A power integration method with energy compensation mechanism for energy state estimation

Abstract

A power integration method with energy compensation mechanism for state of energy estimation relates to the technical field of lithium ion batteries. Through the analysis of state of energy estimation error, the analysis of variable maximum energy and reference maximum energy, the compensation mechanism of state of energy in power integration method is explored, and the Dropout technology is introduced in the power integration method with state of energy compensation. It can reduce the cumulative error in power integration method, improve the accuracy of state of energy estimation, consider the battery aging factor, improve the applicability of energy compensation, integrate Dropout technology in energy compensation, and improve the robustness and adaptability of the algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lithium ion batteries, and particularly relates to a power integration method with an energy compensation mechanism for energy state estimation. BACKGROUND

[0002] Lithium ion batteries are widely used in electric vehicles due to their high energy density, low self-discharge rate, and no memory effect. Battery management systems can provide state estimation, fault diagnosis, and safety warnings by collecting battery voltage, current, and temperature data. Accurate feedback of various battery indicators in electric vehicles is crucial for the driving experience and safety of drivers, and accurate feedback of remaining range is crucial for relieving drivers' range anxiety. Therefore, accurate estimation of battery remaining energy is the basis for accurate feedback of remaining range.

[0003] Currently, the methods for lithium ion battery energy state estimation mainly include coupling calculation method, power integration method, model-based method (such as equivalent circuit method), and data-driven method. Among them, the coupling calculation method first establishes the functional relationship between the energy state and other battery state indicators, and then estimates the energy state indirectly through other battery state estimation indicators. However, since other state indicators themselves have estimation errors, these errors will be passed to the estimation results of the energy state in the coupling calculation process, which is the disadvantage of this method; the power integration method is a method of integrating power to calculate energy during battery operation, which is simple in form and is the basis for other energy estimation modeling methods. The power integration method requires low computing resources and is suitable for embedded devices, but its disadvantage is that as the number of iterations increases, the error will gradually accumulate, eventually leading to a large deviation between the estimated result and the actual energy state; the equivalent circuit method, as a model-based energy state modeling method, still uses the power integration method in the energy state update mechanism, and also fails to effectively solve the problem of cumulative error of the power integration method; with the development of computing resources, data-driven methods have become a novel method for lithium ion battery energy state estimation. As a nonlinear "black box" system, it can effectively capture the data characteristics of the energy state by establishing the relationship between the input features and the energy state indicators, but this method relies too much on the training data set, and the robustness under different working conditions is worth further exploring.

[0004] In summary, the current energy state estimation research mostly uses the power integration and data-driven method, which still has the following problems: the power integration-based modeling method cannot effectively reduce the cumulative error, which leads to the gradual accumulation of energy state estimation error with the increase of iteration number, thereby limiting the performance of the power integration method. The data-driven method is very effective in capturing the data characteristics of energy state estimation, but it is highly dependent on the quality of the data set. When facing unknown working conditions, its robustness needs to be considered. In addition, the current research on how to introduce the core mechanism of the data-driven method into the relatively simple power integration method is not clear. SUMMARY

[0005] To solve the problems in the background art, the present application provides a power integration method with energy compensation mechanism for energy state estimation, which can reduce the cumulative error in the power integration method, improve the energy state estimation accuracy, consider the battery aging factor, improve the applicability of energy compensation, integrate Dropout technology in energy compensation, and improve the robustness and adaptability of the algorithm.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a power integration method with energy compensation mechanism for energy state estimation, comprising the following steps:

[0007] Step one, analysis of energy state estimation error

[0008] The energy state estimated by the power integration method at the kth iteration is denoted as SOE pi (k), the variable maximum energy index E v (k) at the kth iteration is defined as:

[0009]

[0010] wherein,

[0011] E r,0,k = E r · [SOE(0)-SOE r (k)] (2)

[0012] The energy state estimation error ΔSOE(k) of the power integration method at the kth iteration is:

[0013] ΔSOE(k) = |SOEpi(k)-SOEr(k)| (4)

[0014] wherein, E r,0,k represents the cumulative reference energy from the initial state to the kth iteration, SOE(0) represents the initial energy state, Er represents the reference maximum energy index, SOE r (k) represents the reference energy state at the kth iteration;

[0015] Step two: Analysis of variable maximum energy and reference maximum energy

[0016] Maximum energy error ΔE vr (k) between variable maximum energy index and reference maximum energy index at the kth iteration

[0017]

[0018] When SOE pi (k) is greater than or less than SOE r (k), maximum energy error ΔE vr (k) is compensated to the cumulative discharge energy E pi,0,k of the power integration method from the initial state to the kth iteration, when SOE pi (k) is equal to SOE r (k), no compensation is made;

[0019] Step three: Power integration method with energy compensation mechanism

[0020] The ideal form of the power integration method with energy compensation mechanism is:

