A method for designing a hybrid battery system controller for energy saving and safety

Abstract

The application discloses a kind of composite battery system controller design methods for energy saving and safety, including Thevenin equivalent circuit model is established according to main, deputy battery equivalent circuit model parameter, main, deputy battery pack loss model is constructed based on Thevenin equivalent circuit model and with main, deputy battery pack average loss efficiency as optimization target, power distribution coefficient is calculated, the state of charge of main, deputy battery pack is constantly updated, reach the purpose of optimal efficiency, the application can greatly reduce the energy loss caused by composite battery system itself, improve the endurance mileage of pure electric vehicle, power distribution coefficient is calculated using real-time optimization algorithm, the current optimal power configuration scheme can be obtained in real time for different working conditions, with higher flexibility and convenience, select composite battery system single or multiple performance indicators as optimization target to calculate power distribution coefficient, provide a new way of thinking for composite battery system controller design.

Description

Technical Field

[0001] This invention relates to the field of battery management technology for new energy vehicles, specifically a design method for a composite battery system controller that is energy-saving and safe. Background Technology

[0002] The vigorous development of new energy vehicle technology and the encouragement and guidance of consumers to purchase new energy vehicles have been significantly reflected in the formulation and implementation of relevant policies in my country. With the market share of new energy vehicles increasing year by year, the sales of pure electric vehicles have also risen sharply, playing a pivotal role in the new energy vehicle category. Pure electric vehicles typically use lithium-ion battery systems as their sole energy source, driven by one or more motors. They are characterized by sufficient power, fast response, and low noise, and possess inherent advantages in human-machine interaction, making them the best platform for achieving intelligent driving.

[0003] However, due to the inherent properties of lithium-ion battery cells, battery systems composed of a single type of lithium-ion cell often struggle to simultaneously achieve high energy storage, high power, and safety. This leads to varying degrees of anxiety for users of existing pure electric vehicles, with "range anxiety" and "safety anxiety" being the most prominent. Against this backdrop, hybrid battery systems integrating two or more types of lithium-ion battery packs have emerged. Hybrid battery systems typically incorporate both "energy storage" and "power" battery packs to ensure the entire system possesses both high energy storage and high power characteristics. By rationally allocating the required power to different battery packs, energy savings and improved system safety are effectively achieved. The design of the power distribution controller for hybrid battery systems is a significant challenge in the related technologies. When designers lack experience, the battery system often fails to perform optimally, potentially leading to overcharging or over-discharging of a particular battery pack, resulting in safety issues.

[0004] Therefore, it is indeed necessary to improve existing technologies to address their shortcomings. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] Therefore, the purpose of this invention is to provide a design method for a composite battery system controller that is energy-saving and safe, which can greatly reduce the energy loss caused by the composite battery system itself and improve the driving range of pure electric vehicles.

[0007] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution:

[0008] A design method for a hybrid battery system controller that prioritizes energy saving and safety, comprising:

[0009] S1. Establish the Thevenin equivalent circuit model based on the parameters of the main and auxiliary battery equivalent circuit models;

[0010] S2. Construct loss models for the main and auxiliary battery packs based on Thevenin equivalent circuit model;

[0011] S3. Calculate the power distribution coefficient with the average loss efficiency of the main and auxiliary battery packs as the optimization target, and continuously update the state of charge of the main and auxiliary battery packs to achieve the optimal efficiency.

[0012] As a preferred embodiment of the energy-saving and safety-oriented composite battery system controller design method of the present invention, the specific steps of establishing the Thevenin equivalent circuit model based on the equivalent circuit model parameters of the main and auxiliary batteries in step S1 are as follows:

[0013] The DC internal resistance R of the Thevenin equivalent circuit model of the main and auxiliary batteries as a function of SOC was obtained through corresponding battery experiments. d Polarization internal resistance R s and polarization capacitor C s Construct the Thevenin equivalent circuit model;

[0014] The state equation of the Thevenin equivalent circuit model is:

[0015]

[0016] Where SOC is the battery state of charge, U s Cap is the terminal voltage of the RC network in the equivalent circuit model. batt Battery capacity; I batt denoted as battery pack current, with positive values ​​for charging and negative values ​​for discharging; k represents the time series; t represents time.

[0017] The observation equation is:

[0018] U(k+1)=OCV(k+1)+I batt (k+1)·R d (k+1)+U s (k+1) where U represents the battery system terminal voltage; OCV represents the battery open-circuit voltage, and the relevant parameters can be obtained from the following equations:

[0019]

[0020] f Rs (·), f Cs (·) and fRd (·) represent SOC and R respectively. s C s and R d The mapping relationship.

