A method for electrolyte optimization based on actual electrochemical reaction area, a battery pack, a lithium-ion battery and its electrolyte.

Abstract

This invention relates to the field of lithium-ion battery technology, and discloses a method for electrolyte optimization based on the actual electrochemical reaction area, a battery pack, a lithium-ion battery, and its electrolyte. The method includes: constructing an optimal dosage model for the electrolyte additive FEC, i.e., FEC... optimal =K×(ECSA initial )^α+Δ;Measurement of ECSA initial The system constant K was determined by linear sweep voltammetry; ECSA initial The system constant K is input into the optimal dosage model to predict FEC. optimal This invention breaks away from the limitations of traditional single-parameter methods by quantitatively linking the comprehensive performance index of "full cell ECSA" with the amount of FEC used through the system constant K, which characterizes the reactivity of the cathode and anode materials. Therefore, it can more accurately determine the optimal amount of FEC and ensure that the battery is at its best performance.

Description

Technical Field This invention relates to the field of lithium-ion battery technology, and in particular to a method for optimizing electrolyte based on the actual electrochemical reaction area, a battery pack, a lithium-ion battery, and its electrolyte. Background Technology In high-voltage lithium-ion batteries (especially lithium cobalt oxide systems), FEC (fluoroethylene carbonate) is a crucial film-forming additive used to form stable electrolyte interface films (solid electrolyte interface film SEI and positive electrode electrolyte interface film CEI) on the positive and negative electrode surfaces, inhibiting the continuous decomposition of electrolyte and the dissolution of transition metals. However, excessive FEC can lead to kinetic degradation (such as decreased rate performance) and the risk of side reactions (such as gas generation).

[0003] In existing technologies, optimizing the amount of FEC largely depends on its correlation with a single physical parameter of the positive and negative electrode materials. However, these methods all employ simplified models that assume other factors are constant or unaffected. In actual battery systems, the amount of FEC is simultaneously influenced by factors such as the material's specific surface area, pore structure, conductive network, binder distribution, and electrolyte composition. Therefore, determining the amount of FEC solely based on a single material parameter while ignoring the complexity of the system can lead to formulation failure in scenarios including, but not limited to, the following: (1) Materials with the same SSA (specific surface area) but different porosity; (2) Materials with the same particle size but different morphologies (e.g., nanosheets vs. spherical particles); (3) Battery systems using different conductive agents and electrode processes.

[0004] Therefore, existing electrolyte optimization methods cannot guarantee the formation of optimal SEI and CEI films under various complex conditions, making it difficult to universally achieve the best battery cycle life.

[0005] Given the current state of the process, there is an urgent need in this field for a more scientific method for electrolyte optimization. This method needs to consider many factors that affect the interfacial reaction, and then more accurately determine the optimal amount of FEC to ensure that the battery is in optimal performance. Summary of the Invention The purpose of this invention is to provide a method for optimizing electrolytes based on the actual electrochemical reaction area, a battery pack, a lithium-ion battery and its electrolyte, so as to solve or at least partially solve the technical problems mentioned in the background art.

[0007] To achieve this objective, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for electrolyte optimization based on the actual electrochemical reaction area, comprising: Construct a model for the optimal dosage of electrolyte additive FEC, namely: FEC optimal =K×(ECSA initial )^α+Δ; Measure ECSA initial The system constant K was determined by linear sweep voltammetry. ECSA initial The system constant K is input into the optimal dosage model to predict FEC. optimal ; Among them, FEC optimal The optimal concentration or mass percentage of FEC is given, and K is a system constant characterizing the reactivity of the positive and negative electrode materials, with a value ranging from 0.01 to 0.25. (ECSA) initial α represents the initial electrochemical reaction area of ​​the lithium-ion battery, α is an exponential factor determined according to the types of positive and negative electrode materials to describe the marginal effect of FEC usage, with a value range of 0.55 to 0.82, and Δ is a preset safety margin to compensate for the irreversible consumption of FEC during long cycles, with a value range of 0.3 to 0.7 wt%.

[0008] Optionally, ECSA initial The measurement method is as follows: The prepared lithium-ion battery is then subjected to its first charge-discharge formation. A non-Laday zone was determined by cyclic voltammetry scanning; The double-layer capacitance C of a lithium-ion battery was measured by performing cyclic voltammetry tests in the non-Radida region. dl ; Based on the measured double-layer capacitance C dl The initial electrochemical real reaction area of ​​the lithium-ion battery is calculated based on the formula for calculating the electrochemical real reaction area. The formula for calculating the actual electrochemical reaction area is as follows: ECSA initial =C dl / C s C s For the standard double-layer capacitance per unit area, C s The value range is 20~60µF / cm².

[0009] Optionally, the system constant K can be measured as follows: Define K=K c +K a K c K is the cathode contribution coefficient that characterizes the reactivity of cathode materials. a The negative electrode contribution coefficient characterizing the reactivity of the negative electrode material; where Kc =A*(BV onset_c A is the preset positive electrode sensitivity coefficient, and B is the reference voltage of the positive electrode; K a =C*(V onset_a -D), C is the preset negative electrode sensitivity coefficient, and D is the reference voltage of the negative electrode; Using a working electrode coated with a positive electrode material, linear sweep voltammetry was performed in a basic electrolyte solvent containing only lithium salts and no additives. The sweep was performed up to a predetermined high voltage to obtain the LSV curve. The reference voltage B is the intrinsic oxidation decomposition potential of the basic electrolyte solvent on an ideal inert electrode, V. onset_c The voltage at which the current density reaches the predetermined target value or the LSV curve shows its first inflection point; Half-cells were fabricated using negative electrode materials. Low-rate CV and LSV tests were performed in a basic electrolyte solvent containing only lithium salts without any additives. The D value corresponds to the Li value in the low-rate CV test. + The initiation potential for the initial embedding of graphite to form a LiC6 step compound, V onset_a The reduction initiation voltage is determined by the LSV curve; Where A ranges from 0.05 to 0.15, and B ranges from 4.70 to 4.90 V vs. Li / Li + The value of C ranges from 0.08 to 0.20, and the value of D ranges from 1.20 to 1.40V vs. Li / Li. + .

