A low-temperature environment battery heating and charging method

Abstract

The application provides a low-temperature environment battery heating and charging method, comprising the following steps: S1, establishing a battery electro-thermal coupling model, carrying out battery testing, identifying model parameters, and establishing a mapping relationship of parameters-temperature-SoC; S2, determining a battery heating and charging operation mode and a current value as two control variables, setting a variable space of an optimization algorithm based on a multidimensional mapping relationship of an optimization target and battery model parameters; S3, defining variable constraint conditions, a cost function and an objective function, and building an optimization algorithm framework for adaptively switching the operation mode and the current value; S4, collecting feedback battery state parameters, initializing the algorithm, and generating a collaborative heating and charging strategy for an expected duration; and S5, according to an expected control target, optimizing the best collaborative heating and charging control strategy that meets actual demands in S4 under the current battery state. The application realizes heating and charging collaborative optimization control, adaptively switches the heating and charging mode and the current value, and improves the low-temperature charging performance of the battery.

Description

Technical Field

[0001] This invention relates to the field of battery low-temperature charging technology, and in particular to a method for battery heating and co-charging in a low-temperature environment. Background Technology

[0002] Lithium-ion batteries, with their high energy density and long cycle life, are currently the most commonly used power source for electric vehicles. However, in low-temperature environments, lithium-ion batteries suffer from increased charge transfer impedance, reduced electrolyte conductivity, and slow ion diffusion. Charging these batteries can easily induce internal short circuits and lithium plating, severely reducing their durability and safety, and potentially leading to dangerous situations such as thermal runaway. Therefore, improving the low-temperature charging performance of lithium-ion batteries is crucial for the further development of electric vehicles in cold regions.

[0003] While optimizing the charging process alone can improve the low-temperature charging performance of lithium-ion batteries to some extent, it is difficult to overcome the problem of low battery activity in low-temperature environments. In comparison, the strategy of preheating the battery to an empirical temperature threshold before charging is currently a more commonly used low-temperature charging solution for lithium-ion batteries. However, this type of method decouples heating and charging into two independent tasks, giving less consideration to the coupling effect between the two. In reality, on the one hand, the heating method and related parameters are affected by the lithium-ion battery's system-on-chip (SoC), which is determined by the charging behavior; on the other hand, not only is the charging power limit highly dependent on temperature, but heat generation also occurs during the charging process, so the charging process is directly related to the heating process.

[0004] In summary, there is currently a lack of a multi-objective low-temperature charging control strategy for lithium-ion batteries that takes into account the optimal synergy between heating and charging and can achieve adaptive switching between heating and charging modes. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method for coordinated heating and charging of batteries in low-temperature environments. By establishing a multi-objective optimization algorithm model and considering the electro-thermal coupling mechanism of lithium-ion batteries, it achieves optimal coordinated control of heating and charging while adhering to constraints such as the physicochemical limits of lithium-ion batteries. The method proposed in this invention can simultaneously solve the coordinated optimization of the heating and charging processes under multiple constraints and objectives, enabling adaptive switching between heating and charging modes, thereby ensuring improved low-temperature charging performance of the battery.

[0006] The objective of this invention is achieved through the following technical solution: a method for co-charging a battery in a low-temperature environment with heating, comprising the following steps:

[0007] S1. Establish a battery electrothermal coupling model, conduct battery tests at different temperatures, identify model parameters, and establish a mapping relationship between parameters, temperature, and SoC;

[0008] S101. Considering model accuracy and computational complexity, establish a battery electrothermal coupling model;

[0009] The battery electrothermal coupling model includes, but is not limited to, one of the following electrical models: an electrochemical model, an equivalent circuit model, or an impedance model; and the following thermal model includes, but is not limited to, one of the following thermal models: a lumped parameter thermal model or a distributed thermal model.

