set. This data set uses time as its primary dimension and contains multiple synchronously recorded fields: current amplitude change trends, which indicate the current rise and fall and change rate over several future time periods; frequency distribution characteristics, which include the main frequency component, harmonic structure, frequency energy distribution, and periodic change characteristics; and duration series, which clearly defines the start, peak, attenuation, and end timestamps of various pulse signals, and embeds confidence intervals and dynamic correction parameters generated by system model predictions.

[0106] In one example, the temperature gradient distribution field is used to directional drive the lithium ion concentration front of the electrolyte in a heavy-duty truck lithium battery, achieving dynamic coordinated control of the temperature gradient and ion transport in the heavy-duty truck lithium battery under start-stop conditions, including:

[0107] The lithium ion transport in the electrolyte of heavy-duty lithium batteries is guided by the temperature gradient distribution field, and the ion transport guidance scheme of the surface enhanced heat transfer zone, the middle buffer control zone, and the deep stable control zone is obtained.

[0108] The ion transmission guidance scheme is used to control the different current impact stages during the start-stop process to obtain a temperature gradient control mode;

[0109] Real-time tracking of lithium ion concentration front changes under temperature gradient control mode is performed to obtain the ion concentration front position coordinates and propulsion speed change curve;

[0110] Deviation analysis is performed based on the ion concentration front position coordinates and propulsion speed change curves and the theoretical transmission model to obtain the control accuracy deviation value and response delay time;

[0111] The temperature gradient distribution field is corrected in real time according to the control accuracy deviation value and response delay time, realizing dynamic coordinated control of temperature gradient and ion transmission of heavy-duty truck lithium batteries under start-stop conditions.

[0112] In this example, the temperature gradient distribution field is mapped to the electrolyte microstructure, establishing a computable coupling relationship between thermal diffusion and ion migration. Specifically, the thermally driven bias generated in the high-gradient region macroscopically controls the lithium ion concentration gradient and migration direction, achieving spatial guidance of ion transport behavior. The system establishes a strong temperature gradient of 15-25°C / mm in the surface enhanced heat transfer zone, generating a high heat flux and guiding the rapid advance of the ion concentration front during the high-current start-up phase. In the intermediate buffer control zone, the system maintains a moderate temperature gradient of 8-15°C / mm to achieve linear buffering and a stable transition of the transport speed. In the deep stable control zone, a low-intensity gradient of 3-8°C / mm is maintained to maintain a balanced ion distribution and reduce migration instabilities over the long term. This results in a layered ion guidance scheme with surface acceleration, intermediate buffering, and deep stabilization. Based on this partitioned guidance structure, the system maps the current perturbation phase during start-stop conditions to the spatial temperature gradient field. During actual control, the system dynamically invokes gradient excitation strategies in different zones, forming a temperature gradient control mode tailored to specific pulse behavior. For example, when a strong transient current of 40C~50C appears during the startup phase, the system activates the high-intensity gradient drive mode of the surface enhanced heat transfer zone. By increasing the temperature of the surface temperature control point and shortening the heat diffusion path, the thermal drive is maximized on the near-surface area of ​​the electrolyte, so that the lithium ion migration front advances deep into the electrode at a speed of 30%-45%, quickly establishing a power supply path and completing the activation of the reaction front; and when a reverse pulse of 15C-25C appears during the shutdown phase, the system activates the medium gradient mode of the intermediate buffer control zone, so that the thermal drive mechanism changes from a gradual slowing down of the forward direction to a slight reverse adjustment of the backward direction, realizing a natural transition and sudden release of the ion flow direction, avoiding local concentration reversal leading to ion accumulation and side reactions; and when low-amplitude fluctuation pulses frequently occur during the start-stop switching phase, the system mainly activates the deep stable control zone to maintain a low-gradient background heat flow, maintain the continuity and stability of the overall ion transmission path, and form a switchable and combinable temperature gradient control mode under different load stages. During operation in temperature gradient control mode, the system uses a high-precision ion sensor array positioned at different electrode depths to track and monitor the advancement of the lithium-ion concentration front in real time. The monitoring system collects the spatial distribution of lithium ions in the electrolyte with a concentration resolution of 0.01 mol / L, and uses a three-dimensional trajectory reconstruction algorithm to generate dynamic position coordinate data of the concentration front as it advances over time. The system then calculates the curve of the ion front's advancement velocity using a differential velocity model. The system also dynamically analyzes step changes in advancement velocity, acceleration curves, and diffusion directionality indicators to determine whether the temperature gradient effect has achieved the desired effect, and forms a real-time responsive migration velocity database. Based on these measured ion concentration front position coordinates and advancement velocity change curves, the system uses a theoretical transmission model to perform a standard comparative analysis of the current transmission behavior.The model is composed of porous electrode theory, Nernst-Planck equation and Fick diffusion formula, and can deduce the propagation trajectory and velocity curve of the ion concentration front under ideal conditions under given temperature gradient and electrolyte properties. The system uses the Euclidean distance and slope deviation between the current monitoring results and the theoretical curve as evaluation factors to calculate the control accuracy deviation value under the current temperature control strategy, and combines the gap between the actual startup response time and the theoretical response time to obtain the system's response delay time, thereby quantifying the effectiveness and timeliness of the current gradient control strategy in the dynamic collaborative control process. Based on the obtained control accuracy deviation value and response delay time, the system performs real-time correction on the existing temperature gradient distribution field to ensure that the temperature control behavior always evolves in sync with the ion migration requirements. The correction process focuses on three key aspects: First, in areas with significant errors, the system fine-tunes the heating power and target temperature at local temperature control points to minimize the deviation between the actual migration speed and the theoretical speed. Second, the gradient response sequence is replanned for areas with significant response time lag, reducing transmission latency through a preheating lead mechanism. Third, the overall gradient intensity distribution is redistributed to meet the thermal excitation requirements of high-response areas and improve the system's responsiveness in the next cycle. The correction mechanism is automatically executed within each thermal control cycle and iteratively optimized through closed-loop comparison with real-time sensor data, enabling the entire thermal control system to possess adaptive, self-regulating, and self-evolving control capabilities. By deeply coupling the temperature gradient distribution field with the lithium-ion migration process and introducing hierarchical control, real-time monitoring, and closed-loop correction mechanisms, dynamic coordinated temperature-ion migration control is achieved for heavy-duty truck lithium batteries under the complex start-stop operating conditions.

