A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in SOC estimation. However, the selection of an OCV–SOC model must consider several factors: (i) Modeling errors due to approximations, age/temperature effects, and cell-to-cell variations; (ii) Likelihood and severity of errors when the OCV–SOC parameters are rounded; (iii) Computing system requirements to store and process OCV parameters; and (iv) The required computational complexity of real-time OCV lookup algorithms. This paper presents a review of existing OCV–SOC models and proposes a systematic approach to select a suitable OCV–SOC for implementation based on various constraints faced by a BMS designer in practical application.

Battery management systems depend on open circuit voltage (OCV) characterization for state of charge (SOC) estimation in real time. The traditional approach to OCV-SOC characterization involves collecting OCV-SOC data from sample battery cells and then fitting a polynomial model to this data. The parameters of these polynomial models are known as the OCV-parameters, or OCV-SOC parameters, in battery management systems and are used for real-time SOC estimation. Even though traditional OCV-SOC characterization approaches are able to abstract the OCV-SOC behavior of a battery in a few parameters, these parameters are only applicable in high precision computing systems. However, many practical battery applications do not have access to high-precision computing resources. The typical approach in a low-precision system is to round the OCV-parameters. This paper highlights the perils of rounding the OCV parameters and proposes an alternative OCV-SOC table. First, several existing OCV-SOC parameters are compared in terms of their expected system requirements and accuracy losses due to rounding. Then, a systematic optimization-based approach is introduced to create an OCV-SOC table that is robust to rounding. A formal performance evaluation metric is introduced to measure the robustness of the resulting OCV-SOC table. It is shown that the OCV-SOC table obtained through the proposed optimization approach outperforms the traditional parametric OCV-SOC models with respect to rounding.

Temperature has a substantial impact on the safety, efficiency, and reliability of a lithium-ion (Li-ion) battery. The objective of a battery thermal management system (BTMS) is to maintain the battery in preferred temperature range. State-of-the-art BTMS is designed to react to the measured temperature at the battery surface, whereas research suggests that the performance of BTMS will likely improve when used in conjunction with predicted temperature. This article focuses on surface temperature prediction of Li-ion batteries based on an equivalent circuit model-based approach. Particularly, this article presents theoretically sound approaches to model heat generation in the core of the battery and its propagation to its surface. Another novel aspect of the present article is that the heat generation and propagation model identification is tested in various practical scenarios, where the measurement noise is significant. Experimental results using data collected from high precision battery cycler and temperature sensor show an open-loop mean absolute error of 1.678 $^{\circ }$ C during a high-current discharge that lasted approximately 800 s. Using the theoretical models presented in this article, the performance battery temperature prediction error at low SNR conditions can be understood.

Equivalent circuit model (ECM) based approaches are widely used by Li-ion battery management systems. In the ECM approach, the voltage drop within a battery is modelled using resistive and capacitive electrical components. Numerous approaches were reported to estimate the parameters of ECM based on the voltage and current measurements from the battery. Battery ECM parameter estimation is done based on both voltage and current measurements. The measurement noise in these applications results in a doubly noisy observation model and makes theoretical analysis challenging. In this paper, theoretical performance bounds are derived for ECM parameter estimation in batteries where the effect of both voltage and current measurement noises are objectively analyzed. The performance bound is derived in the form of Cramer-Rao lower bound (CRLB) by considering the doubly noisy observation model that represent ECM parameter estimation in batteries. It is shown that traditional least square estimation approach becomes biased and inefficient at low signal to noise ratio (SNR) levels. A new approach, based on the total least squares (TLS) method, is developed for low SNR conditions. It is shown through simulation experiments that the TLS approach can be efficient for ECM parameter estimation. The proposed approach is validated through data collected from cylindrical Li-ion battery cells.

Lithium-ion (Li-ion) batteries are highly sensitive to operating conditions and temperature is one of the critical conditions that affect their performance. This paper proposes a data-driven method for the prediction of the surface temperature of Li-ion batteries so that preemptive measures could be taken to maintain the temperature within the optimum range. Long short-term memory (LSTM) based neural network is one such method that helps in prediction using sequential data. In this paper, a brief and effective comparison between general LSTM (G-LSTM) and LSTM with transfer learning (LSTM-TL) is shown for the prediction of surface temperature. Theoretically, the TL method should reduce the computational burden and improve the prediction performance and the same has been observed in our experiment. This will make the system fault-tolerant. Moreover, the wide generalization and applicability of the developed model are shown through the temperature prediction on two different batteries that were not used in the training. The experimental results demonstrate that the G-LSTM model is capable of temperature prediction with RMSE error of 1.1578 $^\circ$ C for battery 03 and 1.2101 $^\circ$ C for battery 04. This error has been further reduced by around 40% to a value of 0.5012 $^\circ$ C for battery 03 and 0.7480 $^\circ$ C for battery 04 by using the LSTM-TL.