Battery peak power prediction method based on electric-thermal coupling model prediction control
By employing an electro-thermal coupling model predictive control method, the conservative nature of battery peak power prediction and the risk of thermal runaway in existing technologies are addressed. This enables dynamic optimization of battery safety and efficient utilization, thereby improving the accuracy and reliability of the battery management system.
Patent Information
- Application Number
- CN202610017447.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-07
- Publication Date
- 2026-03-03
AI Technical Summary
In existing battery management systems, peak power prediction methods rely on electrical characteristics and ignore temperature factors, resulting in conservative prediction results and the risk of thermal runaway. This makes it impossible to achieve forward-looking thermal management, thus limiting battery performance and safety.
An electro-thermal coupled model predictive control method is adopted to construct an integrated model that combines electrical and thermal states. Peak power prediction is transformed into a dynamic optimal control problem. Through the synergistic optimization of electrical and thermal constraints, the safety and efficient utilization of the battery during operation are achieved.
It significantly improves the accuracy and consistency of peak power prediction, reduces the risk of constraint violations, enhances battery safety and lifespan, prevents thermal runaway, fully taps battery potential, and improves the real-time performance and reliability of the battery management system.
Smart Images
Figure CN121590299A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of new energy vehicle battery management technology, and more specifically, to a method for predicting battery peak power based on electro-thermal coupling model predictive control. Background Technology
[0002] Currently, the most commonly used peak power prediction method in battery management systems is the constant current recursive method based on a purely electrical model. This method is based on the battery's equivalent circuit model (such as a first-order or second-order RC model), and its core state variables only include electrical parameters such as SOC and polarization voltage. During prediction, this method assumes that the battery will operate with a constant current for a fixed prediction time period. Based on this assumption, the system uses discretized electrical model equations to recursively calculate the evolution of the battery terminal voltage over time until the voltage reaches preset upper and lower limits. Finally, the product of the constant current value and the voltage at the voltage boundary is taken as the battery's peak power capability. This method relies entirely on the boundary judgment of electrical characteristics, treating temperature factors only as external correction terms for model parameters, or as an independent protection threshold in extreme cases.
[0003] However, the aforementioned existing technologies have significant drawbacks. First, their "constant current" assumption is overly idealistic, failing to fully utilize the battery's dynamic regulation potential and leading to conservative predictions. Second, this method treats electrical and thermal characteristics separately, posing serious safety hazards: a current scheme perfectly feasible in the electrical model (such as high-current discharge over a short period) may thermodynamically cause a sharp rise in the battery's core temperature, triggering thermal runaway. Due to the lack of predictive capabilities for temperature dynamics, traditional methods can only passively cut off or derating power after the temperature exceeds a fixed threshold, failing to achieve proactive thermal management. This disconnect between electrical and thermal management limits the full potential of battery performance and makes it difficult to guarantee the safe operation of the battery throughout its entire lifespan. Summary of the Invention
[0004] In view of this, the present invention proposes a battery peak power prediction method based on electro-thermal coupling model predictive control. By constructing an integrated model that combines electrical and thermal states, and on this basis, peak power prediction is transformed into a dynamic optimal control problem that includes both electrical and thermal constraints. This enables proactive optimization and planning of future current sequences, and maximizes and accurately predicts and utilizes the battery's peak power capability while ensuring the battery's electro-thermal safety throughout the entire operation process.
[0005] To achieve the above objectives, this invention proposes a battery peak power prediction method based on electro-thermal coupling model predictive control, comprising: An electrothermal coupled state-space model is constructed using electrical and thermal models; A model predictive control framework is constructed based on the electrothermal coupled state-space model. The problem of predicting battery peak power is constructed as a dynamic optimization problem in the prediction time domain; The objective function and constraints are constructed to solve the dynamic optimization problem, obtain the optimal current control sequence, and calculate the instantaneous battery peak power (SOP) based on the optimal current and the corresponding terminal voltage at the current moment.
[0006] Furthermore, the electrothermal coupling state-space model is shown below:
[0007] in, x It is a vector of state variables. u To control variables, d As a disturbance variable, y The output variable vector is represented by A, B, C, D, and E, which are system matrices.
[0008] Furthermore, in the electrothermal coupled state-space model, the state variables include the battery state of charge (SOC), polarization voltage, and battery core temperature; the control variable is the battery operating current; and the output variables include the battery terminal voltage and core temperature.
[0009] Furthermore, the model predictive control framework includes a prediction time domain, a control sequence, a state sequence, and an output sequence.
[0010] Furthermore, the objective function is as follows:
[0011] in, I For battery operating current, V t For observation, Δ T The sampling period.
