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Improving the accuracy of state-of-charge (SoC) estimation is crucial for electric vehicles (EVs) using Lithium-Ion batteries (LiBs). This helps users reliably predict driving range and optimize the charging process, thereby extending battery life and ensuring safety during use. However, due to temperature, driving mode, and charge-dependent electrochemical nonlinear dynamics, SoC estimation for LiB integrated with EVs remains a significant technical challenge. In particular, SoC estimation in the regions of SoC < 30% and SoC > 80% is often inaccurate due to nonlinearity and sensitivity to battery aging. Accurate estimation in these regions is crucial for making decisions regarding recharging and discharging to prolong battery life and prevent damage. To address this issue, this paper proposes a method for SoC estimation using a Long Short-Term Memory (LSTM) network, which is capable of retaining information on battery characteristics related to changes long term electrochemical parameter changes, such as the number of discharge cycles and the aging effects. The method utilizes practical data from 80,000 samples collected from pure electric vehicle testing under different driving modes, temperatures, and road conditions over a 30-day period. The LSTM network was optimized by adjusting the input data sequence and hidden size to minimize the number of hyperparameters. This makes it suitable for use on low-cost processors with moderate computing power. SoC estimation was evaluated across four SoC test regions: SoC < 30%, SoC > 80%, 30% ≤ SoC ≤ 80%, and 0% ≤ SoC ≤ 100%. The results were compared with feedforward neural network (FNN) and convolutional neural network (CNN). Despite having a configuration with a hidden size of 96 and a single layer, the LSTM model achieved estimation accuracy with RMSE = 0.0106, MAE = 0.0077, and MAPE = 1.4116%.

As the use of energy storage systems (ESSs) and electric vehicles (EVs) increases, the importance of lithium-ion (Li-ion) batteries is also growing. The accurate capacity estimation of a battery is useful for detecting the degradation and end-of-life of the battery for scheduled maintenance and replacement, thereby improving the reliability of battery-powered systems. However, many of the capacity estimation methods developed thus far are offline methods and are not suitable for online monitoring. In this study, a novel capacity estimation method for Li-ion batteries is proposed, based on the enhanced coulomb counting (ECC) method. Typically, the battery capacity calculated by ECC has a large error owing to the error in the state of charge estimation. Therefore, additional means are required to obtain an accurate value for the battery capacity. The Kalman filter is an optimal estimator of the hidden state with Gaussian noise and is applied to the capacity values calculated by the ECC method. The performance of the proposed method in estimating capacity is verified through experiments with a Li-ion battery operated using a standard vehicle driving schedule. The results show that the online capacity estimation using the proposed method is performed successfully within 10 h with a 1.7% error.

In this paper, a novel noise adaptive moving horizon estimation (NAMHE) method is proposed to improve the accuracy of state of charge(SOC) estimation of Li-ion batteries under the unknown noise conditions. Specifically, based on the maximum likelihood principle, the unknown statistical characteristics of the noises can be estimated to modify the recurrence expression of the NAMHE. Then, the SOC estimation algorithm is designed by combining the equivalent circuit model and the proposed NAMHE. Furthermore, the convergence of the estimation error expectation is obtained for the NAMHE algorithm. Finally, the simulation results clarify that the SOC estimation error under the different unknown noise conditions can be effectively reduced by the proposed method, compared with the traditional MHE method.

Supercapacitors are an important part of the hybrid energy storage system for electric vehicles (EVs.) They not only protect the battery, but also improve the starting performance, acceleration performance, and regenerative braking performance of the EVs. Accurate modeling and state of charge (SOC) estimation of supercapacitors are essential for reliability, resilience, and safety in hybrid energy storage system operations. In this paper, a fractional order dynamic equivalent circuit model of the supercapacitor is proposed. Through the parameter identification and the experimental analysis, it is proved that the fractional equivalent circuit has better precision. Furthermore, the SOC estimation method of particle filter and Kalman filter is investigated, and a method of estimating supercapacitor SOC is proposed by combining the particle filter and the fractional Kalman filter. The validation results prove that the proposed method has better accuracy and real-time performance based on the fractional order.

This paper proposes an electrochemical-thermal coupling model for state of charge (SOC) estimation of lithium-ion batteries (LIBs). In this paper, to solve two numerical problems in the quasi two-dimensional model of LIBs, a simplified one-dimensional model is built. Considering that the parameters in the model are affected by temperature, the internal parameters of the battery which obey the Arrhenius equation are introduced. Based on the simplified one-dimensional model, the electrochemical-thermal coupling model is built. Finally, an adaptive SOC observer based on electrochemical-thermal coupling model is designed. The simulation results show that the electrochemical-thermal coupling model can be effectively applied to SOC estimation.

With the development of electric vehicles in recent years, lithium-ion batteries have been widely used. Accurate state of charge (SOC) estimation plays an important role in the safety of electric vehicles. Since the temperature has the significant influence on charge and discharge performance of the battery, it is critical to achieve accurate SOC estimation over the wide temperature range. In this paper, a polymer ternary lithium-ion battery is focused, and a Thevenin equivalent circuit model with temperature compensation is established. The validity of the established battery model was verified by the dynamic stress test. On this basis, the ternary lithium-ion battery SOC was estimated using the unscented Kalman filter (UKF). The New European Driving Cycle is used to verify the effectiveness of the proposed algorithm. The simulation and experimental results show that the established Thevenin equivalent circuit model with temperature compensation can accurately represent the battery dynamics. Based on this model, the SOC was estimated using the UKF and the maximum errors are within 3%. Therefore, the proposed SOC estimation method is verified to be effective and robust.

State of Charge (SOC) is a key parameter for battery management and vehicle energy management. Recently used SOC estimation methods for lithium-ion battery for vehicles have problems of too simple a base model for the battery and large sampling noise in both the voltage and current signals. To improve the accuracy of SOC estimation and consider that the extended Kalman filter algorithm needs linear approximation of the system equation, the unscented Kalman filter (UKF) algorithm was used to reduce the influence of sampling noise, and an improved algorithm with better filtering effect and SOC estimation accuracy was proposed. Based on the SOC estimation and battery model, the peak power prediction method for the battery is proposed and used in the power distribution strategy for Series HEV. Considering the frequent changes in load current and sampling noise, an experiment was designed to verify the effectiveness and robustness of the algorithm. The experimental results show that the UKF algorithm and the improved UKF algorithm can achieve 6% and 1.5% estimation error. The power distribution strategy based on battery SOC estimation and peak power prediction is tested and validated.