A Closed-Loop Joint Design Method for Power Batteries and Their Battery Management Systems

Abstract

A closed-loop joint design method for power batteries and their battery management systems is proposed. This method utilizes big data cloud computing and combines digital modeling and parameter joint evaluation and updating to achieve a high-quality closed-loop optimization design that organically integrates power battery design and battery management system design. Through a "know-before-building" approach, a battery with "know-before-testing" capabilities is ultimately obtained. A complete parameter set for the battery to be designed is formed by combining big data cloud computing and battery design parameters. Simulation experiments are then conducted to obtain matching battery characteristic responses, which are used for modeling and developing management algorithms for the battery management system. This results in the manufacture of a digital battery with optimal performance and management indicators, which is then materialized to design a high-performance, well-managed "new battery," achieving the goal of "building for use."

Description

Technical Field

[0001] This invention relates to the technical field of power battery and battery management system design methods, and in particular to a high-quality design method for the entire life cycle of power batteries and a closed-loop design optimization method for the corresponding management system. Background Technology

[0002] In practical applications, the performance of a power battery depends on two aspects: the battery's own design and the performance of the corresponding battery management system (BMS). Currently, the power battery design phase primarily focuses on improving the battery's energy density, power density, and other performance indicators, which directly determine the battery's inherent performance. The BMS design, on the other hand, focuses on improving the accuracy of state estimation and the efficiency, safety, and durability during use, thus determining the performance the battery can deliver during operation. In existing technologies, these two design phases are open-loop and independent. The BMS design phase is often carried out after the power battery design phase, targeting a pre-designed power battery. This design is limited to specific power batteries, significantly sacrificing the design and optimization space of the BMS in order to optimize the performance of a particular battery. This results in low adaptability to the diverse types of power batteries and their entire lifecycle. Summary of the Invention

[0003] In view of this, the present invention aims to improve the existing design process of power batteries and battery management systems implemented separately, and to provide a closed-loop joint design method that organically combines these two design stages and is applicable to different types of power batteries and the entire life cycle of power batteries and their management systems.

[0004] This invention provides a closed-loop joint design method for a power battery and its battery management system, comprising the following steps:

[0005] Collect information on different models of power battery cells, modules and systems from different battery manufacturers to build a big data cloud, which is used to provide the battery parameters required for the design of power batteries and battery management systems.

[0006] The required battery material type and structural indicators are used as pre-selected battery design parameters for preliminary design, and the pre-selected battery design parameters are used for digital modeling of the battery to be designed.

[0007] The digital modeling process first uses big data cloud matching to obtain the corresponding battery performance parameters based on the pre-selected battery design parameters; then, based on the battery design parameters and battery performance parameters, simulation experiments are conducted by applying different stimuli.

[0008] The design parameters of the pre-selected battery management system are used to digitally model the battery using the experimental data from the simulation test, and the corresponding battery management algorithm is developed and the management parameters in the battery management algorithm are extracted.

[0009] The battery performance parameters and management parameters are jointly evaluated, and the battery design parameters and battery management system design parameters are updated and optimized based on the evaluation results. The evaluation criteria are optimal performance parameters such as battery cycle life, and optimal parameters such as accuracy of battery state estimation and consistency management. That is, the following optimization problem is solved:

[0010]

[0011] In the formula, θ1 is the battery design parameter, θ2 is the battery management system design parameter, p1 is the battery performance parameter, and p2 is the battery management parameter.

[0012] The above steps are repeated sequentially to obtain the optimal design parameters for the battery and power battery, which are then used in the manufacture of the physical power battery and the corresponding battery management system.

[0013] Furthermore, the data collected during the establishment of the big data cloud includes information on different models of power battery cells, modules, and systems from different battery manufacturers. This information includes material parameters such as battery electrolyte, separator, and positive and negative electrodes; process parameters such as formulation, slurry preparation, and coating uniformity; structural parameters such as battery shape and size, internal structural shape, and connection sequence and method; and performance parameter data from equipment testing and usage feedback that match the battery material, process, and structural parameters.

