CN-121515830-B - New energy automobile battery management method and system

CN121515830BCN 121515830 BCN121515830 BCN 121515830BCN-121515830-B

Abstract

The invention relates to the technical field of power battery detection and control, in particular to a new energy automobile battery management method and a system thereof. The method comprises the steps of synchronously collecting voltage, current and temperature data of a battery cell, executing frequency scanning to obtain an electrochemical impedance spectrum, extracting impedance parameters and calculating a change rate based on an equivalent circuit model, adopting a double-time scale self-adaptive Kalman filtering algorithm to adjust a noise covariance matrix to realize joint estimation of a state of charge and a health state, determining a target state of charge range of the battery through a multi-target optimization algorithm based on the current state of charge, road condition power requirements and engine fuel consumption rate, regulating the operation of a liquid cooling system by utilizing a temperature threshold strategy, maintaining the temperature of the battery in a safe interval, and predicting battery attenuation and alarming by adopting a random forest based on historical charge and discharge data and temperature information. The method can realize accurate estimation and dynamic management of the battery state, and improves the energy utilization efficiency and operation safety under the mixed working condition.

Inventors

JIA JUNTAO
LU LIXIN
SHAO YIJIA
SHAN XINGHUA
ZHAO FEI

Assignees

河北科技工程职业技术大学

Dates

Publication Date: 20260508
Application Date: 20251204

Claims (8)

1. The new energy automobile battery management method is characterized by comprising the following steps of: Synchronously acquiring battery monomers, including voltage, current and temperature data, and performing frequency scanning in the acquisition process to acquire electrochemical impedance spectrum data; Receiving the electrochemical impedance spectrum data, extracting ohmic impedance, charge transfer impedance and diffusion impedance parameters based on an equivalent circuit model, and calculating the change rate of impedance characteristic parameters; Adopting a double-time scale self-adaptive Kalman filtering algorithm, and dynamically adjusting a noise covariance matrix in the algorithm according to the change rate of the impedance characteristic parameters to realize joint estimation of the state of charge and the state of health of the battery; Dynamically determining a battery target state of charge range through a multi-target optimization algorithm based on the current state of charge, the predicted road condition power demand and the engine fuel consumption rate; According to the temperature distribution of the battery monomers, the working state of the liquid cooling system is regulated through a temperature threshold control strategy, so that the temperature of the battery is maintained within a preset safety range; based on historical charge and discharge data and temperature information of the battery, a local experience attenuation model is established, and an alarm is sent when the state of charge exceeds a preset threshold value so as to assist in battery state monitoring and life management; wherein the step of jointly estimating the state of charge and the state of health of the battery comprises: receiving input data comprising voltage, current, temperature and impedance characteristic parameter change rate; Performing a fast state update based on the real-time voltage and current data in a short time scale to obtain an initial estimate of the state of charge of the battery; Correcting the estimated value of the battery state of health based on the impedance characteristic parameter change rate and the historical operation data in a long time scale; And dynamically adjusting a noise covariance matrix in a filtering algorithm according to the battery operation condition, and fusing a state-of-charge estimation result in a short time scale with a state-of-health estimation result in a long time scale to output a combined state estimation value of the battery.
2. The method for managing the battery of the new energy automobile according to claim 1, wherein the frequency range of the frequency scanning is 0.01Hz to 10kHz, the number of scanning points is not less than 50, and the single scanning time is controlled within 30 seconds.
3. The method for managing a battery of a new energy automobile according to claim 1, wherein the step of calculating a rate of change of the impedance characteristic parameter comprises: Performing frequency domain interpolation and noise filtering treatment on the electrochemical impedance spectrum data to obtain a smooth impedance curve; at a preset frequency point, fitting impedance spectrum based on an equivalent circuit model, and extracting corresponding ohmic impedance, charge transfer impedance and diffusion impedance parameters; comparing the current extracted impedance parameter with the initial parameter in the reference state, and calculating the relative change value of each impedance parameter; And calculating the change rate of the impedance characteristic parameter according to the relative change value.
4. The method of claim 1, wherein the step of dynamically determining the target state of charge range of the battery comprises: Acquiring current battery charge state, vehicle running condition and road condition prediction information; Calculating predicted road condition power demands in a future period based on the vehicle power demand prediction model; determining the energy efficiency of the system under different charge states by combining the fuel consumption rate of the engine; constructing a multi-objective optimization model targeting fuel economy, power responsiveness and battery life decay minimization; And dynamically determining the target state of charge range of the battery according to the optimization result.
5. The method for managing a battery of a new energy automobile according to claim 4, wherein the multi-objective optimization model is solved by Pareto front search, and the solving step includes: s401, initializing an optimized population, and setting population scale, iteration times and multi-target weight parameters; s402, carrying out fitness evaluation on each individual coding SOC target range parameter based on a fuel economy, battery life attenuation, power responsiveness and thermal safety objective function; s403, grading individuals by adopting a non-dominant sorting method, and calculating a crowding distance to maintain population diversity; s404, performing selection, crossing and mutation operations according to individual grades and crowding degree to generate a new generation of candidate solution; And S405, repeating the steps S402 to S404 until the iteration termination condition is met, and obtaining the Pareto optimal solution set meeting the multi-objective constraint.
6. The method for managing a battery of a new energy automobile according to claim 1, wherein the temperature threshold control strategy is implemented by a PID controller, and the controlling step includes: Measuring the temperature of the battery cell and calculating the temperature deviation; the flow of the liquid cooling system is regulated according to a proportion link so as to directly respond to the temperature deviation; accumulating the temperature deviation by using an integration link to eliminate steady-state errors; the differential link is utilized to adjust the cooling flow according to the temperature change rate so as to inhibit the temperature change from being too fast; and integrating output signals of the proportional, integral and differential links to determine the final control flow of the liquid cooling system.
7. The method for managing a battery of a new energy automobile according to claim 1, wherein the step of establishing a local empirical decay model comprises: collecting historical charge and discharge data, current, voltage and temperature information of a battery; training a random forest model based on historical data for predicting battery capacity attenuation and health status indexes; In each charge-discharge cycle process, inputting current battery state data to the random forest model, and outputting predicted capacity attenuation and health state; When the predicted capacity or state of health indicator exceeds a preset threshold, an alarm signal is triggered to assist in battery state monitoring and life management.
8. A new energy automobile battery management system for implementing the battery management method of any one of claims 1 to 7, the system comprising: The data acquisition module is used for synchronously acquiring the battery monomers, including voltage, current and temperature data, and performing frequency scanning in the acquisition process to acquire electrochemical impedance spectrum data; The impedance characteristic extraction module is used for receiving the electrochemical impedance spectrum data, extracting ohmic impedance, charge transfer impedance and diffusion impedance parameters based on an equivalent circuit model, and calculating the change rate of the impedance characteristic parameters; The battery state evaluation module is used for adopting a double-time scale self-adaptive Kalman filtering algorithm, dynamically adjusting a noise covariance matrix in the algorithm according to the change rate of the impedance characteristic parameters, and realizing joint estimation of the battery state of charge and the state of health; The charge state range optimization module is used for dynamically determining a battery target charge state range through a multi-target optimization algorithm based on the current charge state, the predicted road condition power demand and the engine fuel consumption rate; The liquid cooling system control module is used for adjusting the working state of the liquid cooling system according to the temperature distribution of the battery monomers and a temperature threshold control strategy so as to maintain the temperature of the battery within a preset safety range; the battery state early warning module is used for establishing a local experience attenuation model based on battery historical charge and discharge data and temperature information, and sending an alarm when the state of charge exceeds a preset threshold value so as to assist battery state monitoring and service life management.

