Electric vehicle charging method based on user preferences

Abstract

The application discloses a kind of electric vehicle charging methods based on user preferences, comprising the following steps: establishing electric vehicle lithium battery equivalent circuit, according to equivalent circuit, the electric-thermal-user coupling model of lithium battery in charging process is obtained, the charging preference of electric vehicle user is divided into three types;Considering the end point electric quantity SOC in the charging process of electric vehicle, the electricity fee and energy loss generated by electric vehicle charging, and the maximum temperature rise in the charging process, the objective function of electric vehicle charging problem is established;According to different user charging preferences, design time-constant current charging method, optimize charging current sequence through social network search algorithm, obtain the optimized electric vehicle charging current sequence.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging, and specifically relates to an electric vehicle charging method based on user preferences. Background Technology

[0002] With the environmental protection issues brought about by global warming, electric vehicles (EVs) are seen as one of the solutions to reduce environmental pollution and carbon emissions. Unlike traditional gasoline vehicles, EV users have different charging needs, preferences, and driving experiences, resulting in more distinct user characteristics in EV charging demand. Furthermore, time-of-use pricing introduces uncertainty into charging costs. Therefore, incorporating EV users' charging needs and preferences into charging control strategies has become one of the major challenges in the field of intelligent EV charging research. Lithium-ion batteries, due to their low-carbon, environmentally friendly, and long-lasting characteristics, have become the power source for EVs. During use, lithium-ion batteries experience heat loss and temperature rise due to the thermal effect of current and their own electrochemical characteristics, thus affecting battery life. Therefore, designing a well-designed intelligent EV charging strategy is crucial for improving user charging satisfaction and reducing carbon emissions.

[0003] The charging problem of lithium batteries for electric vehicles is currently a hot topic in lithium battery research. Many studies have considered electric vehicle load modeling and electricity prices, but the charging demand for electric vehicles also depends on other factors, such as the initial state of charge (SOC) of the electric vehicle battery, charging duration, charging station location, charging start time, peak charging time, charging speed, and driver experience. This invention incorporates the electric vehicle user into the charging problem. By considering the total charging time set by the user and the charging cost under peak-valley-flat electricity prices, while also taking into account battery temperature and voltage during the charging process, it proposes a user-preference-based electric vehicle charging method. Summary of the Invention

[0004] Purpose of the invention: This invention provides a user-preference-based electric vehicle charging method that is applicable to different user charging preferences and enables intelligent charging of electric vehicles.

[0005] Technical solution: This invention provides a user-preference-based electric vehicle charging method, comprising the following steps:

[0006] (1) Establish the second-order equivalent circuit of the lithium battery of electric vehicle, and derive the electrical and thermal models of the lithium battery during the charging process based on the equivalent circuit. Based on the peak-valley flat electricity price and the charging time and target amount set by the user, establish the user preference model and obtain the electric-thermal-user coupling model of electric vehicle charging.

[0007] (2) Based on the electric-thermal-user coupling model of electric vehicle lithium battery, the final charge of electric vehicle lithium battery is taken as target item J1, the charging cost generated by electric vehicle charging is taken as target item J2, the energy loss generated during charging is taken as target item J3, and the maximum temperature rise during charging is taken as target item J4. The benchmark factor and weight coefficient of the target item are introduced to establish the target function of electric vehicle charging.

[0008] (3) Based on the charging price at different time periods, a time-sharing constant current charging method is adopted, and the charging current sequence is optimized by a social network search algorithm.

[0009] Further, the electro-thermal-user coupling model in step (1) includes an electrical model, a thermal model, and a user preference model for the lithium battery; the electrical model is a second-order RC equivalent circuit of the lithium battery, determined by the open-circuit voltage V. OC The system consists of equivalent resistances R0, R1, and R2, and equivalent capacitances C1 and C2. The thermal model is a two-layer battery thermal model, which includes heat conduction between the battery core and surface, and heat convection between the battery surface and the external environment. The user preference model is a charging preference model for electric vehicle users, categorizing user preferences into power-sensitive, goal-balanced, and economy-sensitive types. Users set the total charging time T according to their needs. set SOC (State of Charge) set .

[0010] Furthermore, the thermal model of the lithium battery in step (1) is a two-layer thermal model consisting of heat conduction between the battery core and the battery surface, and heat convection between the battery surface and the external environment; the formula for the thermal model is:

[0011] Q(t) = I B (t)(V1(t)+V2(t)+R0I B (t))△t

[0012]

[0013]

[0014]

[0015] Where Q(t) represents the heat generated during the battery charging process within the sampling time interval Δt, and 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, respectively; T a (t) represents the average battery temperature at time t during the charging process, R uR c C c With C s These represent thermal convection resistance, thermal conduction resistance, internal capacitance, and surface capacitance, respectively.

[0016] Furthermore, the objective function for charging the electric vehicle in step (2) is:

[0017]

[0018] Where γ1, γ2, γ3, and γ4 are the weight coefficients of each sub-objective item of J1, J2, J3, and J4, respectively, and the benchmark factor J 1b J 2b J 3b J 4b To use a current multiplier Under the constant current and constant voltage charging strategy, the target values ​​of each sub-objective item.

[0019] Furthermore, the time-sharing constant current charging method described in step (3) involves the electric vehicle charging price changing according to the peak-valley-flat electricity price table during the charging process of the lithium battery. Different charging currents are used in different charging time periods, and the charging current value is updated every 1 minute to charge the electric vehicle.

[0020] Furthermore, the social network search algorithm described in step (3) includes the following steps:

[0021] S1: Initialize the number of people in the social network search algorithm and initialize the state variables of the population;

[0022] S2: Select the state with the minimum value of the initial state function and denote it as the current optimal state;

[0023] S3: Randomly select crowd behavior patterns in the social network algorithm and update the state variables of the social network algorithm;

[0024] S4: Calculate the objective function value corresponding to each new state variable, and find the state corresponding to the minimum objective function value as the new optimal state;

[0025] S5: If the objective function value corresponding to the new optimal state is better than the objective function value corresponding to the current optimal state, then update the new optimal state to the current optimal state; otherwise, keep the current optimal state.

[0026] S6: If the number of iterations reaches the maximum number of iterations, then record the current optimal state as the optimal state obtained in the entire iteration process; otherwise, return to S3.

[0027] S7: Output the optimal state variable as the current sequence during the electric vehicle charging process.

[0028] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Compared with the constant current and constant voltage charging strategy, the electric vehicle charging strategy of the present invention reduces the charging cost and energy loss during the charging process while meeting basic power demand, and achieves multi-objective optimization of electric vehicle charging effect; 2. The present invention is applicable to different user charging preferences and can better complete the multi-objective optimization task of electric vehicle charging under various user setting scenarios, demonstrating the effectiveness of the electric vehicle charging strategy. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the electrical-thermal-user coupling model structure;

[0030] Figure 2 This is a schematic diagram of the second-order RC equivalent circuit of a lithium-ion battery;

[0031] Figure 3 This is a flowchart of a social network search algorithm;

[0032] Figure 4 These are charging current graphs for different user preference types;

[0033] Figure 5 These are SOC curves for different user preference types;

[0034] Figure 6 These are average temperature curves under different user preference types;

[0035] Figure 7 These are terminal voltage curves under different user preference types. Detailed Implementation

[0036] The present invention will now be described in further detail with reference to the accompanying drawings.

[0037] This invention provides a fast charging method for lithium-ion batteries based on multi-objective optimization, specifically including the following steps:

[0038] Step 1: Establish the second-order equivalent circuit of the lithium battery of electric vehicle, and derive the electro-thermal model of the lithium battery during the charging process based on the equivalent circuit. Based on the peak-valley flat electricity price and the user's set charging time and target amount, establish a user preference model, and comprehensively obtain the electro-thermal-user coupling model of electric vehicle charging.

[0039] like Figure 1 As shown, the electro-thermal-user coupling model includes the electrical model, thermal model, and user preference model of the lithium battery; the electrical model is the second-order RC equivalent circuit of the lithium battery, which is mainly composed of the open-circuit voltage V. OCThe equivalent resistances R0, R1, R2 and the equivalent capacitances C1, C2 constitute the second-order RC circuit of a lithium battery. The mathematical expression for this circuit is:

[0040]

[0041]

[0042] V OC (t)=f OC (SOC(t))

[0043] V B (t)=V1(t)+V2(t)+V oc (t)+R0I B (t)

[0044]

[0045] Among them, C n Indicates the rated capacity of the battery; SOC(t), I B (t), V1(t), V2(t), V B (t) represent the battery state of charge, charging current, voltage across capacitor C1, voltage across capacitor C2, and voltage across the battery at time t, respectively; V OC Let it be a function with SOC as its independent variable.

