Modeling Automotive Battery Diagnostics

A utomotive 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.