[0014] Furthermore, the pre-selected battery design parameters include: material parameters such as battery electrolyte, separator and positive and negative electrodes; process parameters such as formulation, slurry preparation and coating uniformity; and structural parameters such as battery shape and size, internal structural shape and connection sequence and method.

[0015] Furthermore, the battery performance parameters include: battery capacity, voltage, cycle life, discharge characteristics and internal resistance, operating temperature range, safety performance, and other intrinsic properties.

[0016] Furthermore, the pre-selected design parameters for the battery management system include: management strategy parameters and system hardware design parameters.

[0017] Furthermore, the management parameters in the battery management algorithm include parameters required for different management and control objects, involving functions such as battery state estimation, safety management, charging control management, energy control management, equalization management, thermal management, and information management.

[0018] Furthermore, the simulation test is based on a digital battery. Through reasoning under all operating conditions, the entire system, and the entire life cycle, and by applying different current excitations, it obtains characteristic responses that match the battery design parameters, including but not limited to the battery's operating condition characteristics, temperature characteristics, performance degradation characteristics, and life cycle characteristics. The all operating conditions include, but are not limited to, Dynamic Stress Test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), The New European Driving Cycle (NEDC), and China Typical City Driving Cycle (CTCDC).

[0019] Furthermore, digital modeling of batteries includes equivalent circuit models, electrochemical models, time-domain fractional-order models, fusion models, black-box models based on big data and artificial intelligence algorithms, and variations of the models.

[0020] Furthermore, the development of the battery management algorithm includes algorithms for battery SOX estimation, safety management, charging control management, energy control management, equalization management, thermal management, and information management. The SOX estimation involves using predetermined algorithms and strategies to obtain the power battery state information at each moment, specifically including but not limited to the battery temperature, state of charge (SOC), state of health (SOH), peak power (SOP), and state of energy (SOE).

[0021] Furthermore, the battery design parameters and battery management system design parameters are updated and optimized based on global optimization algorithms such as genetic algorithms and dynamic programming, as well as artificial intelligence algorithms such as neural networks, deep learning, support vector machines, and correlation vector machines.

[0022] The method provided by this invention utilizes big data cloud and combines digital modeling and parameter joint evaluation and updating to achieve high-quality closed-loop optimization design that organically integrates power battery design and battery management system design. Through a "know-before-building" approach, it ultimately obtains a battery that is "know-before-testing." A complete parameter set for the battery to be designed is formed by combining big data cloud and battery design parameters. Simulation experiments are then conducted to obtain matching battery characteristic responses, which are used for modeling and developing management algorithms for the battery management system. This results in the creation of a digital battery with optimal performance and management indicators, which is then materialized to design a "new battery" with good performance and management, achieving the effect of "building for use." This method truly focuses on the power battery itself, starting from its management, and working backwards to drive the power battery design parameters and BMS design parameters. By manufacturing and materializing a digital battery, it fundamentally improves the reliability and safety of the power battery, making battery management simpler. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the principle of the method provided by the present invention.

[0024] Figure 2 This is a structural diagram of the big data cloud in this invention;

[0025] Figure 3 This is a virtual result of using the present invention for closed-loop design of a battery and management system. Detailed Implementation

[0026] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The present invention provides a closed-loop joint design method for a power battery and its battery management system, such as... Figure 1 , 3 As shown, perform the following steps in sequence:

[0028] Collect information on different models of power battery cells, modules, and systems from different battery manufacturers to establish a system. Figure 2 The big data cloud shown is used to provide the battery parameters required for the design of power batteries and battery management systems;

[0029] The required battery material type and structural indicators are used as pre-selected battery design parameters for preliminary design, and the pre-selected battery design parameters are used for digital modeling of the battery to be designed.

