Jiawei Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jiawei Yang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Cheng Yu
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
1 shared publication - Lei Pei
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
1 shared publication - Tiansi Wang
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
1 shared publication - Wanlin Wang
Farasis Energy (GanZhou) Co., Ltd., Ganzhou 341000, China
1 shared publication
Latest Publications
- Open Access
A Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles
To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency.
Open AccessA Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles
To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency.