Battery state of charge estimation using temporal convolutional network based on electric vehicles operating data

An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries

Highly accurate state of charge (SOC) estimation of lithium-ion batteries is one of the key technologies of battery management systems in electric vehicles. The performance of SOC estimation directly influences the driving range and safety of these vehicles. Due to external disturbances, temperature variation and electromagnetic interference, accurate SOC estimation becomes difficult. To accurately estimate the SOC of lithium-ion batteries, this article presents a novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM).The proposed hybrid NARX model embeds LSTM memory, which provides jump-ahead connections in the time-unfolded model. These jump-ahead connections provide a shorter path for the propagation of gradient information, therefore reducing long-term dependence on the recurrent neural network. Experimental results show that the estimation performance root mean square error (RMSE) of the proposed model is less than 1%, and this model has better multitime prediction performance. Finally, the hybrid NARX and LSTM model is compared with the standard back propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) and LSTM existing models under urban dynamometer driving schedule (UDDS) and dynamic stress test (DST) conditions. The proposed hybrid NARX-LSTM model yield relative to other methods and can estimate the battery SOC with high accuracy. The RMSE of proposed model is improved by approximately 60% compared with the standard LSTM.

Battery is one of the main parts in Electrical vehicles as it serves as energy source. In order to prolong battery lifetime and optimally operate the battery, battery performance needs to be monitored and controlled using Battery Management System (BMS). One among important BMS functions is to determine the amount of remaining energy capacity available in a battery, represented by State of Charge (SOC), through estimation. The SOC estimation accuracy is important since it is used as indicator to prevent battery from failures due to overcharging and over discharging. One of the SOC estimation methods is Coulomb counting (CC) which is easy to implement. However, a false initial value of the SOC in this method will degrade the accuracy of the SOC estimation. In this paper an SOC estimation method is proposed by using CC method combined with recursive least square (RLS) algorithm and Proportional Integral (PI) controller to correct the error of SOC estimation due to false initial value of SOC. The result indicates that the proposed method is able to correct the error of SOC estimation due to false initial value of SOC with average of SOC estimation error equals 0.498%.

In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

In an electric vehicle (EV), the battery management system (BMS) is crucial for managing the health and safety of the battery. The accurate estimation of battery state of charge (SOC) offers critical information about the battery’s remaining capacity. The SOC of the battery mainly depends on its non-linear internal parameters, battery chemistry, ambient temperature, aging factor etc. So, accurate SOC estimation is still a significant challenge. Many researchers have developed several model-based methods that are more complex to develop. Another approach is a data-driven based SOC estimation algorithm, which is less complex but requires more data and it may be inaccurate. In this context, this paper presents a robust and accurate SOC estimation algorithm for a Lithium-ion battery using a deep learning feed-forward neural network (DLFFNN) approach. The proposed algorithm accurately characterizes the battery’s non-linear behavior. To develop a robust SOC estimation algorithm, data is collected at different temperatures with 5% error in data (4 mV-voltage, 110 mA-current, 5∘C temperature) is added to battery datasets. The obtained results demonstrated that the performance of the proposed DLFNN is robust and accurate on different drive cycles with 1.14% Root mean squared error (RMSE), 0.66% mean absolute error (MAE), and 6.65% maximum error (MAX).

This paper proposed a battery state of charge (SOC) estimation methodology utilizing the Extended Kalman Filter. First, Extended Kalman Filter for Li-ion battery SOC was mathematically designed. Next, simulation models were developed in MATLAB/Simulink, which indicated that the battery SOC estimation with Extended Kalman filter is much more accurate than that from Coulomb Counting method. This is coincident with the mathematical analysis. At the end, a test bench with Lithium-Ion batteries was set up to experimentally verify the theoretical analysis and simulation. Experimental results showed that the average SOC estimation error using Extended Kalman Filter is <;1%.

The key technology of electric vehicle battery management lies in the battery state of charge (SOC) estimation, accurate and efficient estimation of SOC can provide the reference data for the control system of the electric vehicle in time. On the basis of the Ah measurement method, Calman filtering method, open circuit voltage method to estimate method, the estimation procedure to estimate the charged battery optimized Calman filtering method, effectively shorten the calculation process, improve the estimation accuracy, in the normal operation of electric vehicles under the influence of comprehensive consideration about the discharge voltage, battery temperature to estimate the state of charge, the Calman filter optimization algorithm, and its application in the electric vehicle battery management system. The experimental results show that, the algorithm can calculate the real-time high charge state of surplus value, provides a set of effective estimation scheme of calculating the battery charge state.