[0012] Furthermore, the constraints include kinetic constraints, electro-thermal physical constraints, and terminal voltage constraints; the kinetic constraints are:
[0013] in, A eq The state equality constraint matrix is... B eq To control the input matrix, C eq A vector of constant terms. X It is a state sequence vector. U To control the sequence vector; The electro-thermal physical constraints include current, voltage, state of charge (SOC), and temperature constraints; The terminal voltage constraint is used to make the battery voltage at the end of the prediction time domain approach the set lower limit of discharge voltage or upper limit of charging voltage, provided that a feasible solution to the optimization problem exists.
[0014] Furthermore, the terminal voltage constraint is as follows:
[0015] in, V discharge,min Indicates the lower limit voltage of battery discharge. ε This represents the voltage relaxation parameter. V t For observation purposes, k At the current sampling time, N For prediction in the time domain.
[0016] Furthermore, the process of solving the dynamic optimization problem includes: S1. Based on sensor measurements, obtain the system state at the current moment through a state estimator. S2. Linearize the nonlinear model at the current operation point and calculate the prediction model matrix; S3. Based on the objective function and constraints, construct all the matrices and vectors for the quadratic programming problem; S4. Call the quadratic programming solver to solve for the optimal control sequence. S5. Extract the optimal current at the current moment and, in conjunction with the terminal voltage calculated by the model, calculate the instantaneous peak power; S6 rolls to the next sampling time and repeats steps S1 to S5.
[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention achieves a breakthrough in battery peak power prediction technology through an electro-thermal coupling optimization method based on model predictive control. Compared with traditional constant current recursive methods, this invention significantly improves prediction accuracy, reduces prediction deviations under different operating conditions, and effectively improves the consistency of prediction results. By establishing an electro-thermal coupling model and implementing multi-constraint collaborative optimization, the risk of constraint violations during system operation is greatly reduced, comprehensively enhancing battery safety, especially in thermal safety management, effectively preventing safety hazards such as thermal runaway. While ensuring real-time performance, this invention can fully tap the battery's potential, releasing more usable power within the same safety boundary, and effectively extending battery life through optimized management strategies. Attached Figure Description
[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings: Figure 1 This is a schematic diagram of the overall process of the battery peak power prediction method based on electro-thermal coupling model prediction control according to the present invention. Detailed Implementation
[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0020] This embodiment proposes a battery peak power prediction method based on electro-thermal coupling model predictive control, such as... Figure 1 As shown, the steps for calculating the Standard Operating Procedure (SOP) for a ternary lithium-ion battery system in an electric vehicle are as follows: System Modeling (1) Electro-thermal coupled state-space model This invention adopts an electro-thermal coupling architecture of 1RC electrical model + single thermal capacity model, and the specific modeling is as follows: The state variable is x = [ SOC, V p, T c The parameters are: battery state of charge, polarization voltage, and battery core temperature, respectively; the control variable is the battery operating current. I The discharge is defined as positive; the interference variable is ambient temperature. T a The output variable is the terminal voltage. V t and core temperature T c .
[0021] The battery electrodynamic equation is:
[0022] The thermodynamic equation is as follows:
[0023] The output equation is:
[0024] in, Q This is the battery's rated capacity, measured in Ah. R b It is the internal resistance in ohms, measured in Ω; R p , C p These are polarization resistors and capacitors, measured in Ω and F, respectively. R u It is the battery's external thermal resistance, measured in K / W; C t It is the battery heat capacity, measured in J / K; OCV(SOC) is the open-circuit voltage-SOC function relationship.
[0025] Discretization is performed using the forward Euler method, with a sampling period of Δ. T The discrete-time state-space model is represented as: Among them, current I To control variables, SOC , V p For state variables, V t For the observables. The dynamic equations simplify to:
[0026] (2) Model predictive control framework Define the prediction time domain N, and construct the following sequence: control sequence U = [ I (k), I (k+1),…, I (k+N-1)] T State sequence X = [ x (k+1) T , x (k+2) T , … , x (k+N) T ] T Output sequence Y = [ y (k+1) T , y (k+2) T ,… , y (k+N) T ] T .
[0027] At the current operation point ( SOC 0, V p,0 ,T c,0 , I 0) Perform a first-order Taylor expansion and linearize the nonlinear terms, transforming them into:
[0028] The thermodynamic model can be transformed into:
[0029] The system state-space model can then be linearized as follows:
[0030] in: The output equation is:
[0031] in:
[0032] (3) Optimization problem solving First, we construct the objective function. The optimization problem can be expressed as: maximizing the discharge energy within the prediction time domain, therefore the objective function is:
[0033] Represent it in matrix form:
[0034] in:
[0035] Transform the above objective function into a quadratic programming form:
[0036] in:
[0037] The constraints for the optimization problem are constructed below, including dynamic constraints, electro-thermal physical constraints, and terminal voltage constraints.
