# TRACKSIM: A Multi-Level Simulation Framework for Near-Life Battery Data Generation

Nicolai A Weinreich, Xin Sui, Remus Teodorescu, Kim G Larsen

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Nicolai A Weinreich, Xin Sui, Remus Teodorescu, Kim G Larsen. TRACKSIM: A Multi-Level Simulation Framework for Near-Life Battery Data Generation. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0180. hal-05106093

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# TRACKSIM: A Multi-Level Simulation Framework for Near-Life Battery Data Generation

Nicolai A. Weinreich¹*, Xin Sui¹, Remus Teodorescu¹, Kim G. Larsen²

¹Department of Energy, Aalborg University, Pontoppidanstraede 101, Aalborg, Denmark

²Department of Computer Science, Aalborg University, Selma Lagerløfs Vej 300, Aalborg, Denmark

E-Mail: nawe@energy.aau.dk

URL: https://www.energy.aau.dk/research/project-websites/smartbattery

## ACKNOWLEDGMENT

This work was supported by the Villum Foundation for Smart Battery project (No. 222860). The authors would like to thank Yusheng Zheng and Yunhong Che from the Department of Energy, Aalborg University for the acquirement of experiment data used in this paper.

*Index Terms—Batteries, Artificial intelligence, Automotive application, Simulation, Health assessment.*

*Abstract—*Data-driven methods in the battery intelligence field are highly dependent on data similar to the targeted application. This paper introduces the multi-level simulation framework TRACKSIM as a tool to generate realistic synthetic battery cell data for training data-driven models. A use-case for State-Of-Health (SOH) estimation is presented.

## I. INTRODUCTION

Optimal monitoring and control of the battery pack in an Electric Vehicle (EV) is critical for safe operation and increased longevity of the battery cells and is the main function of any Battery Management System (BMS). Doing this effectively prevents hazardous events and delays the need to replace modules within the pack or the pack itself, thus reducing the negative environmental impacts associated with battery production. Some of the key tasks of the BMS include State of Health (SOH) estimation and prognosis of the battery cells [1], State of Charge (SOC) estimation and balancing [2], [3], remaining driving range prediction, cell fault monitoring [4], State of Temperature (SOT) estimation [5], among many others. Recently, Machine Learning (ML) methods are at the forefront of research into optimizing these tasks. However, the performance of ML models are

highly reliant on the quality of the training data and how similar it is to data in the ultimate application.

Current ML models are typically trained on lab-generated data [6]; however, as discussed in [7], there is a significant gap between this kind of data and data measured in the field, particularly during discharging. This gap can be significant, and may result in otherwise high-performance ML models to not being able to generalize to the final application. Recently, efforts have been taken to try to close this gap. In a recent call to action, the authors in [8] proposed to consolidate various open battery data hubs into a standardized system to promote findability, accessibility, interoperability, and ultimately reuseability (the so-called FAIR principles). While authentic field data is preferable, the authors state that the generation of "synthetic data" can benefit the innovation of new methods by rapidly generating virtual experimental data and provide data for bench-marking the performance of ML algorithms.

Multiple tools are available for synthetic battery data generation. For example, the modeling tool PyBaMM utilizes various physical models of varying theoretical complexity to generate battery voltage responses [9]. However, the tool relies on a user-defined current signal or experimental procedure which may not correspond well to the excitation seen in a real vehicle and may be difficult to obtain from the power demand of the EV motor. In [10], a battery pack simulation procedure is given; however, it is also reliant on a defined current input signal. The author further describes how a simulation can be implemented on the vehicle level to generate a battery power demand for a given desired speed profile. However, the simulation is still reliant on the user to generate this speed profile and the author does not connect the obtained power profile to the battery pack simulation. In this paper, a multi-level simulation framework is
<figure><img src="image_2.png" /><figcaption>Fig. 1. Overview of the TRACKSIM framework.</figcaption></figure>

proposed which incorporates a third simulation layer: the traffic layer. The TRAffic, vehicle, and battery pACK SIMulator (TRACKSIM) is an end-to-end simulation tool which can generate synthetic battery data which is similar to what is expected in the real application. The purpose is to enable researchers to generate dynamic battery data from realistic user-controlled traffic scenarios to develop strategies and algorithms which are more fit to the actual application. The codebase used for the work in this paper is available at https://github.com/nawe-aau/TRACKSIM_EPE2025. The latest version of TRACKSIM is available at https://github.com/crosbat/tracksim.

