Start-stop, aka engine-stop or idle-stop, technologies are increasingly being applied to automotive vehicles to increase fuel economy. Start-stop vehicles turn off the engine during periods of zero speed and/or during prolonged coast down. During engine-stop, the vehicle electronics are powered solely by the battery. To replenish the battery, the battery needs to be recharged. In typical ICE vehicles, the battery is continuously charged. However, fuel economies can be improved if strategic charging of the battery can be achieved through selective charging through the alternator or through regenerative braking. To optimize fuel economy, an accurate estimation of the battery state of charge (SOC) during vehicle operation is required. Although state of charge estimation has mainly focused on Li-ion batteries, lead-acid batteries may be used successfully in start-stop applications. However, SOC estimation for lead-acid batteries is particularly difficult due to side reactions and losses during charging, particularly at high SOC. This is a highly nonlinear function and requires special attention. To estimate the battery SOC, an equivalent-circuit lead-acid battery model is used to simulate the battery dynamics. This model incorporates losses associated with top charging which can affect the battery SOC estimation. In addition, parameter estimation techniques are utilized to identify the dynamic model parameters. Through this research, an online adaptive battery model can be used to estimate the lead-acid battery state of charge for start-stop applications, where the charging patterns may affect the SOC estimation.