[0113] In one example, a deviation analysis is performed based on the ion concentration front position coordinates and propulsion speed change curve and the theoretical transmission model to obtain the control accuracy deviation value and response delay time, including:

[0114] The theoretical ion concentration frontier position benchmark values ​​corresponding to the surface enhanced heat transfer zone, the intermediate buffer control zone and the deep stable control zone are calculated based on the theoretical transmission model;

[0115] Based on the numerical difference analysis between the theoretical ion concentration front position reference value and the ion concentration front position coordinates, the position deviation data and the propulsion direction offset are obtained;

[0116] A time series comparison analysis was conducted based on the propulsion speed variation curve and the speed reference curve in the theoretical transmission model to obtain the speed deviation amplitude and response time difference during the startup, shutdown and transition phases.

[0117] A comprehensive deviation evaluation is performed based on position deviation data, propulsion direction offset, speed deviation amplitude and response time difference to obtain the control accuracy deviation value and response delay time.

[0118] In this example, a theoretical ion transport model was constructed at the system level. This model includes a spatial ion diffusion equation, a thermal-electric field coupling term, and a concentration gradient driving function. A three-layer temperature control structure was used as the computational boundary condition. The model used the set temperature gradient intensity, the region width L2 parameter, and the thermal diffusion coefficient as inputs to solve the functional expression for the theoretical lithium ion concentration front position in the electrolyte as a function of time under different thermal field environments. For the surface enhanced heat transfer zone, the theoretical model considers the linear enhancement effect of the high temperature gradient on the ion migration rate, converting the temperature drive of 15-25°C / mm into the unit time propagation distance, forming a benchmark propagation curve for the ideal concentration front position within 0-50ms. The intermediate buffer control zone handles the transitional thermal drive through a harmonic derivative term, introducing a diffusion damping factor during the ion migration process to determine the smoothness of the propagation path and its ability to suppress high-frequency disturbances. Finally, the deep stable control zone is set in the model to a low-gradient, low-speed, long-period propagation state, simulating the gradual propagation behavior of the concentration front as it gradually approaches a steady state over time scales of more than 100ms. By solving the time-position mapping curves within these three regions, a three-dimensional theoretical concentration front benchmark dataset is generated. Using time as the horizontal axis, regional divisions as the classifications, and concentration gradient drive as the parameters, a structurally partitioned concentration front trajectory benchmark is constructed. The system then compares the spatial coordinates of the real-time concentration front captured by the ion sensor array with these theoretical benchmarks. This analysis uses a time-by-time difference method, performing coordinate differences between the measured ion front position coordinates at each sampling time point and the theoretical benchmark values ​​for the corresponding region. This yields actual-to-theoretical position deviations for the surface, intermediate, and deep regions. The angular offset between the actual and theoretical paths of the front is then used to calculate the propulsion direction offset. This allows the system to assess whether there are ion migration deflections, asymmetric propulsion, or nonlinear offsets under thermal control. The position offset data represents the spatial propulsion error, while the directional offset reflects the coupling consistency between thermal drive and electrochemical effects. When the offset angle exceeds a set threshold (e.g., 5°), it indicates spatial non-uniformity in the heat flux distribution within the temperature-controlled region, necessitating temperature field correction. The system calls up the measured front propulsion speed curve and compares it with the speed reference curve generated by the theoretical transmission model over the entire time axis. During this comparison, the system divides the system into stages according to the start-stop operating conditions: During the startup phase, the system evaluates the degree of consistency between the actual migration speed and the theoretical maximum acceleration range. If there is a speed lag, slow acceleration, or a delay in the speed peak, it is judged as a response lag. During the shutdown phase, the system checks whether the actual deceleration curve is synchronized with the theoretical relief curve. If there is premature decay or insufficient buffering, it indicates excessive temperature control. During the start-stop transition phase, the system further examines whether