Green Energy and Intelligent Transportation最新文献

The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge (SOC) and state-of-temperature (SOT) of Lithium-ion (Li-ion) batteries. Given the influence of cross-interference between the two states indicated above, this study establishs a co-estimation framework of battery SOC and SOT. This framwork is based on an innovative electrothermal model and adaptive estimation algorithms. The first-order RC electric model and an innovative thermal model are components of the electrothermal model. Specifically, the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional (2-D) thermal resistance network (TRN) submodel for the main battery body, capable of capturing the detailed thermodynamics of large-format Li-ion batteries. Moreover, the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances. Besides, the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter (AUKF) and an adaptive Kalman filter (AKF), which adaptively update the state and noise covariances. Regarding the estimation results, the mean absolute errors (MAEs) of SOC and SOT estimation are controlled within 1% and 0.4 ​°C at two temperatures, indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35 ​°C.

Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery, thereby influencing safety of entire electric vehicles. Precise estimation of battery model parameters using key measured signals is essential. However, measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors, potentially diminishing model estimation accuracy. Addressing the challenge of accuracy reduction caused by noise, this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares (BCFFRLS) method. Initially, a variational error model is crafted to estimate the average weighted variance of random noise. Subsequently, an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors, compensating for bias in the parameter estimates. To assess the proposed method's effectiveness in improving parameter identification accuracy, lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule (UDDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC). The proposed method, alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares (FFRLS)—was employed for battery model parameter identification. Comparative analysis reveals substantial improvements, with the mean absolute error reduced by 25%, 28%, and 15%, and the root mean square error reduced by 25.1%, 42.7%, and 15.9% in UDDS, HPPC, and DST operating conditions, respectively, when compared to the FFRLS method.

Accurate state of charge (SOC) estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles, and it is also a key technology component in battery management systems. In recent years, lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity. However, these methods commonly face the issue of poor model generalization and limited robustness. To address such issues, this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression (SA-SVR) combined with minimum error entropy based extended Kalman filter (MEE-EKF) algorithm. Firstly, a probability-based SA algorithm is employed to optimize the internal parameters of the SVR, thereby enhancing the precision of original SOC estimation. Secondly, utilizing the framework of the Kalman filter, the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF, while the ampere-hour integral physical model serves as the state equation, effectively attenuating the measurement noise, enhancing the estimation accuracy, and improving generalization ability. The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training. The results demonstrate that the proposed method achieves a mean absolute error below 0.60% and a root mean square error below 0.73% across all operating conditions, showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms. The high precision and generalization capability of the proposed method are evident, ensuring accurate SOC estimation for electric vehicles.

This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.