[0021] As a preferred embodiment of the energy-saving and safety-oriented composite battery system controller design method of the present invention, in step S2, the battery pack power loss model is as follows:

[0022]

[0023] Among them, I C R in the equivalent circuit model C The polarization capacitance current of the network; L Rd and L Rs These are the power loss due to DC internal resistance and the power loss due to polarization internal resistance, respectively.

[0024] The battery pack loss efficiency model is as follows:

[0025]

[0026] Where θ represents the battery pack efficiency loss.

[0027] As a preferred embodiment of the energy-saving and safety-oriented composite battery system controller design method described in this invention, step S3, which calculates the power distribution coefficient with the average loss efficiency of the main and auxiliary battery packs as the optimization objective, and continuously updates the state of charge of the main and auxiliary battery packs to achieve optimal efficiency, includes:

[0028] Real-time acquisition of the current total current I of the composite battery system sys The current state of charge (SOC) of the main battery pack p and the state of charge (SOC) of the secondary battery pack;

[0029] With the main battery pack loss efficiency θ p and secondary battery pack efficiency loss θ s The optimization objective J is to minimize the average value of the composite battery system, which means maximizing the average efficiency of the composite battery system. The power distribution coefficient K of the composite battery system that minimizes the optimization objective J is calculated using an optimization algorithm. t As shown below:

[0030]

[0031] K t =argmin{}

[0032] StK∈[0,1]

[0033] Wherein, the power allocation coefficient K tThe value range is [0,1], representing the current I allocated to the secondary battery pack. s The ratio of the current in the composite battery system to the total current is:

[0034]

[0035] The main battery pack's required current I p and the auxiliary battery pack's required current I s Substitute back into the equivalent current model, replacing I in the original equation. batt Update the real-time state of charge (SOC) of the main battery pack. p Real-time State of Charge (SOC) of the secondary battery pack s Then return to step 4 to perform the calculation for the next cycle.

[0036] As a preferred embodiment of the energy-saving and safety-oriented composite battery system controller design method described in this invention, the power allocation coefficient K... t The battery will be reassigned under the following six conditions to ensure that the battery pack does not over-discharge or overcharge. The six conditions are as follows:

[0037] (1) When SOC p <= 0% and SOC s When >0%: If I sys If K < 0, then K t =1; if I sys >=0, then K t =0;

[0038] (2) When SOC p >0% and SOC s When <= 0%: If I sys If K < 0, then K t =0; if I sys >=0, then K t =1;

[0039] (3) When SOC p <= 0% and SOC s When <= 0%: If I sys If I < 0, then the battery pack stops discharging; if I sys >=0, then K t constant;

[0040] (4) When SOC p <100% and SOC s When >= 100%: If I sys If K < 0, then K t =1; if I sys >=0, then K t =0;

[0041] (5) When SOC p >=100% and SOC s When <100%: If I sys If K < 0, then K t =0; if I sys >=0, then K t =1;

[0042] (6) When SOC p >=100% and SOC s When >= 100%: If I sys If K < 0, then K t Unchanged; if I sys If the value is greater than or equal to 0, then the battery pack will stop charging.

[0043] Compared with the prior art, the beneficial effects of the present invention are: the present invention can greatly reduce the energy loss caused by the composite battery system itself, improve the driving range of pure electric vehicles, use a real-time optimization algorithm to calculate the power allocation coefficient, and obtain the current optimal power configuration scheme in real time for different operating conditions, which has high flexibility and convenience. Selecting one or more performance indicators of the composite battery system as optimization targets to calculate the power allocation coefficient provides a new idea for the design of composite battery system controllers. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. 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. Wherein:

[0045] Figure 1 This is a flowchart of a design method for a composite battery system controller that is energy-saving and safe according to the present invention;

[0046] Figure 2 This is a schematic diagram of the composite battery system structure of the present invention;

[0047] Figure 3 This is a state-of-charge curve diagram of the main and auxiliary battery packs of the composite battery system of the present invention;

[0048] Figure 4 This is a graph showing the cumulative loss efficiency of the composite battery system of the present invention. Detailed Implementation

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0050] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0052] This invention provides a design method for a composite battery system controller that is energy-saving and safe, the process of which is shown in the attached figure. Figure 1 As shown in the attached figure, the structure of the composite battery system involved is as follows. Figure 2 As shown, a Thevenin equivalent circuit model is first established based on the parameters of the equivalent circuit models of the main and auxiliary batteries. Then, an efficiency loss model is constructed based on the equivalent circuit model, and the power allocation coefficient is calculated with the average loss efficiency of the main and auxiliary battery packs as the optimization objective. The state of charge of the main and auxiliary battery packs is continuously updated to achieve the goal of optimal efficiency. Then, the power allocation coefficient is re-assigned under full charge and full discharge conditions to ensure the safety of the battery system.

