Battery low temperature charging method and system based on pulse self-heating

Abstract

The present application relates to the technical field of battery heating, and particularly relates to a battery low-temperature charging method and system based on pulse self-heating, which realizes the effect of cooperative charging and pulse self-heating by monitoring the double-dimension matching of charging demand and heat source supply parameters, generating a suitable pulse coordination scheme, analyzing the real-time lithium precipitation, supply-demand matching and comprehensive risk of the generated pulse coordination scheme, generating double-target pulse parameters suitable for the current low-temperature working condition, and adjusting the pulse coordination scheme according to the double-target pulse parameters, thereby avoiding the problems of uncontrollable lithium precipitation risk, inaccurate charging acceptance capacity estimation and mismatched energy utilization efficiency of the charging and heating processes caused by single low-temperature charging, improving the charging rate in a low-temperature environment, reducing the battery capacity loss caused by the lithium precipitation risk, reducing the energy loss required for low-temperature charging, and achieving the purpose of safe and efficient charging.

Description

Technical Field

[0001] This invention relates to the field of battery heating technology, and in particular to a low-temperature charging method and system for batteries based on pulse self-heating. Background Technology

[0002] The charging performance of lithium-ion batteries is severely limited in low-temperature environments, mainly due to three major technical challenges: First, low temperatures cause an exponential decrease in the solid-phase diffusion coefficient of lithium ions at the negative electrode, making high-rate charging prone to lithium plating, resulting in irreversible capacity loss and safety risks; Second, during low-temperature charging, ohmic and electrochemical polarization of the electrolyte are significantly enhanced, the charging acceptance is drastically reduced, and the constant-current and constant-voltage charging stage ends prematurely, leading to low charging efficiency; Third, the traditional preheating-before-charging strategy suffers from low energy utilization efficiency and high time costs, while single pulse self-heating can save external heat sources, but it is difficult to simultaneously meet the dual requirements of fast charging and suppressing lithium plating.

[0003] Chinese patent CN117895146A discloses a pulse temperature equalization self-heating system and method for lithium battery packs, relating to the field of low-temperature thermal management technology for lithium batteries. The pulse temperature equalization self-heating system for lithium battery packs consists of several individual battery cells, several heating bypasses, several MOSFETs, several temperature sensors, a signal acquisition unit, a current sensor, and a heating control unit. The established heating system is independent of external heat sources and easy to implement. Due to the use of a time-sharing sequential triggering method, each heated battery alternates with its adjacent batteries, avoiding the high-voltage problems associated with large-scale group heating. The heating process is safe and efficient, with a heating rate >8℃ / min. By dynamically adjusting the duty cycle, the maximum temperature difference of the battery pack during heating can be maintained within 2.6℃. Compared to not using temperature equalization control, the maximum temperature difference at the end of heating can be reduced from 5℃ to 1.3℃, effectively solving the problems of low-temperature capacity degradation and performance decline in electric vehicles under severe cold conditions. However, this solution cannot solve the technical problems of inaccurate estimation of charging acceptance capacity and uncontrollable lithium plating risk during low-temperature charging. Summary of the Invention

[0004] To address these issues, this invention provides a low-temperature battery charging method and system based on pulse self-heating, which overcomes the technical problems of uncontrollable lithium plating risk, inaccurate estimation of charging acceptance capacity, and mismatch between energy utilization efficiency during charging and heating processes in existing technologies.

[0005] To achieve the above objectives, in one aspect, the present invention provides a low-temperature charging method for batteries based on pulse self-heating, comprising:

[0006] Step S1: Collect battery data and demand data;

[0007] Step S2: The charging demand is generated based on the battery data by the charging acceptance capacity prediction model. The heat demand is also generated based on the demand data and battery data by the thermal-electric coupling demand analysis model. The heat source supply parameters are also generated based on the heat source data in the battery data by the heat source supply analysis model.

[0008] Step S3: Determine the charging-heating coordination strategy based on the interface temperature in the battery data, and generate a pulse coordination scheme based on the charging-heating coordination strategy, charging demand, heat demand and heat source supply parameters;

[0009] Step S4: Perform charging-heating coordinated control on the vehicle battery according to the pulse coordination scheme;

[0010] Step S5 involves dynamically correcting the lithium plating boundary of the pulse coordination scheme based on battery data, correcting the supply and demand matching of the pulse coordination scheme generation process based on battery data and heat demand, and comprehensively revising the heat source supply parameters based on battery data to mitigate risks.

[0011] Further, in step S2, the charging demand is generated based on the battery data through the charging acceptance capability prediction model. When the charging demand is obtained, the solid diffusion coefficient and surface temperature set in the battery data are input into the charging acceptance capability prediction model to obtain the lithium plating critical current and target charge output by the charging acceptance capability prediction model. The target charge is calculated with the target state of charge in the demand data to obtain the target charging charge. The lithium plating critical current and target charging charge are output as the charging demand.

[0012] Furthermore, in step S2, the thermal demand is generated based on the demand data and battery data through the thermal-electric coupling demand analysis model. When the thermal demand is obtained, the expected charging time tc, the target charging capacity and the surface temperature set in the demand data and the battery data are input into the thermal-electric coupling demand analysis model to obtain the target heating rate and the heat required for heating output by the thermal-electric coupling demand analysis model. The target heating rate and the heat required for heating are output as the thermal demand.

[0013] In step S2, the heat source supply parameters are generated based on the heat source data in the battery data through the heat source supply analysis model. When the heat source supply parameters are obtained, the initial pulse parameters in the battery data are input into the heat source supply parameter analysis model to obtain the heat source supply parameters output by the heat source supply parameter analysis model. The heat source supply parameters include the supplied heat and the supply response time. The initial pulse parameters refer to the initially set set of pulse characteristic parameters.

[0014] 4. The low-temperature charging method for batteries based on pulse self-heating according to claim 3, characterized in that, in step S3, when determining the charging-heating coordination strategy based on the interface temperature in the battery data, and generating the pulse coordination scheme based on the charging-heating coordination strategy, charging requirements, heat requirements, and heat source supply parameters, the interface temperature t1 is compared with the preset safe charging temperature t0, the charging-heating coordination strategy is determined based on the comparison result, and the pulse coordination scheme is generated based on the determination result, charging requirements, heat requirements, and heat source supply parameters, wherein:

[0015] When t1≥t0, the charging-heating collaborative strategy is determined to be the direct charging mode, and the initial charging scheme is generated as the pulse collaborative scheme.

[0016] When t1 < t0, the charging-heating collaborative strategy is determined to be a pulse collaborative mode. The supply and demand matching in two dimensions is judged based on the charging demand and heat demand. The basic pulse parameters are adjusted based on the supply and demand matching in two dimensions to obtain the adjusted pulse parameters. The adjusted pulse parameters are then input into the dual-objective pulse optimization model to obtain the dual-objective pulse parameters output by the dual-objective pulse optimization model. The dual-objective pulse parameters are then output as the pulse collaborative scheme.

[0017] Furthermore, when the charging-heating collaborative strategy is determined to be a pulse collaborative mode, the supply-demand matching is judged based on the charging demand and heat demand. The basic pulse parameters are adjusted according to the dual-dimensional supply-demand matching to obtain the adjusted pulse parameters. Then, the charge matching index P1 is calculated based on the lithium plating critical current L and the target charging rate V in the charging demand, and P1 = L / V is set. The heat matching index P2 is calculated based on the supplied heat R and the battery heat loss power S in the heat source supply parameters, and P2 = R / S is set. The dual-dimensional supply-demand matching is judged based on the charge matching index P1 and the heat matching index P2. Based on the judgment results, the basic DC charging pulse duty cycle, basic AC heating pulse frequency, and basic pulse interval duration in the basic pulse parameters are adjusted, where:

[0018] When P1≥1 and P2≥1, the two-dimensional supply and demand matching is determined to be a perfect match, and the basic pulse parameters are not adjusted. Instead, the basic pulse parameters are used as the adjusted pulse parameters for output.

