A fast charging method for lithium-ion batteries based on multi-objective optimization

Abstract

The present invention discloses a lithium ion battery fast charging method based on multi-objective optimization, comprising the following steps: establishing a second-order RC equivalent circuit of a lithium ion battery, and deriving an electric-thermal-aging coupling model of the lithium battery during charging according to the equivalent circuit; according to the electric-thermal-aging coupling model, considering the charging time required for the SOC of the lithium battery to reach 90% during charging, the maximum temperature rise during charging, and the capacity loss under the condition of charging for thousands of cycles, establishing a multi-objective optimized fast charging objective function; according to the performance characteristics of the battery at different SOC stages, designing a multi-stage constant current charging method, optimizing the charging current sequence through a social network search algorithm, and obtaining the charging current sequence corresponding to the minimum cost function value. On the basis of meeting the demand for fast charging of lithium batteries, the present invention reduces the maximum temperature rise during charging and reduces the charging capacity loss of the battery.

Description

Technical Field

[0001] The invention belongs to the field of lithium battery charging control, and in particular relates to a lithium ion battery fast charging method based on multi-objective optimization. Background Art

[0002] Lithium-ion batteries have become the core energy storage devices in the field of new energy electric vehicles due to their high energy density, low carbon and environmental protection, and long service life. They have also been widely used in unmanned aerial vehicles, smart wearable devices and other fields. During the use of lithium-ion batteries, due to the thermal effect of current and the electrochemical characteristics of lithium batteries themselves, safety issues such as excessive battery temperature may occur. Due to the high cost of lithium batteries and the fact that they will produce electronic waste that is difficult to handle after being scrapped, the resulting lithium battery service life issue is becoming increasingly important.

[0003] The fast charging problem of lithium-ion batteries is one of the hot topics in the current research field of lithium-ion batteries. Constant Current Constant Voltage (CCCV) is the most commonly used fast charging strategy for lithium-ion batteries. This charging strategy charges the battery in two stages: constant current and constant voltage. It has strong operability and simple implementation. However, this charging strategy will lead to a higher charging temperature rise and a large capacity loss of the battery.

[0004] The multi-stage constant current charging strategy gradually reduces the charging current when the battery voltage reaches the cut-off voltage. This method increases the cycle life of the battery while reducing the charging time, but does not consider the impact of temperature rise on battery safety. The current research on lithium battery fast charging strategies has a large room for optimization in terms of charging time, battery temperature, and battery life. The fast charging strategy can be studied in terms of reducing the impact of charging temperature rise effect and capacity loss effect on battery safety and durability. Summary of the invention

[0005] Purpose of the invention: The present invention provides a lithium-ion battery fast charging method based on multi-objective optimization to achieve fast charging of lithium-ion batteries.

[0006] Technical solution: The present invention provides a lithium-ion battery fast charging method based on multi-objective optimization, comprising the following steps:

[0007] (1) Establish the second-order RC equivalent circuit of lithium-ion batteries, and derive the electrical-thermal-aging coupling model of lithium batteries during the charging process based on the equivalent circuit;

[0008] (2) According to the electric-thermal-aging coupling model of lithium batteries, the charging time required for the SOC of the lithium battery to reach the preset power during charging is taken as the target item J1, the maximum temperature rise of the battery during charging is taken as the target item J2, and the capacity loss of the battery after thousands of cycles of charging is taken as the target item J3. The benchmark factor and weight coefficient are introduced to establish a multi-objective optimization fast charging objective function;

[0009] (3) According to the performance characteristics of the battery at different SOC stages, a multi-stage constant current charging method is adopted, and the charging current sequence is optimized through a social network search algorithm to obtain the charging current sequence corresponding to the minimum objective function value.

[0010] Furthermore, the electric-thermal-aging coupling model in step (1) includes an electric model, a thermal model and an aging model of the lithium-ion battery; the electric model uses a second-order RC equivalent circuit as an equivalent model of the lithium battery, and the open circuit voltage V OC , equivalent resistances R0, R1, R2 and equivalent capacitances C1, C2; the thermal model adopts a double-layer thermal model, including heat conduction between the battery core and the battery surface and heat convection between the battery surface and the ambient temperature; the aging model adopts a lithium battery capacity loss model based on a semi-empirical model, involving the loss of battery capacity due to charging time, ambient temperature, charge and discharge depth, and charging rate.

[0011] Furthermore, the thermal model of the lithium-ion battery is a double-layer thermal model, including heat conduction between the battery core and the battery surface and heat convection between the battery surface and the ambient temperature:

[0012]

[0013]

[0014]

[0015] Among them, T c (t), T s (t), T f (t) represent the core temperature, surface temperature and ambient temperature of the lithium battery at time t during the charging process, T a (t) represents the average battery temperature at time t during the charging process; R u , R c , C c With C s They represent thermal convection resistance, thermal conduction resistance, internal capacitance, and surface capacitance respectively.

[0016] Furthermore, the lithium battery capacity loss model based on the semi-empirical model is:

[0017] A0=32350exp(-0.34C rate )+4041exp(0.13C rate )

[0018] A h =N·DOD·2

[0019]

[0020] Among them, A0 is the pre-factor of the model, A h is the battery’s ampere-hour throughput, N is the number of charge cycles, C rate is the charge rate, DOD is the depth of charge and discharge, and R is the ideal gas constant.

[0021] Furthermore, the fast charging objective function of the multi-objective optimization in step (2) is:

[0022]

[0023] Among them, J1, J2 and J3 are the sub-objective items of the multi-objective function, γ1, γ2 and γ3 are the weight coefficients of each sub-objective item, and the benchmark factor J 1b , J 2b , J 3b It is the target value of each sub-target item under the constant current and constant voltage charging strategy with a current rate of 3C.

[0024] Furthermore, the multi-stage constant current charging method in step (3) is:

[0025] During the charging process of the lithium battery, as the SOC increases, the charging current value that the lithium battery can accept gradually decreases. At different SOC stages, different charging currents are required. The SOC range of 0%-100% is divided into 10 stages. Each stage of SOC corresponds to a constant charging current, that is, a total of 10 current sequences are used to quickly charge the lithium battery.

[0026] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are: 1. Compared with the constant current and constant voltage charging strategy, the multi-objective optimization fast charging control strategy of the lithium-ion battery of the present invention shortens the charging time, reduces the maximum temperature rise during the charging process, reduces the capacity loss, and achieves the multi-objective optimization lithium battery charging effect; 2. The present invention is suitable for different initial SOCs. For batteries with different initial SOCs, it can better complete the multi-objective optimization task of fast charging, which reflects the effectiveness of the fast charging strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the structure of the electrical-thermal-aging coupling model;

[0028] Figure 2 This is a schematic diagram of the second-order RC equivalent circuit of a lithium-ion battery;

[0029] Figure 3 This is a comparison chart of charging current under different charging strategies;

[0030] Figure 4 It is a real-time SOC comparison chart under different charging strategies;

[0031] Figure 5 This is a comparison chart of battery terminal voltage under different charging strategies;

[0032] Figure 6 This is a comparison chart of the average battery temperature under different charging strategies;

[0033] Figure 7 This is a comparison chart of the remaining battery capacity under different charging strategies. DETAILED DESCRIPTION

[0034] The present invention is further described in detail below with reference to the accompanying drawings.

[0035] The present invention provides a lithium ion battery fast charging method based on multi-objective optimization, which specifically comprises the following steps:

[0036] Step 1: Establish a second-order RC equivalent circuit of the lithium-ion battery, and derive the electrical-thermal-aging coupling model of the lithium battery during the charging process based on the equivalent circuit.

[0037] like Figure 1 As shown in Figure 1, the lithium-ion battery electrical-thermal-aging model includes the electrical model, thermal model and aging model of the lithium-ion battery. The second-order RC equivalent circuit of the lithium-ion battery is shown in Figure 1. Figure 2 As shown, it is mainly composed of the open circuit voltage V OC , equivalent resistance R0, R1, R2 and equivalent capacitance C1, C2, the mathematical expression of the second-order RC circuit of lithium battery is:

[0038]

[0039]

[0040]

[0041] V OC (t) = f OC (SOC(t))

[0042] V B (t)=V1(t)+V2(t)+V oc (t)+R0I B (t)

[0043] Among them, Cn Indicates the rated capacity of the battery; SOC(t), I B (t), V1(t), V2(t), V B (t) respectively represent the battery charge state, charging current, voltage across capacitor C1, voltage across capacitor C2, and voltage across the battery at time t. Lithium battery open circuit voltage V OC Related to the battery SOC, so V OC It can be expressed as a function with SOC as the independent variable.

[0044] The present invention adopts a double-layer thermal model to describe the thermal model of lithium-ion batteries. The double-layer thermal model includes heat conduction between the battery core and the battery surface and heat convection between the battery surface and the ambient temperature. The heat generated during the battery charging process within the sampling time interval △t is Q(t):

[0045] Q(t)=I B (t)(V1(t)+V2(t)+R0I B (t))△t

[0046] The heat conduction and heat convection formulas during lithium battery charging are:

[0047]

[0048]

[0049]

[0050] Among them, T c (t), T s (t), T f (t) represent the core temperature, surface temperature and ambient temperature of the lithium battery at time t during the charging process. a (t) represents the average battery temperature at time t during the charging process. u , R c , C c With C s They represent thermal convection resistance, thermal conduction resistance, internal capacitance, and surface capacitance respectively.