[0053] The following examples will provide a detailed explanation.

[0054] Step 1: Obtain the DC internal resistance R of the Thevenin equivalent circuit model of the main and auxiliary batteries as a function of SOC through corresponding battery experiments. d Polarization internal resistance R s and polarization capacitor C s Construct the Thevenin equivalent circuit model, where the state equations are as follows:

[0055]

[0056] The observation equation is as follows:

[0057] U(k+1)=OCV(k+1)+I bat t(k+1)·R d (k+1)+U s (k+1)

[0058] The relevant parameters can be obtained from the following equations:

[0059]

[0060] In the state equation, SOC represents the battery's state of charge, and U... s Cap is the terminal voltage of the RC network in the equivalent circuit model. batt Battery capacity; I battdenoted as , where is the battery pack current, with positive values ​​for charging and negative values ​​for discharging; k represents the time series; and t represents time.

[0061] Furthermore, in the measurement equation, U represents the battery system terminal voltage; OCV represents the battery open-circuit voltage.

[0062] f Rs (·), f Cs (·) and f Rd (·) represent SOC and R respectively. s C s and R d The mapping relationship.

[0063] Step 2: Based on the Thevenin equivalent circuit model in Step 1, further establish the main and auxiliary battery pack loss efficiency models. The battery pack power loss model is as follows:

[0064]

[0065] In the formula, I C R in the equivalent circuit model C The polarization capacitance current of the network; L Rd and L Rs These are the power loss due to DC internal resistance and the power loss due to polarization internal resistance, respectively.

[0066] Furthermore, the battery pack loss efficiency model is as follows:

[0067]

[0068] In the formula, θ represents the battery pack efficiency loss.

[0069] Step 3: Obtain the current total current I of the composite battery system in real time. sys The current state of charge (SOC) of the main battery pack p and the state of charge (SOC) of the secondary battery pack s .

[0070] Step 4: Using the main battery pack loss efficiency θ p and secondary battery pack efficiency loss θ s The optimization objective J is to minimize the average value of the composite battery system, which means maximizing the average efficiency of the composite battery system. The power distribution coefficient K of the composite battery system that minimizes the optimization objective J is calculated using an optimization algorithm. t As shown below:

[0071]

[0072] K t =argmin{J}

[0073] StKt ∈[0,1]

[0074] In the formula, the power distribution coefficient K t The value range is [0,1], representing the current I allocated to the secondary battery pack. s The ratio of the current in the composite battery system to the total current is:

[0075]

[0076] Step 5: Set the main battery pack current requirement I p and the auxiliary battery pack's required current I s Substitute back into the equivalent current model, replacing I in the original equation. batt Update the real-time state of charge (SOC) of the main battery pack. p Real-time State of Charge (SOC) of the secondary battery pack s Then return to step 4 to perform the calculation for the next cycle.

[0077] Furthermore, the power allocation coefficient K calculated in step 4 t The battery will be reassigned under the following conditions to ensure that the battery pack does not over-discharge or overcharge:

[0078] (1) When SOC p <= 0% and SOC s When >0%: If I sys If K < 0, then K t =1; if I sys >=0, then K t =0;

[0079] (2) When SOC p >0% and SOC s When <= 0%: If I sys If K < 0, then K t =0; if I sys >=0, then K t =1;

[0080] (3) When SOC p <= 0% and SOC s When <= 0%: If I sys If I < 0, then the battery pack stops discharging; if I sys >=0, then K t constant;

[0081] (4) When SOC p <100% and SOC s When >= 100%: If I sys If K < 0, then K t =1; if I sys >=0, then K t=0;

[0082] (5) When SOC p >=100% and SOC s When <100%: If I sys If K < 0, then K t =0; if I sys >=0, then K t =1;

[0083] (6) When SOC p >=100% and SOC s When >= 100%: If I sys If K < 0, then K t Unchanged; if I sys If the value is greater than or equal to 0, then the battery pack will stop charging.

[0084] As attached Figure 3 As shown, after introducing an optimized power allocation coefficient, when both the main and auxiliary battery packs have a high SOC, the auxiliary battery pack, with its lower efficiency loss, discharges first, while the main battery pack's SOC changes slowly during this phase. When the auxiliary battery pack is at a low SOC, it no longer possesses a high efficiency advantage, at which point the main battery pack discharges first, and the auxiliary battery pack's SOC changes more slowly. Throughout the entire operation, the SOC of both the main and auxiliary battery packs remained between 0% and 100%, without any over-discharge or over-charge phenomena. (See attached diagram) Figure 4 As shown, after optimizing the power allocation coefficient, the cumulative loss efficiency of the composite battery system remains lower than before optimization throughout the entire operating cycle (real-time average allocation current: K). t The cumulative loss efficiency remains at 0.5%, and gradually becomes significant near the end of operation, indicating that the energy-saving effect of the present invention is good.