[0010] The battery electrothermal coupling model described in step S101 includes the following method for calculating the heat generation rate q:

[0011] If the current through the battery is direct current, then the battery's heat generation rate is:

[0012] q dc =I dc 2 ·|Z|

[0013] In the formula, I dc For DC current, the discharge direction is positive; |Z| is the impedance module, calculated as follows:

[0014]

[0015] In the formula, Z re and Z im These are the real and imaginary parts of the battery impedance, respectively.

[0016] If the current flowing through the battery is alternating current, it will look like this:

[0017]

[0018] In the formula, I ac f is the AC amplitude, and f is the AC frequency. Given the AC phase angle, the heat generation rate of the battery is:

[0019]

[0020] S102. Perform basic performance tests on the battery at different temperatures, and establish a three-dimensional mapping relationship between model parameters, temperature, and SoC based on the test data.

[0021] The basic battery performance tests described in step S102 include capacity calibration test, intermittent discharge-rest test, hybrid pulse power characteristic (HPPC) test, electrochemical impedance spectroscopy (EIS) test, and specific heat capacity test.

[0022] Parameter calculation methods include, but are not limited to, batch least squares, genetic algorithms, or particle swarm optimization.

[0023] S2. Determine the battery heating / charging operation mode and current value as two control variables, and set the variable space of the optimization algorithm based on the multidimensional mapping relationship between the optimization objective and the battery model parameters;

[0024] Step S2 includes the following sub-steps:

[0025] S201. Based on the battery model established in S1, define the optimization objective according to the requirements;

[0026] S202. Determine the operating mode and current value as key control variables in the battery co-heating and charging process, and form a control vector space U. Specifically, set the current value as control variable u1 and the operating mode as control variable u2.

[0027] The control variable u1 mentioned in step S202, if the current through the battery is DC, then u1 represents the DC current value; if the current through the battery is AC, then u1 represents the AC current amplitude; the control variable u2, the operation mode includes charging mode and heating mode.

[0028] S203. Considering the multidimensional mapping relationship that affects the battery model parameters, determine the key state variables in the battery co-heating and charging process as the constituent variables of the state vector space X, and select the state of charge S and the battery surface temperature T as state variables x1 and x2.

[0029] The recursive relationship of the state variables described in step S203 is obtained based on the discretization of the battery model. If the current passing through the battery at adjacent times is alternating current, the value of the state of charge S remains unchanged.

[0030] S3. Define variable constraints, cost functions, and objective functions, and build an optimization algorithm framework for adaptive switching of operating modes and current values;

[0031] Step S3 includes the following sub-steps:

[0032] S301. Based on the state variables and control variables selected in S2, determine the constraints according to the battery model, and define them as the reachable state set and the permissible control set respectively;

[0033] The constraints described in step S301 include requirements that the reachable state set and the permissible control set satisfy the physicochemical limits of the battery:

[0034] 0≤SoC≤1

[0035] |I dc |≤|I dc,max |

[0036] |I ac |≤|I ac,max |

[0037] U t,min ≤U t ≤U t,max

[0038] I ac ·|Z|≤min(U t,max -U OCV U OCV -U t,min )

[0039] In the formula, I dc,max and I ac,max These represent the maximum allowable charging current and the maximum AC current amplitude of the battery, respectively; U t,max and U t,min These are the upper and lower cutoff voltages of the battery, respectively; U t U is the battery's terminal voltage. OCV This is the open-circuit voltage of the battery.

[0040] S302. Based on the optimization objective and constraints, establish a cost function and an objective function, and build an optimization algorithm framework that can adaptively switch operating modes and current values. The optimization algorithm includes either dynamic programming or a genetic algorithm.

[0041] S4. Collect battery status parameters fed back by the battery management system, initialize the algorithm, and generate a collaborative heating and charging strategy with the expected duration;

[0042] Step S4 includes the following sub-steps:

[0043] S401. Collect battery status parameters fed back by the battery management system, initialize the algorithm, k=1;

[0044] S402. Set the total heating-charging time t according to the expected control time. L Taking into account both computational complexity and accuracy, we define the time step Δt and the number of stages N:

[0045]

[0046] S403. Assign different weight factors according to the expected control objectives, focusing on different optimization objectives.