[0046] The battery thermal model used in this invention is a two-layer thermal model consisting of heat conduction between the battery core and the battery surface, and heat convection between the battery surface and the external environment. The thermal model formula for the charging process of an electric vehicle lithium battery is as follows:

[0047] Q(t) = I B (t)(V1(t)+V2(t)+R0I B (t))△t

[0048]

[0049]

[0050]

[0051] Where Q(t) represents the heat generated during the battery charging process within the sampling time interval Δt, and T c (t), T s (t), T f (t) represents the core temperature, surface temperature, and ambient temperature of the lithium battery at time t during the charging process, respectively. a (t) represents the average battery temperature at time t during the charging process. R u Rc C c With C s These represent thermal convection resistance, thermal conduction resistance, internal capacitance, and surface capacitance, respectively.

[0052] The user model describes the charging preference model for electric vehicle users. This model categorizes user preferences into three types: power-sensitive, goal-balanced, and economy-sensitive. Users can set the total charging time T according to their own needs. set (Unit: h) and target energy SOC set (Unit: %); The charging price for electric vehicles adopts the State Grid Beijing charging pile peak-valley flat electricity price table. Electric vehicle users' charging preferences are divided into three types:

[0053] Type 1 (Battery Sensitive): Users are more concerned about the final charge level of the electric vehicle than the final charging cost. This charging preference is suitable when the electric vehicle has a long driving plan or an important driving task.

[0054] Type 2 (Target Balance): This type of charging preference achieves a balance between charging costs and the final amount of electricity needed for charging electric vehicles, reducing charging costs while basically meeting the user's electricity needs.

[0055] Type 3 (Economy Sensitive): Users are more concerned about the final charging cost of electric vehicles than about reaching a full charge. This type of charging preference is suitable for users who drive short distances or are not in a hurry to use the vehicle.

[0056] Therefore, electric vehicle users can choose different charging preferences based on their own driving tasks.

[0057] Step 2: Based on the electro-thermal-user coupling model of electric vehicle lithium batteries, determine the final state of charge (SOC) of the electric vehicle lithium battery. final The expression is used as the objective term J1. Referring to a certain brand of electric vehicle with a battery pack consisting of 7104 lithium batteries, the charging cost required for 7104 batteries is taken as the electric vehicle charging objective term J2, the total energy loss during charging is taken as the objective term J3, and the maximum temperature rise during charging is taken as the objective term J4. By introducing benchmark factors and weighting coefficients for the objective terms, the objective function for the electric vehicle charging problem is established. The expressions for each objective term are as follows:

[0058] J1 = exp(abs(SOC) final -SOC set ))

[0059]

[0060]

[0061] J4 = maxT a (t)-T f (t)

[0062] The multi-objective function for electric vehicle charging is:

[0063]

[0064] Where J1, J2, J3, and J4 are the sub-objectives in the objective function, γ1, γ2, γ3, and γ4 are the weight coefficients of each sub-objective, and the benchmark factor J is... 1b J 2b J 3b J 4b To use a current multiplier Under the constant current and constant voltage charging strategy, the target values ​​of each sub-objective item.

[0065] With fixed weights γ1 = 10, γ3 = 1, and γ4 = 1, and using the weight γ2 of the target item J2 as the economic weight, the economic weight options are divided into six levels: 0, 2, 4, 6, 8, and 10. Each of these six levels corresponds to a different electric vehicle charging preference. The larger the economic weight, the higher the user's economic sensitivity, meaning they place greater importance on the electricity costs incurred during electric vehicle charging.

[0066] According to the three types of charging preferences proposed in this invention, those with economic weight levels of 0, 2, and 4 are classified as power-sensitive, those with economic weight level of 6 are classified as target-balanced, and those with economic weight levels of 8 and 10 are classified as economic-sensitive.

[0067] Step 3: Based on the charging electricity price at different time periods, adopt the time-sharing constant current charging method and optimize the charging current sequence through a social network search algorithm.

