阅读说明：本技术 一种基于改进随机森林联合容积卡尔曼动力电池荷电状态估计的算法 (Algorithm based on improved random forest combined cubature Kalman power battery state of charge estimation ) 是由 寇发荣 王甜甜 张宏 王思俊 于 2019-12-03 设计创作，主要内容包括：本发明公开了一种基于改进随机森林联合容积卡尔曼动力电池荷电状态估计的算法,用来解决动力电池在工作中荷电状态准确估计的问题。该方法结合了随机森林回归和容积卡尔曼滤波算法联合估计动力电池荷电状态,并通过含有禁忌鲸鱼搜索算法加权优化随机森林的参数,以达到对算法剪枝阈值、预测试样本数、决策树数量最优化处理,优化算法能够快速找到最优解,提高算法效率；通过双向长短时记忆人工神经网络,对动力电池剩余寿命进行预测,以达到修正电池最大可用容量,提高全时工况动力电池荷电状态估计的精度的目的；通过联合估计算法综合了随机森林和容积卡尔曼滤波两种算法,发挥两种算法的优点,避免两者的缺点,使动力电池荷电状态估计精度更高。(The invention discloses an algorithm based on improved random forest combined cubature Kalman power battery state of charge estimation, which is used for solving the problem of accurate estimation of the state of charge of a power battery in working. The method combines random forest regression and a cubature Kalman filtering algorithm to jointly estimate the state of charge of the power battery, and weights and optimizes parameters of the random forest by a search algorithm containing a taboo whale so as to optimize the pruning threshold, the number of pretest samples and the number of decision trees of the algorithm, so that the optimization algorithm can quickly find the optimal solution, and the algorithm efficiency is improved; the residual service life of the power battery is predicted by memorizing the artificial neural network in a bidirectional long-time and short-time manner, so that the aims of correcting the maximum available capacity of the battery and improving the estimation precision of the state of charge of the power battery under the full-time working condition are fulfilled; the combined estimation algorithm integrates two algorithms of random forest and cubature Kalman filtering, the advantages of the two algorithms are brought into play, the defects of the two algorithms are avoided, and the estimation precision of the power battery charge state is higher.)

1. An algorithm based on improved random forest combined cubature Kalman power battery state of charge estimation is characterized in that: the algorithm based on the improved random forest combined cubature Kalman power battery state of charge estimation comprises the following steps:

step one, preparation work: and performing data offline acquisition on the power lithium ion battery under the working condition of a cycle test, and training a random forest based on the optimization of a taboo whale optimization algorithm by using offline data.

Step two, preparation work: and performing data offline acquisition on the power lithium ion battery under the cyclic test working condition, and training a bidirectional long-time and short-time memory artificial neural network by using offline data to complete the construction of an offline partial model.

Thirdly, collecting parameters of the power battery on line under the working condition of the real vehicle, and realizing the prediction of the residual service life of the battery through a bidirectional long-time memory artificial neural network; and inputting parameters such as residual life, current, voltage, temperature and the like into a random forest model under the whale taboo optimization, and realizing the real-time estimation of the charge state of the power battery.

And fourthly, power battery parameters are acquired on line under the actual vehicle working condition, the parameters are identified on line through a recursive least square method containing forgetting factors, and the posterior estimation of the power battery charge state by the volume Kalman filter is realized through the identified parameters.

And fifthly, predicting the residual life of the battery by using a bidirectional long-time memory artificial neural network, further obtaining the maximum available capacity through a corresponding formula, and correcting the prior estimation of the charge state of the volume Kalman filter.

And step six, using the innovation of the cubature Kalman filtering algorithm as a judgment standard, and fusing the two algorithms to realize more accurate state of charge estimation.

2. The algorithm for improving the estimation of the state of charge of the stochastic forest joint cubature kalman power battery according to claim 1, wherein the step one comprises the following steps: the method comprises the steps of collecting external characteristic data of the power lithium ion battery, establishing an SOC estimation random forest model, and collecting the historical capacity of the power lithium ion battery.

3. The algorithm for improving the estimation of the state of charge of the stochastic forest joint cubkalman power battery according to claim 1, wherein the second step specifically comprises the following steps: the method comprises the steps of conducting RUL prediction, establishing a bidirectional LSTM neural network RUL prediction model in the second step, and conducting a whale taboo search algorithm optimization algorithm in the third step.

4. The algorithm for improving the estimation of the state of charge of the stochastic forest joint cubkalman power battery according to claim 1, wherein the fourth step specifically comprises the following steps: the method comprises the steps of firstly establishing least square method model parameter online identification containing forgetting factors, secondly proposing a second-order equivalent model, thirdly carrying out model-based ampere-hour method SOC prior estimation and fourthly stepping volume Kalman filtering algorithm SOC posterior estimation.