[0047] S5. Based on the expected control objectives, select the optimal collaborative heating and charging control strategy in S4 that meets the actual needs under the current battery state.

[0048] The beneficial effects of this invention are: based on the electrothermal coupling mechanism of the battery, this invention can coordinate and regulate the heating and charging actions of the battery, achieve optimal control that takes into account multiple objectives and constraints, adaptively switch heating and charging modes and current values, and effectively improve the low-temperature charging performance of the battery. Attached Figure Description

[0049] Figure 1 This is a flowchart of the method of the present invention.

[0050] Figure 2 This is an electrothermal coupling model of the lithium-ion battery in the embodiment.

[0051] Figure 3 This is a dynamic programming algorithm framework for a multi-objective optimized collaborative heating and charging strategy in the embodiment.

[0052] Figure 4(a) shows the optimal current control strategy under the collaborative heating and charging method when the weighting factor is 1;

[0053] Figure 4(b) shows the simulation and measured results of the battery surface temperature change under the synergistic heating charging method when the weighting factor is 1;

[0054] Figure 4(c) shows the battery SoC change under the synergistic heating and charging method when the weighting factor is 1;

[0055] Figure 5(a) shows the current control strategy for preheating to different temperatures before recharging;

[0056] Figure 5(b) shows the simulation and experimental results of the surface temperature change of the battery after preheating to different temperatures and then recharging.

[0057] Figure 5(c) shows the changes in battery SoC after preheating to different temperatures and then recharging. Detailed Implementation

[0058] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0059] like Figure 1 As shown, a method for co-charging a battery in a low-temperature environment includes the following steps:

[0060] S1. Establish a battery electrothermal coupling model, conduct battery tests at different temperatures, identify model parameters, and establish a parameter-temperature-SoC mapping relationship. Step S1 includes the following sub-steps:

[0061] S101. Establish the battery model. Specifically, in this embodiment, the electrical part adopts a second-order RC equivalent circuit model, and the state-space equation is:

[0062]

[0063]

[0064]

[0065] U t =U OCV -I·R0-U p1 -U p2

[0066] In the formula, t is time, I is current (with the discharge direction as positive), S is SoC, and C is the current. max For nominal capacity, U p1 and U p2 U is the polarization voltage. t U is the terminal voltage. OCV R0, R1, R2, C1, and C2 are the model parameters to be identified. Specifically, R0 is the ohmic internal resistance, R1 and R2 are the polarization internal resistances, and C1 and C2 are the polarization capacitors.

[0067] S102. Perform capacity calibration tests on the battery at different temperatures, and determine the nominal capacity C of the battery at different temperatures based on the test data. max Specifically, in this embodiment, six temperature points—-20℃, -10℃, 0℃, 5℃, 10℃, and 25℃—are selected as the ambient temperatures for testing.

[0068] S103. Charge the battery to 100% SoC and perform intermittent discharge-rest test at different temperatures. Establish a three-dimensional mapping relationship between OCV-temperature-SoC based on the test data. Specifically, in this embodiment, six temperature points of -20℃, -10℃, 0℃, 5℃, 10℃, and 25℃ are selected as the ambient temperature for the test.

[0069] S104. Perform HPPC or EIS tests on the battery at different temperatures. Specifically, in this embodiment, six temperature points of -20℃, -10℃, 0℃, 5℃, 10℃, and 25℃ are selected as the ambient temperatures for HPPC testing.

[0070] Based on the test data, the ohmic internal resistance R0, polarization internal resistance R1 and R2, and polarization capacitance C1 and C2 of the battery model are identified offline. The three-dimensional mapping relationship between the electrical parameters, temperature and SoC is established respectively. In this embodiment, the batch least squares method is used to identify the above battery model parameters.