[0030] The digital modeling process can be viewed as a virtual digital battery manufacturing workshop. First, based on the pre-selected battery design parameters, the corresponding battery performance parameters are obtained using big data cloud matching. Based on the battery design parameters and battery performance parameters, simulation experiments are conducted by applying different stimuli.

[0031] The design parameters of the pre-selected battery management system are used to digitally model the battery using the experimental data from the simulation test, and the corresponding battery management algorithm is developed and the management parameters in the battery management algorithm are extracted.

[0032] The battery performance parameters and management parameters are jointly evaluated, and the battery design parameters and battery management system design parameters are updated and optimized based on the evaluation results. The evaluation criteria are optimal battery performance parameters such as cycle life, and optimal battery state estimation accuracy and consistency management parameters, i.e., solving the following optimization problem:

[0033]

[0034] In the formula, θ1 is the battery design parameter, θ2 is the battery management system design parameter, p1 is the battery performance parameter, and p2 is the battery management parameter.

[0035] By repeating the above steps sequentially, the optimal design parameters for the battery and power battery are obtained. That is, the designed battery form is obtained from the digital battery manufacturing workshop, and the designed management system is also obtained from the BMS design stage. This can be used to guide the manufacturing of physical power batteries and corresponding battery management systems.

[0036] In a preferred embodiment of the present invention, the information collected when establishing a big data cloud includes different models of power battery cells, modules, and system products from different battery manufacturers. This information includes material parameters such as battery electrolyte, separator, and positive and negative electrodes; process parameters such as formulation, slurry preparation, and coating uniformity; structural parameters such as battery shape and size, internal structural shape, and connection sequence and method; and performance parameter data from equipment testing and usage feedback that match the battery material, process, and structural parameters.

[0037] In a preferred embodiment of the present invention, the pre-selected battery design parameters include: material parameters such as battery electrolyte, separator and positive and negative electrodes, process parameters such as formulation, slurry preparation and coating uniformity, and structural parameters such as battery shape and size, internal structural shape and connection sequence and method.

[0038] In a preferred embodiment of the present invention, the battery performance parameters include: battery capacity, voltage, cycle life, discharge characteristics and internal resistance, operating temperature range, safety performance and other intrinsic properties.

[0039] In a preferred embodiment of the present invention, the pre-selected design parameters of the battery management system include: management strategy-related parameters of the battery management system and system hardware design parameters.

[0040] In a preferred embodiment of the present invention, the management parameters in the battery management algorithm include parameters required for different management and control objects, involving functions such as battery state estimation, safety management, charging control management, energy control management, equalization management, thermal management, and information management;

[0041] In a preferred embodiment of the present invention, the simulation test is based on a digital battery. Through reasoning under all operating conditions, the entire system, and the entire life cycle, and by applying different current excitations, a characteristic response matching the battery design parameters is obtained. This includes, but is not limited to, the battery's operating condition characteristics, temperature characteristics, performance degradation characteristics, and life cycle characteristics. The all operating conditions include, but are not limited to, Dynamic Stress Test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), The New European Driving Cycle (NEDC), and China Typical City Driving Cycle (CTCDC).

[0042] In a preferred embodiment of the present invention, digital modeling of the battery includes equivalent circuit models, electrochemical models, time-domain fractional-order models, fusion models, black-box models based on big data and artificial intelligence algorithms, and variations of the models.

[0043] In a preferred embodiment of the present invention, the development of the battery management algorithm includes algorithms for battery SOX estimation, safety management, charging control management, energy control management, equalization management, thermal management, and information management. The SOX estimation involves using predetermined algorithms and strategies to obtain the power battery state information at each moment, specifically including but not limited to the battery temperature, state of charge (SOC), state of health (SOH), peak power (SOP), and state of energy (SOE).

[0044] In a preferred embodiment of the present invention, the battery design parameters and the battery management system design parameters are updated and optimized based on global optimization algorithms such as genetic algorithms and dynamic programming, as well as artificial intelligence algorithms such as neural networks, deep learning, support vector machines, and correlation vector machines.