the number of step points and the change range in the speed curve exceed the disturbance tolerance allowed by the theoretical model, thereby judging the stability and control accuracy of this stage.Ultimately, the system extracts velocity deviation magnitude metrics (maximum velocity difference, average velocity difference) and response time difference metrics (initial response lag time, peak response offset time) from these time-velocity trajectories to quantify the responsiveness and dynamic consistency of the entire control system in the temporal dimension. A comprehensive deviation assessment is performed based on position deviation data, propulsion direction offset, velocity deviation magnitude, and response time difference. The system constructs a multi-weighted deviation evaluation function, normalizing and fusing the four types of deviation data using regional importance, thermal driving intensity, and response time as weighting factors. Position deviation and propulsion direction offset are primarily used to evaluate spatial response accuracy, while velocity deviation magnitude and response time difference form the basis for evaluating temporal response performance. This evaluation function outputs two key control evaluation parameters: control accuracy deviation, which represents the error between the spatial propulsion control of the lithium ion concentration front under the current temperature control strategy and the desired target; smaller values ​​indicate more precise spatial control; and response delay, which reflects the time offset between the system's thermal field changes and the ion response, determining the temperature control strategy's pre-conditioning capability and dynamic response efficiency.

[0119] Reference Figure 2 This embodiment provides a device for optimizing the low-temperature performance of a heavy truck start-stop lithium battery, comprising:

[0120] Acquisition module 1 is used to collect the transient impact current of the heavy truck lithium battery during the start-stop process and perform spectrum analysis to obtain the frequency components and harmonic characteristics;

[0121] Division module 2 is used to determine the L1 spacing parameter of the temperature control points at the positive and negative electrode ends by using the frequency components and harmonic characteristics, and to divide the L2 width parameter of the temperature gradient control area corresponding to the thickness direction of the electrode;

[0122] A generation module 3 is used to perform transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field;

[0123] The driving module 4 is used to implement directionally driven lithium ion concentration frontier of the electrolyte in the heavy-duty truck lithium battery with the help of the temperature gradient distribution field, so as to realize dynamic coordinated control of temperature gradient and ion transmission of the heavy-duty truck lithium battery under start-stop conditions.

[0124] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0125] The present invention uses FFT fast Fourier transform technology to perform spectrum analysis on the 10C-50C transient impact current during the start-stop process of heavy trucks. It can accurately identify the three typical current types of starting large current impact, shutdown reverse pulse, and start-stop transition pulse. According to the frequency component and harmonic characteristics of the pulse current, a non-uniformly distributed temperature control point network is established. Through the differentiated configuration of the L1 spacing parameter, targeted response to different current impact types is achieved, which significantly improves the response delay problem of the traditional uniform layout method. A three-level control structure of surface enhanced heat transfer zone, intermediate buffer control zone, and deep stable control zone is established along the thickness direction of the pole piece. Through the precise setting of the L2 width parameter, a technological leap from plane temperature control to three-dimensional temperature gradient control is achieved. Taking the L1 spacing parameter and the L2 width parameter as input, the pulse prediction module, the temperature response module, and the gradient optimization module work together to achieve early prediction and dynamic response to the start-stop pulse current of heavy trucks, shortening the response time to milliseconds. The use of a temperature gradient distribution field to directionally drive the lithium-ion concentration front in the LiPF6-EC / DMC electrolyte changes the traditional technology model of passively responding to changes in ion transport, achieving active regulation and optimization of the ion transport process. A dynamic coupling control system between temperature gradient and ion transport under heavy-duty truck start-stop conditions has been established. Through real-time deviation analysis and feedback correction, synchronous coordination between the two is achieved, effectively solving the technical problems of local overheating at the electrode end and ion transport imbalance, ensuring the stable and efficient operation of lithium batteries under heavy-duty truck start-stop conditions.