This paper considers the issue of Li-Ion batteries State of Health (SoH) and State of Charge (SoC) accurate and robust estimation for electric vehicle applications. SoC and SoH are two monitoring indicators of primary importance that are used by the Battery Management System (BMS) , amongst other benefits, to manage and equalize the battery cells. Improving the estimation precision and reliability of the SoC and the SoH indicators is highly beneficial during operation and maintenance of the vehicle. We propose in this paper a new scheme of SoC and SoH estimation using an hybridization of Kalman filtering, Recursive Least Squares approach and Support Vector Machines learning. The battery SoC and SoH indicators are estimated using an adaptive-Sigma Point Kalman Filter. The battery cell impedance equivalent filter is obtained in real-time by a Recursive Least Square. Furthermore, the cell capacity evolution tracking is achieved by using a Support Vector Machine (SVM) method. Finally, the battery cell capacity and impedance equivalent filter are provided to the SoC estimator in order to update its state and observation models. This architecture yields to a complete SoC and SoH algorithmic solution exhibiting a high level of accuracy and robustness. The SVM method which requires the highest computational load in the architecture is designed to be used only for estimating the variable with the lowest evolution dynamics.

For autonomous mobile robots moving in unknown environment, accurate estimation of available power along with the robot power demand for each mission is paramount to successful completion of that mission. Regarding the power consumption, the control unit deals with two tasks simultaneously: 1) it has to monitor the power supply (batteries) state of charge (SoC) constantly. This leads to estimation of robot current available power. Besides, batteries are sensitive to deep discharge or overcharge. The battery SoC is an essential factor in power management of a mobile robot. Accurate estimation of the battery SoC can improve power management, optimize the performance, extend the lifetime, and prevent permanent damage to the batteries. 2) The dynamic characteristics of the terrain the robot traverse requires rapid online modifications in its behaviour. The power required for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving on different terrains, slip of the wheels should be checked and compensated for to keep the robot moving with less power consumption. To reduce the power consumption, the target robot moving with less power consumption. To reduce the power consumption, the target of the control system is to keep the slip ratio of the driving wheels around the desired value of the control system is to keep the slip ratio of the driving wheels around the desired value. To fulfill the above mentioned tasks, in this thesis, to increase model validity of lithium-ion battery in various charge/discharge scenarios during the mobile robot operation, the battery capacity fade and internal resistance change are modeled by adding them as state variables to a state space model. Using the output measured data, adaptive unscented Kalman Filter (AUKF) is employed for online model parameters identification of the equivalent circuit model at each sampling time. Subsequently, based on the updated model parameters, SoC estimation is conducted using AUKF. The effectiveness of the proposed method is verified through experiments under different power duties in the lab environment through experiments under different power duties in the lab environment. Better results are obtained both in battery model parameters estimation and the battery SoC estimation in comparison with other Kalman filter extensions. Furthermore, for effective control of the slip ratio, a model-based approach to estimating the longitudinal velocity of the mobile robot is presented. The AUKF is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity using measurements from wheel encoders. Based on the estimated slip ratio, a sliding mode controller is designed for slip control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter variations, and disturbances. Experiments are carried out in real time on a four-wheel mobile robot to verify the effectiveness of the estimation algorithm and the controller. It is shown that the controller is able to control the slip ratio of the mobile robot on different terrains while adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.

Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data

Accurately estimation of battery state of charge (SOC) has great significance for predicting the remaining mileage of electric vehicles. In order to improve the estimation accuracy of battery SOC and enhance the stability of traditional unscented Kalman filter algorithm, an improved unscented Kalman filter based on singular value decomposition was proposed to estimate the battery SOC. First, the LiFePO4 was used as the research object to establish the Thevenin model of the battery. By using the forgetting factor recursive least squares to identify parameters such as ohmic internal resistance, polarization resistance, and polarization capacitance in the model under actual working conditions, the identified model parameters were imported into the improved algorithm in real time to estimate the SOC of the lithium battery and compared with the extended Kalman filter. Experiments proved that the maximum error estimated by the algorithm used in this paper is not more than 2%, which can effectively improve the accuracy of SOC estimation and ensure the normal operation of the algorithm.

The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.

Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model to enable accurate SoC estimation. The proposed method is evaluated using two dynamic battery tests: the dynamic street test (DST) and the federal urban driving schedule (FUDS) test. The simulation results show that the proposed method achieved high estimation accuracy and fast response speed. Additionally, real-time battery information, including battery output voltage and SoC, was acquired and displayed by an automatic monitoring system. The designed system is valuable for all battery application cases.

In order to effectively achieve MH-Ni battery state of charge estimation, grey system neural network model is put forward to predict battery state of charge by using the parameters of battery pulse current response signal as input for grey system neural network. The state of charge is as the network output and the response parameters of the battery pulse current as the input. The results show that its prediction accuracy of the state of charge can be achieved to requirements of the electric vehicles in applications by this method to predict the state of charge.