[0045] In a preferred embodiment of the present invention, a fractional-order model of the battery is selected for modeling, and the management algorithm of the battery management system is such as the SOC estimation of the adaptive extended Kalman filter algorithm. The genetic algorithm searches for the optimal battery design parameters and the parameters of the adaptive extended Kalman filter algorithm, so that the estimation effect is optimal, and the battery design parameters and battery management system design parameters under optimal performance and management are obtained, and then they are materialized and manufactured.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A closed-loop joint design method for a power battery and its battery management system, characterized in that: Perform the following steps in sequence: Collect information on different models of power battery cells, modules and systems from different battery manufacturers to build a big data cloud, which is used to provide the battery parameters required for the design of power batteries and battery management systems. The required battery material type and structural indicators are used as pre-selected battery design parameters for preliminary design, and the pre-selected battery design parameters are used for digital modeling of the battery to be designed. The digital modeling process first uses big data cloud matching to obtain the corresponding battery performance parameters based on the pre-selected battery design parameters; then, based on the battery design parameters and battery performance parameters, simulation experiments are conducted by applying different stimuli. The design parameters of the pre-selected battery management system are used to digitally model the battery using the experimental data from the simulation test, and corresponding battery management algorithms are developed, including algorithms for SOX estimation, safety management, charging control management, energy control management, equalization management, thermal management, and information management. The SOX estimation is to obtain the power battery state information at each moment using predetermined algorithms and strategies, specifically including the battery temperature, state of charge (SOC), state of health (SOH), peak power (SOP), and state of energy (SOE); and the management parameters in the battery management algorithm are extracted. The battery performance parameters and management parameters are jointly evaluated, and the battery design parameters and battery management system design parameters are updated and optimized based on the evaluation results. The evaluation criteria are optimal battery performance parameters and optimal battery management parameters, i.e., solving the following optimization problem: In the formula, θ 1 represents the battery design parameters. θ 2 represents the design parameters of the battery management system. p 1 represents battery performance parameters. p 2 represents battery management parameters; Battery performance parameters specifically include: intrinsic performance parameters related to battery capacity, voltage, cycle life, discharge characteristics and internal resistance, operating temperature range, and safety performance; battery management system design parameters specifically include: management strategy parameters and system hardware design parameters. The above steps are repeated sequentially to obtain the optimal design parameters for the battery and power battery, which are then used in the manufacture of the physical power battery and the corresponding battery management system.

2. The method as described in claim 1, characterized in that: The data collected during the establishment of the big data cloud includes information on different models of power battery cells, modules, and systems from different battery manufacturers. This information includes material parameters related to battery electrolytes, separators, and positive and negative electrodes; process parameters related to formulation, slurry preparation, and coating uniformity; structural parameters related to battery shape and size, internal structural shape, and connection sequence and method; and performance parameter data from equipment testing and usage feedback that match the battery materials, processes, and structural parameters.

3. The method as described in claim 1, characterized in that: The pre-selected battery design parameters include: material parameters related to the battery electrolyte, separator, and positive and negative electrodes; process parameters related to the formulation, slurry preparation, and coating uniformity; and structural parameters related to the battery shape and size, internal structural shape, and connection sequence and method.

4. The method as described in claim 1, characterized in that: The management parameters in the battery management algorithm include parameters required for different management and control objects, involving functions such as battery state estimation, safety management, charging control management, energy control management, equalization management, thermal management, and information management.

5. The method as described in claim 1, characterized in that: The simulation test is based on a digital battery. Through reasoning under all operating conditions, the entire system, and the entire life cycle, and by applying different current excitations, the characteristic response matching the battery design parameters is obtained, including the battery's operating condition characteristics, temperature characteristics, performance degradation characteristics, and life cycle characteristics.

6. The method as described in claim 1, characterized in that: Digital modeling of batteries includes equivalent circuit models, electrochemical models, time-domain fractional-order models, fusion models, black-box models based on big data and artificial intelligence algorithms, and variations of the above models.

Images