[0126] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0127] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery, characterized in that: include: Collect the transient impact current of the heavy-duty truck lithium battery during the start-stop process and perform spectrum analysis to obtain the frequency components and harmonic characteristics; The frequency component and the harmonic characteristics are used to determine the L1 spacing parameters of the temperature control points at the positive and negative electrode ends, and the L2 width parameters of the temperature gradient control areas corresponding to the thickness direction of the electrode are divided; specifically, the method includes: calculating the coordinated response strength of the temperature control points based on the frequency component and the harmonic characteristics to obtain the temperature control point spacing range corresponding to different frequency ranges; performing a non-uniform layout design on the positive and negative electrode ends according to the temperature control point spacing range to obtain the L1 spacing parameter; performing spatial division of the temperature gradient control area along the thickness direction of the electrode based on the L1 spacing parameter to obtain a three-level control structure of the surface enhanced heat transfer area, the intermediate buffer control area and the deep stable control area; performing temperature gradient heat transfer path analysis and L2 width optimization based on the three-level control structure to obtain the L2 width parameter; Executing transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field; specifically comprising: performing parameter analysis and historical pulse data matching on the L1 spacing parameter and the L2 width parameter to obtain current historical characteristic data; performing pulse prediction based on the current historical characteristic data to obtain pulse current amplitude and duration prediction results; transmitting the pulse current amplitude and duration prediction results to a temperature response module to calculate temperature control requirements to obtain response strength and response timing requirements corresponding to each temperature control point; performing gradient optimization according to the response strength and the response timing requirements to obtain pulse current prediction accuracy and prediction time window; Based on the pulse current prediction accuracy and the prediction time window, pulse current change data including current amplitude change trend, frequency distribution characteristics and duration sequence are generated; based on the pulse current change data, the target temperature value of each temperature control point is calculated to obtain the temperature setting value and response time sequence corresponding to different areas of the positive and negative electrode ends; based on the temperature setting value and the response time sequence, the temperature gradient intensity is distributed to the three-level control structure to obtain specific temperature gradient values ​​of the surface enhanced heat transfer area, the intermediate buffer control area and the deep stable control area; based on the specific temperature gradient values, the spatial temperature field distribution is calculated to obtain a temperature gradient distribution field covering the entire positive and negative electrode ends and the thickness direction of the electrode sheet; By means of the temperature gradient distribution field, the lithium ion concentration front of the electrolyte in the heavy-duty truck lithium battery is directionally driven, thereby realizing dynamic coordinated control of the temperature gradient and ion transport of the heavy-duty truck lithium battery under start-stop conditions.

2. The method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery according to claim 1, characterized in that: The transient impact current of the heavy truck lithium battery during the start-stop process is collected and the spectrum analysis is performed to obtain the frequency components and harmonic characteristics, including: Collect current data during the start-stop process of heavy-duty truck lithium batteries to obtain transient impact current; Classifying the transient impact current according to current amplitude and duration to obtain a current classification result, wherein the current classification result includes: a startup large current impact type, a shutdown reverse pulse type, and a start-stop transition pulse type; Based on the current classification result, an FFT fast Fourier transform is performed on the transient impulse current to obtain start-stop current spectrum data, and harmonic component extraction and frequency component analysis are performed on the start-stop current spectrum data to obtain frequency components and harmonic characteristics.

3. The method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery according to claim 1, characterized in that: The non-uniform layout design of the positive and negative electrode terminals is performed according to the temperature control point spacing range to obtain the L1 spacing parameters, including: Based on the temperature control point spacing range, the temperature response characteristics of the startup large current impact type, the shutdown reverse pulse type, and the start-stop transition pulse type are analyzed to obtain a coordinated response time; Calculate the temperature control point spacing values ​​according to the coordinated response time to obtain a first spacing value required for the startup high current impact type, a second spacing value required for the shutdown reverse pulse type, and a third spacing value required for the start-stop transition pulse type, and use the first spacing value, the second spacing value, and the third spacing value as spacing calculation results; Performing a non-uniform distribution analysis of the spatial layout of the positive and negative electrode terminals based on the spacing calculation results to obtain a spatial coordinate distribution and a temperature control point arrangement scheme at the positive and negative electrode terminals; Based on the spatial coordinate distribution and the temperature control point arrangement scheme, L1 spacing parameter calibration and area boundary division are performed to obtain L1 spacing parameters.

