The battery is a fundamental component of electric vehicles, which represent a step forward towards sustainable mobility. Lithium chemistry is now acknowledged as the technology of choice for energy storage in electric vehicles. However, several research points are still open. They include the best choice of the cell materials and the development of electronic circuits and algorithms for a more effective battery utilization. This paper initially reviews the most interesting modeling approaches for predicting the battery performance and discusses the demanding requirements and standards that apply to ICs and systems for battery management. Then, a general and flexible architecture for battery management implementation and the main techniques for state-of-charge estimation and charge balancing are reported. Finally, we describe the design and implementation of an innovative BMS, which incorporates an almost fully-integrated active charge equalizer.

This paper presents an accurate state of charge (SOC) estimation algorithm using a recurrent neural network with long short-term memory (LSTM) for lithium-ion batteries (LIB) performing under real conditions. With its self-learning ability, this data-driven approach is able to model the highly non-linear behavior of LIB due to changes of environment and working conditions all along the battery lifetime. It is shown that the LSTM approach outperforms common physical-based models using Extended Kalman Filters (EKF) regarding accuracy and stability. To demonstrate this benefit for real-world applications, the provided network is trained and tested with data gathered from commercial industry applications in the domain of energy storage. The LSTM is evaluated and compared with an equivalent circuit model (ECM) using EKF under different working conditions. For dynamic loading profiles, the ECM-EKF achieves an error (RMSE) of 9.5% whereas the LSTM achieves an error (RMSE) of 5.0%.

The market breakthrough of electric vehicles is mainly delayed by the still too high costs of the battery system. The novel distributed battery cell monitoring and management concept presented in this paper allows a significant reduction of the final battery pack costs. Further, due to economies of scale, it provides reduced development costs and much lower time-to-market. The proposed concept provides a contactless cost-efficient data transmission interface with capacitive coupling, thus making the development of a battery monitoring circuit for each battery module type needless. The costs are mainly reduced thanks to the high volume manufacturing approach of novel smart battery cells integrating all the sensors of the monitoring electronics together with passive cell balancing and cell heating function. This paper describes the possibilities offered by the proposed concept and shows implementation examples of such a contactless distributed battery cell monitoring.

Today, battery electric vehicles as well as stationary battery system demand lithium-ion batteries. Lithium titanate battery cells with their excellent charge and discharge rate capabilities in combination with their long lifetime have become an attractive option for these kinds of applications. However, to ensure safe operation of lithium batteries - especially in high power applications - temperature observation is mandatory. So, it is highly important to have a reliable thermal management system with proper temperature estimation. Normally, an external temperature sensor is placed on the surface of the battery case. This method is not very appropriate because only the external battery temperature can be detected, which is delayed by the thermal coupling between sensor and cell, and thermal capacity of the battery cell. This paper describes the development of a circuit that performs sensorless temperature estimation of a lithium-ion battery cell. A functional prototype was developed and comprehensive tests are performed to verify the functionality of this prototype. It is demonstrated that it is possible to determine the internal equivalent temperature of the cell using the measurement results of the developed circuit. Further, the circuit concept can be easily integrated into the monitoring system of high power battery systems. As a result, the developed prototype may enhance the safety of battery systems by proper temperature estimation of lithium-ion batteries used in electric vehicles and stationary energy storage systems.

Large lithium-ion battery systems rely on battery monitoring and management systems to ensure safe and efficient operation. Typically the battery current, the cell voltages, and the cell temperatures are monitored. This paper describes the use of gas sensors in large lithium-ion battery systems in addition to conventionally used means of battery monitoring. An undetected electrolyte leak in a cell can pose a serious threat to users and maintenance personnel. Experiments described in this paper show that a gas sensor can easily detect volatile organic compounds (VOC) from the leaking electrolyte, whereas standard cell monitoring methods can only detect a leak indirectly over premature cell performance degradation. Therefore, gas sensors offer a fast, simple, and cost efficient way to increase the safety of battery systems. This paper gives a description of a suitable gas sensor and its application in a battery system, followed by the identification of relevant use cases. In the experimental section the performance of the gas sensor in these use cases is investigated and evaluated. The paper ends with a summary of the results and a short outlook.