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

An alternative method for determining the SG is an indirect measurement of SoC using the dependence of SG on load conditions and other battery parameters. These parameters include temperature, current drawn, voltage (load), internal resistance, corrosion rate and the time duration for which the battery has been used.

In-system measurement of these input parameters is central to any modeling technique. However, measuring all these parameters will be costly given the amount of hardware required. Therefore, a careful selection of the most critical parameters for determining SoC is needed to make SoC modeling cost effective. The selected parameters can be used to estimate the cost of the instrumentation required for modeling SoC.

In the present work, we have experimented with optimizing the number of input parameters to determine the SG for SoC indication. The three essential parameters turn out to be internal resistance, voltage and current consumption. For more accurate results, it's preferable to measure two additional parameters: the battery run-time and temperature.

A first step in designing the model was the selection of an actual battery from which data could be collected. The present study was made on an Exide model MF40sv/ 38 LM 20 car battery. For the purpose of the model design it was necessary to measure sufficient data on the battery under study.^[3]

Data was collected while keeping the battery on a 12-A constant load corresponding to a slow discharge rate and drawing about 150 A of current for a few seconds to simulate real cranking. The latter action was simulated in the laboratory by 15 seconds of constant discharge at 150 A, followed by a rest of 15 seconds. Several data sets were taken for different environmental temperatures, battery ages and states of charge. Fig. 1 shows the activity chart for a single set of data collection.

As expected, the behavioral pattern observed was similar in the two cases of data collected on the MF40sv battery for slow discharge and real cranking as summarized below.^[3]

Given a constant percentage of charge, the following occur as a result of increasing ambient temperature:

• The battery can run for a longer period of time
• The internal resistance of the battery decreases
• A very small variation occurs in battery terminal voltage
• The value of the SG decreases.

Conversely, when the ambient temperature was held constant and the battery's SoC was varied, it was seen that:

1. A battery with a high SoC runs longer

2. The battery's internal resistance increases with a decrease in charged state

3. The SG of the battery decreases along with a decrease in charged state

4. The initial voltage decreases with a decrease in charged state.

SoC Model

Artificial neural networks (ANNs) are well known for simulating nonlinear physical processes, and ANNs coupled with fuzzy logic provide a powerful mechanism to linguistically translate the behavior of a complex physical process. The nonlinear adaptive-learning capability of ANNs is used here to simulate the discharging process of a battery, which is translated linguistically using fuzzy logic to represent the charged state of the battery for maneuvering battery operations. The term linguistically refers to the fact that the battery's SoC is expressed in relative terms such as fully charged, half charged or fully discharged.

A schematic of the model^[4] is shown in Fig. 2. In this schematic, 1 through 5 represent the in-system inputs to be given to the ANN to generate the output in terms of SoC. These inputs are directly measurable in-system battery parameters. These parameters include terminal voltage, current drawn, internal resistance, internal battery temperature and time of battery consumption.

The ANN output is the SG, which along with temperature is the input to a “fuzzifier” outputting the SoC of the battery in linguistic form such as very high, high, half, low and very low. The ANN architecture and weights have to be obtained with the battery out of the system using a prior training of the ANN on the specific battery under study.