In Section II, an overview of the TRACKSIM framework is presented including the fundamental workings of the individual levels of simulation. Section III present an exemplary use case for the TRACKSIM framework in training an SOH estimator. The work is concluded in Section IV where future extensions to the framework are proposed.

## II. OVERVIEW OF TRACKSIM

Figure 1 shows the general TRACKSIM framework. The simulation framework is comprised of 3 levels. On the traffic level, a traffic network is modelled and the overall traffic is simulated, yielding dynamic driving profiles of the individual vehicles. On the vehicle level, the battery power required to realize the driving profile is inferred using an appropriate model of the vehicle. On the battery level, the individual cell currents, voltages,

and SOCs are simulated based on the defined topology of the battery pack and models of the cells within the pack. In the following sections, the 3 levels of simulation, and how they are implemented, will be discussed in detail.

### A. Traffic Level Simulation

TRACKSIM uses the open source traffic simulator SUMO (see [11]) as a backend for the traffic level simulation due to its high maturity, open availability, high customizability, extensive documentation, and large community of users. SUMO is operating based on user-designed scenarios which include the traffic network of a specific area, the traffic infrastructure e.g. traffic lights, and a model of the overall traffic demand. While SUMO provides tools to conveniently make simple scenarios, more sophisticated scenarios have been published in the literature (see e.g. [12] for a list of complete scenarios).

TRACKSIM uses the python package Libsumo to interface with SUMO and run the traffic simulation of a given scenario. At each time step, the speed of every vehicle in the traffic network is recorded as well as the current time of the simulation in seconds. The user is able to set the duration of the traffic simulation in hours (starting from 00:00) and the time resolution down to 0.1s between each time step.

### B. Vehicle Level Simulation

The vehicle level simulation translates the dynamic speed profile of the vehicle into the battery power required to reach the desired speed. The equations used
<figure><img src="image_3.png" /><figcaption>Fig. 2. Summary of the procedure given in Section II-C1.</figcaption></figure>

to implement this are adapted from [10, Sec. 2.5] and the associated code is structured similarly to the code in [10, Sec. 2.6] with minor changes. The vehicle simulation depends on a complete set of vehicle parameters such as weight, frontal area, and drag coefficient as well as drivetrain parameters such as internal efficiencies, gear ratio, motor torque limits etc. An example of a complete parameter set will be given in Section III-A.

### C. Battery Level Simulation

Fig. 3 The battery level simulation infers individual cell currents, voltages, and SOCs from the battery power profile, based on the defined cell models and initial SOCs. We will assume that the battery pack is comprised of $N_s$ parallel cell modules (PCMs) connected in series, each with $N_p$ cells connected in parallel. At time $k$, the individual cell currents, $I^{(i,j)}[k]$, voltages, $V^{(i,j)}[k]$, and SOC, $SOC^{(i,j)}[k]$, should be determined for $i = 1, 2, \dots, N_s$ and $j = 1, 2, \dots, N_p$ based on the desired power of the battery pack, $P[k]$. The SOC of each cell is updated by Coulomb counting i.e.

$$SOC^{(i,j)}[k+1] = SOC^{(i,j)}[k] - \frac{\rho[k]\Delta t}{Q \cdot 3600} I^{(i,j)}[k] \quad (1)$$

where $\Delta t$ is the time step of the simulation, $Q$ is the capacity of the battery cell in Ah, and

$$\rho[k] = \begin{cases} \eta[k], & I^{(i,j)}[k] < 0 \text{ (charging)} \\ 1, & I^{(i,j)}[k] \ge 0 \text{ (discharging)} \end{cases} \quad (2)$$

where $\eta[k]$ is the (possibly time dependent) Coulombic efficiency of the battery cell. The determination of the cell currents and voltages depends on the cell model used in the simulation. TRACKSIM currently supports two types of models. The first type comprises models where the output voltage can be written on the form