5. The algorithm for improving the estimation of the state of charge of the stochastic forest joint cubkalman power battery according to claim 1, wherein the fifth step specifically comprises the following steps: the method comprises the steps of initializing a bidirectional LSTM neural network structure, training a bidirectional cyclic neural network, performing unidirectional prediction, performing bidirectional prediction in a fourth step, and performing RUL prediction in a fifth step.

6. The algorithm for improving the estimation of the state of charge of the stochastic forest joint volumetric kalman power battery according to claim 1, wherein the sixth step specifically comprises the following steps: the first step is to perform an innovation switching algorithm and the second step is to obtain an SOC estimation result.

Technical Field

The invention relates to the technical field of battery state of charge estimation of power battery systems of electric vehicles, in particular to an algorithm based on improved random forest combined cubature Kalman power battery state of charge estimation.

Background

The near-pure electric vehicle (BEV), as one of new energy vehicles, is an automobile that uses a power battery as an energy storage power source and provides electric energy to a motor through the power battery, so as to propel the automobile to travel. Compared with the traditional diesel locomotive, the diesel locomotive has the characteristics of no emission, low noise, high energy conversion efficiency and the like, and gradually replaces the traditional diesel locomotive to become the mainstream form of vehicles in the future.

In the field of electric automobiles, a power battery is an indispensable part in the three-electricity technology and provides motion energy of the whole vehicle system. The lithium ion power battery has the advantages of high energy density, high power density, long service life, high safety, high reliability, low self-discharge rate, light weight, no memory and the like. Because the lithium ion power battery has the defects of irreversible overcharge and overdischarge, severe characteristic change along with temperature change and the like, a complete Battery Management System (BMS) needs to be equipped so as to feed back and control the real-time state of the battery pack and ensure the safety and reliability of the power battery pack.

The State of charge (SOC) is the most important parameter in the power battery management system, and is also the most important part in the battery State detection function, and can only be estimated according to a model or a corresponding algorithm. However, due to the fact that the interior of the chemical battery is complex, the quantity of internal measurable parameters is very limited, characteristics of the chemical battery are mutually coupled, namely the parameters are attenuated immediately after use, strong time variation and high nonlinearity, and in addition, parameters such as current and temperature under the working condition of an actual vehicle are wide in variation range and high in variation rate, and the research of an estimation algorithm with high precision and high robustness is the key point of the estimation of the state of charge of the power battery.

The electrochemical reaction process inside the power lithium ion battery is complex, the actual working condition is complex and severe, and the estimation method of the charge state of the invisible state quantity can be roughly divided into four categories: an ampere-hour integration method, a characterization parameter method, a model-based method and a data-driven method. The estimation values based on a residual capacity method, an impedance spectroscopy method and an open-circuit voltage method in the characterization parameter method are very accurate, but all the estimation values need to be calibrated in a laboratory environment, otherwise, the precision cannot be guaranteed; the charge state estimation based on the ampere-hour integration method has the mutual influence of factors such as acquisition of an initial charge state, sensor error accumulation, capacity decline and the like, and the precision of the charge state estimation is often difficult to ensure after the battery is used for a long time; the model-based estimation method usually needs to establish a power battery equivalent circuit model and a state equation thereof, apply a filtering algorithm and an observer and establish a state of charge estimation algorithm, and the estimation accuracy of the method is determined by an estimation process and a correction process, so that the accuracy is high; based on a data driving method, a mapping network between power battery parameters and the state of charge is established and trained through massive offline data, and the method has good advantages for solving the problem of high nonlinearity, and is high in estimation accuracy and fitting performance.

At present, a plurality of methods for estimating the state of charge exist, but all the methods are respectively a single method and have the advantages and the defects. The method combines a random forest and neural network algorithm based on a data driving method and a cubature Kalman filtering algorithm based on a model method, and effectively combines the advantages of the two algorithms through innovation switching, so that the state of charge estimation is more accurate. Parameters of random forests are weighted and optimized through a search algorithm containing the taboo whale so as to achieve optimization processing of an algorithm pruning threshold, the number of pretest samples and the number of decision trees, the optimization algorithm can quickly find an optimal solution, and algorithm efficiency is improved; the residual service life of the power battery is predicted by memorizing the artificial neural network in a bidirectional long-time and short-time manner, so that the aims of correcting the maximum available capacity of the battery and improving the estimation precision of the state of charge of the power battery under the full-time working condition are fulfilled; the combined estimation algorithm integrates two algorithms of random forest and cubature Kalman filtering, and the advantages of the two algorithms are adopted, so that the defects of the two algorithms are avoided, and the estimation precision of the power battery charge state is higher. The algorithm overcomes the influence of the fluctuation of working conditions and environments on the estimation precision of the state of charge, and improves the generalization and the robustness of the state of charge estimation.

