Phophongviwat, Teeraphon

Preferred name

Alternative Name

Phophongviwat, T.

Main Affiliation

School of Engineering

teeraphon.ph@kmitl.ac.th

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Capacity Forecasting of Lithium-Ion Batteries Using Empirical Models: Toward Efficient SOH Estimation with Limited Cycle Data
(2025-07-01)
Sivalertporn, Kanchana
;
Poopanya, Piyawong
;
Phophongviwat, Teeraphon
Accurate prediction of lithium-ion battery capacity degradation is crucial for reliable state-of-health estimation and long-term performance assessment in battery management systems. This study presents an empirical modeling approach based on experimental data collected from four lithium iron phosphate (LFP) battery packs cycled over 75 to 100 charge–discharge cycles. Several mathematical models—including linear, quadratic, single-exponential, and double-exponential functions—were evaluated for their predictive accuracy. Among these, the linear and single-exponential models demonstrated strong performance in early-cycle predictions. It was found that using 30 to 40 cycles of data is sufficient for reliable forecasting within a 100-cycle range, reducing the mean absolute error by over 80% compared to using early-cycle data alone. Although these models provide reasonable short-term predictions, they fail to capture the nonlinear degradation behavior observed beyond 80 cycles. To address this, a modified linear model was proposed by introducing an exponentially decaying slope. The modified linear model offers improved long-term prediction accuracy and robustness, particularly when data availability is limited. Capacity forecasts based on only 40 cycles yielded results comparable to those using 100 cycles, demonstrating the model’s efficiency. End-of-life estimates based on the modified linear model align more closely with typical LFP specifications, whereas conventional models tend to underestimate the cycle life. The proposed model offers a practical balance between computational simplicity and predictive accuracy, making it well suited for battery health diagnostics.
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Quantitative Assessment of EV Energy Consumption: Applying Coast Down Testing to WLTP and EPA Protocols
(2025-07-01)
Phophongviwat, Teeraphon
;
Poopanya, Piyawong
;
Sivalertporn, Kanchana
This study presents a comprehensive methodology for evaluating electric vehicle (EV) energy consumption by integrating coast down testing with standardized chassis dynamometer protocols under WLTP Class 3b and EPA driving cycles. Coast down tests were conducted to determine road load coefficients—critical for replicating real-world resistance profiles on a dynamometer. Energy usage data were measured using On-Board Diagnostics II (OBD-II) and dynamometer measurements to assess power flow from the battery to the wheels. The results reveal that OBD-II consistently recorded higher cumulative energy usage, particularly under urban driving conditions, highlighting limitations in dynamometer responsiveness to transient loads and regenerative events. Notably, the WLTP low-speed cycle exhibited a significantly lower efficiency of 62.42%, with nearly half of the battery energy consumed by non-propulsion systems. In contrast, the EPA cycle demonstrated consistently higher efficiencies of 84.52% (low-speed) and 93.00% (high-speed). Interestingly, high-speed efficiencies between WLTP and EPA were nearly identical, despite differences in total energy consumption. These findings underscore the importance of aligning test protocols with actual driving conditions and demonstrate the effectiveness of combining coast down data with real-time diagnostics for robust EV performance assessments.
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Technical Assessment of Reusing Retired Electric Vehicle Lithium-Ion Batteries in Thailand
(2023-06-01)
Phophongviwat, Teeraphon
;
Polmai, Sompob
;
Maneeinn, Chaitouch
;
Hongesombut, Komsan
;
Sivalertporn, Kanchana
A rapid growth in electric vehicles has led to a massive number of retired batteries in the transportation sector after 8–10 years of use. However, retired batteries retain over 60% of their original capacity and can be employed in less demanding electric vehicles or stationary energy storage systems. As a result, the management of end-of-life electric vehicles has received increased attention globally over the last decade due to their environmental and economic benefits. This work presents knowledge and technology for retired electric vehicle batteries that are applicable to the Thai context, with a particular focus on a case study of a retired lithium-ion battery from the Nissan X-Trail Hybrid car. The disassembled battery modules are designed for remanufacturing in small electric vehicles and repurposing in energy storage systems. The retired batteries were tested in a laboratory under high C-rate conditions (10C, 20C, and 30C) to examine the limitations of the batteries’ ability to deliver high current to electric vehicles during the driving operation. In addition, the electric motorcycle conversion has also been studied by converting the gasoline engine to an electric battery system. Finally, the prototypes were tested both in the laboratory and in real-world use. The findings of this study will serve as a guideline for the sorting and assessment of retired lithium-ion batteries from electric vehicles, as well as demonstrate the technical feasibility of reusing retired batteries in Thailand.
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A Comparative Study on the Parameter Identification of an Equivalent Circuit Model for an Li-ion Battery Based on Different Discharge Tests
(2022-03-01)
Poopanya, Piyawong
;
Sivalertporn, Kanchana
;
Phophongviwat, Teeraphon
An effective model of battery performance is important for battery management systems to control the state of battery and cell balancing. The second-order equivalent circuit model of a lithium-ion battery is studied in the present paper. The identification methods that include the multiple linear regression (MLR), exponential curve fitting (ECF) and Simulink design optimization tool (SDOT), were used to determine the model parameters. The aim of this paper is to compare the validity of the three proposed algorithms, which vary in complexity. The open circuit voltage was measured based on the pulse discharge test. The voltage response was collected for every 10% SOC in the interval between 0–100% SOC. The battery voltages calculated from the estimated parameters under the constant current discharge test and dynamic discharge tests for electric vehicles (ISO and WLTP) were compared to the experimental data. The mean absolute error and root mean square error were calculated to analyze the accuracy of the three proposed estimators. Overall, SDOT provides the best fit with high accuracy, but requires a heavy computation burden. The accuracy of the three methods under the constant current discharge test is high compared to other experiments, due to the nonlinear behavior at a low SOC. For the ISO and WLTP dynamic tests, the errors of MLR are close to that of SDOT, but have less computing time. Therefore, MLR is probably more suitable for EV use than SDOT.

Phophongviwat, Teeraphon

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