[0021]

[0022] wherein,

[0023]

[0024] The energy state compensation considering battery aging is as follows:

[0025]

[0026] In the formula, SOE pic (k) represents the energy state calculated by the power integration method with energy compensation mechanism at the kth iteration, represents the energy conversion coefficient, V(i) and I(i) represent the voltage and current at the ith iteration, Δt represents the sampling time, ΔSOE c (i) represents the compensation value of the energy state, ΔE vr (i) represents the maximum energy error at the ith iteration, ΔSOE ca (i) represents the compensation value of the energy state considering battery aging at the ith iteration, Y = C r / C t , C r represents the current cycle maximum capacity, C t represents the rated maximum capacity;

[0027] Step four: power integration method with energy state compensation under Dropout technology

[0028] On the basis of step three, the Dropout technology is used to provide an energy state compensation value ΔSOE every iteration ca (i) random masking part of the compensation value, and the final power integration method is as follows:

[0029]

[0030] Ψ 0～1 represents the masking probability of energy state compensation.

[0031] Further, the energy state compensation value ΔSOE c (i) is a percentage value, and at the ith iteration, the state of charge remains the same order of magnitude.

[0032] Compared with the prior art, the beneficial effects of the present application are: in order to analyze the causes of the cumulative error in the power integration method, the present application designs a variable maximum energy index, and based on the index, an energy compensation mechanism is designed to reduce the cumulative error in the power integration method, improve the energy state estimation accuracy, consider the battery aging factor, improve the applicability of energy compensation under different battery aging stages, adapt to different environmental temperature and charge-discharge rate conditions, integrate Dropout technology in energy compensation to prevent over-correction of the state of charge under unknown operating conditions, improve the robustness and adaptability of the algorithm, provide a theoretical basis for accurate energy state estimation, ensure the safe driving of electric vehicles, and improve the driving experience. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of the method of the present application;

[0034] Figure 2 is a principle diagram of the energy compensation mechanism in the method of the present application;

[0035] Figure 3 is a schematic diagram of the application of Dropout technology in the method of the present application;

[0036] Figure 4 is an energy state result graph of the original power integration method and the method of the present application in the embodiment;

[0037] Figure 5 is an energy state estimation error graph of the original power integration method and the method of the present application in the embodiment. DETAILED DESCRIPTION

[0038] The technical solutions in the present application will be described clearly and completely below with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0039] As shown in Figures 1-3 , the analysis of energy state estimation error, the analysis of variable maximum energy and reference maximum energy, the exploration of the compensation mechanism of energy state in power integration method, and the introduction of Dropout technology in power integration method with energy state compensation, a power integration method with energy compensation mechanism for energy state estimation is proposed, and its flow is shown in Figure 1 , including the following steps:

[0040] (1) Analysis of energy state estimation error

[0041] Assume that the energy state estimated by the power integration method at the kth iteration is SOE pi (k), and the variable maximum energy index E v (k) at the kth iteration is defined as:

[0042]

[0043] In the formula, E r,0,k represents the cumulative reference energy from the initial state to the kth iteration, and SOE(0) represents the initial energy state.

[0044] And the cumulative reference energy from the initial state to the kth iteration can be expressed as:

[0045] E r,0,k = E r · [SOE(0) - SOE r (k)] (2)

[0046] In the formula, Er represents the reference maximum energy index, and SOE r (k) represents the reference energy state at the kth iteration.

[0047] Substitute formula (2) into formula (1) to obtain the relationship between the variable maximum energy index and the reference maximum energy index:

[0048]

[0049] Then the energy state estimation error of the power integration method at the kth iteration is:

[0050] ΔSOE(k) = |SOE pi (k) - SOEr (k)| (4)

[0051] If SOE pi (k) is equal to SOE r (k), E v (k) is equal to E r , it indicates that the definition of variable maximum energy index E v (k) is reasonable.

[0052] (2) Analysis of variable maximum energy and reference maximum energy

[0053] The maximum energy error of variable maximum energy index and reference maximum energy index at the kth iteration is:

[0054]

[0055] The purpose of the present application is to design a compensation method for energy state estimation by using the maximum energy error ΔE vr (k) to improve the accuracy of SOE pi (k), and the relationship between SOE pi (k) and ΔE vr (k) is discussed as follows:

[0056] ① When the energy state SOE pi (k) calculated by the power integration method is greater than the energy state reference value SOE r (k), the cumulative discharge energy E pi,0,k from the initial state to the kth iteration of the power integration method is less than the cumulative reference energy E r,0,k , and the energy should be increased on the basis of E pi,0,k to reduce the error between E pi,0,k and E r,0,k . At the same time, the maximum energy error ΔE vr (k) satisfies:

[0057]