[0010] Optionally, after constructing the optimal dosage model for electrolyte additive FEC, the method further includes: verifying and revising the optimal dosage model; The specific methods for model validation are as follows: The target battery was calculated and prepared using the optimal dosage model. FEC predicted using the optimal dosage model optimal A validation set was prepared with an FEC concentration gradient of ±βwt% centered on [center]; the validation set contained FEC concentrations of [missing information - likely FEC concentrations]. optimal -βwt%, FEC optimal and FEC optimal Three groups of batteries with +βwt%; where β is less than FEC optimal ; Perform n standard cycles on all batteries in the verification group; n is a natural number greater than 1; Measure and calculate the initial ECSA degradation rate V for each cell. ECSA : V ECSA =[(ECSA initial -ECSAn ) / ECSA_initial]÷n; where ECSA n The actual electrochemical reaction area of ​​the battery after n standard cycles; Judgment FEC optimal The battery's V ECSA Whether it is the smallest in the verification group, or whether it is below a predetermined acceptable threshold; If so, then the optimal dosage model is determined to be without modification, and the verification ends; If not, according to V in the verification group ECSA Minimum FEC concentration best The K value of the optimal dosage model is corrected, and the process returns to the step: Calculate and prepare the target battery using the optimal dosage model; The method for correcting the K value is as follows: K corrected =(FEC best -Δ) / (ECSA initial )^α).

[0011] Optionally, after validating the optimal dosage model, the method further includes: The optimal dosage range for FEC is calculated and set based on the validated optimal dosage model; the optimal dosage range for FEC is set as follows: [FEC] optimal '-ε,FEC optimal '+ε]; Among them, FEC optimal ' represents the optimal concentration or optimal mass percentage of FEC calculated using the validated optimal dosage model, and ε is the preset floating margin, ranging from 0.1 to 0.4 wt%.

[0012] Optionally, Δ is 0.5wt%.

[0013] Optionally, α is selected from a predetermined correspondence table based on the types of positive and negative electrode materials; the contents of the predetermined correspondence table include: When the positive electrode is lithium iron phosphate and the negative electrode is lithium titanate, α is 0.55; When the positive electrode is lithium iron phosphate and the negative electrode is artificial graphite, α is 0.60; When the positive electrode is high-voltage lithium cobalt oxide and the negative electrode is artificial graphite, α is 0.68. When the positive electrode is high-pressure lithium cobalt oxide and the negative electrode is a silicon-carbon composite material, α is 0.78. When the positive electrode is a high-nickel ternary material and the negative electrode is artificial graphite, α is 0.70; When the positive electrode is a high-nickel ternary material and the negative electrode is a silicon-carbon composite material, α is 0.80; When the positive electrode is high-pressure lithium cobalt oxide and the negative electrode is metallic lithium, α is 0.82; When the positive electrode is a ternary nickel electrode and the negative electrode is artificial graphite, α is 0.65; When the positive electrode is lithium manganese oxide and the negative electrode is artificial graphite, α is 0.66.

[0014] Secondly, the present invention provides an electrolyte for a lithium-ion battery comprising a lithium salt, an organic solvent, and an FEC, wherein the mass percentage of the FEC is an optimal mass percentage determined by an electrolyte optimization method based on the actual electrochemical reaction area as described above.

[0015] Thirdly, the present invention provides a lithium-ion battery comprising a positive electrode, a negative electrode, a separator, and an electrolyte, wherein the electrolyte is an electrolyte as described above.

[0016] Fourthly, the present invention also provides a battery pack comprising a plurality of lithium-ion batteries as described above.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes to quantitatively correlate the comprehensive performance index of "full cell ECSA" with the amount of FEC used through the system constant K, which is used to characterize the reactivity of the cathode and anode materials. ECSA is the interfacial area that actually participates in the reaction under a specific electrochemical environment. It is a dynamic, functional, and systematic parameter, and its value is affected by a variety of factors. It breaks away from the limitations of single parameters of traditional materials. Therefore, it can more accurately determine the optimal amount of FEC and ensure that the battery is in the best performance. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 The flowchart illustrates a method for optimizing electrolytes based on the actual electrochemical reaction area, as provided in this embodiment of the invention. Detailed Implementation

[0020] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] Example 1: Please refer to Figure 1 , Figure 1 A flowchart of a method for electrolyte optimization based on the actual electrochemical reaction area provided in this embodiment of the invention is shown. The method specifically includes: Step 100: Construct an optimal dosage model for the electrolyte additive FEC.

[0022] The optimal dosage model for the electrolyte additive FEC constructed in this step is as follows: FEC optimal =K×(ECSA initial )^α+Δ; Among them, FEC optimal The optimal concentration or mass percentage of FEC is given, where K is a system constant characterizing the reactivity of the positive and negative electrode materials, and ECSA is the optimal concentration or mass percentage of FEC. initial α represents the initial electrochemical reaction area of ​​the lithium-ion battery, α is an exponential factor determined based on the types of positive and negative electrode materials to describe the marginal effect of FEC usage, and Δ is a preset safety margin to compensate for the irreversible consumption of FEC during long cycles.

[0023] It should be noted that this step optimizes the amount of FEC using ECSA (Electrochemical Active Surface Area) as the core indicator. The true reaction area is the "effective" reaction area: it represents the total area of ​​surface sites actually participating in the reaction under specific reaction conditions (temperature, pressure, reactants, solvent, electric field, light, etc.). This is a dynamic, functional concept. The true reaction area is much smaller than the specific surface area, mainly because not all surface sites are reactive or accessible. Its main influencing factors include: (1) Pore structure (accessibility of pores and mass transfer): Hole size and distribution; Hole connectivity: The surface area within dead-end holes and isolated holes contributes zero or very little to the actual reaction area; only highly interconnected hole networks can effectively utilize the internal surface area. Pore ​​tortuosity: Tortuous pores increase the diffusion path length and resistance of reactants / products, and reduce the effective utilization of internal surface area.

[0024] (2) Surface chemical properties (density and nature of active sites): Active site density: Specific surface area measures the sum of all atoms / sites, but only surface sites with specific chemical compositions, coordination environments, and oxidation states (such as metal atoms, defect sites, acidic / basic sites, and specific functional groups) possess catalytic / reactive activity. The proportion of these active sites in the total surface area determines the effectiveness of surface chemistry; The nature of active sites: Different active sites may have different selectivity and activity for a specific reaction; Surface contamination / passivation: Surface oxides, carbon deposits, and impurity adsorption formed during preparation, storage, or reaction can cover or poison active sites.