[0019] When P1≥1 and P2<1, the dual-dimensional supply and demand matching is determined to be thermal shortage and power shortage. The basic pulse parameters are adjusted. The basic AC heating pulse frequency jp is adjusted by the heating enhancement coefficient k1. K1 is set to 1+0.3×(1-P2) to obtain the first adjusted AC heating pulse frequency jpt1. jpt1 is set to jp×k1. The basic DC charging pulse duty cycle jd is also adjusted by the heating sacrifice coefficient rx. The heating sacrifice coefficient rx is set to 0.85 to obtain the first adjusted DC charging pulse duty cycle jdt1. jdt1 is set to jd×rx. The AC heating pulse frequency and DC charging pulse duty cycle in the basic pulse parameters are replaced with the first adjusted AC heating pulse frequency and the first adjusted DC charging pulse duty cycle to obtain the adjusted pulse parameters.

[0020] When P1 < 1 and P2 ≥ 1, the dual-dimensional supply and demand matching is determined to be power shortage and heat sufficiency. The basic pulse parameters are adjusted. The duty cycle jd of the basic DC charging pulse is adjusted by the charging conservatism coefficient Cb. Cb = 0.7 is set to obtain the second adjusted DC charging pulse duty cycle. jdt2 = jd × Cb is set. The duration ys of the basic pulse interval is also adjusted by the extension coefficient ty. ty = 1.2 is set to obtain the adjusted pulse interval duration yst. yst = ys × ty is set. The DC charging pulse duty cycle and pulse interval duration in the basic pulse parameters are replaced with the second adjusted DC charging pulse duty cycle and the adjusted pulse interval duration to obtain the adjusted pulse parameters.

[0021] When P1 < 1 and P2 < 1, the dual-dimensional supply and demand matching is determined to be a double shortage of supply and demand. The basic pulse parameters are adjusted. The basic AC heating pulse frequency jp is adjusted by the preheating priority coefficient yr, and yr = 1.5 is set to obtain the second adjusted AC heating pulse frequency jpt2, and jpt2 = jp × yr is set. The basic DC charging pulse duty cycle jd is also adjusted by the charging suppression coefficient Cy, and Cy = 0.5 is set to obtain the third adjusted DC charging pulse duty cycle jdt3, and jdt3 = jd × Cy is set. The AC heating pulse frequency and DC charging pulse duty cycle in the basic pulse parameters are replaced with the second adjusted AC heating pulse frequency and the third adjusted DC charging pulse duty cycle to obtain the adjusted pulse parameters.

[0022] Furthermore, in step S4, when performing charging-heating coordinated control of the vehicle battery according to the pulse coordination scheme, the pulse coordination scheme in step S3 is sent to the vehicle battery control system, and the vehicle battery control system controls the vehicle battery to perform charging-heating coordinated control according to the DC charging pulse duty cycle, DC charging pulse amplitude, AC heating pulse frequency, AC heating pulse amplitude and pulse interval duration in the dual target pulse parameters.

[0023] Furthermore, in step S5, when performing dynamic correction of the lithium plating boundary for the pulse-coordinated scheme based on battery data, the negative electrode potential W in the battery data is compared with the lithium plating overpotential boundary W0. The lithium plating situation is judged based on the comparison result, and the dynamic correction of the lithium plating boundary is performed on the pulse-coordinated scheme based on the judgment result, wherein:

[0024] When W≥W0, the lithium plating situation is determined to be no lithium plating, and no dynamic correction of the lithium plating boundary is performed on the pulse collaborative scheme;

[0025] When W < W0, lithium plating is determined to be present. The lithium plating boundary is dynamically corrected for the pulse coordination scheme. The correction method is to trigger the pulse intermittent re-embedding protocol. The pulse intermittent re-embedding protocol refers to correcting the pulse intermittent period ys in the pulse coordination scheme according to the extended pulse intermittent period coefficient Mx to obtain the extended pulse intermittent period yt. Mx is set to 1.5, yt = ys × Mx, and the value of the pulse intermittent period in the pulse coordination scheme is replaced with the value of the extended pulse intermittent period.

[0026] Further, in step S5, when performing supply-demand matching correction on the generation process of the pulse coordination scheme based on battery data and heat demand, the actual charging rate vd is calculated based on the actual charging capacity d and actual charging time tcl in the battery data, and vd = d / tcl is set. The charge achievement rate g1 is calculated based on the actual charging rate vd and the target charging rate vm, and g1 = vd / vm is set. The heat achievement rate g2 is calculated based on the actual heat generation rs in the battery data and the heat required for temperature rise sp in the heat demand, and g2 = rs / sp is set. The supply-demand coupling coefficient GX is calculated based on the charge achievement rate g1 and the heat achievement rate g2, and GX = (g1 + g2) / 2 is set. The supply-demand coupling coefficient GX is compared with a preset coupling coefficient range. Based on the comparison result, the supply-demand matching situation is judged, and the supply-demand matching correction is performed on the generation process of the pulse coordination scheme based on the judgment result. Wherein:

[0027] When the supply and demand coupling coefficient is within the preset coupling coefficient range, the supply and demand matching is determined to be a match, and no supply and demand matching correction is performed in the generation process of the pulse coordination scheme.

[0028] When the supply and demand matching coefficient is outside the preset coupling coefficient range, the supply and demand matching situation is determined to be mismatched. The supply and demand matching correction is performed on the generation process of the pulse coordination scheme. The charge matching index P1 is corrected according to the charge achievement rate g1 to obtain the corrected charge matching index P1g. P1g is set to P1×g1. The heat demand matching index P2 is corrected according to the heat achievement rate g2 to obtain the corrected heat demand matching index P2g. P2g is set to P2×g2. The values ​​of the charge matching index and the heat demand matching index are replaced with the values ​​of the corrected charge matching index and the corrected heat demand matching index.

[0029] Further, in step S5, when revising the heat source supply parameters based on battery data for comprehensive risk assessment, the comprehensive risk index F is calculated based on the lithium plating risk coefficient q1, thermal runaway risk coefficient q2, charging capacity loss coefficient q3, first weighting coefficient α1, second weighting coefficient α2, and third weighting coefficient α3 in the battery data. F is set as F = q1 × α1 + q2 × α2 + q3 × α3. The comprehensive risk index F is compared with a preset risk index threshold F0. Based on the comparison result, the risk situation is judged, and the heat source supply parameters are revised based on the judgment result. Wherein:

[0030] When F≤F0, the risk situation is determined to be no risk, and the heat source supply parameters are not revised;

[0031] When F > F0, the risk situation is determined to be risky, and the heat source supply parameters are revised. The supply heat R in the heat source supply parameters is reduced by the risk degradation coefficient fx. The risk degradation coefficient fx = 0.6 is set to obtain the revised supply heat Rx. Rx = R × fx is set, and the value of the supply heat R is replaced with the value of the revised supply heat Rx.

[0032] On the other hand, the present invention also provides a system for a low-temperature battery charging method based on pulse self-heating, comprising:

[0033] The data acquisition module is used to collect battery data and demand data;

[0034] The demand generation module is used to generate charging demand based on battery data through a charging acceptance capability prediction model, and to generate thermal demand based on demand data and battery data through a thermal-electric coupling demand analysis model, and to generate thermal supply parameters based on thermal source data in battery data through a thermal source supply analysis model.

[0035] The pulse scheme generation module is used to determine the charging-heating coordination strategy based on the interface temperature in the battery data, and to generate the pulse coordination scheme based on the determination result, charging demand, heat demand and heat source supply parameters.

[0036] The collaborative control module performs charging-heating collaborative control of the vehicle battery according to the pulse collaborative scheme.

[0037] The pulse scheme correction module is used to dynamically correct the lithium plating boundary of the pulse coordination scheme based on battery data, to perform supply and demand matching correction on the generation process of the pulse coordination scheme based on battery data and heat demand, and to perform comprehensive risk revision of heat source supply parameters based on battery data.

