Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

Resonant-Inductive Wireless Power Transfer for Autonomous Mobile Robots

Due to the increasing commercial interest in autonomy and sustainability,this paper reviews and presents a comprehensive summary of the resonant-inductive power transmission(RPT)technology for autonomous mobile robots.It outlines historic and recent research activities in wireless power transmission,covering the fundamental operation of microwave,capacitive and inductive power transfer technologies,state-of-the-art developments in RPT for high-power applications,current design and health standards,technological drawbacks,and possible future trends.In this paper,coupling-enhanced pad designs,adaptive tuning techniques,compensation network designs,and control techniques are explored.Major design issues such as coupling variation,frequency splitting,and bifurcation are reviewed.The difference between maximum power transfer and maximum energy efficiency is highlighted.Human exposure guidelines are summarized from documentations provided by the Institute of Electrical and Electronics Engineers(IEEE)and the International Commission on Non-ionizing Radiation Protection(ICNIRP).Other standards like WPC’s Qi and Airfuel design standards are also summarized.Finally,the possible trends of the relevant research and development,particularly dynamic charging,are discussed.The intention of this review is to encourage designs that will relieve robot operators of the burden of frequent manual recharging,and to reduce downtime and increase the productivity of autonomous mobile robots in industrial environments.

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

Battery packs are applied in various areas(e.g.,electric vehicles,energy storage,space,mining,etc.),which requires the state of health(SOH)to be accurately estimated.Inconsistency,also known as cell variation,is considered a significant evaluation index that greatly affects the degradation of battery pack.This paper proposes a novel joint inconsistency and SOH estimation method under cycling,which fills the gap of joint estimation based on the fast-charging process for electric vehicles.First,fifteen features are extracted from current change points during the partial charging process.Then,a joint estimation system is designed,where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency.A wrapper is used to select the optimal feature subset,and Gaussian process regression is implemented to estimate the SOH.Finally,the estimation performance is assessed by the test data.The results show that the inconsistency evaluation can reflect the aging conditions,and the inconsistency does affect the aging process.The wrapper selection method improves the accuracy of SOH estimation by about 75.8%compared to the traditional filter method when only 10%of data is used for model training.The maximum absolute error and root mean square error are 2.58%and 0.93%,respectively.

Sewage sludge ditch for recovering heavy metals can improve crop yield and soil environmental quality

The treatment and disposal of municipal sewage sludge(MSS)is an urgent problem to be resolved in many countries.Safely using the nutrients within MSS to increase crop yield and enhance the fertility of poor soil could contribute to achieving sustainable development.An indirect use of MSS in ditches alongside Pennisetum hybridum plants was studied in field plots for 30 months and the contents of heavy metals and macronutrients were monitored in soil,sludge and plant samples.We found that the yield of P.hybridum was significantly increased by 2.39 to 2.80 times and the treated plants had higher N content compared with no sludge.In addition,the organic matter(OM)and N contents in the planted soil increased significantly compared with the initial soil.The OM content in the planted soil of the MSS treatment was 2.9 to 5.2 times higher than that with no sludge,and N increased by 2.0 to 3.8 times.However,MSS had no significant effect on the N,P and K contents in the soil at the bottom of the MSS ditch,and the content of heavy metals(Cd,Pb,Cu and Zn)were also within the safe range.Moreover,the moisture content and phytotoxicity of MSS after this indirect use were reduced and the heavy metal contents changed little,which is favorable to the further disposal of recovered MSS.Therefore,this indirect use of MSS is beneficial to agricultural production,soil quality and environmental sustainability.