[0068] During the charging process of an electric vehicle, the lithium battery's charge, terminal voltage, temperature, and real-time electricity price will change. Therefore, different charging currents can be used at different charging time stages to achieve multi-objective optimization. This invention employs a time-sharing constant current charging method for intelligent charging of electric vehicles, making a current value decision every 1 minute. Therefore, the charging current sequence is I. B =[I (1) ,I (2) ,...,I (N) ], where N = 60·T set .

[0069] This invention employs a social network search algorithm to optimize the charging current sequence in order to solve the multi-objective optimization problem proposed in this invention. The state variable X consists of nPop groups of states, where nPop represents the number of social users in the social network search algorithm.

[0070] X = [X1, X2, ..., X nPop ]

[0071] Set status X i ,i=1,2,...,nPop;

[0072]

[0073] The objective function of the social network search algorithm is set to the objective function of the multi-objective optimization problem established above:

[0074] f(X i )=J(I B =X i )

[0075] Social network search algorithms, by simulating user behavior in social networks, can be used to solve constrained multi-objective optimization problems. Their mathematical models are as follows, representing social group initialization, imitation behavior, dialogue behavior, debate behavior, and innovation behavior, respectively:

[0076] X 0 =LB+r (0,1) ×(UB-LB)

[0077]

[0078]

[0079]

[0080]

[0081] Among them, X 0 This is the initial state; X j The state is selected randomly; X i The state is updated based on behaviors such as imitation, dialogue, debate, and innovation in the social network search algorithm; UB is the upper limit of the state quantity, and LB is the lower limit of the state quantity, which is set here. N r r is a random integer between 1 and nPop; (a,b) This represents a random value between a and b. sgn() is the sign function and round() is the rounding function.

[0082] The flowchart of the social network search algorithm is as follows: Figure 3 As shown, it includes the following steps:

[0083] S1: Initialize the number of people in the social network search algorithm and initialize the state variables of the population;

[0084] S2: Select the state with the minimum value of the initial state function and denote it as the current optimal state;

[0085] S3: Randomly select crowd behavior patterns in the social network algorithm and update the state variables of the social network algorithm;

[0086] S4: Calculate the objective function value corresponding to each new state variable, and find the state corresponding to the minimum objective function value as the new optimal state;

[0087] S5: If the objective function value corresponding to the new optimal state is better than the objective function value corresponding to the current optimal state, then update the new optimal state to the current optimal state; otherwise, keep the current optimal state.

[0088] S6: If the number of iterations reaches the maximum number of iterations, then record the current optimal state as the optimal state obtained in the entire iteration process; otherwise, return to S3.

[0089] S7: Output the optimal state variable as the current sequence during the electric vehicle charging process.

[0090] In a scenario where charging starts at 6:30 AM and the user sets a total charging time of 1 hour, the electricity price from 6:30 AM to 7:00 AM is 0.335 yuan / kWh, and the electricity price from 7:00 AM to 7:30 AM is 0.781 yuan / kWh. Under the electric vehicle charging strategy proposed in this invention, the charging current, state of charge (SOC), average battery temperature, and battery terminal voltage curves for different user charging preference types are as follows: Figures 4-7 As shown. From Figure 4 It can be seen that the charging current is relatively high during the low electricity price period from 6:30 to 7:00; and relatively low during the high electricity price period from 7:00 to 7:30. Figure 5 The SOC curves show that under the power-sensitive user preference setting with an economic weight of 2, the battery's final charge level is the highest; under the economic-sensitive user preference setting with an economic weight of 8, the current value remains at a low level throughout the charging process, resulting in the lowest charging cost; while under the target-balanced user preference setting with an economic weight of 6, a good balance is achieved between the final charge level and the charging cost. Figure 6 , Figure 7 As can be seen, since the battery temperature and terminal voltage during the charging process are considered in the objective function, the battery temperature during the charging process and the terminal voltage at the end point are lower in this invention compared to the CCCV charging method.

[0091] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Those skilled in the art can make various equivalent changes and improvements based on the above embodiments, and all equivalent variations and modifications made within the scope of the claims should fall within the protection scope of the present invention.