$$V^{(i,j)}[k] = V_{f}^{(i,j)}[k] - R_{0}^{(i,j)} I^{(i,j)}[k] \quad (3)$$

where $V_{f}^{(i,j)}[k]$ is a fixed voltage at time $k$ which only depends on states and input current at time $k-1$ and $R_{0}^{(i,j)}$ is an internal resistance which provides an instantaneous voltage change based on the current at time $[k]$. Equivalent Circuit Models (ECMs) generally follow the form above with the fixed voltage comprising of the open circuit voltage, diffusion voltages and possibly hysteresis. Models which can not reasonably be written on the form in Eq. (3), can be written more generally as

$$V^{(i,j)}[k] = f^{(i,j)}(I^{(i,j)}[k], z[k]) \quad (4)$$

where $f$ is a non-linear function and $z[k]$ is a collection of internal states including SOC. In the following, the battery pack simulation is detailed for both types of models as described above.

1) Equivalent Circuit Methods: Equation (3) can be modelled as an equivalent Thevenin circuit with a thevenin equivalent voltage source $V_{f}^{(i,j)}[k]$ and resistance $R_{0}^{(i,j)}$. The Thevenin equivalent resistance and
voltage of the i'th PCM are given by

$$R_T^{(i)} = 1 / \sum_{j=1}^{N_p} \frac{1}{R_0^{(i,j)}}, \quad V_T^{(i)}[k] = R_T^{(i)} \sum_{j=1}^{N_p} \frac{V_f^{(i,j)}[k]}{R_0^{(i,j)}} \quad (5)$$

The Thevenin equivalent resistance and voltage for the whole pack are then given by

$$R_T = \sum_{i=1}^{N_s} R_T^{(i)}, \quad V_T[k] = \sum_{i=1}^{N_s} V_T^{(i,j)}[k]. \quad (6)$$

The voltage of the pack can then be modelled as

$$V[k] = V_T[k] - R_T I[k] \quad (7)$$

where $I[k]$ is the load current for the whole pack. Using that $P[k] = V[k]I[k]$, the load current at time $k$ can be calculated as

$$I[k] = \frac{V_T - \sqrt{V_T^2 - 4R_T P[k]}}{2R_T} \quad (8)$$

The individual cell currents are then calculated as

$$I^{(i,j)}[k] = \frac{V_f^{(i,j)}[k] - V^{(i)}[k]}{R_0^{(i,j)}} \quad (9)$$

where

$$V^{(i)}[k] = V_T^{(i)}[k] - R_T^{(i)} I[k]. \quad (10)$$

The resulting cell voltages are then calculated using Eq. (3) and the SOCs are updated by Eq. (1).

2) Optimization Methods: In the case where the cell model is on the form in Eq. (4), closed form solutions of cell currents and voltages may not exist. Instead, a nested non-linear optimization approach is used to provide reasonable estimates of the load current on the cells and the pack. We note that for the i'th PCM, the individual cell currents can be written as

$$I^{(i,j)}[k] = a^{(i,j)}[k] I[k], \quad j = 1, 2, \dots, N_p \quad (11)$$

where $a^{(i,j)}[k]$ are unknown mixing coefficients which depend on the internal parameters of the battery cells and are defined such that

$$\sum_{j=1}^{N_p} a^{(i,j)}[k] = 1, \quad i = 1, 2, \dots, N_s. \quad (12)$$

The individual cell currents are found by a nested optimization approach. First, an initial guess of $I[k]$ is chosen, either based on the nominal voltage of the battery

pack or $I[k-1]$. The load current for time $k$ is found by solving the optimization problem

$$I[k] = \arg \min_{I} |VI - P[k]| \\ \text{s.t.} \quad V = \sum_{i=1}^{N_s} V^{(i,1)}. \quad (13)$$

For each choice of $I$, the voltages $V^{(i,1)}$ are found by choosing an appropriate choice of mixing coefficients. This is done by solving the optimization problem

$$a^{(i,j)}[k] = \begin{array}{ll} \underset{a^{(i,j)}, j=1,2,\dots,N_p}{\arg\min} & \max_j (V^{(i,j)}) - \min_j (V^{(i,j)}) \\ \text{s.t.} & V^{(i,j)} = f^{(i,j)}(a^{(i,j)} I, \mathbf{z}[k]) \\ & V^{(i,j)} > 0 \\ & \sum_{j=1}^{N_p} a^{(i,j)} = 1 \end{array} \quad (14)$$

for every $i$. Once an optimal choice of load current and mixing coefficients have been found, the cell currents, voltages, and SOCs can be found using Eqs. (11), (4), and (1).