[0051] Lithium-ion batteries lose battery capacity during the charging process. The present invention adopts a lithium battery capacity loss model based on a semi-empirical model, which involves the effects of charging time, ambient temperature, charge and discharge depth, and charging rate on battery capacity loss.

[0052] The semi-empirical capacity loss model of the battery in a cyclic charging scenario is:

[0053] A0=32350exp(-0.34C rate)+4041exp(0.13C rate )

[0054] A h =N·DOD·2

[0055]

[0056] Among them, A0 is the pre-factor of the model, A h is the battery’s ampere-hour throughput, N is the number of charge cycles, C rate is the charge rate, DOD is the depth of charge and discharge, and R is the ideal gas constant.

[0057] Step 2: According to the electric-thermal-aging coupling model of lithium batteries, the charging time required for the lithium battery to reach 90% SOC (State of Charge, SOC) during charging is taken as the target item J1, the maximum temperature rise of the battery during charging is taken as the target item J2, and the capacity loss of the battery after thousands of charging cycles is taken as the target item J3. The benchmark factor and weight coefficient are introduced to establish a multi-objective optimization fast charging objective function.

[0058] Compared with the time required for the battery SOC to reach 100%, the time required for the battery SOC to reach 90% is more practical and can be regarded as an important indicator to measure the superiority of the charging strategy. The present invention takes the time required for the battery SOC to reach 90% during the charging process as the target item J1:

[0059] J1=t SOC＝90％

[0060] Lithium batteries will generate energy loss during the charging process. This energy loss is the main reason for the battery temperature rise. At the same time, battery temperature is an important factor affecting the life and safety of lithium batteries. Therefore, the maximum temperature rise during the charging process is taken as the target item J2:

[0061] J2=maxT a (t)-T f (t)

[0062] Since a single charge has little effect on the battery capacity, in order to fully demonstrate the impact of the adopted charging strategy on the capacity loss in multiple cycle charging scenarios, the battery capacity loss caused by the cycle charging number N = 1000 is taken as the target item J3:

[0063] J3=Q loss (N=1000)

[0064] In order to avoid the situation where a certain target item cannot effectively affect the overall target due to its small magnitude, the present invention introduces the concept of reference factor, and uses the target value obtained when the constant current and constant voltage charging strategy with a current ratio of 3C is used as the reference factor of each target item. 1b , J 2b , J 3b They are the benchmark factors for each target item, indicating the time required for the SOC to reach 90%, the maximum temperature rise, and the cycle charging capacity loss when a constant current and constant voltage charging strategy with a charging rate of 3C is adopted.

[0065] Under the condition of meeting the basic requirements of fast charging, the charging temperature rise effect and the capacity loss effect caused by cyclic charging are considered, and the weighted coefficient method is used to construct the following objective function:

[0066]

[0067] Among them, γ1, γ2, and γ3 are weight coefficients of each target item. In order to reflect the purpose of the charging strategy proposed in the present invention to optimize multiple target items, each weight coefficient is set to 1. And during the charging process, each physical quantity in the battery must satisfy in They are the upper current limit, lower current limit, upper voltage limit, and lower voltage limit.

[0068] Taking into account users' fast charging needs, temperature rise effect and capacity loss, the optimal charging control problem of lithium batteries is constructed. In this way, the fast charging problem of lithium-ion batteries is transformed into a multi-objective optimization problem with constraints.

[0069] Step 3: According to the performance characteristics of the battery at different SOC stages, a multi-stage constant current charging method is adopted, and the charging current sequence is optimized through a social network search algorithm to obtain the charging current sequence corresponding to the minimum objective function value.

[0070] During the charging process of lithium batteries, as the SOC increases, the charging current value that the lithium battery can accept gradually decreases. Therefore, different charging currents need to be used at different SOC stages. The present invention designs a multi-stage constant current charging method according to the different intervals of SOC, and uses a 10-stage current sequence to quickly charge the lithium battery. (k) is the charging current value in each stage, k=1,2,...,10.

[0071] The present invention adopts a social network search algorithm to optimize the charging current sequence to solve the multi-objective optimization problem proposed by the present invention. The state quantity X is composed of nPop group states, and nPop is the number of social groups in the social network search algorithm:

[0072] X=[X1,X2,...,XnPop ]

[0073] Set Status X i ,i=1,2,...,nPop:

[0074]

[0075] Set the objective function of the social network search algorithm to the objective function of the multi-objective optimization problem established above:

[0076] f(X i )=J(I B =X i )

[0077] Social network search algorithms can be used to solve constrained multi-objective optimization problems by simulating the behavior of users in social networks.

