The tracking and behavior recognition of heavy-duty trucks on roadways are keys for the development of automated heavy-duty trucks and an advanced driver assistance system. The spatiotemporal information of trucks from trajectory tracking and motions learnt from behavior analysis can be employed to predict possible driving risks and generate safe motion to avoid roadway accidents. This article presents a unified tracking and behavior recognition algorithm that can model the mobility of heavy-duty trucks on long inclined roadways. Random noise within the sampled elevation data is addressed by time-based segmentation to extract time-continuous samples at geographical locations. A Kalman filter is first used to distinguish error offsets from random noise and to estimate the distribution of truck elevations for different time intervals. A Markov chain Monte Carlo model is then applied to classify truck behaviors based on the change in elevation between two geographical locations. A heavy-duty truck mobility (HVMove) model is constructed based on the map information to apply the roadway geometry to the tracking and behavior recognition algorithm. We develop an extended Metropolis-Hastings algorithm to tune the parameters of the HVMove model. The proposed model is verified and evaluated through extensive experiments based on a real-world trajectory dataset covering sections of an expressway and national and provincial highways. From the experimental results, we conclude that the HVMove model provides sufficient accuracy and efficiency for automated heavy-duty trucks and advanced driver assistance system applications. In addition, HVMove can generate maps with the elevation information marked automatically.
With the development of intelligent vehicles, increasing number of electronic and electrical devices are applied in vehicles, the necessary power is surging, battery protection is increasingly required, and higher demands are made for energy management. Most previous studies on battery characteristics have only focused on the battery itself, which is chemical characteristics. Further considering battery’s actual operating conditions on vehicles, battery’s functional characteristics are proposed and studied in this paper. Aging adaptive functional state model of battery for internal combustion engine vehicles is proposed, comprehensively revealing the operating characteristics covering the battery full life cycle. Thereafter, based on the model, a battery protection scheme is developed, including over-discharge and graded over-current protection to achieve comprehensive battery protection. Furthermore, a model-based energy management strategy is presented to achieve integrated optimization of fuel economy, battery protection, and vehicle power performance. Finally, tests are performed on the vehicle and test bench to verify the validity and feasibility of the proposed model and management scheme. Results reveal that the model can reflect battery’s functional features, over-current protection and over-discharge protection of the battery are achieved, and the vehicle start-up capability is secured. The proposed energy management strategy can effectively improve fuel economy.
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