[0025] (3) Reaction conditions (kinetic and thermodynamic limitations): Reactant / product diffusion rate: Slow diffusion within pores or between particles limits the rate at which reactants reach internal active sites and products leave, resulting in some internal sites not being fully utilized during the reaction time. Reaction rate constant: If the surface reaction itself is very fast (fast reaction), the reaction rate may be limited by diffusion, resulting in the internal surface area not being able to effectively participate in the reaction (low efficiency factor). Conversely, slow reactions can make fuller use of the surface area; Temperature, pressure, and concentration affect the reaction rate, adsorption equilibrium, and diffusion coefficient, thereby affecting the utilization rate of active sites and the actual reaction area. Solvent / medium: Affects the solubility, diffusion, wettability on material surfaces, and solvation of active sites of reactants / products.

[0026] (4) Macroscopic shape and size of the material: Particle size / thickness: Large particles or thick coatings present significant mass transfer resistance. Reducing particle size or preparing ultrathin layers / nanofashelves is an effective way to increase the actual reaction area (shorten the diffusion path); Aggregation: Nanoparticles tend to aggregate, and the "secondary pores" inside the aggregates may be difficult for reactants to access, reducing the effective specific surface area.

[0027] In this step, the value of α in the optimal dosage model of electrolyte additive FEC ranges from 0.55 to 0.82. This factor describes the marginal effect of FEC dosage, and is preferably 0.65 to 0.75. The safety margin Δ (safety margin) ranges from 0.3 to 0.7 wt%, and in this embodiment, it is 0.5 wt%.

[0028] The boundary conditions for this step are: FEC optimal Its practical application range is limited to 0.5~30wt%.

[0029] Furthermore, α is uniquely determined by the type of positive and negative electrode materials and does not require calculation. For example, Table 1 below provides α values ​​for some common combinations: Table 1 Step 110: Measure ECSA initial The system constant K was determined by linear scanning voltammetry.

[0030] Specifically, in this step, ECSA initial The measurement method is as follows: Step 1, Battery Preparation and Formation: The prepared lithium-ion battery is subjected to its first charge-discharge formation according to standard procedures.

[0031] Step 2: Select the test interval, that is: determine a non-Radida region by scanning with cyclic voltammetry.

[0032] For example, a voltage window (i.e., non-Radida region) free from lithium-ion insertion / extraction reactions is determined by cyclic voltammetry scanning; for full cells (such as graphite|lithium cobalt oxide), this window is typically 50% to 60% of the battery's rated voltage (e.g., for a battery with a nominal voltage of 3.7V, the window is approximately 3.5V to 3.8V).

[0033] Step 3: Cyclic voltammetry test, i.e., measuring the double-layer capacitance C of the lithium-ion battery by performing a cyclic voltammetry test in the non-Radida region. dl .

[0034] For example, within the selected non-Radal region, CV (cyclic voltammetry) tests are performed at at least five different scan rates (V) (e.g., 0.5, 1, 2, 5, 10 mV / s); the current value (i) corresponding to the voltage at the midpoint of the scan direction (e.g., 3.65 V) is taken to minimize interference from non-Radal currents; Plotting the scan rate (v) on the x-axis and the current value (i) on the y-axis, the slope of the graph obtained through linear fitting is the double-layer capacitance C of the battery at that voltage. dl The actual electrochemical reaction area is given by the formula ECSA = C dl / C s Calculated; where C s The standard double-layer capacitance per unit area is 20-60µF / cm².

[0035] To obtain the absolute ECSA value, calibration using a standard substance (such as a smoothed platinum electrode) is required; to obtain the relative ECSA value for the optimal dosage model used in this embodiment, C can be fixed. s It is a constant (in this embodiment, a preferred value of 40 μF / cm² is used) to ensure that the benchmark for all tests is consistent.

[0036] Step 4: Calculate ECSA initial .

[0037] Based on the measured double-layer capacitance C dl The initial electrochemical real reaction area of ​​the lithium-ion battery is calculated based on the formula for calculating the electrochemical real reaction area. The formula for calculating the actual electrochemical reaction area is as follows: ECSA initial =C dl / C s C s This refers to the standard double-layer capacitance per unit area.

[0038] Specifically, the method for measuring the system constant K is as follows: Step 1: Define K = K c +K a K c K is the cathode contribution coefficient that characterizes the reactivity of cathode materials. a The negative electrode contribution coefficient is used to characterize the reactivity of the negative electrode material; Among them, K c =A*(BV onset_c A is the preset positive electrode sensitivity coefficient, and B is the reference voltage of the positive electrode; K a =C*(V onset_a -D), C is the preset negative electrode sensitivity coefficient, and D is the reference voltage of the negative electrode.

[0039] Step 2: Using a working electrode coated with positive electrode material, perform linear sweep voltammetry testing in a basic electrolyte solvent containing only lithium salt without any additives, sweeping up to a predetermined high voltage to obtain the LSV curve; Wherein, the reference voltage B is the intrinsic oxidation decomposition potential of the base electrolyte solvent on the working electrode, V onset_c The voltage at which the current density reaches the predetermined target value or the LSV curve shows its first inflection point.

[0040] Step 3: Fabricate a half-cell using the negative electrode material. Perform low-rate CV and LSV tests in a basic electrolyte solvent containing only lithium salts and no additives. The D value corresponds to the Li value in the low-rate CV test. + The initiation potential for the initial embedding of graphite to form a LiC6 step compound, Vonset_a The reduction initiation voltage is determined by the LSV curve; Among them, the value of A ranges from 0.05 to 0.15, and the value of B ranges from 4.70 to 4.90 V vs. Li / Li+.

[0041] To facilitate understanding, a specific example of a K-value test is given below: Using a glassy carbon electrode coated with a positive electrode material, in a 1M LiPF6EC / EMC electrolyte, the voltage was scanned from 3.8V to 5.0V at 1mV / s (vs. Li / Li + Among them, the LiPF6EC / EMC electrolyte is a benchmark electrolyte system that has been industrially validated over a long period of time and achieves the best balance between performance, cost, and safety. EC is the cornerstone for the formation of the SEI film on the graphite anode, and EMC is a key co-solvent for adjusting low-temperature performance and viscosity; vs. Li / Li + This indicates that the potential of the working electrode in the circuit is measured using a lithium metal electrode as a reference.

[0042] For K c Its physical meaning is: the lower the oxidation initiation voltage, the stronger the oxidizing power of the positive electrode, K c The larger.