[0038] Compared with existing technologies, the beneficial effects of this invention are as follows: by monitoring the dual-dimensional matching of charging demand and heat source supply parameters, a suitable pulse coordination scheme is generated. The real-time lithium plating situation, supply-demand matching situation, and comprehensive risk situation of the generated pulse coordination scheme are analyzed to generate dual-target pulse parameters suitable for the current low-temperature operating conditions. The pulse coordination scheme is then adjusted based on these dual-target pulse parameters to achieve a synergistic effect between charging and pulse self-heating. This avoids the problems of uncontrollable lithium plating risk, inaccurate estimation of charging acceptance capacity, and mismatch in energy utilization efficiency between charging and heating processes caused by single low-temperature charging. This improves the charging rate in low-temperature environments, reduces battery capacity loss due to lithium plating risk, and reduces energy loss required for low-temperature charging, achieving safe and efficient charging. The method collects battery data and demand data in step S1 to facilitate the subsequent generation of the pulse coordination scheme. Step S2 obtains charging demand, heat demand, and heat source supply parameters to facilitate subsequent analysis of the dual-dimensional supply-demand matching. The method determines the charging-heating coordination strategy in step S3 and generates a pulse coordination scheme based on the dual-dimensional supply-demand matching judgment result. This allows for subsequent charging-heating coordination control of the vehicle battery based on the pulse coordination scheme. Step S4 executes the pulse coordination scheme, enabling the vehicle battery to charge and heat according to dual-target pulse parameters, avoiding situations where charging is difficult at low temperatures. Step S5 analyzes the real-time lithium plating situation of the vehicle and performs dynamic correction of the lithium plating boundary of the pulse coordination scheme based on the analysis results. This prevents battery safety issues caused by high lithium plating risks, thereby improving the low-temperature charging safety of the vehicle battery. Furthermore, the method corrects the supply-demand matching of the pulse coordination scheme based on the supply-demand matching situation, avoiding low energy utilization efficiency caused by charging under difficult-to-match supply-demand conditions, thus reducing energy waste during low-temperature charging. Finally, the method comprehensively revises the heat source supply parameters based on the overall risk situation, preventing battery thermal runaway caused by multiple risks, thereby improving the low-temperature charging reliability of the vehicle battery. Attached Figure Description

[0039] Figure 1 This is a schematic flowchart of the low-temperature battery charging method based on pulse self-heating in this embodiment;

[0040] Figure 2 This is a schematic diagram of the system structure of the low-temperature battery charging method based on pulse self-heating in this embodiment. Detailed Implementation

[0041] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0042] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0043] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0044] Please see Figure 1 As shown, this is a schematic flowchart of the low-temperature charging method for batteries based on pulse self-heating in this embodiment. The method includes:

[0045] Step S1: Collect battery data and demand data;

[0046] Step S2: The charging demand is generated based on the battery data by the charging acceptance capacity prediction model. The heat demand is also generated based on the demand data and battery data by the thermal-electric coupling demand analysis model. The heat source supply parameters are also generated based on the heat source data in the battery data by the heat source supply analysis model.

[0047] Step S3: Determine the charging-heating coordination strategy based on the interface temperature in the battery data, and generate a pulse coordination scheme based on the charging-heating coordination strategy, charging demand, heat demand and heat source supply parameters;

[0048] Step S4: Perform charging-heating coordinated control on the vehicle battery according to the pulse coordination scheme;

[0049] Step S5 involves dynamically correcting the lithium plating boundary of the pulse coordination scheme based on battery data, correcting the supply and demand matching of the pulse coordination scheme generation process based on battery data and heat demand, and comprehensively revising the heat source supply parameters based on battery data to mitigate risks.

[0050] Specifically, the pulse self-heating-based low-temperature battery charging method is applied to vehicle battery charging control scenarios, such as the battery management system of a pure electric vehicle equipped with an 800V high-voltage platform. By monitoring the dual-dimensional matching of charging demand and heat source supply parameters, a suitable pulse coordination scheme is generated. The real-time lithium plating situation, supply-demand matching, and overall risk of the generated pulse coordination scheme are analyzed to generate dual-target pulse parameters suitable for the current low-temperature conditions. The pulse coordination scheme is then adjusted based on these dual-target pulse parameters to achieve a synergistic effect between charging and pulse self-heating. This avoids the problems of uncontrollable lithium plating risk, inaccurate estimation of charging acceptance capacity, and mismatch in energy utilization efficiency between charging and heating processes caused by single low-temperature charging. This improves the charging rate in low-temperature environments, reduces battery capacity loss due to lithium plating risk, and reduces energy loss required for low-temperature charging, achieving safe and efficient charging. The method collects battery data and demand data in step S1 to generate the pulse coordination scheme. Step S2 analyzes the charging demand, heat demand, and heat source supply parameters... The method acquires data to facilitate subsequent judgment on the dual-dimensional supply and demand matching. In step S3, the charging-heating coordination strategy is determined, and a pulse coordination scheme is generated based on the dual-dimensional supply and demand matching judgment result. This allows for subsequent charging-heating coordination control of the vehicle battery according to the pulse coordination scheme. In step S4, the pulse coordination scheme is executed, enabling the vehicle battery to charge and heat according to dual-target pulse parameters, avoiding situations where charging is difficult at low temperatures. In step S5, the real-time lithium plating situation of the vehicle is analyzed, and the lithium plating boundary of the pulse coordination scheme is dynamically corrected based on the analysis results. This prevents battery safety issues caused by high lithium plating risks, thereby improving the low-temperature charging safety of the vehicle battery. Furthermore, the supply and demand matching of the pulse coordination scheme is corrected based on the supply and demand matching situation to avoid low energy utilization efficiency caused by charging under difficult-to-match supply and demand conditions, thus reducing energy waste during low-temperature charging. Finally, the heat source supply parameters are comprehensively revised based on the overall risk situation to avoid battery thermal runaway caused by multiple risks, thereby improving the low-temperature charging reliability of the vehicle battery.