[0071] S105. The thermal model is described by the following formula:

[0072]

[0073]

[0074] In the formula, T a T and T i These are the ambient temperature, the battery surface temperature, and the internal temperature, respectively, C. p For the battery's equivalent heat capacity, R in and R out Here, q represents the equivalent thermal resistance inside and outside the battery, respectively, and q is the heat generation rate. The calculation method is as follows:

[0075] Specifically, if the current through the battery is direct current, then the battery's heat generation rate is:

[0076] q dc =I dc 2 ·(R0+R1+R2)

[0077] If the current flowing through the battery is alternating current, it will look like this:

[0078]

[0079] In the formula, I ac f is the AC amplitude, and f is the AC frequency. Let the phase angle be AC, then the heat generation rate of the battery is:

[0080]

[0081] In the formula, Z re The real part of the impedance is calculated as follows:

[0082]

[0083] S106. Perform a specific heat capacity test on the battery, and determine the equivalent heat capacity C of the battery based on the test data and the battery mass m. p ;

[0084] S107. Perform periodic charge-discharge cycle tests on the battery, and calculate the external equivalent thermal resistance R of the battery model based on the data obtained from the isothermal test. out Based on the temperature rise data obtained from the tests, the internal equivalent thermal resistance R of the battery model was identified using the least squares method. in In this embodiment, the equivalent circuit model and the thermal model are coupled together, such as... Figure 2 As shown.

[0085] S2. Determine the battery heating / charging operation mode and current value as two control variables. Based on the multi-dimensional mapping relationship between the optimization objective and battery model parameters, set the variable space of the optimization algorithm. Step S2 includes the following sub-steps:

[0086] S201. Based on the battery model established in S1, define the optimization objective according to the requirements. Specifically, in this embodiment, the optimization objective, which balances charging speed and energy loss, can be described as:

[0087]

[0088] In the formula, k represents time, which is the temporal expression of the physical quantity; α is a weighting factor characterizing the importance of different targets, with a value of 0 to 1; M is a compensation coefficient to ensure L SoC and L energy They are on the same order of magnitude; L SoC and L energy These are used to describe charging speed and energy loss, respectively, and the calculation method is as follows:

[0089] L SoC,k =S k-1 -S k

[0090] L energy =L energy_h +L energy_c

[0091] In the formula, S k-1 and S k The SoC values ​​at time (k-1) and time k are respectively; L energy_h and L energy_c The energy lost during AC heating and DC charging processes are respectively calculated as follows:

[0092]

[0093] L energy_c =|I dc ·(U t -U OCV )|

[0094] S202. The operating mode and current value are determined as key control variables in the battery co-heating and charging process, forming a control vector space U. In this embodiment, the current value is set as control variable u1, and the operating mode is set as control variable u2. The operating mode refers to both the charging mode and the heating mode. Specifically, if the current through the battery is DC, then u1 represents the DC current value; if the current through the battery is AC, then u1 represents the AC current amplitude.

[0095] S203. Considering the multidimensional mapping relationship affecting the battery model parameters, the key state variables in the battery co-heating and charging process are determined as the constituent variables of the state vector space X. In this embodiment, the state of charge S and the battery surface temperature T are selected as state variables x1 and x2. Specifically, the recursive calculation method for the state variables is as follows:

[0096] If the current through the battery is DC, then the state variable at time (k+1) is:

[0097]

[0098]

[0099] If the current through the battery is alternating current, then the state variable at time (k+1) is:

[0100] S k+1 =S k

[0101]

[0102] In the formula, the subscript k represents time and is the temporal expression of the physical quantity; Δt is the time step between two adjacent time points.