[0038] The dynamic constraints are:
[0039] in, A eq The state equality constraint matrix is... B eq To control the input matrix, C eq A vector of constant terms. XIt is a state sequence vector. U To control the sequence vector; Specifically, it is expressed as follows:
[0040] Current, voltage, SOC, and temperature constraints are as follows:
[0041] The terminal voltage relaxation constraint is mainly used to limit the voltage to the lower discharge limit at the end of the discharge, so as to make full use of energy in the prediction time domain. Therefore, this constraint uses the lower voltage limit for relaxation to ensure that the optimal problem has a solution. This constraint is expressed as:
[0042] Among them, among them, V discharge,min Indicates the lower limit voltage of battery discharge. ε This represents the voltage relaxation parameter. V t For observation purposes, k At the current sampling time, N For prediction in the time domain.
[0043] Transform the relaxation constraints into matrix inequality form:
[0044] Finally, construct the quadratic programming problem:
[0045] (4) Online solution At each sampling time k Perform the following steps: ① State estimation: Estimate the current state based on sensor measurements. x ( k ); ② Linearization: Linearize the nonlinear model at the current operation point; ③ Matrix construction: Calculate the prediction matrix Ψ,Θ,Θ d ,Θ nl ④QP Construction: Constructing the Hessian matrix H and gradient vector f ⑤ Constraint Construction: Construct all constraint matrices and vectors; ⑥ QP Solving: Solve for the optimal sequence using the QP solver. U * ⑦ Power extraction: Calculate the instantaneous peak power SOP(k) = V t ( k )· I * ( k); ⑧ Rolling update: Repeat steps 1-7 at the next moment.
[0046] The formula for calculating the starting point of charging (SOP) is similar to that for discharging, with the key differences being as follows:
[0047] In summary, the implementation of this invention will generate extensive social benefits: it will provide more reliable battery safety guarantees for electric vehicles and energy storage systems, and strongly promote the healthy development of the new energy vehicle industry; by improving battery utilization efficiency and extending service life, it will significantly reduce resource consumption and environmental impact, and promote the development of a green, low-carbon, and circular economy; it will provide an advanced technical framework for battery management systems, promote the upgrading of my country's battery management technology towards intelligence and precision, enhance its international competitiveness in the field of core components for new energy vehicles, and provide important technical support for achieving the goal of carbon neutrality.
[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A battery peak power prediction method based on electro-thermal coupling model predictive control, characterized in that, include: An electrothermal coupled state-space model is constructed using electrical and thermal models; A model predictive control framework is constructed based on the electrothermal coupled state-space model. The problem of predicting battery peak power is constructed as a dynamic optimization problem in the prediction time domain; The objective function and constraints are constructed to solve the dynamic optimization problem, obtain the optimal current control sequence, and calculate the instantaneous battery peak power (SOP) based on the optimal current and the corresponding terminal voltage at the current moment.
2. The method according to claim 1, characterized in that, The electrothermal coupling state-space model is shown below: ， in, x It is a vector of state variables. u To control variables, d As a disturbance variable, y The output variable vector is represented by A, B, C, D, and E, which are system matrices.
3. The method according to claim 2, characterized in that, In the electrothermal coupled state-space model, the state variables include the battery state of charge (SOC), polarization voltage, and battery core temperature; the control variable is the battery operating current; and the output variables include the battery terminal voltage and core temperature.
4. The method according to claim 1, characterized in that, The model predictive control framework includes a prediction time domain, a control sequence, a state sequence, and an output sequence.
5. The method according to claim 1, characterized in that, The objective function is as follows: ， in, I For battery operating current, V t For observation, Δ T The sampling period.
6. The method according to claim 1, characterized in that, The constraints include kinetic constraints, electro-thermal physical constraints, and terminal voltage constraints; the kinetic constraints are: ， in, A eq The state equality constraint matrix is... B eq To control the input matrix, C eq A vector of constant terms. X It is a state sequence vector. U To control the sequence vector; The electro-thermal physical constraints include current, voltage, state of charge (SOC), and temperature constraints; The terminal voltage constraint is used to make the battery voltage at the end of the prediction time domain approach the set lower limit of discharge voltage or upper limit of charging voltage, provided that a feasible solution to the optimization problem exists.
7. The method according to claim 6, characterized in that, The terminal voltage constraint is as follows: ， in, Vdischarge,min Indicates the lower limit voltage of battery discharge. ε This represents the voltage relaxation parameter. Vt For observation purposes, k At the current sampling time, N For prediction in the time domain.
8. The method according to claim 1, characterized in that, The process of solving the dynamic optimization problem includes: S1. Based on sensor measurements, obtain the system state at the current moment through a state estimator. S2. Linearize the nonlinear model at the current operation point and calculate the prediction model matrix; S3. Based on the objective function and constraints, construct all the matrices and vectors for the quadratic programming problem; S4. Call the quadratic programming solver to solve for the optimal control sequence; S5. Extract the optimal current at the current moment and combine it with the terminal voltage calculated by the model to calculate the instantaneous peak power; S6 rolls to the next sampling time and repeats steps S1 to S5.