Claims

1. A method for charging an electric vehicle based on user preferences, characterized by, It comprises the following steps: (1) Establishing a second-order equivalent circuit of the lithium battery of the electric vehicle, and obtaining an electrical model and a thermal model of the lithium battery during charging according to the equivalent circuit, establishing a user preference model according to the peak-valley-flat electricity price of charging and the target electricity quantity set by the user according to the charging time, and comprehensively obtaining an electrical-thermal-user coupling model of the charging of the electric vehicle; (2) According to the electrical-thermal-user coupling model of the lithium battery of the electric vehicle, taking the terminal electricity quantity of the lithium battery of the electric vehicle as a target item J1, taking the charging cost generated by the charging of the electric vehicle as a target item J2, taking the energy loss generated during the charging process as a target item J3, and taking the maximum temperature rise during the charging process as a target item J4, introducing a reference factor and a weight coefficient of the target item, and establishing an electric vehicle charging target function; (3) According to the charging electricity price in different time periods, a time-division constant-current charging method is adopted, and a social network search algorithm is used to optimize the charging current sequence; The thermal model of the lithium battery in step (1) is a double-layer thermal model composed of heat conduction between the battery core and the battery surface and heat convection between the battery surface and the external environment. The thermal model formula is: Q(t) = I B (t)(V1(t) + V2(t) + R0I B (t))Δt wherein I B is the charging current value during the charging process of the lithium battery; V1 is the voltage across the capacitor C1 in the lithium battery electrical model; V2 is the voltage across the capacitor C2 in the lithium battery electrical model; R0 is the equivalent internal resistance in the lithium battery electrical model; Q(t) is the heat generated in the battery during the charging process within the sampling time interval Δt, T c (t), T s (t), T f (t) respectively 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 temperature of the battery at time t during the charging process, R u , R c , C c and C s respectively represent the thermal convection resistance, thermal conduction resistance, internal capacitance and surface capacitance; The electric vehicle charging target function in step (2) is: J1 = exp(abs(SOC final -SOC set )) J4 = max T a (t) - T f (t) Wherein, γ1, γ2, γ3, γ4 are weight coefficients of J1, J2, J3 and J4 respectively, and J 1b , J 2b , J 3b , J 4b are target values of each sub-target item under the constant current constant voltage charging strategy with a current multiplication factor of ; SOC final is the terminal point of power of the electric vehicle lithium battery, SOC set is the target power of the user; T set is the total charging time set by the user according to his own needs; The social network search algorithm in step (3) comprises the following steps: S1: initializing the number of people in the social network in the social network search algorithm, and initializing the state quantity of the crowd; S2: selecting the state with the minimum initial state quantity target function value as the current optimal state; S3: randomly selecting a crowd behavior mode in the social network algorithm, and updating each state quantity in the social network algorithm; S4: calculating the target function value corresponding to each new state quantity, and finding the state corresponding to the minimum target function value as the new optimal state; S5: If the target function value corresponding to the new optimal state is better than the target function value corresponding to the current optimal state, the new optimal state is updated as the current optimal state, otherwise the current optimal state is maintained; S6: If the number of iterations reaches the maximum number of iterations, the current optimal state is recorded as the optimal state quantity obtained in the entire iteration process, otherwise returning to S3; S7: outputting the optimal state quantity as the current sequence of the electric vehicle during charging.

2. The electric vehicle charging method based on user preferences according to claim 1, characterized in that, The electro-thermal-user coupling model in step (1) comprises an electric model, a thermal model and a user preference model of the lithium battery; the electric model is a second-order RC equivalent circuit of the lithium battery, which is composed of open circuit voltage V OC , equivalent resistances R0, R1 and R2, and equivalent capacitances C1 and C2; the thermal model is a double-layer thermal model of the battery, which comprises heat conduction between the battery core and the battery surface, and heat convection between the battery surface and the external environment; and the user preference model is a charging preference model of the electric vehicle user, which divides the user preference into power-sensitive type, target-balancing type and economy-sensitive type, and the user sets the total charging time T set and target power SOC set according to the own demand.

3. The electric vehicle charging method based on user preferences according to claim 1, wherein, The time-division constant-current charging method in step (3) is that during the charging process of the lithium battery, the charging electricity price of the electric vehicle changes according to the peak-valley-flat electricity price table with time, different charging currents are used in different charging time periods, and the charging current value is updated every 1 min to charge the electric vehicle.

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