[0051] Specifically, the battery data includes solid-phase diffusion coefficient, surface temperature set, negative electrode potential, actual charging capacity, actual heat generation, actual charging time, initial pulse parameters, state of charge (SOC), number of sampling points, battery rated capacity, electrochemical impedance spectroscopy (EIS), current, and voltage. The required data includes the target SOCe and the expected charging time tc. The solid-phase diffusion coefficient refers to the solid-state diffusion coefficient of lithium ions in the negative electrode active material, characterizing the solid-phase transport capability of lithium ions. Step S1 collects the solid-phase diffusion coefficient using an online EIS identification system. The EIS refers to the impedance response data of the battery at different frequencies. Step S1 collects the EIS using an electrochemical workstation. The surface temperature set refers to the collection of battery surface temperature data collected based on the number of sampling points. The number of sampling points refers to the number of temperature sensors arranged on the battery surface. Step S1 collects the surface temperature set using a distributed temperature sensor array, which includes NTCs (Negative Temperature Sensors) arranged on the battery surface. The negative temperature coefficient (NTC) thermistor; the negative electrode potential refers to the potential of the battery's negative electrode relative to the lithium reference electrode, directly reflecting the electrochemical state of the negative electrode; step S1 collects the negative electrode potential through the reference electrode in the three-electrode battery system; the actual charging capacity refers to the cumulative charge added to the battery from the start of charging to the current moment; step S1 collects the actual charging capacity through the ampere-hour integration function of the battery management system; the actual heat generation refers to the cumulative heat generated by the battery during charging due to ohmic heat and polarization heat; step S1 collects the actual heat generation through a calorimeter; the actual charging duration refers to the time length from the start of charging to the current moment; step S1 collects the actual charging duration through the system clock; the initial pulse parameters refer to the pre-stored initial characteristic parameters of pulse charging and heating, including the basic DC charging pulse duty cycle, basic DC... The battery data includes the following parameters: charging pulse amplitude, basic AC heating pulse frequency, basic AC heating pulse amplitude, and basic pulse interval duration. Step S1 collects initial pulse parameters through the battery management system configuration table. The State of Charge (SOC) refers to the percentage of the battery's remaining capacity relative to its rated capacity at the current moment. Step S1 collects the SOC using the ampere-hour integration method or open-circuit voltage method of the battery management system. The rated battery capacity refers to the total amount of charge the battery can release under standard conditions. Step S1 collects the rated battery capacity through the battery factory parameter database. The current refers to the charging and discharging current flowing through the battery. Step S1 collects the current using a Hall effect current sensor. The voltage refers to the battery's operating voltage. Step S1 collects the voltage using a high-precision voltage sensor. The battery data also includes lithium plating risk coefficient, thermal runaway risk coefficient, and charging capacity loss coefficient.The lithium plating risk coefficient is a normalized parameter used to quantify the probability of lithium plating occurring. It is calculated based on the deviation between the negative electrode potential and the lithium plating overpotential boundary, and is set as FLi=exp[(ηLi-Uneg) / k×T], where ηLi is the lithium plating overpotential boundary, Uneg is the negative electrode potential, k is the Boltzmann constant, and T is the mean of the surface temperature set. The more negative the negative electrode potential (the easier it is for lithium plating to occur), the larger the lithium plating risk coefficient. The thermal runaway risk coefficient is a normalized parameter used to quantify the probability of thermal runaway occurring. It is calculated based on the maximum temperature change rate in the surface temperature set, and is set as Fthermal=(dTmax / dt) / vthreshold, where dTmax / dt is the maximum temperature change rate over time in the surface temperature set, and vthreshold is a preset value. The temperature change rate threshold is set to 2℃ / min in this embodiment. The faster the temperature rises, the greater the thermal runaway risk coefficient. The charging capacity loss coefficient is a normalized parameter used to quantify capacity loss during charging. This coefficient is calculated based on the deviation between the actual charging capacity and the theoretical usable capacity. The charging capacity loss coefficient is set as Fcap = 1 - (Qinjected / Qtheoretical), where Qinjected is the actual cumulative charging capacity, and Qtheoretical is the theoretical chargeable capacity under the current state of charge. The theoretical chargeable capacity is calculated based on the difference between the battery's rated capacity Cnominal and the current battery state of charge percentage SOC, i.e., Qtheoretical = Cnominal × (1 - SOC). The lower the actual charging efficiency, the greater the charging capacity loss coefficient.

[0052] Specifically, in step S2, the charging demand is generated based on the battery data by the charging acceptance capability prediction model. When the charging demand is obtained, the solid diffusion coefficient and surface temperature set in the battery data are input into the charging acceptance capability prediction model to obtain the lithium plating critical current and target charge output by the charging acceptance capability prediction model. The target charge is calculated with the target state of charge in the demand data to obtain the target charging charge. The lithium plating critical current and target charging charge are output as the charging demand.

[0053] Specifically, the charge acceptance prediction model refers to a recurrent neural network model that takes the solid-phase diffusion coefficient and surface temperature set as inputs and the lithium plating critical current and target charging capacity as outputs. The model is trained on an initial neural network using a charge acceptance training dataset, which includes historical solid-phase diffusion coefficients, historical surface temperature sets, and corresponding historical lithium plating critical currents and target charging capacities. This embodiment constructs the charge acceptance prediction model through the following steps: collecting battery samples under different aging states and temperature conditions; identifying the solid-phase diffusion coefficient using electrochemical impedance spectroscopy; and measuring the lithium plating critical current using a three-electrode battery testing system. The lithium plating critical current is defined as the negative electrode potential dropping to 0 mV (relative to Li / Li). + The current threshold corresponding to the reference electrode is used to calculate the target charging capacity by the difference between the rated capacity and the target state of charge. A charging acceptance capacity training dataset containing 5000 samples is constructed. A Long Short-Term Memory (LSTM) network is used as the basic architecture. The number of input layer nodes is set to 2 (to receive the ensemble mean of solid diffusion coefficient and surface temperature, respectively), the hidden layer is 2 LSTM layers (64 neurons per layer, dropout rate of 0.2 to prevent overfitting), and the number of output layer nodes is 2 (to output the critical lithium plating current and the target charging capacity, respectively). The activation function is ReLU. The Adam optimizer is used with an initial learning rate of 0.001, a batch size of 32, and 200 training iterations. The mean squared error (MSE) is used as the loss function. Training stops when the validation set loss does not decrease for 10 consecutive iterations, thus obtaining the charging acceptance capacity prediction model.

[0054] Specifically, in step S2, the thermal demand is generated based on the demand data and battery data through the thermal-electric coupling demand analysis model. When the thermal demand is obtained, the expected charging time tc, the target charging capacity and the surface temperature set in the demand data and the battery data are input into the thermal-electric coupling demand analysis model to obtain the target heating rate and the heat required for heating output by the thermal-electric coupling demand analysis model. The target heating rate and the heat required for heating are output as the thermal demand.

[0055] Specifically, the thermal demand analysis model refers to a deep neural network model that takes the expected charging time and surface temperature set as inputs and outputs the heat required for heating and the response time required for heating. The thermal demand analysis model is obtained by training an initial neural network using a thermal demand training dataset. This training dataset includes historical expected charging times, historical surface temperature sets, and corresponding historical heat required for heating and historical response times required for heating. This embodiment constructs the thermal demand analysis model through the following steps: Based on battery thermal properties (specific heat capacity, mass, heat transfer coefficient) and the energy conservation equation, the heat required to reach the preset safe charging temperature and the maximum allowable response time are calculated under different initial temperatures and expected charging times, constructing a thermal demand training dataset containing 3000 operating conditions; a fully connected neural network (FCN) architecture is adopted, with 3 input layer nodes (expected charging time, mean of surface temperature set, and range of surface temperature set), and 3 hidden layers (128 neurons per layer, using batch processing). Normalization accelerates convergence. The output layer has 2 nodes (outputting the heat required for heating and the response time required for heating, respectively). The activation function is Tanh. The Adagrad optimizer is used with a learning rate of 0.01 and a batch size of 64. The training is conducted for 150 epochs, using the mean absolute percentage error (MAPE) as the loss function. Training is stopped when the MAPE of the training set is lower than 5%, thus obtaining the heat demand analysis model.

[0056] Specifically, in step S2, the heat source supply parameters are generated by the heat source supply analysis model based on the heat source data in the battery data. When the heat source supply parameters are obtained, the initial pulse parameters in the battery data are input into the heat source supply parameter analysis model to obtain the heat source supply parameters output by the heat source supply parameter analysis model. The heat source supply parameters include the supplied heat and the supply response time. The initial pulse parameters refer to the initially set set of pulse characteristic parameters.

[0057] Specifically, the heat source supply analysis model refers to a recurrent neural network model that takes initial pulse parameters as input and outputs heat source supply parameters. The supplied heat refers to the heat value that the initial pulse parameters can provide within the response time required for heating. The supply response time refers to the time required for the initial pulse parameters to provide the supplied heat. The heat source supply analysis model is obtained by training the initial neural network using a heat source supply training dataset. The heat source supply training dataset includes historical initial pulse parameters and corresponding historical supplied heat and historical supply response times. This embodiment constructs the heat source supply analysis model through the following steps: testing different DC charging pulse duty cycles (range 0.1-0.9) and different AC heating pulse frequencies (10...) on an experimental platform. The heat generation power of pulses within the 0Hz-10kHz range was calculated by integration, and the supply response time was determined by the inflection point of the temperature rise curve. A training dataset containing 4000 parameter combinations was constructed. A gated recurrent unit (GRU) network architecture was adopted, with 5 input layer nodes (receiving the duty cycle of the basic DC charging pulse, the amplitude of the basic DC charging pulse, the frequency of the basic AC heating pulse, the amplitude of the basic AC heating pulse, and the duration of the basic pulse interval), 2 hidden layers (32 neurons per layer), and 2 output layer nodes (outputting the supply heat and the supply response time). The sigmoid activation function was used. The RMSprop optimizer was used with a learning rate of 0.005 and a batch size of 16. The training was conducted for 300 epochs, and the Huber loss function was used to balance the mean square error and the absolute error. Training was stopped when the validation set loss was below 0.01, resulting in the heat source supply analysis model.