[0103] S3. Define variable constraints, cost function, and objective function, and build an optimization algorithm framework for adaptive switching of operating modes and current values. Step S3 includes the following sub-steps:

[0104] S301. Based on the state variables and control variables selected in S2, and using the battery model, determine the constraints, defining them as the reachable state set and the permissible control set, respectively. In this embodiment, considering the physicochemical limits of the battery, the reachable state set and the permissible control set satisfy:

[0105] 0≤SoC≤1

[0106] |I dc |≤|I dc,max |

[0107] |I ac |≤|I ac,max |

[0108] U t,min ≤U OCV -I dc ·(R0+R1+R2)≤U t,max

[0109] I ac ·|Z|≤min(U t,max -U OCV U OCV -U t,min )

[0110] In the formula, I dc,max and I ac,max These are the maximum charging current and maximum AC current amplitude that the battery is allowed to pass, respectively. Specifically, I dc,max =1C,I ac,max=3C;U t,max and U t,min These are the upper and lower cutoff voltages of the battery, specifically, U t,max =4.2V, U t,min =2.7V; |Z| is the impedance module, calculated as follows:

[0111]

[0112]

[0113] S302. Based on the optimization objective and constraints, establish the cost function and objective function, and build an optimization algorithm framework for strategy selection. In this embodiment, dynamic programming (DP) algorithm is used to solve for the optimal control that satisfies the optimization objective.

[0114] The global optimization control problem is decomposed into solving several sub-optimization problems based on constraints. The recursive relationship of the objective function is determined according to the Bellman optimization principle as follows:

[0115] J k =min{v k +J k+1}

[0116] Among them, J N+1 =0; v k The cost function for each stage is defined as follows:

[0117] v k =αL SoC,k +(1-α)·M·L energy,k

[0118] To ensure the solution satisfies the aforementioned constraints, the admissible control set and controllable state set are discretized into a finite computational grid. First, a reverse computation is employed, starting from time N and ending at the initial time. Taking time k as an example, the state vector X is traversed. k and control vector U k Calculate X based on the state transition equation k+1 X is determined by interpolation. k+1 Corresponding performance index J k+1 U is obtained through calculation k Cost v in stage k k Then take (v) k +J k+1 Find the minimum value of ) and record the corresponding control variable. This is the optimal control variable for this stage. The same principle applies to the remaining stages; the above steps are repeated in reverse iteration based on the recursive equation of the objective function until the initial time is reached.

[0119] Secondly, a forward calculation method is used, starting from the initial time and ending at time N. Given the initial state variables, the results of the backward recursive calculation are used as known data, and the optimal control values ​​for each stage are obtained sequentially through interpolation.

[0120] This completes the construction of the dynamic programming algorithm framework for solving the coordinated heating and charging control strategy. The algorithm logic diagram is shown below. Figure 3 As shown.

[0121] S4. Collect battery status parameters fed back by the battery management system, initialize the algorithm, and generate a collaborative heating and charging strategy for the expected duration. Step S4 includes the following sub-steps:

[0122] S401. Collect battery status parameters fed back by the battery management system, initialize the algorithm, k=1. In this embodiment, SoC1=50%, T1=-15℃.

[0123] S402. Set the total heating-charging time t according to the expected control time. L Taking into account both computational complexity and accuracy, we define the time step Δt and the number of stages N:

[0124]

[0125] In this embodiment, t L =7200s, Δt=300s, N=24.

[0126] S403. Assign different weighting factors according to the expected control objectives, focusing on different optimization objectives. Specifically, when the weighting factor α is 1, it means that only the single objective of fast charging is considered.

[0127] S5. Based on the expected control objective, the optimal collaborative heating and charging control strategy in S4 that meets the actual requirements under the current battery state is selected. In this embodiment, the requirements of both energy consumption and charging speed are comprehensively considered, and the two conflicting optimization objectives are balanced to determine the optimal collaborative heating and charging strategy that meets the control expectations under the current initial battery state.

[0128] Following the steps above, based on the physicochemical limits of the battery, a multi-objective collaborative heating and charging strategy is generated through an optimization algorithm; according to the expected control objectives, the objective weights are adjusted to determine the optimal current control scheme, effectively improving the charging performance of the battery in low-temperature environments.

