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

Automotive electrical systems are becoming increasingly complex as more and more electrical and electronic equipment is incorporated into new vehicles. With this trend, the growing demand for electrical power is placing greater demands on the automobile's primary source of electrical energy storage — the lead-acid battery.

Whether the vehicle relies on an internal combustion engine, is a hybrid electric vehicle or is fully electric, the reliability of the battery must be ensured. To do so requires monitoring the car battery's diagnostic parameters in the system while the vehicle is running.

Even when not in active use, a battery discharges and its health deteriorates. The task of identifying a faulty battery for replacement or a depleted battery in need of recharging is essential. If these actions are not performed in a timely manner, a system breakdown is likely.

Although there are devices and methods for monitoring a battery's state of charge (SoC) and its state of health (SoH), they rarely operate in-system while the vehicle is running. Consequently, they do not provide timely warning for corrective action.

A new approach to measuring these diagnostic parameters overcomes this limitation by indicating the SoC in terms of the current-delivery capacity of the battery and the SoH in terms of the remaining percentage of battery life while the vehicle is operating with its various electrical loads. By modeling the SoC and SoH of a lead-acid battery using neuro-fuzzy and regression techniques, it's possible to display the battery charge status and battery health in real time for the driver.

Indirect Measurement

In any automotive system, robustness is a necessity, and a graceful degradation in system performance is preferred over a sudden breakdown. Therefore, recording the battery status in-system is a value-added feature for the driver, because it helps avoid a sudden breakdown of the vehicle due to a battery malfunction.

Unfortunately, neither SoC nor SoH are directly measurable. Instead, these parameters need to be inferred from other measurements. The model for SoC described here uses a neuro-fuzzy approach coupled with in-system sensing of the charge status of the battery to provide a timely detection and warning of battery failure. SoC is determined from measurable battery parameters such as terminal voltage, discharge/charge current, internal resistance, discharge/charge cycles, temperature as an input and specific gravity (SG) of a lead-acid battery as an output through a neural network model.

SoH can be expressed in terms of battery parameters using a regression equation. SoH is a function of the aging of the battery and its run-time consumption. Therefore, the regression equation for SoH is expressed as a function of those battery parameters that affect the aging and run-time consumption. The aging effect can be seen through various slopes of SG, terminal voltage and internal resistance (IR) with respect to discharge time. Run-time consumption can be observed through the battery's ampere-hour (Ah) consumption. This work also has an important application in heavy mobile systems such as rocket launchers, missile launchers, submarines, satellites and trucks.

There are two major modes of battery operation in an automobile: slow discharge and engine cranking.^[1] When the alternator voltage is less than the battery voltage (when the engine is not running), the direction of the current flow is from the battery to the load. Otherwise, the current flows from the alternator to the load and to the battery (when the engine is running). This situation is known as the slow discharge of the battery through the car's electrical load.

The electrical load of a car consists of many different vehicle subsystems such as sidelights, taillights, license-plate lights, headlights (main and dip), dashboard lights, radio/cassette/CD, indicators, wipers, heater and other accessories. On average, the battery is required to supply the electrical load with 12 A of current when the engine is off.

At engine startup, when the alternator is not running, the engine requires an initial high torque of about 100 rev/min (engine cranking). This high torque, in turn, requires that the battery supply a pulse of high current.

Again, the ability to reach this high torque depends on several factors, among which battery characteristics play an important role along with the engine cranking resistance (torque required at the starting limit temperature) and the voltage drop between the battery and the starter. Thus, the battery should be able to supply a heavy current for a very short duration until the alternator can take over the function of supplying electrical power to the load.

Battery parameters affecting SoC are voltage, current, charge/discharge cycles (rate and method of charging), temperature, internal resistance, internal pressure, grid material (the grid refers to the frame of the battery's electrodes), electrode health, electrolytic strength, corrosion (rate of corrosion), SG and consumption time.^[2] Many of these parameters are impossible to measure while the battery is supplying current to the load. Furthermore, the dependence of SoC on these parameters is usually nonlinear, making mathematical modeling of the behavior of a battery a prohibitive task.

The SG of a battery is an indirect indicator of its SoC. Its direct measurement is based on a chemical process whereby an electrolyte is siphoned from the battery into a digital or analog hygrometer — a technique that is impractical when the battery is operating in the system.

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