Description

New energy automobile battery management method and system Technical Field The invention relates to the technical field of power battery detection and control, in particular to a new energy automobile battery management method and a system thereof. Background With the rapid development of new energy automobile industry, the performance and the service life of the power battery serving as a core energy device directly influence the endurance, the safety and the economical efficiency of the whole automobile. In order to ensure the efficient and safe operation of the battery, the battery management system becomes an important component of the new energy automobile. The main functions of the BMS include battery state monitoring, state of charge estimation, state of health estimation, thermal management, safety protection, and the like. In hybrid electric vehicles and plug-in hybrid electric vehicles, a battery cooperates with an engine, the energy flow of the system is complex, and the battery is frequently in a charge-discharge alternating state. This significantly increases the difficulty of SOC and SOH estimation of the battery, and conventional SOC estimation methods based on open circuit voltage or coulomb integration are susceptible to noise accumulation and model errors, resulting in insufficient estimation accuracy. Meanwhile, SOH estimation generally depends on long-term historical data, cannot reflect battery performance changes in real time, and is difficult to meet dynamic response requirements under mixed operating conditions. Disclosure of Invention In order to make up for the defects, the invention provides a new energy automobile battery management method and a system thereof, which aim to improve the accuracy of battery state evaluation and the operation safety, prolong the service life of the battery and optimize the energy utilization efficiency of the whole automobile. In a first aspect, the present invention provides a method for managing a new energy automobile battery, including: Synchronously acquiring battery monomers, including voltage, current and temperature data, and performing frequency scanning in the acquisition process to acquire electrochemical impedance spectrum data; receiving the electrochemical impedance spectrum data, extracting ohmic impedance, charge transfer impedance and diffusion impedance parameters based on an equivalent circuit model, and calculating the change rate of the impedance characteristic parameters; Adopting a double-time scale self-adaptive Kalman filtering algorithm, and dynamically adjusting a noise covariance matrix in the algorithm according to the change rate of the impedance characteristic parameters to realize joint estimation of the state of charge and the state of health of the battery; Dynamically determining a battery target state of charge range through a multi-target optimization algorithm based on the current state of charge, the predicted road condition power demand and the engine fuel consumption rate; According to the temperature distribution of the battery monomers, the working state of the liquid cooling system is regulated through a temperature threshold control strategy, so that the temperature of the battery is maintained within a preset safety range; based on the historical charge and discharge data of the battery and the temperature information, a local empirical decay model is established, and an alarm is sent when the state of charge exceeds a preset threshold value so as to assist in battery state monitoring and life management. Preferably, the frequency range of the frequency scanning is 0.01Hz to 10kHz, the number of scanning points is not less than 50 frequency points, and the single scanning time is controlled within 30 seconds. Preferably, the step of calculating the rate of change of the impedance characteristic parameter includes: Performing frequency domain interpolation and noise filtering treatment on the electrochemical impedance spectrum data to obtain a smooth impedance curve; at a preset frequency point, fitting impedance spectrum based on an equivalent circuit model, and extracting corresponding ohmic impedance, charge transfer impedance and diffusion impedance parameters; comparing the current extracted impedance parameter with the initial parameter in the reference state, and calculating the relative change value of each impedance parameter; And calculating the change rate of the impedance characteristic parameter according to the relative change value. Preferably, the step of jointly estimating the state of charge and the state of health of the battery comprises: receiving input data comprising voltage, current, temperature and impedance characteristic parameter change rate; Performing a fast state update based on the real-time voltage and current data in a short time scale to obtain an initial estimate of the state of charge of the battery; Correcting the estimated value of the battery state of health based o