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Algorithms for On-board Battery Manageme

Algorithms for On-board Battery Management System in Electric Vehicle for State of
Charge Estimation and Dynamic Voltage Simulation of Lithium-ion Batteries (LIB):
Considering Cell Aging
Kamyar Makinejad, Amin Sakka, Andreas Jossen*, Wen Changyun**
TUM CREATE, Singapore, [email protected]
* Technische Universität München (TUM), Institute for Electrical Energy Storage Systems (EES), Munich, Germany
[email protected]
** Nanyang Technological University (NTU), school of electrical & electronic engineering, Singapore, [email protected]
1. Introduction
Start of the test
Battery management system (BMS) is an on-board integrated part for an electric
vehicle (EV) and must perform certain tasks in real time to provide high storage
RPT test at 25˚C
utilization, long lifetime and safe operation. Biggest challenge in BMS, is accurate
battery state estimation. This work focuses on algorithms with functions of
Cycling at 15/25/40/50/60/˚C for 100 cycles
estimating these states such as state of charge (SOC), state of health (SOH) and
additionally, functions to calculate cell/pack voltage and to estimate required
1c charge/
RPT test at 25˚C
resistances and capacitances for further processes. Aside from the slight
differences in manufacturing process of the commercial cells, some cells within
Cell capacity<80% initial capacity
the battery pack can be exposed to elevated temperatures based on their location,
different mechanical stresses such as compression or vibrations and many other
factors which affect their performance like loose connections and wirings. All
these will eventually lead to inhomogeneity among the cells and brings different
End of Test (Cell EOL)
aging levels and SOC variations between them. Therefore in this work we suggest
Figure 1. Aging test flowchart used in this work
an algorithm for SOC estimation that takes cell to cell inhomogeneity and aging
into account
2. Experimental
Test setup includes high capacity battery cyclers (up to 1200A), Temperature chambers (600L Temperature and humidity control), fast
data acquisition systems, hardware in the loop system (HIL) and electrochemical impedance spectroscopy (EIS) meters among others.
High power pouch LIBs with NMC technology are used for the experiments, these are same cells used in the EVA battery pack, an
electric taxi developed for Singapore [1]. Reference performance tests (RPT) as well as drive cycle tests were performed on single
cell; modules and the battery pack itself. To study the temperature and aging effects on cell performance and monitoring algorithms,
Cells underwent accelerated aging tests at various temperatures as shown in figure 1. For each test plan, three cells are used to achieve
statistically reliable results. After every 100 cycles (cycle life aging), EIS and Hybrid pulse power Characterization (HPPC) and drive
cycle tests as part of RPT tests were repeated on the cells until cell’s end of life (EOL).
3. Results and Discussion
From the experimental, test data were used to develop real time up-scalable equivalent circuit model (ECM) simulating driving cycle
as shown in figure 2. ECM models are studied at [2] to create voltage simulation model for single cell. Additionally algorithms were
developed and adopted to estimate state SOC and SOH of the individual cells within the battery pack by EKF to evaluate the pack
performance since the SOC/capacity of the pack is influenced by the lowest SOC/capacity of the cell within the pack. The work is
developed in offline mode and evaluated in real time with HIL setup. After evaluation of the models and algorithms, the code is
optimizes to use for in house developed BMS. Algorithms are proven to be stable, fast convergence and low cost efficient.
Figure 2. (A) Battery pack voltage model from up scaled cell voltage model, (B) total SOC of the battery pack vs weakest cell Test at 25˚C on new battery pack
“EVA by TUM CREATE – Electric Taxi for Tropical Megacities: Home.” [Online]. Available:
J. Jamnik and J. Maier, “Generalised equivalent circuits for mass and charge transport: chemical capacitance and its
implications,” Phys. Chem. Chem. Phys., vol. 3, no. 9, pp. 1668–1678, 2001.