[0058] The maximum energy error ΔE vr (k) is greater than 0, and on the basis of the power integration method, a compensation method for energy state estimation can be designed by using ΔE vr (k) to reduce the cumulative error;

[0059] ② When SOE pi (k) is less than SOE r (k), the cumulative discharge energy E pi,0,k from the initial state to the kth iteration of the power integration method is greater than the cumulative reference energy E r,0,k , and the energy should be reduced on the basis of E pi,0,k to reduce Epi,0,k With E r,0,k The error between them. Meanwhile, the maximum energy error ΔE vr (k) satisfies:

[0060]

[0061] Maximum energy error ΔE vr (k) is less than 0, and based on the power integration method, ΔE can be used. vr (k) Design a compensation method for energy state estimation to reduce cumulative error;

[0062] ③When SOE pi (k) equals SOE r At time (k), the cumulative discharge energy E from the initial state to the kth iteration using the power integration method is... pi,0,k Equal to the cumulative reference energy E r,0,k E pi,0,k No energy compensation is required. Simultaneously, the maximum energy error ΔE vr (k) satisfies:

[0063]

[0064] Based on the above analysis, SOE pi (k) and ΔE vr The relationship between (k) is shown in Table 1:

[0065] Table 1 SOE pi (k) and ΔE vr The relationship between (k)

[0066]

[0067] It can be seen that the energy state SOE estimated in the k-th iteration calculated by the power integral method is... pi (k) and the maximum energy error ΔE vr (k) is related. When SOE pi (k) is greater than SOE r At time (k), the cumulative discharge energy E pi,0,k Less than the reference value E r,0,k Due to ΔE vr (k) is positive, so ΔE vr (k) compensate to E pi,0,k Reduce cumulative error; when SOE pi (k) is less than SOE r At time (k), the cumulative discharge energy E pi,0,k Greater than the reference value E r,0,k Due to ΔE vr (k) is negative, so ΔE can also be used. vr(k) to reduce accumulated error; when SOE pi,0,k (k) is equal to SOE pi (k), accumulated discharge energy E r (k) is equal to reference value E pi,0,k (k), ΔE r,0,k (k) is 0, no additional energy compensation is needed. The above energy state compensation principle is shown in Fig. 1, which gives the design idea of the above three types of energy compensation. For known data, the data-driven method is used to obtain ΔE vr (k), and the obtained ΔE vr (k) is used as an offline compensation parameter for energy state estimation. vr (k) is used as an offline compensation parameter for energy state estimation. Figure 2 (k) is used as an offline compensation parameter for energy state estimation.

[0068] (3) Power integration method with energy compensation mechanism

[0069] The ideal form of the power integration method with energy compensation mechanism is as follows:

[0070]

[0071] wherein SOE pic (k) represents the energy state calculated by the power integration method with energy compensation mechanism at the kth iteration, ζ represents the energy conversion coefficient, V(i) and I(i) represent the voltage and current at the ith iteration, respectively, Δt represents the sampling time, and ΔSOE c (i) represents the compensation value of the energy state.

[0072] The compensation value of the energy state ΔSOE c (i) is a percentage value, and at the ith iteration, it remains the same order of magnitude as the discharge energy state ζV(i)I(i)Δt / E r According to the maximum energy error in formula (5), the compensation value of the energy state is as follows:

[0073]

[0074] To consider the energy change of the battery at different aging stages, the energy state compensation considering battery aging is as follows:

[0075]

[0076] wherein ΔSOE ca (i) represents the compensation value of the energy state considering battery aging at the ith iteration, Y=C r / C t , C r represents the current cycle maximum capacity, C t represents the rated maximum capacity, and ΔE vr(i) represents the maximum energy error at the i-th iteration.

[0077] (4) Power integration method with energy state compensation under Dropout technology

[0078] The energy state compensation method considering battery aging is designed as shown in formula (11), but the maximum energy error ΔE vr (k) may not be applicable to different temperatures, charge and discharge rates, or charge and discharge strategies. Taking temperature as an example, due to the difference in discharge characteristics of lithium ion batteries at different temperatures, the maximum energy error measured at 25℃ may not be directly used to compensate for the energy state at 5℃. Assuming that the energy state compensation value obtained in formula (11) at 25℃ is directly applied to the energy state estimation at 5℃, the problem of “over-correction” may occur, that is, after using the energy state compensation mechanism, the estimation result deviates from the reference value, as shown below:

[0079]

[0080] In the formula, K represents the total number of iterations.

[0081] The problem of “over-correction” in formula (12) is due to the difference in battery discharge characteristics under different working conditions, which causes the power integration method to have a correction deviation at each iteration. The core idea of Dropout technology is to randomly mask some nodes, and the present application uses Dropout technology to solve the “over-correction” problem in formula (12), to improve the robustness of the energy state compensation mechanism. The Dropout technology in the power integration method with energy state compensation is combined as shown in formula (13). Figure 3 In the power integration method based on energy state compensation, the energy state compensation value ΔE ca (i) is provided at each iteration, but a part of the compensation value is randomly masked, and no energy compensation is performed at the masked place, which can solve the “over-correction” problem in energy state compensation.