[0078] The mathematical model of the social network search algorithm is as follows, which represents social group initialization, imitation behavior, dialogue behavior, debate behavior and innovation behavior respectively.

[0079] X 0 =LB+rand (0,1) ×(UB-LB)

[0080]

[0081]

[0082]

[0083]

[0084] Among them, X 0 is the initial state; X j is a randomly selected state; X i The updated state through imitation, dialogue, debate, innovation and other behaviors in the social network search algorithm; UB is the upper limit of the state quantity, LB is the lower limit of the state quantity, and here we set N r A random integer between 1 and nPop; rand (a,b) Represents a random value between a and b, sign() is the sign function, and round() is the rounding function.

[0085] like Figure 3 , Figure 4 As shown, the fast charging strategy proposed in the present invention adopts high current charging when the SOC is low, and as the SOC gradually increases, the charging current gradually decreases; Figure 5, Figure 6 It can be seen that the charging strategy proposed by the present invention has a lower maximum voltage and a lower maximum temperature during the charging process, thereby effectively reducing the capacity loss of the battery; Figure 7 The comparison of cyclic charging capacity attenuation under different charging strategies is demonstrated. The method proposed in the present invention reduces the cyclic charging capacity loss of the battery while meeting the fast charging target.

[0086] The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Those skilled in the art can make various equivalent changes and improvements based on the above embodiments, and all equivalent changes and modifications made within the scope of the claims should fall within the protection scope of the present invention.

Claims

1. A lithium-ion battery fast charging method based on multi-objective optimization, characterized in that: The following steps are involved: (1) Establish the second-order RC equivalent circuit of lithium-ion batteries, and derive the electrical-thermal-aging coupling model of lithium batteries during the charging process based on the equivalent circuit; (2) According to the electric-thermal-aging coupling model of lithium batteries, the charging time required for the SOC of the lithium battery to reach the preset power during charging is taken as the target item J1, the maximum temperature rise of the battery during charging is taken as the target item J2, and the capacity loss of the battery after thousands of cycles of charging is taken as the target item J3. The benchmark factor and weight coefficient are introduced to establish a multi-objective optimization fast charging objective function; (3) According to the performance characteristics of the battery at different SOC stages, a multi-stage constant current charging method is adopted, and the charging current sequence is optimized through a social network search algorithm to obtain the charging current sequence corresponding to the minimum objective function value; The electric-thermal-aging coupling model in step (1) includes an electric model, a thermal model and an aging model of a lithium-ion battery; the electric model uses a second-order RC equivalent circuit as an equivalent model of a lithium battery, and the open circuit voltage V OC , equivalent resistances R0, R1, R2 and equivalent capacitances C1, C2; the thermal model adopts a double-layer thermal model, including heat conduction between the battery core and the battery surface and heat convection between the battery surface and the ambient temperature; The aging model adopts a lithium battery capacity loss model based on a semi-empirical model, which involves the loss of battery capacity due to charging time, ambient temperature, charge and discharge depth, and charging rate; The thermal model of the lithium-ion battery is a double-layer thermal model, which includes heat conduction between the battery core and the battery surface and heat convection between the battery surface and the ambient temperature: Among them, T c (t), T s (t), T f (t) represent the core temperature, surface temperature and ambient temperature of the lithium battery at time t during the charging process, T a (t) represents the average battery temperature at time t during the charging process; R u , R c , C c With C s They represent thermal convection resistance, thermal conduction resistance, internal capacitance, and surface capacitance respectively; The lithium battery capacity loss model based on the semi-empirical model is: A0=32350exp(-0.34C rate )+4041exp(0.13C rate ) A h =N·DOD·2 Among them, A0 is the pre-factor of the model, A h is the battery’s ampere-hour throughput, N is the number of charge cycles, C rate is the charge rate, DOD is the depth of charge and discharge, and R is the ideal gas constant.

2. According to the multi-objective optimization-based lithium-ion battery fast charging method of claim 1, it is characterized in that: The fast charging objective function of the multi-objective optimization in step (2) is: Among them, J1, J2 and J3 are the sub-objective items of the multi-objective function, γ1, γ2 and γ3 are the weight coefficients of each sub-objective item, and the benchmark factor J 1b , J 2b , J 3b It is the target value of each sub-target item under the constant current and constant voltage charging strategy with a current rate of 3C.

3. According to the multi-objective optimization-based lithium-ion battery fast charging method of claim 1, it is characterized in that: The multi-stage constant current charging method in step (3) is: During the charging process of the lithium battery, as the SOC increases, the charging current value that the lithium battery can accept gradually decreases. At different SOC stages, different charging currents are required. The SOC range of 0%-100% is divided into 10 stages. Each stage of SOC corresponds to a constant charging current, that is, a total of 10 current sequences are used to quickly charge the lithium battery.

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