[0058] Specifically, in step S3, when determining the charging-heating coordination strategy based on the interface temperature in the battery data, and generating the pulse coordination scheme based on the charging-heating coordination strategy, charging demand, heat demand, and heat source supply parameters, the interface temperature t1 is compared with the preset safe charging temperature t0. The charging-heating coordination strategy is determined based on the comparison result, and the pulse coordination scheme is generated based on the determination result, charging demand, heat demand, and heat source supply parameters, wherein:

[0059] When t1≥t0, the charging-heating collaborative strategy is determined to be the direct charging mode, and the initial charging scheme is generated as the pulse collaborative scheme.

[0060] When t1 < t0, the charging-heating collaborative strategy is determined to be a pulse collaborative mode. The supply and demand matching in two dimensions is judged based on the charging demand and heat demand. The basic pulse parameters are adjusted based on the supply and demand matching in two dimensions to obtain the adjusted pulse parameters. The adjusted pulse parameters are then input into the dual-objective pulse optimization model to obtain the dual-objective pulse parameters output by the dual-objective pulse optimization model. The dual-objective pulse parameters are then output as the pulse collaborative scheme.

[0061] Specifically, the initial charging scheme refers to the standard constant current and constant voltage charging scheme preset at the vehicle's factory, including the initial charging current, initial charging voltage, and initial charging cutoff conditions. It is executed directly in direct charging mode without the need for pulse heating assistance. The interface temperature refers to the actual temperature of the electrode / electrolyte interface, distinct from the battery casing temperature characterized by the surface temperature set. The preset safe charging temperature refers to the minimum temperature threshold that allows for safe high-rate charging; in this embodiment, the preset safe charging temperature is set to 10°C. The direct charging mode refers to a charging mode that directly uses DC charging without auxiliary heating. The pulse coordination mode refers to alternating charging... The coordinated operation mode of DC charging pulse and AC heating pulse, wherein the basic pulse parameters refer to the set of parameters initially set in the pulse coordination mode, including the duty cycle of the basic DC charging pulse, the amplitude of the basic DC charging pulse, the frequency of the basic AC heating pulse, the amplitude of the basic AC heating pulse, and the duration of the basic pulse interval; the adjusted pulse parameters refer to the set of parameters obtained after adjusting the basic pulse parameters according to the dual-dimensional supply and demand matching judgment results; the dual-objective pulse optimization model refers to a multi-objective genetic algorithm optimization model that takes the adjusted pulse parameters as input and the dual-objective pulse parameters as output; the dual-objective pulse optimization model is implemented through the following methods... Formula Construction: Define the decision variables as the adjusted DC charging pulse duty cycle (range 0.1-0.8), the adjusted AC heating pulse frequency (range 100Hz-8kHz), the adjusted pulse interval duration (range 1s-10s), the DC charging pulse amplitude (range 0.1C-0.5C), and the AC heating pulse amplitude (range 0.05C-0.3C); Establish a dual-objective optimization function: the first objective function f1 is the expected charging time (f1=target charging capacity / (DC charging pulse amplitude × adjusted DC charging pulse duty cycle)), and the second objective function f2 is the energy efficiency (f2=actual charging capacity / (actual charging capacity × adjusted DC charging pulse duty cycle)). The constraints include: adjusted AC heating pulse frequency ≥ 100Hz (to avoid low-frequency electrolyte decomposition), DC charging pulse amplitude ≤ lithium plating critical current × 0.9 (safety margin), and adjusted pulse interval duration ≥ 1s (to ensure re-embedding time). The NSGA-II algorithm parameters are set as follows: population size 100, crossover probability 0.9, mutation probability 0.1, maximum number of iterations 200, and elite retention strategy retaining the top 10% of the best individuals. The Pareto optimal front is solved using fast non-dominated sorting and crowding calculation. The solution located at the "knee point" (i.e., the optimal point for the two objectives) is selected from the Pareto solution set as the dual-objective pulse parameter output. The dual-objective pulse parameters include DC charging pulse duty cycle, DC charging pulse amplitude, AC heating pulse frequency, AC heating pulse amplitude, and pulse interval duration.

[0062] Specifically, when the charging-heating collaborative strategy is determined to be a pulse collaborative mode, the supply-demand matching is assessed based on both charging and heating demands. The basic pulse parameters are then adjusted according to this matching. To obtain the adjusted pulse parameters, the charge matching index P1 is calculated based on the lithium plating critical current L and the target charging rate V in the charging demand, with P1 set to L / V. The heat matching index P2 is calculated based on the supplied heat R and the battery heat loss power S in the heat source supply parameters, with P2 set to R / S. The supply-demand matching is then assessed based on the charge matching index P1 and the heat matching index P2. Based on the assessment results, the basic DC charging pulse duty cycle, basic AC heating pulse frequency, and basic pulse interval duration in the basic pulse parameters are adjusted.

[0063] When P1≥1 and P2≥1, the two-dimensional supply and demand matching is determined to be a perfect match, and the basic pulse parameters are not adjusted. Instead, the basic pulse parameters are used as the adjusted pulse parameters for output.

[0064] When P1≥1 and P2<1, the dual-dimensional supply and demand matching is determined to be thermal shortage and power shortage. The basic pulse parameters are adjusted. The basic AC heating pulse frequency jp is adjusted by the heating enhancement coefficient k1. K1 is set to 1+0.3×(1-P2) to obtain the first adjusted AC heating pulse frequency jpt1. jpt1 is set to jp×k1. The basic DC charging pulse duty cycle jd is also adjusted by the heating sacrifice coefficient rx. The heating sacrifice coefficient rx is set to 0.85 to obtain the first adjusted DC charging pulse duty cycle jdt1. jdt1 is set to jd×rx. The AC heating pulse frequency and DC charging pulse duty cycle in the basic pulse parameters are replaced with the first adjusted AC heating pulse frequency and the first adjusted DC charging pulse duty cycle to obtain the adjusted pulse parameters.

[0065] When P1 < 1 and P2 ≥ 1, the dual-dimensional supply and demand matching is determined to be power shortage and heat sufficiency. The basic pulse parameters are adjusted. The duty cycle jd of the basic DC charging pulse is adjusted by the charging conservatism coefficient Cb. Cb = 0.7 is set to obtain the second adjusted DC charging pulse duty cycle. jdt2 = jd × Cb is set. The duration ys of the basic pulse interval is also adjusted by the extension coefficient ty. ty = 1.2 is set to obtain the adjusted pulse interval duration yst. yst = ys × ty is set. The DC charging pulse duty cycle and pulse interval duration in the basic pulse parameters are replaced with the second adjusted DC charging pulse duty cycle and the adjusted pulse interval duration to obtain the adjusted pulse parameters.

[0066] When P1 < 1 and P2 < 1, the dual-dimensional supply and demand matching is determined to be a double shortage of supply and demand. The basic pulse parameters are adjusted. The basic AC heating pulse frequency jp is adjusted by the preheating priority coefficient yr, and yr = 1.5 is set to obtain the second adjusted AC heating pulse frequency jpt2, and jpt2 = jp × yr is set. The basic DC charging pulse duty cycle jd is also adjusted by the charging suppression coefficient Cy, and Cy = 0.5 is set to obtain the third adjusted DC charging pulse duty cycle jdt3, and jdt3 = jd × Cy is set. The AC heating pulse frequency and DC charging pulse duty cycle in the basic pulse parameters are replaced with the second adjusted AC heating pulse frequency and the third adjusted DC charging pulse duty cycle to obtain the adjusted pulse parameters.

