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| 001 | 1035298 | ||
| 005 | 20250203103432.0 | ||
| 037 | _ | _ | |a FZJ-2025-00356 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Raijmakers, Luc |0 P:(DE-Juel1)176196 |b 0 |
| 111 | 2 | _ | |a Advanced Battery Power Conference |c Münster |d 2024-04-09 - 2024-04-11 |w Germany |
| 245 | _ | _ | |a Battery simulations of real-world electric vehicle data |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
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| 520 | _ | _ | |a Electric vehicles (EVs) have gained significant attention as a sustainable mode of transportation, emphasizing the critical role of Li-ion batteries in their performance. Accurate battery state estimation is essential for improving the reliability and efficiency of EVs. To develop state estimation algorithms, existing testing methods, often primarily performed in laboratory settings under controlled conditions, are utilized. However, these methods differ substantially from real-world scenarios, posing challenges in translating laboratory findings to practical applications. Introducing the complexities of real-world conditions, so-called field data acquisition is essential for providing insights into EV operation beyond laboratory tests. However, field data acquisition introduces complexities such as uncontrolled operating conditions, less accurate sensors, and concerns regarding data collection and storage, including data gaps [1]. Moreover, the unknown battery cell parameters within EV battery packs, if not being a battery or EV manufacturer, pose additional hurdles, particularly in simulating and predicting battery cell states, such as battery cell voltage.In this study, we employ a physics-based battery modelling technique, specifically the extended single particle model (ESPM), to simulate the battery cell voltage under various test drive conditions, with battery pack current as input. The ESPM is chosen for its ability to achieve a favourable balance between accuracy, given the relatively low average C-rates for EVs, and simulation speed [2]. A research EV (Nissan Leaf) is used to perform measurements of EV states, including battery pack current, (cell) voltage, and temperature. The EV states are obtained via the OBD-II interface using an AutoPi telematics device [3], which logs and sends the data via mobile network to a server (in real time) during driving.Before performing battery simulations, a parameter sensitivity analysis was performed to identify the most sensitive parameters of the ESPM model. Subsequently, highly and medium-sensitive parameters were prioritized for parameter optimization, while setting low-sensitive parameters to typical average values obtained from literature. The sensitivity analysis results are shown in Fig. 1a and illustrate the impact of various parameters on the model's performance. Following the sensitivity analysis, battery parameter optimization was performed with the highly and medium-sensitive parameters. For parameter optimization, the difference between measured and simulated battery cell voltage was minimized, while the battery current served as input for the ESPM. The same was repeated for various test drives under different conditions. Fig. 1b shows an example of a comparison between the measured (blue line) and simulated (red line) voltage of a battery cell during a test drive, which highlights the model's ability to capture real-world behaviour.These findings serve as a first step towards achieving state estimation and forecasting through model-based approaches under real-world conditions. In the future, we plan to expand our research by performing parameter optimization under a wider range of operating conditions, aiming to optimize the robustness and accuracy of our simulations. Additionally, we anticipate achieving better simulation accuracy by extracting physical parameters directly from batteries, thereby capturing the intricacies of real-world behaviour more effectively.[1] V. Sulzer, P. Mohtat, A. Aitio, S. Lee, Y.T. Yeh, F. Steinbacher, M.U. Khan, J.W. Lee, J.B. Siegel, A.G. Stefanopoulou, D.A. Howey, The challenge and opportunity of battery lifetime prediction from field data, Joule. 5 (2021) 1934–1955. doi:10.1016/j.joule.2021.06.005.[2] H.A.A. Ali, L.H.J. Raijmakers, K. Chayambuka, D.L. Danilov, P.H.L. Notten, R.-A. Eichel, A Comparison between Physics-Based Li-Ion Battery Models, Preprint. (2023). doi:http://dx.doi.org/10.2139/ssrn.4664038.[3] AutoPi.io, (n.d.). https://www.autopi.io/ (accessed November 27, 2023). |
| 536 | _ | _ | |a 1223 - Batteries in Application (POF4-122) |0 G:(DE-HGF)POF4-1223 |c POF4-122 |f POF IV |x 0 |
| 536 | _ | _ | |a LLEC::VxG - Integration von "Vehicle-to-grid" (BMBF-03SF0628) |0 G:(DE-Juel1)BMBF-03SF0628 |c BMBF-03SF0628 |x 1 |
| 700 | 1 | _ | |a Ali, Haider Adel |0 P:(DE-Juel1)190784 |b 1 |u fzj |
| 700 | 1 | _ | |a Tempel, Hermann |0 P:(DE-Juel1)161208 |b 2 |
| 700 | 1 | _ | |a Riebesel, Lea |0 P:(DE-Juel1)180104 |b 3 |
| 700 | 1 | _ | |a Xhonneux, André |0 P:(DE-Juel1)8457 |b 4 |
| 700 | 1 | _ | |a Kasselmann, Stefan |0 P:(DE-Juel1)7250 |b 5 |
| 700 | 1 | _ | |a Müller, Dirk |0 P:(DE-Juel1)172026 |b 6 |
| 700 | 1 | _ | |a Eichel, Rüdiger-A. |0 P:(DE-Juel1)156123 |b 7 |u fzj |
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