[0085] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A design method for a composite battery system controller oriented towards energy saving and safety, characterized in that, include: S1. Establish the Thevenin equivalent circuit model based on the parameters of the main and auxiliary battery equivalent circuit models. The state equation of the Thevenin equivalent circuit model is as follows: ； Where SOC is the battery state of charge, U s Cap is the terminal voltage of the RC network in the equivalent circuit model. batt Battery capacity; I batt denoted as battery pack current, with positive values ​​for charging and negative values ​​for discharging; k represents the time series; t represents time. The observation equation is: ； Where U represents the battery system terminal voltage; OCV represents the battery open-circuit voltage, and the relevant parameters are obtained from the following equations: ； f Rs (·), f Cs (·) and f Rd (·) represent SOC and R respectively. s C s and R d The mapping relationship; S2. Construct loss models for the main and auxiliary battery packs based on Thevenin equivalent circuit model; S3. Calculate the power distribution coefficient with the average loss efficiency of the main and auxiliary battery packs as the optimization target, and continuously update the state of charge of the main and auxiliary battery packs to achieve the optimal efficiency. In step S3, the power allocation coefficient is calculated with the average loss efficiency of the main and auxiliary battery packs as the optimization objective, and the state of charge of the main and auxiliary battery packs is continuously updated to achieve the optimal efficiency. This includes: Real-time acquisition of the current total current I of the composite battery system sys The current state of charge (SOC) of the main battery pack p and the state of charge (SOC) of the secondary battery pack; With the main battery pack loss efficiency θ p and secondary battery pack efficiency loss θ s The optimization objective J is to minimize the average value of the composite battery system, which means maximizing the average efficiency of the composite battery system. The power distribution coefficient K of the composite battery system that minimizes the optimization objective J is calculated using an optimization algorithm. t As shown below: ； ； Among them, the power allocation coefficient K t The value range is [0, 1], representing the current allocated to the secondary battery pack. I s The ratio of the current in the composite battery system to the total current is: ； The main battery pack's required current I p and the auxiliary battery pack's required current I s Substitute back into the equivalent current model, replacing I in the original equation. batt Update the real-time state of charge (SOC) of the main battery pack. p Real-time State of Charge (SOC) of the secondary battery pack s Return to step 4 to perform the calculation for the next cycle; The power allocation coefficient K t The battery will be reassigned under the following six conditions to ensure that the battery pack does not over-discharge or overcharge. The six conditions are as follows: (1) When SOC p <=0% and SOC s When >0%: If I sys <0, then K t =1; if I sys If K >= 0, then t =0; (2) When SOC p >0% and SOC s When <=0%: If I sys <0, then K t =0; if I sys If K >= 0, then K t =1; (3) When SOC p <=0% and SOC s When <= 0%: If I sys If I < 0, then the battery pack will stop discharging; if I sys If K >= 0, then K t constant; (4) When SOC p <100% and SOC s When I >= 100%: sys <0, then K t =1; if I sys If K >= 0, then t =0; (5) When SOC p >=100% and SOC s When <100%: If I sys <0, then K t =0; if I sys If K >= 0, then t =1; (6) When SOC p >= 100% and SOC s When I >= 100%: sys <0, then K t Unchanged; if I sys If the value is greater than or equal to 0, the battery pack will stop charging.

2. The design method for a composite battery system controller oriented towards energy saving and safety according to claim 1, characterized in that, In step S1, the specific steps for establishing the Thevenin equivalent circuit model based on the parameters of the main and secondary battery equivalent circuit models are as follows: The DC internal resistance of the main and auxiliary batteries as a function of SOC was obtained through corresponding battery experiments using the Thevenin equivalent circuit model. R d Polarization internal resistance R s and polarization capacitor C s Construct the Thevenin equivalent circuit model.

3. The design method for a composite battery system controller oriented towards energy saving and safety according to claim 1, characterized in that, In step S2, the battery pack power loss model is as follows: ； Among them, I C R in the equivalent circuit model C The polarization capacitance current of the network; L Rd and L Rs These are the power loss due to DC internal resistance and the power loss due to polarization internal resistance, respectively. The battery pack loss efficiency model is as follows: ； Where θ represents the battery pack efficiency loss.

Images