@article{huang_cheng_chow_2021, title={A Robust and Efficient State-of-Charge Estimation Methodology for Serial-Connected Battery Packs: Most Significant Cell Methodology}, volume={9}, ISSN={["2169-3536"]}, DOI={10.1109/ACCESS.2021.3081619}, abstractNote={Safely and efficiently managing a battery pack consisting of hundreds to thousands of battery cells is a critical but challenging task due to commonly observed uncertainties, e.g. temperature, battery degradation and SOC estimation inaccuracy. This paper proposes a robust and efficient most significant cell methodology that estimates the battery pack SOC depending on the determined most significant cells. The estimation adopting this methodology is robust to variations of temperature, battery degradation and battery cell SOC estimation inaccuracy. A battery pack simulator and a real battery pack designed for electric vehicles were used as prototypes to illustrate the high performance, robustness and effectiveness of the proposed methodology. Moreover, the proposed algorithm requires light computational effort, making it suitable for real-time operation.}, journal={IEEE ACCESS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Huang, Cong-Sheng and Cheng, Zheyuan and Chow, Mo-Yuen}, year={2021}, pages={74360–74369} }
 @article{chen_huang_2021, title={An Efficient Phase Shift Full Bridge DC/DC Converter with Wide Range Voltage Output: Design and Hardware Implementation}, ISSN={["2093-7423"]}, DOI={10.1007/s42835-021-00970-8}, journal={JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY}, author={Chen, Cheng-Lun and Huang, Cong-Sheng}, year={2021}, month={Dec} }
 @inproceedings{balagopal_huang_chow_2018, title={Effect of calendar ageing on SEI growth and its impact on electrical circuit model parameters in lithium ion batteries}, DOI={10.1109/ieses.2018.8349846}, abstractNote={This paper discusses the effect of calendar aging on the growth rate of SEI. It discusses the reasons for the growth of SEI and the implementation of SEI growth in the 3D First Principle Based Degradation Model (3DM) of the battery. The growth of SEI is simulated over a period of 3 years and the impact it has over the terminal voltage and current generated by the battery are plotted and discussed. The results are then applied to the Equivalent Circuit Model (ECM) of the battery to study and identify the impacts that calendar aging and SEI growth has on the parameters of the circuit. The increase in the internal resistance and the decrease in the capacity are also discussed.}, booktitle={2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)}, author={Balagopal, B. and Huang, C. S. and Chow, M. Y.}, year={2018}, pages={32–37} }
 @inproceedings{salamati_huang_balagopal_chow_2018, title={Experimental battery monitoring system design for electric vehicle applications}, DOI={10.1109/ieses.2018.8349847}, abstractNote={Li-ion batteries are considered as main energy sources for next generation of transportation systems. This paper presents a systematic way to design an efficient hardware testbed for Battery Monitoring System (BMS) applications in Electric Vehicle (EV) industry following the standard industrial communication protocol. The hardware testbed performs both the battery voltage/current data acquisition and the Co-Estimation algorithm. Co-Estimation is an electric circuit model based SOC estimation algorithm which takes model parameter variations into account. In this paper, the Co-Estimation algorithm is firstly discussed. A battery hardware testbed design is then elaborated, and reasons for selecting main components, including microcontroller and voltage/current sensors are explained. The performance of the hardware testbed is compared with MATLAB simulation result using the same Co-Estimation algorithm, showing similar performance between two different platforms: hardware testbed and software simulation.}, booktitle={2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)}, author={Salamati, S. M. and Huang, C. S. and Balagopal, B. and Chow, M. Y.}, year={2018}, pages={38–43} }
 @inproceedings{balagopal_huang_chow_2017, title={Effect of calendar aging on Li ion battery degradation and SOH}, DOI={10.1109/iecon.2017.8217340}, abstractNote={This paper discusses the impact of calendar ageing on the anode and the concentration of lithium ions in the anode structure. It discusses the modeling of the calendar ageing, its implementation in the 3D First Principle Based Degradation Model (3DM) of the battery and the results that were observed as a result of calendar ageing over a period of 4–6 years. The paper also relates these physical degradation phenomena with the impact that they have on the parameters of the Equivalent Circuit Model of the battery. The paper uses the capacity degradation and the increase in the internal resistance of the battery to showcase the impact of calendar ageing on the State of Health of the battery.}, booktitle={Iecon 2017 - 43rd annual conference of the ieee industrial electronics society}, author={Balagopal, B. and Huang, C. S. and Chow, M. Y.}, year={2017}, pages={7647–7652} }
 @inproceedings{huang_chow_chow_2017, title={Li-ion battery parameter identification with low pass filter for measurement noise rejection}, DOI={10.1109/isie.2017.8001575}, abstractNote={The advent of Energy Management (EM) and Electric Vehicles (EV) have completely changed the use of batteries. Accurately estimating the remaining power in batteries has become increasingly important. In order to estimate precise battery state of charge (SOC)/state of health (SOH) value, accurate parameter identification is essential when constructing an accurate battery model. Even though we are able to exactly identify battery parameters offline, the precision of online parameter identification usually suffers from measurement noise, which is an unavoidable phenomenon. In this paper we investigate how battery parameter identification is influenced by measurement noise. The selection of a low pass filter is also discussed and a fourth order Butterworth filter is adopted to effectively reject high frequency measurement noise. This algorithm can help with the identification of battery parameter that rejects measurement noise and maintains the accuracy of online battery parameter identification for future online model-based battery SOC/SOH estimation.}, booktitle={Proceedings of the ieee international symposium on industrial}, author={Huang, C. S. and Chow, T. W. S. and Chow, M. Y.}, year={2017}, pages={2075–2080} }
 @inproceedings{huang_chow_2016, title={Accurate Thevenin's circuit-based battery model parameter identificaiton}, DOI={10.1109/isie.2016.7744902}, abstractNote={Batteries are becoming the main energy storage devices in following decades. Highly accurate battery models are needed to fulfill accurate controlling and monitoring purpose. For real-time operations, the calculation complexity of the battery model is an issue. Thevenin's circuit model is a tradeoff between fast calculation and accuracy. The model uses resistor and RC pairs to capture the dynamic of terminal voltage. Even though there are few components in the model, to accurately identify those components value is a difficult question. In this paper, an algorithm to separate effective data and ineffective data is proposed by comparing the condition number. This algorithm can help identify correct parameters for future model-based battery monitoring and controlling.}, booktitle={Proceedings of the ieee international symposium on industrial}, author={Huang, C. S. and Chow, M. Y.}, year={2016}, pages={274–279} }