[0043] For A, it determines the oxidation initiation voltage relative to K. c The degree of influence. In this example, the value is 0.1.

[0044] For B, it is the reference voltage, representing the intrinsic oxidation-decomposition potential of the base electrolyte solvent (without any additives) on an ideal inert electrode. B is determined as follows: Use an absolutely inert working electrode (such as a smooth platinum electrode or glassy carbon electrode). Prepare a base solvent (EC:EMC=3:7 vol%) containing only lithium salt (such as 1M LiPF6) without any additives; perform LSV testing in this system, scanning to the predetermined high voltage; The B-value is defined as the voltage at which a current density reaches 20 μA / cm² in this ideal system. For common EC-based carbonate solvents, this value is typically between 4.7V and 4.9V on platinum electrodes (vs. Li / Li). + Therefore, in this embodiment, the value of B is fixed at 4.8V in the model. This is a constant that does not change with the cathode material.

[0045] For V onset_cThe voltage V is determined by the LSV curve, specifically the voltage at which the oxidation current density reaches 20 μA / cm² or the curve shows its first inflection point. In this embodiment, a glassy carbon electrode coated with a positive electrode material is used as the working electrode, and a lithium sheet is used as the counter electrode and reference electrode. A slow linear scan is performed in a specific electrolyte, and the voltage at which the current density reaches 20 μA / cm² is defined as V. onset_c .

[0046] For the negative pole contribution coefficient K a Its physical meaning is: the higher the reduction initiation voltage, the stronger the reducing power of the negative electrode, K a The larger.

[0047] In this example, during testing, a glassy carbon electrode coated with the negative electrode material was used, and the voltage was scanned from 2.5V to 1.0V (vs. Li / Li) at 1mV / s in a 1M LiPF6EC / EMC electrolyte. + ); For C, the value in this example is 0.15; For D, it is a reference voltage anchored to the onset potential of lithium-ion intercalation, representing the thermodynamic potential at which lithium ions begin to significantly intercalate into the graphite or other negative electrode substrates. This process precedes the violent reduction and decomposition of the solvent. The method for determining D is as follows: Half-cells were fabricated using practical negative electrode materials (such as artificial graphite). Low-scan rate CV tests were performed in a basic electrolyte without additives. The D value corresponds to Li... + The onset potential for the initial insertion of graphite into the LiC6 step compound is shown on the differential capacity curve (dQ / dV vs. V), which is a distinct value at 1.2V (vs. Li / Li). + Since the reduction peak starts near the V, D is set to 1.2V in this example.

[0048] V onset_a To restore the starting voltage, it is determined by the LSV curve, which in this example is defined as the voltage at which the current density reaches -50 μA / cm² or the inflection point.

[0049] It should be added that the difference between the B-value and D-value testing methods stems from the fact that they represent completely different physical meanings: B value (positive electrode reference): This measures the oxidation resistance of the base electrolyte itself. Therefore, an inert electrode (such as platinum or glassy carbon) must be used to eliminate interference from the catalytic activity of the positive electrode material and to measure its intrinsic decomposition potential. LSV is the standard method for determining the oxidative stability of electrolytes.

[0050] D-value (negative electrode reference): This measures the thermodynamic potential at which lithium ions begin to intercalate into the negative electrode. It is a principal reaction potential determined by the properties of the negative electrode material itself, rather than the solvent decomposition potential. Therefore, a real negative electrode material (such as graphite) must be used as the working electrode, and the starting point of the lithium intercalation reaction must be accurately determined using methods that clearly distinguish the phase transition process, such as CV or differential capacity curves (dQ / dV).

[0051] Step 120: ECSA initial The system constant K is input into the optimal dosage model of electrolyte additive FEC to predict FEC. optimal .

[0052] Step 130: Verify and revise the optimal dosage model of the electrolyte additive FEC that has been constructed.

[0053] Any theoretical model needs practical verification. Therefore, we add a "model verification and iterative correction" step, which greatly enhances the practicality and reliability of the scheme. The corrected model aims to calibrate the systematic deviation between the theoretical calculation results and the actual best results. This deviation may stem from insufficiently considered interface complexity effects, etc. The correction mechanism in this step is a feedback closed loop based on the "initial ECSA decay rate." The specific method for model verification is as follows: Step 131: Calculate and prepare the target battery using the optimal dosage model of the electrolyte additive FEC that has been constructed.

[0054] Step 132: Using the model-predicted FEC optimal A validation set with an FEC concentration gradient of ±βwt% was prepared centered on the target group.

[0055] The validation group contained FEC concentrations of FEC. optimal -βwt%, FEC optimal and FEC optimal For the three battery groups with +βwt%, if the optimal dosage deviates, additional tests with other concentrations are conducted until the optimal concentration is determined; where β is less than FEC optimal In this example, β is set to 0.5. If 2.0% is the best among 2.0%, 2.5%, and 3.0%, then a test of 1.5% is added. If 1.5% is better, then a test of 1.0% is added, until the optimal concentration remains unchanged.

[0056] Step 133: Perform n standard cycles on all batteries in the verification group.

[0057] In this step, n is a natural number greater than 1. For example, n is 10 in this step, that is, 10 standard cycles are performed on all batteries in the verification group.

[0058] Step 134: Measure and calculate the initial ECSA degradation rate V for each cell. ECSA The calculation formula is: V ECSA =[(ECSA initial -ECSA n ) / ECSA_initial]÷n; Among them, V ECSA That is, the average percentage of ECSA loss per cycle, ECSA n The actual electrochemical reaction area of ​​the battery after n standard cycles.

[0059] Step 135: Determine FEC optimal The battery's V ECSA Is it the smallest in the verification group, or is it below the predetermined acceptable threshold? If yes, proceed to step 1351; if no, proceed to step 1352.

[0060] Case A is considered successful validation, meaning: if the model predicts the V of the formulation (2.5%)... ECSA It is the smallest in the validation group, or below the acceptable threshold (e.g., V). ECSA If the accuracy is less than 0.3%, the model is considered accurate and no correction is needed.

[0061] Case B requires correction, namely: if the V of other formulations (e.g., 2.0%) in the validation group... ECSA If it is significantly better, then the correction process will be initiated.

[0062] Step 1351: Determine that the model does not require modification; verification complete. Step 1352: Based on V in the verification group ECSA Minimum FEC concentration best Correct the K value of the optimal dosage model and return to step 131; The method for correcting the K value is as follows: K corrected =(FEC best -Δ) / (ECSA initial )^α).

[0063] After step 130, the following is also included: Step 140: Calculate and set the optimal dosage range for FEC based on the validated optimal dosage model.

[0064] The optimal dosage range for FEC is set as follows: [FEC] optimal '-ε,FEC optimal '+ε]; Among them, FEC optimal' represents the optimal concentration or optimal mass percentage of FEC calculated using the validated optimal dosage model, and ε is the preset floating margin, ranging from 0.1 to 0.3 wt%.

[0065] Furthermore, four specific experimental cases are given below: Case 1: High-voltage lithium cobalt oxide / artificial graphite system Positive electrode: high voltage lithium cobalt oxide, cutoff voltage 4.48V; Negative electrode: artificial graphite; The target is to determine the optimal FEC dosage to achieve a capacity retention of >90% after 500 cycles.

[0066] The experimental results of Case 1 are as follows: The initial double-layer capacitance C of the full cell was measured. dl =4500μF, ECSA initial =4500 / 40=112.5cm²; α is determined by looking up the table (LCO / artificial graphite) and is set to α = 0.68; Positive LSV test yielded V onset_c =4.42V, calculate K c =0.1*(4.8-4.42)=0.038; The negative electrode LSV test yielded V onset_a =1.35V, calculate K a =0.15*(1.35-1.2)=0.0225; The system constant K = 0.038 + 0.0225 = 0.0605; Δ is set to 0.5 wt%; FEC optimal =0.0605×(112.5)^0.68+0.5≈2.00wt%.

[0067] The verification results for Case 1 are as follows: Batteries with FEC contents of 1.5%, 2.0%, and 2.5% were prepared for verification.

[0068] After 10 cycles, the 2.0% battery ECSA exhibits the lowest degradation rate.

[0069] Using 2.0wt% for long-cycle testing, the capacity retention rate reached 91.5% after 500 cycles, far exceeding the predetermined target.

[0070] Case 2: High-voltage lithium cobalt oxide / silicon-carbon composite system Positive electrode: high-voltage lithium cobalt oxide, cutoff voltage 4.48V; Negative electrode: silicon-carbon composite material; The intended goal is to solve the problems of low initial efficiency and rapid cycle decay of silicon-based negative electrodes.

[0071] C was measured dl =14270μF, ECSA initial =14270 / 40=356.75cm².

[0072] α is determined by looking up the table (LCO / silicon-carbon) and is set to α = 0.78. Positive LSV test yielded V onset_c =4.40V, calculate K c =0.1*(4.8-4.40)=0.04; The negative electrode LSV test yielded V onset_a =1.58V, calculate K a =0.15*(1.58-1.2)=0.057, indicating that the silicon-carbon anode has significantly higher reduction activity; The system constant K = 0.04 + 0.057 = 0.097 Δ is set to 0.5wt%.

[0073] FEC optimal =0.097×(356.75)^0.78+0.5≈10.0wt%.

[0074] The optimal dosage range for FEC is 9.6~10.4 wt%. (ε=0.4 wt%) The verification results for Case 2 are as follows: Validation groups with FEC contents of 8.0%, 10.0%, and 12.0% were prepared.

[0075] After 10 cycles, the battery interface with an FEC of 10.0% was found to be the most stable (V). ECSA lowest).

[0076] Using 10.0wt% directly for long-cycle testing, the battery's initial efficiency is improved by 5%, and the capacity retention rate after 500 cycles is improved by 15% compared to the empirical formula.

[0077] Case 3: NMC811 / Artificial Graphite System Positive electrode: NMC811; Negative electrode: Artificial graphite; Target: Optimize the high-nickel ternary system and suppress gas generation and interfacial impedance growth.

[0078] C was measured dl ==4000μF, calculate ECSA initial =4000 / 40=100cm².

[0079] α is determined by looking up the table (NMC811 / Artificial Graphite) and is set to α = 0.70.

[0080] Positive LSV test yielded V onset_c=4.35V (high-nickel materials have strong catalytic activity); Calculate K c ==0.1*(4.8-4.35)=0.045; The negative electrode LSV test yielded V onset_a =1.33V, calculate Ka=0.15*(1.33-1.2)=0.0195.

[0081] The system constant K = 0.045 + 0.0195 = 0.0645.

[0082] Δ is set to 0.5wt%.

[0083] FEC optimal =0.0645×(100)^0.7+0.5≈2.1wt%.

[0084] The optimal range for FEC dosage is 1.9~2.3wt%.

[0085] The verification results for Case 3 are as follows: Empirical formulations typically use about 1.5% FEC, but this results in significant gas production.

[0086] The recommended formulation (2.1wt%) in this application was tested and found that after the battery was stored at 45°C for 7 days, the gas production was reduced by 60% compared with the empirical formulation, while the interfacial impedance was significantly reduced.

[0087] Case 4: Novel Surface-Modified High-Pressure Lithium Cobalt Oxide / Conventional Artificial Graphite System Positive electrode: novel surface-modified high-voltage lithium cobalt oxide; Negative electrode: conventional artificial graphite; Target: improve interface stability.

[0088] C was measured dl ==4400μF, calculate ECSA initial =4400 / 40=110cm².

[0089] α is taken as 0.68 by referring to the table (traditional high-voltage lithium cobalt oxide / graphite system).

[0090] Positive LSV test yielded V onset_c =4.46V (high-nickel materials have strong catalytic activity); Calculate K c ==0.1*(4.8-4.46)=0.034.

[0091] The negative electrode LSV test yielded V onset_a =1.33V, calculate K a =0.15*(1.33-1.2)=0.0195.

[0092] The system constant K = 0.034 + 0.0195 = 0.0535.

[0093] Δ is set to 0.5wt%.

[0094] FEC optimal =0.0535×(110)^0.68+0.5≈1.81wt%.

[0095] The optimal range for FEC dosage is 1.31~2.31 wt%.

[0096] The verification results for Case 4 are as follows: With the predicted value of 1.81wt% as the center, verification battery packs with FEC contents of 1.31wt%, 1.81wt%, 2.31wt%, and 2.81wt% were prepared according to a gradient of ±0.5%.