[0082] Combining the energy state compensation of formula (9) and formula (11) and Dropout technology, the final power integration method is as follows:

[0083]

[0084] In the formula, Ψ 0～1 represents the masking probability of energy state compensation.

[0085] The present application is applicable not only to the power integration method, but also to other energy state modeling methods based on the power integration method, and the design idea of the energy compensation mechanism is consistent.

[0086] Embodiment

[0087] Based on a nickel-cobalt-manganese lithium ion battery with a rated capacity of 2000mAh, dynamic test experimental data are obtained under FUDS working conditions. Based on the power integration method with energy compensation of the application, energy state estimation results are obtained by running on a PyTorch platform.

[0088] Combining Figure 4 As shown in FIG. 6, the estimation results of the original power integration method and the power integration method with energy compensation of the application are compared with reference values, it can be seen that the estimation results of the original power integration method are obviously far away from the reference values, while the estimation results of the power integration method with energy compensation of the application are closer to the reference values, and the energy state estimation accuracy using the energy compensation mechanism is higher.

[0089] Combining Figure 5 As shown in FIG. 7, the energy state estimation error of the original power integration method and the power integration method with energy compensation of the application are compared, it can be seen that the power integration method with energy compensation of the application can obviously reduce the cumulative error and is more reasonable and effective.

[0090] It is obvious to those skilled in the art that the application is not limited to the details of the above exemplary embodiments, and the application can be implemented in other forms without departing from the spirit or essential characteristics of the application. Therefore, the embodiments should be regarded as exemplary and non-limiting, the scope of the application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent conditions of the claims are intended to be included in the application. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0091] In addition, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description manner of the specification is only for the sake of clarity, those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be properly combined to form other embodiments that those skilled in the art can understand.

Claims

1. A power integration method with an energy compensation mechanism for energy state estimation, characterized in that: Includes the following steps: Step 1: Analysis of Energy State Estimation Error The energy state estimated by the power integral method in the kth iteration is denoted as SOE. pi (k), defining the variable maximum energy index E at the k-th iteration. v (k) is: in, E r,0,k =E r ·[SOE(0)-SOE r (k)] (2) The energy state estimation error ΔSOE(k) of the power integral method in the kth iteration is: ΔSOE(k)=|SOE pi (k)-SOE r (k)| (4) In the formula, E r,0,k SOE(0) represents the cumulative reference energy from the initial state to the k-th iteration, where SOE(0) represents the initial energy state, Er represents the reference maximum energy index, and SOE(0) represents the cumulative reference energy from the initial state to the k-th iteration. r (k) represents the reference energy state at the k-th iteration; Step 2: Analysis of Variable Maximum Energy and Reference Maximum Energy The maximum energy error ΔE between the variable maximum energy index and the reference maximum energy index at the k-th iteration. vr (k) is: When SOE pi (k) is greater than or less than SOE r When (k), the maximum energy error ΔE vr (k) Compensation to the cumulative discharge energy E from the initial state to the kth iteration using the power integral method. pi,0,k In the middle, when SOE pi (k) equals SOE r When (k), no compensation is performed; Step 3: Power Integration Method with Energy Compensation Mechanism The ideal form of the power integral method with an energy compensation mechanism is: in, The energy state compensation considering battery aging is as follows: In the formula, SOE pic (k) represents the energy state calculated by the power integral method with energy compensation mechanism at the k-th iteration. Let V(i) represent the energy conversion coefficient, and I(i) represent the voltage and current at the i-th iteration, respectively. Let Δt represent the sampling time, and ΔSOE represent the energy conversion coefficient. c (i) represents the compensation value for the energy state, ΔE vr (i) represents the maximum energy error ΔSOE at the i-th iteration. ca (i) represents the energy state compensation value considering battery aging in the i-th iteration, Υ=C r / C t C r C represents the maximum capacity of the current loop. t Indicates the rated maximum capacity; Step 4: Power Integration Method with Energy State Compensation under Dropout Technology Building upon step three, the Dropout technique is used to provide an energy state compensation value ΔSOE for each iteration. ca (i) When randomly shielding a portion of the compensation value, the final power integration method is as follows: In the formula, Ψ 0～1 This represents the shielding probability of energy state compensation.

2. The power integral method with energy compensation mechanism for energy state estimation according to claim 1, characterized in that: The compensation value ΔSOE of the energy state c (i) is a value in percentage form, and at the i-th iteration, it is related to the discharge energy state. Keep the same order of magnitude.

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