### III. USE CASE: TRAINING AN SOH ESTIMATOR ON SIMULATED MOTORWAY DATA

In this section, the TRACKSIM framework is used to generate dynamic battery cell data to train a Deep Neural Network (DNN) in estimating the SOH of the cell. The purpose of this section is to give an example of how TRACKSIM can be used to develop estimation strategies and models based on data which is comparable to what you would expect to see in the field.

#### A. Experimental Setup

A diagram of the experimental procedure is shown in Fig. 3. First, an appropriate SUMO scenario needs to be constructed for the traffic simulation. In this example, traffic has been simulated on the E45 motorway in North Jutland, Denmark, going from Aalborg to Frederikshavn. A view of the network can be seen in Fig. 4. The trip of each vehicle was defined such that the start and end of the trip were randomly chosen from roads at the edge of the network. A new vehicle was inserted into the network every 10 seconds for 48 hours. We found that this gave a reasonable traffic behavior without too much traffic on the motorway. For each simulated vehicle, the associated battery power demand was simulated using a modified model of a first-generation General Motor's Chevy Volt as described in [10, Sec. 2.6]. A summary of the model parameters can be seen in Table II.
<figure><img src="image_4.png" /><figcaption>Fig. 3. Experimental procedure.</figcaption></figure>

The individual battery cells were modelled using a 1RC ECM. The terminal voltage of the (i, j)'th cell is given by

$$V^{(i,j)} = \underbrace{OCV(SOC[k]) - R_1^{(i,j)} I_{R_1}^{(i,j)}[k] - R_0^{(i,j)} I^{(i,j)}[k]}_{V_f^{(i,j)}[k]} \quad (15)$$

where SOC[k] is given by Eq. (1) and

$$I_{R_1}^{(i,j)}[k+1] = \exp\left\{-\frac{\Delta t}{\tau}\right\} + \left(1 - \exp\left\{-\frac{\Delta t}{\tau}\right\}\right) I^{(i,j)}[k] \quad (16)$$

with τ being the time constant. The fixed voltage at time of the model at time k is given by the SOC dependent

Open Circuit Voltage (OCV) and diffusion voltage. In the simulation, we set Δt = 1s.

Multiple parameter sets were constructed by performing a parameter characterization of prismatic CALB NMC battery cells (model L148N50B) with a nominal capacity of 50Ah. The cells have been aged using a CT-8008-5V200A-NTFA Neware battery cycler, as pictured in Fig. 3. At different stages of aging, Reference Performance Tests (RPTs) were performed to measure the discharge capacity of the cells and obtain estimates of the model parameters including the series resistance, RC resistance and time constant. 13 parameter sets of varying SOH levels were chosen for this experiment. The SOC-OCV curve was obtained from a fresh cell, and is assumed to be constant across the cells over their lifetime. We also assume a constant Coulombic efficiency of η = 0.99. The SOH value from each RPT is defined as the discharge capacity divided by the nominal capacity. The parameter sets for different SOH are given in Table I.

To assess how the constructed model generalizes to different cells and trips, 80% of the simulated vehicles were used for training while the remaining 20% were used for final validation. Similarly, 3 cell models were chosen as test cells and were not used in the generation of training data. For each trip in the training set, the battery pack was simulated for each cell model used for training. This was done provide data which show how the cell measurements change under different SOH conditions for the same trip. The same was done for test trips and test cells. To simplify the example, each battery pack is comprised of identical cells. Furthermore, the cells in the battery pack are assumed to be at rest at the start of the trip with an initial SOC of 80%.