[0067] Specifically, the charge matching index refers to the ratio of the lithium plating critical current to the target charging rate, used to characterize the sufficiency of charging capacity. When the charge matching index is greater than or equal to 1, it indicates that the current maximum allowable charging current meets the target rate; when the charge matching index is less than 1, it indicates that the current maximum allowable charging current is insufficient. The target charging rate refers to the ratio of the target charging capacity to the expected charging time. The thermal matching index refers to the ratio of supplied heat to battery heat loss power, used to characterize the sufficiency of heating capacity. When the thermal matching index is greater than or equal to 1, it indicates that the heat generation power is sufficient to compensate for heat dissipation loss; when the thermal matching index is less than 1, it indicates that the heat generation power is insufficient. The battery heat loss power refers to the power of heat dissipated from the battery to the environment, calculated based on the temperature difference between the ambient temperature and the surface temperature set and the heat dissipation coefficient. The dual-dimensional supply and demand matching refers to the matching state that simultaneously considers the charge transfer demand and the heat supply demand, including four states: sufficient supply and demand, sufficient heat and sufficient charge, sufficient charge and sufficient heat, and insufficient supply and demand. The basic DC charging pulse duty cycle refers to the conduction time of the initially set DC charging pulse in one cycle. The following parameters are defined as follows: The basic AC heating pulse frequency refers to the initially set frequency of the AC heating pulse; the basic pulse interval duration refers to the initially set interval time between adjacent pulses; the first adjusted AC heating pulse frequency refers to the AC heating pulse frequency increased according to the heat shortage state; the first adjusted DC charging pulse duty cycle refers to the DC charging pulse duty cycle adjusted according to the charge surplus state; the adjusted pulse interval duration refers to the pulse interval duration extended according to electrochemical safety requirements; the heating enhancement coefficient refers to the frequency enhancement ratio calculated based on the degree of heat shortage; the heating sacrifice coefficient refers to the duty cycle reduction ratio that sacrifices charging power to ensure heating; the charging conservatism coefficient refers to the conservative charging ratio used when charge supply is insufficient; the interval extension coefficient refers to the interval extension ratio used when charge supply is insufficient; the preheating priority coefficient refers to the frequency enhancement ratio that prioritizes preheating when both supply and demand are insufficient; and the charging suppression coefficient refers to the duty cycle reduction ratio that suppresses charging power when both supply and demand are insufficient.

[0068] Specifically, in step S4, when the vehicle battery is subjected to charging-heating coordinated control according to the pulse coordination scheme, the pulse coordination scheme in step S3 is sent to the vehicle battery control system, and the vehicle battery control system controls the vehicle battery to perform charging-heating coordinated control according to the DC charging pulse duty cycle, DC charging pulse amplitude, AC heating pulse frequency, AC heating pulse amplitude and pulse interval duration in the dual target pulse parameters.

[0069] Specifically, this embodiment does not limit the specific method of sending the pulse coordination scheme in step S3 to the vehicle battery control system. Those skilled in the art can set it according to the actual situation, such as sending the pulse coordination scheme to the vehicle battery control system through the CAN bus communication protocol. The vehicle battery control system controls the on / off state and current magnitude of the DC charging circuit according to the DC charging pulse duty cycle and DC charging pulse amplitude in the dual target pulse parameters, controls the oscillation frequency and energy output of the AC heating circuit according to the AC heating pulse frequency and AC heating pulse amplitude, and controls the intermittent rest time of the two circuits according to the pulse interval duration.

[0070] Specifically, in step S5, when performing dynamic correction of the lithium plating boundary for the pulse-coordinated scheme based on battery data, the negative electrode potential W in the battery data is compared with the lithium plating overpotential boundary W0. The lithium plating situation is judged based on the comparison result, and the dynamic correction of the lithium plating boundary is performed on the pulse-coordinated scheme based on the judgment result. Wherein:

[0071] When W≥W0, the lithium plating situation is determined to be no lithium plating, and no dynamic correction of the lithium plating boundary is performed on the pulse collaborative scheme;

[0072] When W < W0, lithium plating is determined to be present. The lithium plating boundary is dynamically corrected for the pulse coordination scheme. The correction method is to trigger the pulse intermittent re-embedding protocol. The pulse intermittent re-embedding protocol refers to correcting the pulse intermittent period ys in the pulse coordination scheme according to the extended pulse intermittent period coefficient Mx to obtain the extended pulse intermittent period yt. Mx is set to 1.5, yt = ys × Mx, and the value of the pulse intermittent period in the pulse coordination scheme is replaced with the value of the extended pulse intermittent period.

[0073] Specifically, the lithium plating overpotential boundary refers to the critical potential for lithium plating calculated according to the Sand equation. When the negative electrode potential is lower than this boundary, the deposition rate of lithium ions on the negative electrode surface exceeds the insertion rate, leading to lithium plating. The pulse intermittent re-insertion protocol refers to a corrective control strategy that promotes the re-insertion of reversible lithium plating into the negative electrode by extending the pulse intermittent period and utilizing the residual heat inside the battery when lithium plating risk is detected. The extended pulse intermittent period coefficient is a proportional coefficient used to calculate the extended pulse intermittent period, and the down-adjustment coefficient is a proportional coefficient used to reduce the amplitude of the DC charging pulse.

[0074] Specifically, in step S5, when performing supply-demand matching correction on the generation process of the pulse coordination scheme based on battery data and heat demand, the actual charging rate vd is calculated based on the actual charging capacity d and actual charging time tcl in the battery data, and vd = d / tcl is set. The charge achievement rate g1 is calculated based on the actual charging rate vd and the target charging rate vm, and g1 = vd / vm is set. The heat achievement rate g2 is calculated based on the actual heat generation rs in the battery data and the heat required for temperature rise sp in the heat demand, and g2 = rs / sp is set. The supply-demand coupling coefficient GX is calculated based on the charge achievement rate g1 and the heat achievement rate g2, and GX = (g1 + g2) / 2 is set. The supply-demand coupling coefficient GX is compared with a preset coupling coefficient range. Based on the comparison result, the supply-demand matching situation is judged, and the supply-demand matching correction is performed on the generation process of the pulse coordination scheme based on the judgment result. Wherein:

[0075] When the supply and demand coupling coefficient is within the preset coupling coefficient range, the supply and demand matching is determined to be a match, and no supply and demand matching correction is performed in the generation process of the pulse coordination scheme.

[0076] When the supply and demand matching coefficient is outside the preset coupling coefficient range, the supply and demand matching situation is determined to be mismatched. The supply and demand matching correction is performed on the generation process of the pulse coordination scheme. The charge matching index P1 is corrected according to the charge achievement rate g1 to obtain the corrected charge matching index P1g. P1g is set to P1×g1. The heat demand matching index P2 is corrected according to the heat achievement rate g2 to obtain the corrected heat demand matching index P2g. P2g is set to P2×g2. The values ​​of the charge matching index and the heat demand matching index are replaced with the values ​​of the corrected charge matching index and the corrected heat demand matching index.

[0077] Specifically, the actual charging time refers to the time from the start of charging to the current moment, which is collected by a timer. The charge achievement rate refers to the ratio of the actual charging rate to the target charging rate. The heat achievement rate refers to the ratio of the actual heat generation to the heat required for heating. The supply and demand coupling coefficient is the average of the charge achievement rate and the heat achievement rate. The preset coupling coefficient range is a reasonable range set according to the battery type and charging conditions. When the supply and demand coupling coefficient is lower than 0.85, it indicates that the actual execution deviates from the expectation by more than 15%, and there is a risk of insufficient heat supply or slow charging. When it is higher than 1.15, it indicates that the actual execution exceeds the expectation by more than 15%, and there is a risk of overheating or overcharging. In both cases, the supply and demand matching calculation needs to be re-performed to correct the deviation. Therefore, in this embodiment, the preset coupling coefficient range is set to [0.85, 1.15].

[0078] Specifically, in step S5, when revising the heat source supply parameters based on battery data to account for overall risk, the comprehensive risk index F is calculated based on the lithium plating risk coefficient q1, thermal runaway risk coefficient q2, charging capacity loss coefficient q3, first weighting coefficient α1, second weighting coefficient α2, and third weighting coefficient α3 in the battery data. F is set as F = q1 × α1 + q2 × α2 + q3 × α3. The comprehensive risk index F is compared with a preset risk index threshold F0. Based on the comparison result, the risk situation is judged, and the heat source supply parameters are revised based on the judgment result.