Disclosure of Invention

In order to solve the defects in the prior art, the invention provides an algorithm based on the improved random forest combined Kalman volume Kalman power battery state of charge estimation, and aims to solve the problem of accurate estimation of the state of charge of a power battery in working.

In order to achieve the purpose, the invention adopts the technical scheme that:

an algorithm based on improved random forest combined cubature Kalman power battery state of charge estimation is characterized in that: the algorithm for improving the estimation of the state of charge of the random forest combined cubature Kalman power battery comprises the following steps:

step one, preparation work: performing data offline acquisition on the power lithium ion battery under a cycle test working condition, and training a random forest based on optimization of a taboo whale optimization algorithm by using offline data;

step two, preparation work: and performing data offline acquisition on the power lithium ion battery under the cyclic test working condition, and training a bidirectional long-time and short-time memory artificial neural network by using offline data to complete the construction of an offline partial model.

Thirdly, collecting parameters of the power battery on line under the working condition of the real vehicle, and realizing the prediction of the residual service life of the battery through a bidirectional long-time memory artificial neural network; and inputting parameters such as residual life, current, voltage, temperature and the like into a random forest model under the whale taboo optimization, and realizing the real-time estimation of the charge state of the power battery.

Fourthly, power battery parameters are acquired on line under the working condition of the real vehicle, the parameters are identified on line through a recursive least square method containing forgetting factors, and the posterior estimation of the power battery charge state by the volume Kalman filter is realized through the identified parameters;

and fifthly, predicting the residual life of the battery by using a bidirectional long-time memory artificial neural network, further obtaining the maximum available capacity through a corresponding formula, and correcting the prior estimation of the charge state of the volume Kalman filter.

And step six, using the innovation of the cubature Kalman filtering algorithm as a judgment standard, and fusing the two algorithms to realize more accurate state of charge estimation.

Further, the step one specifically includes the steps of: the method comprises the steps of collecting external characteristic data of the power lithium ion battery, establishing an SOC estimation random forest model, and collecting the historical capacity of the power lithium ion battery.

Further, the second step specifically includes the following steps: the method comprises the steps of conducting RUL prediction, establishing a bidirectional LSTM neural network RUL prediction model in the second step, and conducting a whale taboo search algorithm optimization algorithm in the third step.

Further, the fourth step specifically includes the following steps: the method comprises the steps of firstly establishing least square method model parameter online identification containing forgetting factors, secondly proposing a second-order equivalent model, thirdly carrying out model-based ampere-hour method SOC prior estimation and fourthly stepping volume Kalman filtering algorithm SOC posterior estimation.

Further, the step five specifically comprises the following steps: the method comprises the steps of initializing a bidirectional LSTM neural network structure, training a bidirectional cyclic neural network, performing unidirectional prediction, performing bidirectional prediction in a fourth step, and performing RUL prediction in a fifth step.

Further, the sixth step specifically includes the following steps: the first step is to perform an innovation switching algorithm and the second step is to obtain an SOC estimation result.

The invention has the beneficial effects that: the method combines random forest regression and a cubature Kalman filtering algorithm to jointly estimate the state of charge of the power battery, and weights and optimizes parameters of the random forest by a search algorithm containing a taboo whale so as to optimize the pruning threshold, the number of pretest samples and the number of decision trees of the algorithm, so that the optimization algorithm can quickly find the optimal solution, and the algorithm efficiency is improved; the residual service life of the power battery is predicted by memorizing the artificial neural network in a bidirectional long-time and short-time manner, so that the aims of correcting the maximum available capacity of the battery and improving the estimation precision of the state of charge of the power battery under the full-time working condition are fulfilled; the combined estimation algorithm integrates two algorithms of random forest and cubature Kalman filtering, the advantages of the two algorithms are brought into play, the defects of the two algorithms are avoided, and the estimation precision of the power battery charge state is higher.

The foregoing description is only an overview of the technical solutions of the present invention, and in order to clearly understand the technical solutions of the present invention and to implement the technical solutions according to the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

FIG. 1 is a flow chart of algorithm for optimizing whale by the taboo search algorithm of the invention.

FIG. 2 is a flow chart of optimizing random forests by a whale taboo algorithm according to the invention.

FIG. 3 is a flow chart of the bidirectional long-time and short-time memory artificial neural network of the present invention.

FIG. 4 is a flow chart of estimating the state of charge of the power battery by the cubature Kalman filter.

FIG. 5 is a flow chart of an algorithm based on improved stochastic forest combined cubature Kalman power battery state of charge estimation.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.