In this example, the cell measurement data from the first 10 minutes of motorway driving is collected and subsequently used for SOH estimation. Two example speed and current profiles generated by TRACKSIM are shown in Fig. 5. The cell measurements extracted in the 10 minute interval include voltage, current, and SOC. In addition, relative SOC over a small time interval has been shown to be an effective predictor of SOH [13]. The relative SOC is calculated as

$$\Delta SOC[i] = SOC[il-l] - SOC[il-1], i = 1, 2, \dots, 600/l \quad (17)$$

where i is the index of the time interval and l is the length of the time interval. In this example, we set l = 40 seconds, yielding 15 adjacent time intervals to calculate
<figure><img src="image_5.png" /><figcaption>Fig. 4. The road network used in the example.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5. Example trips and corresponding current profiles generated by TRACKSIM. The dashed lines indicate the time interval used in the SOH estimator.</figcaption></figure>

ΔSOC[i]. The current signals are normalized such that

$$I_{norm}[k] = \frac{I[k] - I_{min}}{I_{max} - I_{min}} \qquad (18)$$

where $I_{max}$ and $I_{min}$ are the maximum and minimum recorded current across all training trips. Similar normalization is done for the voltage, SOC, and ΔSOC to obtain $V_{norm}$, $SOC_{norm}$, and ΔSOC$_{norm}$. The input data for the DNN are constructed by concatenating the normalized current signal, voltage signal, SOC signal, and ΔSOC for each 40 second interval. The architecture of the DNN is shown in Fig. 6. The DNN is trained by minimizing the Mean Square Error (MSE) using Adam optimization. The performance of the model is evaluated based on the Mean Average Error (MAE) given by

$$MAE = \frac{1}{N} \sum_{i=1}^{N} |SOH_{i} - S\hat{OH}_{i}| \qquad (19)$$

where N is the number of data points in the test set and $SOH_i$ and $S\hat{OH}_i$ are the true and estimated SOH respectively.

<figure><img src="image_7.png" /><figcaption>Fig. 6. Architecture of DNN used in the example.</figcaption></figure>

## B. Results & Discussion

The results of the SOH estimator on the training and test data are shown in Fig. 7 and Fig. 8, respectively. The figures show good agreement between the predicted SOH and the true SOH for each SOH level. The MAE for the training data is 0.304% while it is 0.347% for the test data, thus showing good generalization towards unseen trips and cell models for both low, medium, and
<table><caption>TABLE I
1RC ECM PARAMETERS FOR CELLS OF VARYING SOH USED IN THE EXPERIMENT.</caption><thead><tr><th colspan="4">Training Cells</th></tr><tr><th>SOH [%]</th><th>R<sub>0</sub> [mΩ]</th><th>R<sub>1</sub> [mΩ]</th><th>τ [s]</th></tr></thead><tbody><tr><td>95.268%</td><td>1.298</td><td>0.866</td><td>22.75</td></tr><tr><td>93.293%</td><td>1.416</td><td>1.052</td><td>22.96</td></tr><tr><td>92.522%</td><td>1.588</td><td>1.154</td><td>24.15</td></tr><tr><td>91.297%</td><td>1.581</td><td>1.172</td><td>24.68</td></tr><tr><td>89.9907%</td><td>1.586</td><td>1.174</td><td>24.91</td></tr><tr><td>87.9543%</td><td>1.570</td><td>1.150</td><td>25.82</td></tr><tr><td>87.5903%</td><td>1.496</td><td>1.108</td><td>24.03</td></tr><tr><td>85.5325%</td><td>1.608</td><td>1.191</td><td>24.76</td></tr><tr><td>81.39785%</td><td>1.592</td><td>1.225</td><td>26.23</td></tr><tr><td>79.882%</td><td>1.605</td><td>1.244</td><td>25.55</td></tr></tbody><thead><tr><th colspan="4">Test Cells</th></tr><tr><th>SOH [%]</th><th>R<sub>0</sub> [mΩ]</th><th>R<sub>1</sub> [mΩ]</th><th>τ [s]</th></tr></thead><tbody><tr><td>94.568%</td><td>1.311</td><td>0.866</td><td>22.85</td></tr><tr><td>88.940%</td><td>1.646</td><td>1.215</td><td>25.36</td></tr><tr><td>82.819%</td><td>1.743</td><td>1.285</td><td>25.70</td></tr></tbody></table>

high degradation. It is important to note that the voltage, current, and SOC data are assumed to be exact, which is not the case in actual application due to measurement and estimation errors. Thus, the robustness of the SOH estimator towards noise is yet to be demonstrated. Moreover, with the data set consisting exclusively on simulated motorway trips, the estimator is not expected to generalize well to other types of trips such as city driving. However, since the SOH changes very slowly, it is acceptable to have an estimator which is biased towards a particular type of driving, e.g. motorway driving, as long as this happens frequently enough across the EV's lifetime.