[0097] The test results (capacity retention rate after 100 cycles) are as follows: 1.31wt% group: 91.5% 1.81wt% group: 94.8% 2.31wt% group: 96.5% (optimal) 2.81wt% group: 96.0% (performance has reached an inflection point and has begun to decline) Deviation calculation: δ = |2.31 ​​- 1.81| / 1.81 ≈ 27.6% 3. Iterative correction Calculate the correction factor: K_corrected=(2.31-0.5) / (110^0.68)≈1.81 / 24.45≈0.0740.

[0098] In this case, the reasonable explanation for the deviation is: The lower-than-expected initial forecast (1.81 wt%) is mainly attributed to the new interfacial chemical environment introduced by the coating (such as different lithium-ion insertion / extraction kinetics or surface catalytic activity). These complex effects increase the actual demand for FEC, and their impact outweighs the theoretical demand reduction brought about by the increase in V_onset_c alone.

[0099] The modified model in this embodiment does not need to exhaust all complex mechanisms. Through a simple mathematical operation, it successfully incorporates and reflects the net results of all these unquantified complex effects into the calibrated system constants (from 0.0535 to 0.0740), thereby achieving high-precision formulation guidance for this specific high-pressure coating system.

[0100] Case 5: Mild System – Lithium Iron Phosphate / Artificial Graphite System Target System Cathode: Lithium iron phosphate (LiFePO4, LFP) Negative electrode: Artificial graphite Objective: To verify the applicability of the present invention in low electrochemical stress systems and to determine the optimal amount of FEC.

[0101] Implementation steps and results Step 1: ECSA Measurement Electrochemical impedance spectroscopy was used to measure the total double-layer capacitance C of the positive and negative electrodes through equivalent circuit fitting. dl =6800μF.

[0102] The specific capacitance C calibrated based on the three-electrode system s =40μF / cm², calculate the initial ECSA: ECSA initial =6800 / 40=170cm².

[0103] Step 2: Parameter Determination α is determined by looking up the table (LFP / artificial graphite) and is set to α = 0.60.

[0104] Positive electrode LSV testing shows that the LFP material is very stable, V onset_c >5.0V. Taking A=0.08V and B=4.8V, according to the formula Kc=A×(B-V_onset_c), when V_onset_c>B, Kc takes its minimum value of 0.002. In this example, Kc is taken as... c =0.002.

[0105] The solvent reduction initiation potential V was obtained by LSV testing at the negative electrode. onset_a =1.28V. Taking coefficient C=0.10 and constant D=1.20V, calculate K. a =0.10×(1.28-1.20)=0.008.

[0106] The system constant K = 0.002 + 0.008 = 0.01.

[0107] Δ: Take 0.5wt%.

[0108] Step 3: Calculation and Output Calculation (ECSA) initial )^α=170^0.60≈21.8, FEC optimal =0.01×21.8+0.5=0.218+0.5=0.72wt%.

[0109] Considering a floating margin ε = 0.2 wt%, the optimal range for FEC dosage is 0.52~0.92 wt%. Taking into account practical preparation convenience, 0.8 wt% is preferred as the recommended dosage.

[0110] Step 4: Verification and Results Batteries with FEC contents of 0.3%, 0.8%, and 1.3% were prepared for comparative verification.

[0111] Cyclic testing (1C charge / 1C discharge) was conducted at 55℃. Results showed: The battery with 0.3% FEC retains 82% of its capacity after 300 cycles.

[0112] The battery with 0.8% FEC performed best, with a capacity retention of 93.5% after 300 cycles.

[0113] The battery performance of the 1.3% FEC is comparable to that of the 0.8% group, but the DC internal resistance is slightly increased.

[0114] This case demonstrates that in LFP systems, which have a low demand for FEC, the model of this invention accurately predicts the optimal dosage to be approximately 0.8%, avoiding the waste that may be caused by adding based on experience.

[0115] Case 6: High-nickel ternary / high-silicon-oxygen anode system (silicon content ~20%) Target System Cathode: NCA (LiNi) 0.8 Co 0.15 Al 0.05 O2) Anode: High silicon-oxygen anode (SiOx, silicon content approximately 20%, doped with graphite) Objective: To verify the predictive ability of this invention in systems with medium to high silicon content.

[0116] Implementation steps and results Step 1: ECSA Measurement The total double-layer capacitance C of the positive and negative electrodes was measured. dl =22000μF. (This case uses a high-load electrode design) Calculate ECSA initial =22000 / 40=550cm².

[0117] Step 2: Parameter Determination α: By referring to the table (high nickel ternary / silicon-carbon composite material), we take α=0.80.

[0118] Positive LSV test yielded V onset_c =4.33V. Taking A=0.12V and B=4.8V, calculate K. c=0.12×(4.8-4.33)=0.0564.

[0119] The negative electrode LSV test yielded V onset_a =1.55V. Taking C=0.16V and D=1.20V, calculate K. a =0.16×(1.55-1.20)=0.056.

[0120] The system constant K = 0.0564 + 0.056 = 0.1124.

[0121] Δ: Take 0.5wt%.

[0122] Step 3: Calculation and Output Calculation (ECSA) initia )^α=550^0.80≈155.7.

[0123] FEC optimal =0.1124×155.7+0.5≈17.5+0.5=18.0wt%.

[0124] The optimal range for FEC dosage is 17.50~18.50 wt% (ε=0.5 wt%).

[0125] Step 4: Verification and Results Batteries with FEC contents of 15.0%, 18.0%, and 21.0% were prepared for verification.

[0126] Cyclic test results (300 cycles, 1C / 1C) showed that the 18.0% FEC group had the slowest interfacial impedance growth and a capacitance retention of 87% after 300 cycles, which was better than other control groups.

[0127] Case 7: High-voltage lithium cobalt oxide / high-silicon-oxygen anode system (silicon content ~30%) Target System Positive electrode: High-voltage lithium cobalt oxide, cutoff voltage 4.48V Negative electrode: High silicon-oxygen negative electrode (SiOx, silicon content approximately 30%) Objective: To verify the accuracy of this invention in predicting the amount of FEC used in high-silicon content systems.

[0128] Implementation steps and results Step 1: ECSA Measurement The full cell C was measured dl =21000μF.

[0129] Calculate ECSA initial =21000 / 40=525cm².

[0130] Step 2: Parameter Determination α is determined by referring to the table (LCO / silicon-carbon composite material) and is set to α = 0.78.

[0131] Positive V onset_c =4.40V, take A=0.12, B=4.8V, calculate K. c =0.12×(4.8-4.40)=0.048.