[0079] When F≤F0, the risk situation is determined to be no risk, and the heat source supply parameters are not revised;

[0080] When F > F0, the risk situation is determined to be risky, and the heat source supply parameters are revised. The supply heat R in the heat source supply parameters is reduced by the risk degradation coefficient fx. The risk degradation coefficient fx = 0.6 is set to obtain the revised supply heat Rx. Rx = R × fx is set, and the value of the supply heat R is replaced with the value of the revised supply heat Rx.

[0081] Specifically, the first weighting coefficient refers to the weight corresponding to the lithium plating risk coefficient when calculating the comprehensive risk index; the second weighting coefficient refers to the weight corresponding to the thermal runaway risk coefficient when calculating the comprehensive risk index; and the third weighting coefficient refers to the weight corresponding to the charging capacity loss coefficient when calculating the comprehensive risk index. Lithium plating risk directly leads to permanent capacity loss and may trigger internal short circuits, so its weight is set to 0.5, which is dominant. Thermal runaway risk, although serious, has a relatively low probability of occurrence, so its weight is set to 0.3. Capacity loss risk is a cumulative consequence, so its weight is set to 0.2. The preset risk index threshold refers to the critical value for determining the risk situation. When the comprehensive risk index exceeds 0.7, it indicates that the battery is in a high risk state. In the high-risk operating zone, the thermal excitation intensity must be immediately reduced, and a conservative control mode must be switched to, reducing the supplied heat. Therefore, this embodiment sets the preset risk index threshold to 0.7. This embodiment sets the risk degradation coefficient fx=0.6, reducing the supplied heat by 40% to avoid thermal runaway caused by temperature changes, which meets the requirements of thermodynamic stability. The degradation charging mode refers to a conservative control strategy that sacrifices the charging rate to gain a safety margin. It reduces the risk of lithium plating by reducing the DC charging pulse amplitude and ensures thermal safety by extending the AC heating pulse duration. The risk situation refers to the charging situation where there is no risk, as determined by the comprehensive risk index and the preset risk index threshold. The risk situation includes no risk and risk.

[0082] Please see Figure 2 The diagram shown is a structural schematic of the system for the low-temperature battery charging method based on pulse self-heating in this embodiment. The system includes:

[0083] The data acquisition module is used to collect battery data and demand data;

[0084] The demand generation module is used to generate charging demand based on battery data through a charging acceptance capability prediction model, and to generate thermal demand based on demand data and battery data through a thermal-electric coupling demand analysis model, and to generate thermal supply parameters based on thermal source data in battery data through a thermal source supply analysis model. The demand generation module is connected to the data acquisition module.

[0085] The pulse scheme generation module is used to determine the charging-heating coordination strategy based on the interface temperature in the battery data, and to generate a pulse coordination scheme based on the determination result, charging demand, heat demand and heat source supply parameters. The pulse scheme generation module is connected to the demand generation module.

[0086] The collaborative control module performs charging-heating collaborative control of the vehicle battery according to the pulse collaborative scheme, and the collaborative control module is connected to the pulse scheme generation module.

[0087] The pulse scheme correction module is used to dynamically correct the lithium plating boundary of the pulse coordination scheme based on battery data, to perform supply and demand matching correction on the generation process of the pulse coordination scheme based on battery data and heat demand, and to perform comprehensive risk revision of the heat source supply parameters based on battery data. The pulse scheme correction module is connected to the coordination control module.

[0088] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A low-temperature charging method for batteries based on pulse self-heating, characterized in that, include: Step S1: Collect battery data and demand data; Step S2: The charging demand is generated based on the battery data by the charging acceptance capacity prediction model. The heat demand is also generated based on the demand data and battery data by the thermal-electric coupling demand analysis model. The heat source supply parameters are also generated based on the heat source data in the battery data by the heat source supply analysis model. Step S3: Determine the charging-heating coordination strategy based on the interface temperature in the battery data, and generate a pulse coordination scheme based on the charging-heating coordination strategy, charging demand, heat demand and heat source supply parameters; Step S4: Perform charging-heating coordinated control on the vehicle battery according to the pulse coordination scheme; Step S5 involves dynamically correcting the lithium plating boundary of the pulse coordination scheme based on battery data, correcting the supply and demand matching of the pulse coordination scheme generation process based on battery data and heat demand, and comprehensively revising the heat source supply parameters based on battery data to mitigate risks.

2. The low-temperature charging method for batteries based on pulse self-heating according to claim 1, characterized in that, In step S2, the charging demand is generated based on the battery data by the charging acceptance capability prediction model. When the charging demand is obtained, the solid diffusion coefficient and surface temperature set in the battery data are input into the charging acceptance capability prediction model to obtain the lithium plating critical current and target charge output by the charging acceptance capability prediction model. The target charge is calculated with the target state of charge in the demand data to obtain the target charging charge. The lithium plating critical current and target charging charge are output as the charging demand.

3. The low-temperature charging method for batteries based on pulse self-heating according to claim 2, characterized in that, In step S2, the thermal demand is generated based on the demand data and battery data through the thermal-electric coupling demand analysis model. When the thermal demand is obtained, the expected charging time tc, the target charging capacity and the surface temperature set in the demand data and the battery data are input into the thermal-electric coupling demand analysis model to obtain the target heating rate and the heat required for heating output by the thermal-electric coupling demand analysis model. The target heating rate and the heat required for heating are output as the thermal demand. In step S2, the heat source supply parameters are generated based on the heat source data in the battery data through the heat source supply analysis model. When the heat source supply parameters are obtained, the initial pulse parameters in the battery data are input into the heat source supply parameter analysis model to obtain the heat source supply parameters output by the heat source supply parameter analysis model. The heat source supply parameters include the supplied heat and the supply response time. The initial pulse parameters refer to the initially set set of pulse characteristic parameters.

4. The low-temperature charging method for batteries based on pulse self-heating according to claim 3, characterized in that, In step S3, the charging-heating coordination strategy is determined based on the interface temperature in the battery data. When generating the pulse coordination scheme based on the charging-heating coordination strategy, charging demand, heat demand, and heat source supply parameters, the interface temperature t1 is compared with the preset safe charging temperature t0. The charging-heating coordination strategy is determined based on the comparison result, and the pulse coordination scheme is generated based on the determination result, charging demand, heat demand, and heat source supply parameters, wherein: When t1≥t0, the charging-heating collaborative strategy is determined to be the direct charging mode, and the initial charging scheme is generated as the pulse collaborative scheme. When t1 < t0, the charging-heating collaborative strategy is determined to be a pulse collaborative mode. The supply and demand matching in two dimensions is judged based on the charging demand and heat demand. The basic pulse parameters are adjusted based on the supply and demand matching in two dimensions to obtain the adjusted pulse parameters. The adjusted pulse parameters are then input into the dual-objective pulse optimization model to obtain the dual-objective pulse parameters output by the dual-objective pulse optimization model. The dual-objective pulse parameters are then output as the pulse collaborative scheme.