4. The method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery according to claim 3, characterized in that: The temperature gradient heat transfer path analysis and L2 width optimization based on the three-level control structure are performed to obtain the L2 width parameters, including: The heat transfer characteristics of the surface enhanced heat transfer zone, the intermediate buffer control zone, and the deep stable control zone in the three-level control structure are analyzed to obtain the corresponding heat transfer coefficient and thermal resistance distribution characteristics of each zone; The temperature gradient strength is calculated based on the heat transfer coefficient and the thermal resistance distribution characteristics, and the temperature gradient requirement of the surface enhanced heat transfer zone is a first gradient strength, the temperature gradient requirement of the intermediate buffer control zone is a second gradient strength, and the temperature gradient requirement of the deep stable control zone is a third gradient strength; Calculate the width of each region according to the temperature gradient requirement to obtain a width calculation result; According to the width calculation results, the spatial configuration and parameter calibration of the three-level control structure are performed, and the L2 width parameter of the surface enhanced heat transfer zone is obtained as the first width value, the L2 width parameter of the intermediate buffer control zone is obtained as the second width value, and the L2 width parameter of the deep stable control zone is obtained as the third width value.

5. The method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery according to claim 1, characterized in that: The method of implementing a directional drive on the lithium ion concentration front of the electrolyte in the heavy-duty truck lithium battery by means of the temperature gradient distribution field to achieve dynamic coordinated control of the temperature gradient and ion transport of the heavy-duty truck lithium battery under start-stop conditions includes: The lithium ion transport of the electrolyte in the heavy-duty lithium battery is directed and guided by the temperature gradient distribution field, thereby obtaining an ion transport guidance scheme of a surface enhanced heat transfer zone, an intermediate buffer control zone, and a deep stable control zone; The ion transmission guidance scheme is used to control different current impact stages during the start-stop process to obtain a temperature gradient control mode; Tracking the lithium ion concentration front change in the temperature gradient control mode in real time to obtain the ion concentration front position coordinates and the propulsion speed change curve; Based on the ion concentration front position coordinates and the propulsion speed change curve and the theoretical transmission model, a deviation analysis is performed to obtain a control accuracy deviation value and a response delay time; The temperature gradient distribution field is corrected in real time according to the control accuracy deviation value and the response delay time, thereby realizing dynamic coordinated control of the temperature gradient and ion transmission of the heavy-duty truck lithium battery under start-stop conditions.

6. The method for optimizing the low-temperature performance of a heavy truck start-stop lithium battery according to claim 5, characterized in that: The deviation analysis based on the ion concentration front position coordinates and the propulsion speed change curve and the theoretical transmission model to obtain the control accuracy deviation value and the response delay time includes: The theoretical ion concentration frontier position benchmark values ​​corresponding to the surface enhanced heat transfer zone, the intermediate buffer control zone and the deep stable control zone are calculated based on the theoretical transmission model; Performing a numerical difference analysis based on the theoretical ion concentration front position reference value and the ion concentration front position coordinates to obtain position deviation data and a propulsion direction offset; A time series comparison analysis is performed based on the propulsion speed variation curve and the speed reference curve in the theoretical transmission model to obtain the speed deviation amplitude and response time difference in the startup phase, shutdown phase and transition phase; A comprehensive deviation evaluation is performed based on the position deviation data, the propulsion direction offset, the speed deviation amplitude and the response time difference to obtain a control accuracy deviation value and a response delay time.

7. A device for optimizing the low-temperature performance of a heavy truck start-stop lithium battery, characterized in that: The steps for implementing the method for optimizing the low-temperature performance of a heavy-duty truck start-stop lithium battery according to any one of claims 1 to 6, wherein the device for optimizing the low-temperature performance of a heavy-duty truck start-stop lithium battery comprises: The acquisition module is used to collect the transient impact current of the heavy-duty truck lithium battery during the start-stop process and perform spectrum analysis to obtain the frequency components and harmonic characteristics; a division module, configured to determine an L1 spacing parameter of the temperature control points at the positive and negative electrode ends by using the frequency components and the harmonic characteristics, and to divide an L2 width parameter of a temperature gradient control area corresponding to the thickness direction of the electrode; A generating module, configured to perform transient pulse prediction based on the L1 spacing parameter and the L2 width parameter to generate a temperature gradient distribution field; The driving module is used to implement directionally driven lithium ion concentration front of the electrolyte in the heavy-duty truck lithium battery with the help of the temperature gradient distribution field, so as to realize dynamic coordinated control of temperature gradient and ion transmission of the heavy-duty truck lithium battery under start-stop conditions.

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