[0132] negative electrode V onset_a =1.60V, take C=0.18, D=1.20V, calculate K. a =0.18×(1.60-1.20)=0.072.

[0133] The system constant K = 0.048 + 0.072 = 0.12.

[0134] Δ: Take 0.5wt%.

[0135] Step 3: Calculation and Output Calculation (ECSA) initial )^α=525^0.78≈132.4.

[0136] FEC optimal =0.12×132.4+0.5≈15.9+0.5=16.4wt%.

[0137] The optimal range for FEC dosage is 15.9~16.9 wt% (ε=0.5 wt%).

[0138] Step 4: Verification and Results Validation groups with FEC contents of 13.4%, 16.4%, and 19.4% were prepared.

[0139] After 10 cycles, the 16.4% group had the lowest ECSA decay rate. Long-cycle testing (300 cycles) showed that the 16.4% group maintained 82% capacity, an improvement of 10 percentage points compared to the 13.4% group.

[0140] Case 8: High-nickel ternary / lithium metal anode system Target System Positive electrode: NCM811 Negative electrode: Lithium metal foil Objective: To explore its applicability in lithium metal anode systems.

[0141] Implementation steps and results Step 1: ECSA Measurement The full cell C was measured dl =16000μF.

[0142] Calculate ECSAinitial =16000 / 40=400cm².

[0143] Step 2: Parameter Determination α is determined by consulting (high-nickel ternary / lithium metal) and is set to α = 0.82.

[0144] Positive V onset_c =4.32V, take A=0.12, B=4.8V, calculate K. c =0.12×(4.8-4.32)=0.0576.

[0145] negative electrode V onset_a =1.65V (reduction of lithium surface oxide layer), take C=0.18, D=1.20V, calculate K. a =0.18×(1.65-1.20)=0.081.

[0146] The system constant K = 0.0576 + 0.081 = 0.1386.

[0147] Δ: Take 0.5wt%.

[0148] Step 3: Calculation and Output Calculation (ECSA) initial )^α=400^0.82≈136.0.

[0149] FEC optimal =0.1386×136.0+0.5≈18.86+0.5=19.36wt%.

[0150] The optimal range for FEC dosage is 18.86~19.86 wt% (ε=0.5 wt%).

[0151] Step 4: Verification and Results Validation groups with FEC contents of 16.4%, 19.4%, and 22.4% were prepared.

[0152] Cyclic tests showed that the 19.4% group had the highest coulombic efficiency (99.2%), the densest lithium deposition morphology, and no significant dendrite formation after 100 cycles.

[0153] Unlike existing technologies that directly establish a linear relationship between FEC dosage and a specific material parameter (such as silicon content WSi), this invention proposes a method based on ECSA. initial The model provides a higher-dimensional and more universal systemic solution.

[0154] In practical battery systems, various factors, such as silicon content (WSi), lithium replenishment content (WL), conductive agent morphology, binder distribution, and electrode porosity, collectively determine the complex state of the battery interface. These factors are difficult to enumerate, and their interactions are nonlinear. The model of this invention cleverly integrates and characterizes the combined effects of all these factors into a single, physically meaningful macroscopic indicator that can be directly measured using standard electrochemical methods—the initial electrochemical true reaction area (ECSA). initial Meanwhile, the intrinsic reactivity of the positive and negative electrode materials is characterized by the system constant (K) determined independently by linear sweep voltammetry.

[0155] Therefore, regardless of how WSi or WL varies in a specific formulation, as long as it ultimately creates a similar interfacial state (manifested as a similar ECSA), initial If the activity of the material (reflected by similar K) is similar, then the optimal amount of FEC calculated by the model of this invention will be similar. This allows this invention to overcome the shortcomings of existing technologies that rely on a single parameter and have poor universality, and can provide rapid and accurate guidance for electrolyte formulations of various complex systems.

[0156] In summary, this application is the first to propose a quantitative correlation between the comprehensive performance index of "full-cell ECSA" and the amount of FEC used, and establishes a systematic scheme that includes a complete parameter determination method and a verification and correction process. This solution breaks away from the limitations of traditional single-parameter materials and solves the long-standing technical challenge of "how to comprehensively, quickly, and accurately optimize additives." It provides a framework for the entire system and specific methods for determining each parameter, including multiple physical steps such as measurement, modeling, calculation, verification, and correction. Any battery engineer can operate according to this document, solve technical problems (improve battery life), and achieve positive results.

[0157] The electrolyte optimization method based on the actual electrochemical reaction area provided in this application can greatly shorten the R&D cycle, transforming the lengthy "trial and error" cyclical testing into "predictive" rapid calculation and short-term verification, which can save enterprises a lot of R&D time and costs. By precisely optimizing the amount of FEC used, the best balance between cost and performance can be found, significantly improving the cycle life and interface stability of the battery. When developing a new positive or negative electrode material, this method can be used to immediately determine its matching electrolyte formulation, accelerating the implementation of the new material and providing guidance for its development.

[0158] Example 2: This embodiment provides an electrolyte for a lithium-ion battery, comprising lithium salt, organic solvent and FEC, wherein the mass percentage of FEC is the optimal mass percentage determined by an electrolyte optimization method based on the actual electrochemical reaction area described in Example 1.

[0159] This embodiment provides a lithium-ion battery comprising a positive electrode, a negative electrode, a separator, and an electrolyte, wherein the electrolyte is one as described above.

[0160] This embodiment provides a battery pack comprising a plurality of lithium-ion batteries as described above.

[0161] Since the method for optimizing the electrolyte based on the actual electrochemical reaction area has been described in detail in Example 1, it will not be repeated in this example.

[0162] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0163] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for electrolyte optimization based on the actual electrochemical reaction area, characterized in that, include: Construct a model for the optimal dosage of electrolyte additive FEC, namely: FEC optimal =K×(ECSA initial )^a+D; Measure ECSA initial The system constant K was determined by linear sweep voltammetry. ECSA initial The system constant K is input into the optimal dosage model to predict FEC. optimal ; Among them, FEC optimal The optimal concentration or mass percentage of FEC is given, and K is a system constant characterizing the reactivity of the positive and negative electrode materials, with a value ranging from 0.01 to 0.

