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Summary The estimation of state-of-charge (SOC) is crucial to determine the remaining capacity of the Lithium-Ion battery, and thus plays an important role in many electric vehicle control and energy storage management problems. The accuracy of the estimated SOC depends mostly on the accuracy of the battery model, which is mainly affected by factors like temperature, State of Health (SOH), and chemical reactions. Also many characteristic parameters of the battery cell, such as the output voltage, the internal resistance and so on, have close relations with SOC. Battery models are often identified by a large amount of experiments under different SOCs and temperatures. To resolve this difficulty and also improve modeling accuracy, a multiple input parameter fitting model of the Lithium-Ion battery and the factors that would affect the accuracy of the battery model are derived from the Nernst equation in this paper. Statistics theory is applied to obtain a more accurate battery model while using less measurement data. The relevant parameters can be calculated by data fitting through measurement on factors like continuously changing temperatures. From the obtained battery model, Extended Kalman Filter algorithm is applied to estimate the SOC. Finally, simulation and experimental results are given to illustrate the advantage of the proposed SOC estimation method. It is found that the proposed SOC estimation method always satisfies the precision requirement in the relevant Standards under different environmental temperatures. Particularly, the SOC estimation accuracy can be improved by 14% under low temperatures below 0 °C compared with existing methods. Copyright © 2017 John Wiley & Sons, Ltd.

Summary State of Health (SOH) is one of the most important parameters of lead-acid batteries. Most of the existing SOH estimation methods only take the influence of charge cycles into consideration, and the estimation accuracy is limited. Batteries in the substations have two typical states: one is the check-discharge state, in which the batteries are discharged for 8 h at 0.1 C (Capacity) to determine whether the battery pack has certain reliability. The other is the floating charge state, in which the batteries are connected to the charger to maintain full power. This paper proposes a novel SOH estimation method based on the two states. In the check-discharge state, the relationship between the voltage and the age of battery is analysed. The health index, which is introduced in this model, is affected by the age of battery. In the floating state, the relationship between the internal resistance and the age of battery is discussed. Another SOH model is established based on the change of the internal resistance. By combining the two models, the estimation method can achieve real-time estimation and high accuracy for substation application. An accelerated life test is applied to verify the theoretical analysis. The experimental results demonstrate that the SOH estimation error is less than 3% which is very satisfactory for practical applications. Copyright © 2016 John Wiley & Sons, Ltd.