Modeling Automotive Battery Diagnostics

Mar 1, 2008 12:00 PM
By Neeta Khare and Rekha Govil, Apaji Institute of Mathematics and Applied Computer Technology, Banasthali University

SoH Modeling

The SoH of a battery is defined as the remaining life of the battery given a specific load. The cause of battery health deterioration is the effect of aging on the grid, electrodes, contacts, corrosion and charging /discharging cycles.

The SoH of a battery is modeled using multivariate linear regression^[9] on the aging effect and the on-time consumption of the battery. The values of various slopes for the Exide car battery under study are given in tabular form with different SoC conditions at different temperatures for ages of batteries in the face of real cranking and slow discharge, respectively. (This data is available in a table in the online version of this article.)

It is seen that the slopes of parameters like SG, terminal voltage and internal resistance indicate the effect of age on battery performance. SG and terminal voltage decrease with discharge duration, and internal resistance increases with the discharge of the battery. The negative slope of SG and terminal voltage has a sharper decrease with age, and the positive slope of internal resistance also shows an incremental rise with age.

From the online table it can also be observed that smaller values of slopes of SG and terminal voltage were not as significant as values of the IR slope. This fact is verified later with the results that internal resistance affects the SoH more than SG and terminal voltage.

Initially, the regression technique had been applied to only two factors on which the SoH depends: SG and open circuit voltage (OCV). The various slopes of the SG and OCV have been used to obtain a formula. The results obtained were not very satisfactory, so we realized that internal resistance is also an important factor on which SoH depends. Therefore, internal resistance should be included in the formula developed to model SoH.

The formula obtained after applying the multiple regression technique is:

SoH = 1.0043 + 0.0088(TT × C) + 3.8925 m(SG) + 0.2444m'(OCV) - 0.0863m"(IR),

where TT is the run-time of the battery and C is the discharge rate and IR is the internal resistance. TT × C gives the ampere-hour consumption of the battery and m(SG), m' (OCV), m"(IR) are slopes.

The regression results, from the data collected from the car battery, show that the current consumption affects 60% (for real cranking of a car for 15 sec), the IR slope affects 30% and the remaining two paremeters — the SG slope and the terminal voltage slope — affect only 10% of the battery's SoH. The SG in-system measurement is difficult and its slope values also are not significant, so it can be ignored.

This work concentrates on an intelligent modeling of the nonlinear behavior of a battery, not through mathematical/ algorithmic approach like prior work in this field, but through a simulation of the entire process based on real data, using battery parameters measured in-system. A nonlinear behavior mapping of the battery has been conducted using an ANN and the regression technique. This has been found to provide a more-reliable and accurate estimation of SoC and SoH than previous methods. A relative indication of SoC is implemented through the use of fuzzy logic and SoH is expressed as a remaining percentage of battery life.

The objective of the study was also to achieve a desired accuracy with an optimized hardware model (i.e., highly accurate and low cost). The process model has the potential to be implemented in product form as a panel display in automobiles. While preferred model parameters have been given and described, various modifications may be made without departing from the spirit and scope of the process. The hardware used to implement these models can be a low-cost, easy-to-build module consisting of a DSP or microcontroller and signal-conditioning circuits.

Acknowledgement

We gratefully acknowledge the assistance provided by Exide, R&D Lab, Kolkata, Exide Industries Ltd. India in terms of financing the project and facilitating the experimentation in their laboratory without which this research work could not have been accomplished.

References

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3. Khare, N., “Intelligent Battery Monitoring.” Thesis work under Prof. Rekha Govil, Banasthali Vidyapith, Rajasthan, India, 2006.

4. Khare, N.; Govil, R.; and Mittal, S.K., “A Process of Determining State of Charge and State of Health of a Battery,” Indian patent 813/KOL/2005.

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6. Hwang, J.N.; Lay, S.R.; Maechlar, M.; Martin, D.; and Schimert, J., “Regression Modeling in Back-Propagation and Projection Pursuit Learning,” IEEE Trans. on Neural Network, 5(3), May 1994, pp. 342-353.

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8. Pritpal, S. “Development of Fuzzy Logic Based Lead Acid Battery Management Techniques with Applications to 42V Systems.” U.S. patent 6,668,247.

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