25. (ECSA) initial α represents the initial electrochemical reaction area of ​​the lithium-ion battery, α is an exponential factor determined according to the types of positive and negative electrode materials to describe the marginal effect of FEC usage, with a value range of 0.55 to 0.82, and Δ is a preset safety margin to compensate for the irreversible consumption of FEC during long cycles, with a value range of 0.3 to 0.7 wt%.

2. The method for electrolyte optimization based on the actual electrochemical reaction area according to claim 1, characterized in that, ECSA initial The measurement method is as follows: The prepared lithium-ion battery is then subjected to its first charge-discharge formation. A non-Laday zone was determined by cyclic voltammetry scanning; The double-layer capacitance C of a lithium-ion battery was measured by performing cyclic voltammetry tests in the non-Radida region. dl ; Based on the measured double-layer capacitance C dl The initial electrochemical real reaction area of ​​the lithium-ion battery is calculated based on the formula for calculating the electrochemical real reaction area. The formula for calculating the actual electrochemical reaction area is as follows: ECSA initial =C dl / C s C s For the standard double-layer capacitance per unit area, C s The value range is 20~60µF / cm².

3. The method for electrolyte optimization based on the actual electrochemical reaction area according to claim 2, characterized in that, The method for measuring the system constant K is as follows: Define K=K c +K a K c K is the cathode contribution coefficient that characterizes the reactivity of cathode materials. a The negative electrode contribution coefficient characterizing the reactivity of the negative electrode material; where K c =A*(BV onset_c A is the preset positive electrode sensitivity coefficient, and B is the reference voltage of the positive electrode; K a =C*(V onset_a -D), C is the preset negative electrode sensitivity coefficient, and D is the reference voltage of the negative electrode; Using a working electrode coated with a positive electrode material, linear sweep voltammetry was performed in a basic electrolyte solvent containing only lithium salts and no additives. The sweep was performed up to a predetermined high voltage to obtain the LSV curve. The reference voltage B is the intrinsic oxidation decomposition potential of the basic electrolyte solvent on an ideal inert electrode, V. onset_c The voltage at which the current density reaches the predetermined target value or the LSV curve shows its first inflection point; Half-cells were fabricated using negative electrode materials. Low-rate CV and LSV tests were performed in a basic electrolyte solvent containing only lithium salts without any additives. The D value corresponds to the Li value in the low-rate CV test. + The initiation potential for the initial embedding of graphite to form a LiC6 step compound, V onset_a The reduction initiation voltage is determined by the LSV curve; Where A ranges from 0.05 to 0.15, and B ranges from 4.70 to 4.90 V vs. Li / Li + The value of C ranges from 0.08 to 0.20, and the value of D ranges from 1.20 to 1.40V vs. Li / Li. + .

4. The method for electrolyte optimization based on the actual electrochemical reaction area according to claim 1, characterized in that, After constructing the optimal dosage model for electrolyte additive FEC, the method further includes: verifying and revising the optimal dosage model; The specific methods for model validation are as follows: The target battery was calculated and prepared using the optimal dosage model. FEC predicted using the optimal dosage model optimal A validation set was prepared with an FEC concentration gradient of ±βwt% centered on [center]; the validation set contained FEC concentrations of [missing information - likely FEC concentrations]. optimal -βwt%, FEC optimal and FEC optimal Three groups of batteries with +βwt%; where β is less than FEC optimal ; Perform n standard cycles on all batteries in the verification group; n is a natural number greater than 1; Measure and calculate the initial ECSA degradation rate V for each cell. ECSA : V ECSA =[(ECSA initial -ECSA n ) / ECSA_initial]÷n; where ECSA n The actual electrochemical reaction area of ​​the battery after n standard cycles; Judgment FEC optimal The battery's V ECSA Whether it is the smallest in the verification group, or whether it is below a predetermined acceptable threshold; If so, then the optimal dosage model is determined to be without modification, and the verification ends; If not, according to V in the verification group ECSA Minimum FEC concentration best The K value of the optimal dosage model is corrected, and the process returns to the step: using the optimal dosage model to calculate and prepare the target battery; The method for correcting the K value is as follows: K corrected =(FEC best -Δ) / (ECSA initial )^α).

5. The method for electrolyte optimization based on the actual electrochemical reaction area according to claim 4, characterized in that, After validating the optimal dosage model, the following steps are also included: The optimal dosage range for FEC is calculated and set based on the validated optimal dosage model; the optimal dosage range for FEC is set as follows: [FEC] optimal '-ε,FEC optimal '+ε]; Among them, FEC optimal ' represents the optimal concentration or optimal mass percentage of FEC calculated using the validated optimal dosage model, and ε is the preset floating margin, ranging from 0.1 to 0.4 wt%.

6. The method for electrolyte optimization based on the actual electrochemical reaction area according to claim 1, characterized in that, Δ is 0.5wt%.

7. The method for electrolyte optimization based on the actual electrochemical reaction area according to claim 2, characterized in that, α is selected from a predetermined correspondence table based on the types of positive and negative electrode materials; the contents of the predetermined correspondence table include: When the positive electrode is lithium iron phosphate and the negative electrode is lithium titanate, α is 0.55; When the positive electrode is lithium iron phosphate and the negative electrode is artificial graphite, α is 0.60; When the positive electrode is high-voltage lithium cobalt oxide and the negative electrode is artificial graphite, α is 0.

68. When the positive electrode is high-pressure lithium cobalt oxide and the negative electrode is a silicon-carbon composite material, α is 0.

78. When the positive electrode is a high-nickel ternary material and the negative electrode is artificial graphite, α is 0.70; When the positive electrode is a high-nickel ternary material and the negative electrode is a silicon-carbon composite material, α is 0.80; When the positive electrode is high-pressure lithium cobalt oxide and the negative electrode is metallic lithium, α is 0.82; When the positive electrode is a ternary nickel electrode and the negative electrode is artificial graphite, α is 0.65; When the positive electrode is lithium manganese oxide and the negative electrode is artificial graphite, α is 0.

66.

8. An electrolyte for a lithium-ion battery, comprising a lithium salt, an organic solvent, and FEC, characterized in that, The mass percentage of FEC is the optimal mass percentage determined by an electrolyte optimization method based on the actual electrochemical reaction area as described in any one of claims 1 to 7.

9. A lithium-ion battery, comprising a positive electrode, a negative electrode, a separator, and an electrolyte, characterized in that, The electrolyte is the electrolyte described in claim 8.

10. A battery pack, characterized in that, It includes several lithium-ion batteries as described in claim 9.

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