## IV. CONCLUSION

Synthetic battery data generation can prove highly useful for the development of ML tools designed for use in real-life applications. TRACKSIM provides a multi-level simulation framework to produce synthetic battery data from realistic traffic patterns, thus closing the gap between the laboratory and the field. Multiple improvements and extensions are planned for TRACKSIM including support for more advanced battery models including SOC and temperature dependent model parameters, methods for introducing degradation of the data quality e.g. packet loss and sensor noise, and further increased interoperability with existing open battery software.

<table><caption>TABLE II
VEHICLE AND BATTERY PACK PARAMETER VALUES USED IN THE EXPERIMENT</caption><thead><tr><th>Vehicle Parameters</th><th>Value [unit]</th></tr></thead><tbody><tr><td>No. wheels</td><td>4</td></tr><tr><td>Wheel radius</td><td>0.35 [m]</td></tr><tr><td>Wheel inertia</td><td>8 [kg/m<sup>2</sup>]</td></tr><tr><td>Brake drag</td><td>0 [N]</td></tr><tr><td>Drag coefficient</td><td>0.22</td></tr><tr><td>Frontal area</td><td>1.84 [m<sup>2</sup>]</td></tr><tr><td>Vehicle mass</td><td>1425 [kg]</td></tr><tr><td>Payload mass</td><td>75 [kg]</td></tr><tr><td>Overhead power consumption</td><td>200 [W]</td></tr><tr><td>Inverter efficiency</td><td>0.94</td></tr><tr><td>Fractional regen torque efficiency</td><td>0.9</td></tr><tr><td>Gear ratio</td><td>12</td></tr><tr><td>Gear inertia</td><td>0.05 [kg/m<sup>2</sup>]</td></tr><tr><td>Gear efficiency</td><td>0.97</td></tr><tr><td>Rolling coefficient</td><td>0.0111</td></tr><tr><td>Max motor torque</td><td>1420 [Nm]</td></tr><tr><td>Rated RPM</td><td>4000 [RPM]</td></tr><tr><td>Max RPM</td><td>23000 [RPM]</td></tr><tr><td>Motor efficiency</td><td>0.95</td></tr><tr><td>Motor inertia</td><td>0.2 [kg/m<sup>2</sup>]</td></tr><tr><th>Battery Pack Parameters</th><th>Value [unit]</th></tr><tr><td>Battery pack efficiency</td><td>0.96</td></tr><tr><td>No. battery pack modules</td><td>12</td></tr><tr><td>No. cells in series per module</td><td>12</td></tr><tr><td>No. cells in parallel per module</td><td>2</td></tr><tr><td>Cell mass</td><td>0.865 [kg]</td></tr><tr><td>Cell Nominal Capacity</td><td>50 [Ah]</td></tr><tr><td>Cell Nominal Voltage</td><td>3.6 [V]</td></tr><tr><td>Battery Pack Nominal Capacity</td><td>51.84 [kWh]</td></tr><tr><td>Battery Pack Initial SOC</td><td>80%</td></tr><tr><td>Battery module mass overhead</td><td>0.08</td></tr><tr><td>Battery pack mass overhead</td><td>0.1</td></tr><tr><td>Cell interconnect resistance</td><td>0 [Ω]</td></tr></tbody></table>

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<figure><img src="image_8.png" /><figcaption>Fig. 7. Violin plot of SOH estimation results on training trips and cell models. The white dots represent the median predicted SOH.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8. Violin plot of SOH estimation results on test trips and cell models. The white dots represent the median predicted SOH.</figcaption></figure>

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