Mingyu Gao

State of Charge Estimation Online Based on EKF-Ah Method for Lithium-Ion Power Battery

As for battery management systems (BMS), it is the most important and significant aspect to estimate state of charge (SOC) accurately, which can provide the judgment basis to system control strategy. In view of the lithium-ion power batterys properties and its operation condition in electric vehicles, we propose a new method named EKF-Ah that derives from extended Kalman filtering (EKF) algorithm and ampere hour counting method. This method has a good performance on SOC estimation in complicated environment and is able to accomplish the requirements on power batteries. The paper covers the definition of SOC, analyzes and compares some common used estimations , finally discusses the EKF-Ah method in detail. Results of laboratory tests show that the maximal SOC estimation error is under 6.5%, which validates the feasibility and availability of the EKF-Ah online estimation.

Design and Study on the State of Charge Estimation for Lithium-ion Battery Pack in Electric Vehicle

State of charge (SOC) estimation is an increasingly important issue in battery management system (BMS) and has become a core factor to promote the development of electric vehicle (EV). In addition to offering the real time display of battery parameters to user, the accurate SOC information would exert some controls over the charging and discharging process that in turn reduces the risk of cell over voltage. Considering the shortcoming of traditional estimation methods and the harsh requirements in EV environment, a new method named combination algorithm is proposed in this paper in accordance with the characteristics of lithium-ion power battery. Some capacity effect factors, such as current rate and cell temperature, are also taken into consideration in the algorithm. The dynamic discharge test shows that the maximal SOC estimation error is less than 5%, which validates the feasibility and availability of the combination algorithm in the SOC estimation of electric vehicle.

EKF-Ah Based State of Charge Online Estimation for Lithium-ion Power Battery

One of the most essential and significant aspect of the battery management systems (BMS) is to estimate the state of charge (SOC) accurately, which can provide the judgment basis to system control strategy. In view of the lithium-ion power battery’ s properties and its operation condition in electric vehicles, a new method named EKF-Ah that derives from the extended Kalman filtering (EKF) algorithm and ampere hour counting method is proposed, which has a good performance on SOC estimation in complicated environment and is able to accomplish the requirements on power batteries. Results of tests show that the maximal SOC estimation error is fewer than 6.5%, which validates the feasibility and reliability of the proposed method.

Battery Model Parameters Estimation with the Sigma Point Kalman Filter

Accurate estimation of the State of Charge (SOC) of the battery is one of the key problems to the battery management system. The SOC should be obtained indirectly according to some algorithms under a mathematical model, along with some measurable quantities. A Sigma Point Kalman Filter based battery model parameters estimation method is proposed. The parameters can be estimated accurately while efficiently with the proposed method. Compared to the classical least squares method, the proposed method consumes much less memory and calculation time, which makes it suitable for embedded applications.

A joint model and SOC estimation method for lithium battery based on the sigma point KF

Lithium-ion batteries have been widely used in electric vehicles (EV). The working state of the battery is very important to the safety of an EV. Online estimation of the state of charge (SOC) is essential in obtaining the battery working conditions. In order to achieve an accurate estimation of the SOC, the battery model should be adjustable when the battery is aged. A joint battery model and SOC estimation method based on the sigma point kalman filter (SPKF) is presented. A combined battery model is used to depict the relationship between the open circuit voltage (OCV) and the SOC of the battery. The main battery model parameter for estimation is the internal resistance and it is jointly estimated with the SOC online. Experimental results show that the SPKF based joint estimation method is effective.

Dual estimation of lithium-ion battery internal resistance and SOC based on the UKF

Lithium-ion batteries have been widely used in many fields. In order to make the battery work on good conditions, people should monitor its working states continuously. The two important working state of a battery are its internal resistance and the State of Charge (SOC). A dual estimation method of the internal resistance and the SOC based on the Unscented Kalman Filter (UKF) is proposed. The internal resistance is regarded as a parameter of a mathematical model, which reflects the relationship between the voltage and the SOC, the discharging rate and the temperature. The dual estimation of the internal resistance and the SOC are then done alternately with different UKF algorithms. Experimental results show that the proposed method is effective.

An Improved Example-Based Texture Synthesis Algorithm

Texture synthesis is a modern technology which grows rapidly in recent ten years. An improved example based texture synthesis algorithm is proposed. The basic idea is from the famous texture synthesis algorithm of WL00[4].Two improvements are done in this paper. The first one is to automatically determine the neighborhood size during the neighborhood match process, the second one is that each time three pixels other than one are synthesized to accelerate the synthesis procedure. Experimental results show that the proposed algorithm is effective and efficient.

Underground garage LED lighting control system based on video analysis

The reliability of the movement of the target discrimination based on the traditional control mode such as infrared sensor and radar sensor is poor. This paper designs a control system of underground garage LED lighting based on moving target detection. It is an intelligent energy saving control for underground garage lighting based on video image processing technology. The experimental results show that the designed system can accurately detect moving object. The system can save the under-ground garage lighting energy more than 60% by the system quickly automatically increase the LED lamp b rightness when the moving target assesses to the garage and down the LED lamp brightness when the target leaves the garage.

WC-SWFA algorithm in the application of constant tension control of the yarn

The aim of constant tension control of the yarn is to keep the yarns tension to be consistent during the winding process by controlling the speed of the motor according to the output voltages of the tension sensor. The mechanical structure of spinning machines such as the winding machine makes the fluctuation of the yarns tension changes rapidly. In this paper, we propose a new tension control algorithm, which is called the weighted curve sliding window filtering algorithm (WC-SWFA). According to this algorithm, the brushless direct current (DC) motor can be controlled to operate stably and efficiently, and the yarns will be shaped well. Theoretical analysis is firstly carried out to verify its effectiveness. Laboratory experiments are then put in force. In the experiments, an arbitrary waveform generator (AFG3252) is used to generate the simulated fluctuation signals of the tension, and an STM32 micro-controller is used to control the rotation of the motor, with the proposed WC-SWFA served as the control algorithm. Practical experiments on the winding machine show that the proposed algorithm works effectively.