Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.

Accurate estimation of the state of charge (SOC) and temperature of batteries is essential to ensure the safety of energy storage systems. However, it is very difficult to obtain multiple states of the battery with fewer sensors. In this paper, a joint estimation method for a lithium iron phosphate battery’s SOC and temperature based on ultrasonic reflection waves is proposed. A piezoelectric transducer is affixed to the surface of the battery for ultrasonic–electric transduction. Ultrasonic signals are excited at the transducer, transmitted through the battery, and transmitted back to the transducer by reaching the underside of the battery. Feature indicator extraction intervals of the battery state are determined by sliding–window matching correlation analysis. Virtual samples are used to expand the data after feature extraction. Finally, a backpropagation (BP) neural network model is applied to the multistate joint estimation of a battery in a wide temperature range. According to the experimental results, the root mean square error (RMSE) of the lithium-ion battery’s SOC and temperature estimation results is 7.42% and 0.40 °C, respectively. The method is nondestructive and easy to apply in battery management systems. Combined with the detection of gas production inside the battery, this method can improve the safety of the battery system.

In this paper, a foreign object detection (FOD) method based on symmetrical coil sets is proposed to prevent wireless power transfer (WPT) systems from heating, which could possibly cause an accidental fire. First, the influence of foreign metal object on magnetic field distribution of the WPT system is analyzed. Then, a two-layer symmetrical detection coil set is proposed considering the magnetic field characteristics. This kind of FOD method can reduce the design complexity and the detection strategy is easily realized. The mutual inductance variation patterns are used to detect the presence and location of metal objects. Four scenarios of foreign object intrusion, including one coin with different locations, a coin on top of two adjacent detection coils with the same cover area, different test bodies, and different numbers of coins are taken into consideration for validating the effectiveness of the proposed FOD method. Finally, a WPT system prototype with symmetrical coil sets for FOD detection is established. Both the simulation and experimental results indicate that the proposed method can effectively detect the presence and location of foreign objects. INDEX TERMS Wireless power transfer, Symmetrical coil sets, Foreign object detection, Heating effect.

State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.

An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.