Search Results (3)

The pure pursuit (PP) method has been widely employed in automated guided vehicles (AGVs) to address path tracking challenges. However, the traditional pure pursuit method exhibits certain limitations in tracking performance. For instance, selecting a look-ahead point that is too close can lead to oscillations during tracking, while selecting one that is too far away can result in slow tracking and corner-cutting issues. To address these challenges, this paper proposes a multistep prediction pure pursuit method. First, the look-ahead distance calculation equation is adjusted by incorporating path curvature, allowing it to adaptively adjust according to road conditions. Next, to avoid oscillations caused by constant changes in the look-ahead distance, this paper adopts the prediction concept of model predictive control (MPC) to make multistep predictions for the pure pursuit method. The final input is derived from a linear weighted combination of the multistep prediction results. Simulation analyses and experiments demonstrate that the multistep predictive pure pursuit method significantly enhances the tracking performance of the traditional pure pursuit method. Full article

Lithium-ion (Li-ion) batteries are considered to be one of the ideal energy sources for automotive and electronic products due to their size, high levels of charge, higher energy density, and low maintenance. When Li-ion batteries are used in harsh environments or subjected to poor charging habits, etc., their degradation will be accelerated. Thus, online state of health (SOH) estimation becomes a hot research topic. In this study, normalized capacity is considered as SOH for the estimation and calculation of remaining useful lifetime (RUL). A multi-step look-ahead forecast-based deep learning model is proposed to obtain SOH estimates. A total of six batteries, including three as source datasets and three as target datasets, are used to validate the deep learning model with a transfer learning approach. Transferability measures are used to identify source and target domains by accounting for cell-to-cell differences in datasets. With regard to the SOH estimation, the root mean square errors (RMSEs) of the three target batteries are 0.0070, 0.0085, and 0.0082, respectively. Concerning RUL prediction performance, the relative errors of the three target batteries are obtained as 2.82%, 1.70%, and 0.98%, respectively. In addition, all 95% prediction intervals of RUL on the three target batteries include the end-of-life (EOL) value (=0.8). These results indicate that our method can be applied to battery SOH estimation and RUL prediction. Full article

For their emergent application in electric vehicles, the development of fast and accurate algorithms to monitor the health status of batteries and aid decision-making in relation to maintenance and replacement is now of paramount importance. Data-driven approaches are preferred due to the difficulties associated with defining valid models for system and parameter identification. In recent years, the use of features to enhance data-driven methods has become commonplace. Unless the data sets are from multiple batteries, however, such approaches cannot be used to predict more than one cycle ahead because the features are unavailable for future cycles, in the absence of different embedding strategies. In this paper, we propose a novel approach in which features are predicted for future cycles, enabling predictions of the state of health for an arbitrary number of cycles ahead, and, therefore, predictions for the end-of-life. This is achieved by using a data-driven approach to predict voltage and temperature curves for future cycles, from which important signatures of degradation can be extracted and even used directly for degradation predictions. The use of features is shown to enhance the state-of-health predictions. The approach we develop is capable of accurate predictions using a data set specific to the battery under consideration. This avoids the need for large multi-battery data sets, which are hampered by natural variations in the performance and degradation of batteries even from the same batch, compromising the prediction accuracy of approaches based on such data. Full article