5. The low-temperature charging method for batteries based on pulse self-heating according to claim 4, characterized in that, When the charging-heating coordinated strategy is determined to be a pulse coordinated mode, the supply-demand matching is assessed based on both charging and heating demands. The basic pulse parameters are then adjusted according to this matching. After obtaining the adjusted pulse parameters, the charge matching index P1 is calculated based on the lithium plating critical current L and the target charging rate V in the charging demand, and P1 = L / V is set. The heat matching index P2 is calculated based on the supplied heat R and the battery heat loss power S in the heat source supply parameters, and P2 = R / S is set. The supply-demand matching is then assessed based on the charge matching index P1 and the heat matching index P2. Based on the assessment results, the basic DC charging pulse duty cycle, basic AC heating pulse frequency, and basic pulse interval duration in the basic pulse parameters are adjusted. When P1≥1 and P2≥1, the two-dimensional supply and demand matching is determined to be a perfect match, and the basic pulse parameters are not adjusted. Instead, the basic pulse parameters are used as the adjusted pulse parameters for output. When P1≥1 and P2<1, the dual-dimensional supply and demand matching is determined to be thermal shortage and power shortage. The basic pulse parameters are adjusted. The basic AC heating pulse frequency jp is adjusted by the heating enhancement coefficient k1. K1 is set to 1+0.3×(1-P2) to obtain the first adjusted AC heating pulse frequency jpt1. jpt1 is set to jp×k1. The basic DC charging pulse duty cycle jd is also adjusted by the heating sacrifice coefficient rx. The heating sacrifice coefficient rx is set to 0.85 to obtain the first adjusted DC charging pulse duty cycle jdt1. jdt1 is set to jd×rx. The AC heating pulse frequency and DC charging pulse duty cycle in the basic pulse parameters are replaced with the first adjusted AC heating pulse frequency and the first adjusted DC charging pulse duty cycle to obtain the adjusted pulse parameters. When P1 < 1 and P2 ≥ 1, the dual-dimensional supply and demand matching is determined to be power shortage and heat sufficiency. The basic pulse parameters are adjusted. The duty cycle jd of the basic DC charging pulse is adjusted by the charging conservatism coefficient Cb. Cb = 0.7 is set to obtain the second adjusted DC charging pulse duty cycle. jdt2 = jd × Cb is set. The duration ys of the basic pulse interval is also adjusted by the extension coefficient ty. ty = 1.2 is set to obtain the adjusted pulse interval duration yst. yst = ys × ty is set. The DC charging pulse duty cycle and pulse interval duration in the basic pulse parameters are replaced with the second adjusted DC charging pulse duty cycle and the adjusted pulse interval duration to obtain the adjusted pulse parameters. When P1 < 1 and P2 < 1, the dual-dimensional supply and demand matching is determined to be a double shortage of supply and demand. The basic pulse parameters are adjusted. The basic AC heating pulse frequency jp is adjusted by the preheating priority coefficient yr, and yr = 1.5 is set to obtain the second adjusted AC heating pulse frequency jpt2, and jpt2 = jp × yr is set. The basic DC charging pulse duty cycle jd is also adjusted by the charging suppression coefficient Cy, and Cy = 0.5 is set to obtain the third adjusted DC charging pulse duty cycle jdt3, and jdt3 = jd × Cy is set. The AC heating pulse frequency and DC charging pulse duty cycle in the basic pulse parameters are replaced with the second adjusted AC heating pulse frequency and the third adjusted DC charging pulse duty cycle to obtain the adjusted pulse parameters.

6. The low-temperature charging method for batteries based on pulse self-heating according to claim 5, characterized in that, In step S4, when the vehicle battery is charged and heated in coordination according to the pulse coordination scheme, the pulse coordination scheme in step S3 is sent to the vehicle battery control system. The vehicle battery control system then controls the vehicle battery to perform charging and heating coordination based on the DC charging pulse duty cycle, DC charging pulse amplitude, AC heating pulse frequency, AC heating pulse amplitude, and pulse interval duration in the dual target pulse parameters.

7. The low-temperature charging method for batteries based on pulse self-heating according to claim 6, characterized in that, In step S5, when performing dynamic lithium plating boundary correction on the pulse-coordinated scheme based on battery data, the negative electrode potential W in the battery data is compared with the lithium plating overpotential boundary W0. The lithium plating situation is judged based on the comparison result, and the pulse-coordinated scheme is dynamically corrected for lithium plating boundary based on the judgment result. Specifically: When W≥W0, the lithium plating situation is determined to be no lithium plating, and no dynamic correction of the lithium plating boundary is performed on the pulse collaborative scheme; When W < W0, lithium plating is determined to be present. The lithium plating boundary is dynamically corrected for the pulse coordination scheme. The correction method is to trigger the pulse intermittent re-embedding protocol. The pulse intermittent re-embedding protocol refers to correcting the pulse intermittent period ys in the pulse coordination scheme according to the extended pulse intermittent period coefficient Mx to obtain the extended pulse intermittent period yt. Mx is set to 1.5, yt = ys × Mx, and the value of the pulse intermittent period in the pulse coordination scheme is replaced with the value of the extended pulse intermittent period.

8. The low-temperature charging method for batteries based on pulse self-heating according to claim 7, characterized in that, In step S5, when performing supply-demand matching correction on the generation process of the pulse coordination scheme based on battery data and heat demand, the actual charging rate vd is calculated based on the actual charging capacity d and actual charging time tcl in the battery data, and vd = d / tcl is set. The charge achievement rate g1 is calculated based on the actual charging rate vd and the target charging rate vm, and g1 = vd / vm is set. The heat achievement rate g2 is calculated based on the actual heat generation rs in the battery data and the heat required for temperature rise sp in the heat demand, and g2 = rs / sp is set. The supply-demand matching coupling coefficient GX is calculated based on the charge achievement rate g1 and the heat achievement rate g2, and GX = (g1 + g2) / 2 is set. The supply-demand matching coupling coefficient GX is compared with a preset coupling coefficient range. Based on the comparison result, the supply-demand matching situation is judged, and the supply-demand matching correction is performed on the generation process of the pulse coordination scheme based on the judgment result. Wherein: When the supply and demand coupling coefficient is within the preset coupling coefficient range, the supply and demand matching is determined to be a match, and no supply and demand matching correction is performed in the generation process of the pulse coordination scheme. When the supply and demand matching coefficient is outside the preset coupling coefficient range, the supply and demand matching situation is determined to be mismatched. The supply and demand matching correction is performed on the generation process of the pulse coordination scheme. The charge matching index P1 is corrected according to the charge achievement rate g1 to obtain the corrected charge matching index P1g. P1g is set to P1×g1. The heat demand matching index P2 is corrected according to the heat achievement rate g2 to obtain the corrected heat demand matching index P2g. P2g is set to P2×g2. The values ​​of the charge matching index and the heat demand matching index are replaced with the values ​​of the corrected charge matching index and the corrected heat demand matching index.

9. The low-temperature charging method for batteries based on pulse self-heating according to claim 8, characterized in that, In step S5, when revising the heat source supply parameters based on battery data for comprehensive risk assessment, the comprehensive risk index F is calculated based on the lithium plating risk coefficient q1, thermal runaway risk coefficient q2, charging capacity loss coefficient q3, first weighting coefficient α1, second weighting coefficient α2, and third weighting coefficient α3 in the battery data. F is set as F = q1 × α1 + q2 × α2 + q3 × α3. The comprehensive risk index F is compared with a preset risk index threshold F0. Based on the comparison result, the risk situation is judged, and the heat source supply parameters are revised based on the judgment result. Wherein: When F≤F0, the risk situation is determined to be no risk, and the heat source supply parameters are not revised; When F > F0, the risk situation is determined to be risky, and the heat source supply parameters are revised. The supply heat R in the heat source supply parameters is reduced by the risk degradation coefficient fx. The risk degradation coefficient fx = 0.6 is set to obtain the revised supply heat Rx. Rx = R × fx is set, and the value of the supply heat R is replaced with the value of the revised supply heat Rx.

10. A system applied to the pulse self-heating-based low-temperature battery charging method as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect battery data and demand data; The demand generation module is used to generate charging demand based on battery data through a charging acceptance capability prediction model, and to generate thermal demand based on demand data and battery data through a thermal-electric coupling demand analysis model, and to generate thermal supply parameters based on thermal source data in battery data through a thermal source supply analysis model. The pulse scheme generation module is used to determine the charging-heating coordination strategy based on the interface temperature in the battery data, and to generate the pulse coordination scheme based on the determination result, charging demand, heat demand and heat source supply parameters. The collaborative control module performs charging-heating collaborative control of the vehicle battery according to the pulse collaborative scheme. The pulse scheme correction module is used to dynamically correct the lithium plating boundary of the pulse coordination scheme based on battery data, to perform supply and demand matching correction on the generation process of the pulse coordination scheme based on battery data and heat demand, and to perform comprehensive risk revision of heat source supply parameters based on battery data.

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