[0129] In this embodiment, a Samsung 18650 lithium-ion battery with a rated capacity of 2.5Ah is used as a benchmark. Step 1 of the method described in this invention is performed to identify model parameters and build an electrothermal coupling model of the battery. Then, taking a battery with an initial SoC of 50% placed in a -15℃ environment as the research object, the total heating-charging time is set to 7200s. The multi-objective collaborative heating-charging optimization control strategy is verified and compared with the currently widely used low-temperature charging solutions for batteries, which first heat to a certain temperature threshold (5℃ / 10℃ / 15℃ / 20℃) before charging. Specifically, the charging speed comparison results between the optimized strategy and the traditional method are as follows: Figures 4(a) to 5(c) Figure 4(a) shows the optimal current control strategy under the collaborative heating and charging method with a weight factor of 1; Figure 4(b) shows the simulation and experimental results of the battery surface temperature change under the collaborative heating and charging method with a weight factor of 1; and Figure 4(c) shows the battery SoC change under the collaborative heating and charging method with a weight factor of 1. The results show that the method can achieve adaptive switching between heating and charging modes. Figure 5(a) shows the current control strategy for preheating to different temperatures before recharging; Figure 5(b) shows the simulation and experimental results of the battery surface temperature change under preheating to different temperatures before recharging; and Figure 5(c) shows the battery SoC change under preheating to different temperatures before recharging. Comparing Figures 4(c) and 5(c) shows that the method can charge more electricity within the same specified time, i.e., the charging speed is faster. Changing the target weights, the results show that, overall, the collaborative heating and charging strategy of the method of this invention under different weight factors focuses on different optimization objectives and can meet the low-temperature charging requirements of different expected control objectives.

[0130] In summary, this invention, by considering the electrothermal coupling mechanism and based on the expected control objectives, employs a dynamic programming algorithm for optimization, achieving low-temperature heating and charging synergistic control that complies with the physicochemical limits of lithium-ion batteries. Compared to the traditional method of heating to a certain temperature threshold before charging, this invention effectively utilizes the interaction between the heating and charging processes, adaptively switching between heating and charging modes to achieve optimal control that balances conflicting objectives such as increasing charging speed and reducing energy loss, effectively improving the low-temperature charging performance of batteries.

[0131] The above description represents preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in other combinations, modifications, and environments, and can be modified within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for co-charging a battery in a low-temperature environment with heating, characterized in that, Includes the following steps: S1. Establish a battery electrothermal coupling model, conduct battery tests at different temperatures, identify model parameters, and establish a mapping relationship between parameters, temperature, and SoC; S2. Determine the battery heating / charging operation mode and current value as two control variables, and set the variable space of the optimization algorithm based on the multidimensional mapping relationship between the optimization objective and the battery model parameters; Includes the following sub-steps: S201. Based on the battery model established in S1, define the optimization objective according to the requirements; the optimization objective is to balance charging speed and energy loss, and is described as follows: ； In the formula, N is the number of stages; k represents time; α is a weighting factor characterizing the importance of different targets, with a value ranging from 0 to 1; M is the compensation coefficient; L SoC and L energy These are used to describe charging speed and energy loss, respectively, and the calculation method is as follows: ， ； In the formula, S k-1 and S k The SoC values ​​at time k-1 and time k are respectively; L energy_h and L energy_c The energy lost during AC heating and DC charging processes are respectively calculated as follows: ， ； I ac Z represents the AC amplitude. re This represents the real part of the battery impedance. I dc The current is direct current, with the discharge direction as positive; U t U is the battery's terminal voltage. OCV This is the open-circuit voltage of the battery; S202. Determine the operating mode and current value as key control variables in the battery co-heating and charging process, forming a control vector space U; set the current value as control variable u1 and the operating mode as control variable u2; where the operating mode is the charging mode and the heating mode; if the current through the battery is DC, then u1 represents the DC current value; if the current through the battery is AC, then u1 represents the AC current amplitude. S203. Considering the multidimensional mapping relationship affecting the battery model parameters, the key state variables in the battery co-heating and charging process are determined as the constituent variables of the state vector space X; the state of charge S and the battery surface temperature T are selected as state variables x1 and x2; the recursive calculation method for the state variables is as follows: If the current through the battery is DC, then the state variables at time k+1 are: ， ； If the current through the battery is alternating current, then the state variables at time k+1 are: ， ； In the formula, Δt is the time step between two adjacent moments; C max Nominal capacity; T a Ambient temperature; C p R is the battery's equivalent heat capacity; in and R out These are the equivalent thermal resistances inside and outside the battery, respectively. The heat generation rate of the direct current flowing through the battery; The heat generation rate of alternating current flowing through the battery; S3. Define variable constraints, cost functions, and objective functions, and build an optimization algorithm framework for adaptive switching of operating modes and current values; S301. Based on the state variables and control variables selected in S2, and using the battery model, determine the constraints, defining them as the reachable state set and the permissible control set, respectively; the reachable state set and the permissible control set satisfy: ， ， ， ， ； In the formula, I dc,max and I ac,max These represent the maximum allowable charging current and the maximum AC current amplitude of the battery, respectively. dc,max = 1 C, I ac,max = 3 C; U t,max and U t,min These are the upper and lower cutoff voltages of the battery, U. t,max = 4.2 V, U t,min = 2.7 V; U OCV R is the open-circuit voltage of the battery, R0 is the ohmic internal resistance, and R1 and R2 are the polarization internal resistances; |Z| is the impedance module, calculated as follows: ， ； In the formula, Z re and Z im C1 and C2 are the real and imaginary parts of the battery impedance, respectively; f is the AC frequency; S302. Based on the optimization objective and constraints, establish the cost function and objective function, and build an optimization algorithm framework for strategy selection; use dynamic programming algorithm to solve for the optimal control that satisfies the optimization objective; The global optimization control problem is decomposed into solving several sub-optimization problems based on constraints. The recursive relationship of the objective function is determined according to the Bellman optimization principle as follows: ， Among them, J N+1 = 0; The cost function for each stage is defined as follows: ； S4. Collect battery status parameters fed back by the battery management system, initialize the algorithm, and generate a collaborative heating and charging strategy with the expected duration; S5. Based on the expected control objectives and the current battery state, the optimal collaborative heating and charging control strategy in S4 meets the actual needs.

2. The method for co-charging a battery in a low-temperature environment according to claim 1, characterized in that, Step S1 includes the following sub-steps: S101. Considering model accuracy and computational complexity, establish a battery electrothermal coupling model; S102. Perform basic performance tests on the battery at different temperatures, and establish a three-dimensional mapping relationship between model parameters, temperature, and SoC based on the test data.

3. The method for co-charging a battery in a low-temperature environment according to claim 2, characterized in that, The battery electrothermal coupling model described in step S101 includes the following method for calculating the heat generation rate q: If the current through the battery is direct current, then the battery's heat generation rate is: ， In the formula, I dc The current is direct current, with the discharge direction being positive; |Z| represents the impedance module. If the current flowing through the battery is alternating current, it will look like this: ， In the formula, t is time, and I ac Let f be the AC amplitude, f be the AC frequency, and φ be the AC phase angle. Then the heat generation rate of the battery is: 。 4. The method for co-charging a battery in a low-temperature environment according to claim 2, characterized in that, The basic battery performance tests described in step S102 include capacity calibration test, intermittent discharge-rest test, hybrid power pulse capability characteristic test, electrochemical impedance spectroscopy test, and specific heat capacity test. Parameter calculation methods include, but are not limited to, batch least squares, genetic algorithms, or particle swarm optimization.

5. The method for co-charging a battery in a low-temperature environment according to claim 1, characterized in that, Step S4 includes the following sub-steps: S401. Collect battery status parameters fed back by the battery management system, initialize the algorithm, k = 1; S402. Set the total heating-charging time t according to the expected control time. L Taking into account both computational complexity and accuracy, we define the time step Δt and the number of stages N: ； S403. Assign different weight factors according to the expected control objectives, focusing on different optimization objectives.

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