Abstract

International audience; This paper addresses the challenge of monitoring battery in fleets of electric vehicles (EV). The goal is to identify the different EV utilization modes, during real use, and to highlight the constraining conditions to manage battery health evolution. The other part of this study aims to build a battery capacity estimation model, permitting to estimate the remaining capacity of an EV during its real use. Finally, is explained a strategy to improve capacity estimates, based on the identified EV utilization modes.

Abstract

The overall performance of battery packs may be affected by imbalances between the series connected cells which is more likely in packs with high number of cells needed to provide a high voltage as needed for example in electric vehicles. In this case, the overall capacity and power capability of the pack are limited by the weakest cell in the stack which results in incomplete utilisation of the pack's capabilities. In traditional centralised battery systems (TCBS), this is addressed by implementing cell active/passive balancing circuitry/techniques which restore some of the pack's energy capability. This paper proposes the use of cascaded modular battery systems (CMBS) to remove the need for extra balancing circuitry and maximises the performance and reliability of a battery system containing unequal matched/aged cells. The analysis is assessing the CMBS overall system efficiency, reliability and weight compared to the TCBS for a design of a 300 V/3.6 kW battery system as a case study.

Abstract

This paper focuses on the hardware aspects of battery management systems (BMS) for electric vehicle and stationary applications. The purpose is giving an overview on existing concepts in state-of-the-art systems and enabling the reader to estimate what has to be considered when designing a BMS for a given application. After a short analysis of general requirements, several possible topologies for battery packs and their consequences for the BMS’ complexity are examined. Four battery packs that were taken from commercially available electric vehicles are shown as examples. Later, implementation aspects regarding measurement of needed physical variables (voltage, current, temperature, etc.) are discussed, as well as balancing issues and strategies. Finally, safety considerations and reliability aspects are investigated. Document type: Article

Abstract

In battery-electric vehicles, an accurate knowledge of the current state of charge (SOC) of the battery is crucial for safe and efficient operation. Offset-free SOC estimation for Lithium-ion batteries (LIB) during operation still is a challenging problem, due to the uncertain output nonlinearity present in battery models. In battery management systems employed in such vehicles, the age- and cycle-dependent relationship between the open circuit voltage (OCV) and the state of charge of each cell (SOC) can be kept track of using lookup tables. Between the scattered data points, linear interpolation is often used. Another common approach is to switch between models, each with a different piecewise polynomial fit of low order. In this contribution, a single polynomial fit of a higher order with state-dependent coefficients for the whole SOC-OCV range is proposed, so as to avoid switching and facilitate usage of Taylor-based linearization algorithms like in the extended Kalman filter. While the polynomial order is high, the number of parameters is only three; the parameters are estimated and updated by the Kalman filter itself. This augmentation of the observer's state vector with said polynomial parameters is inspired by the idea of an increased “stochastization,” introducing redundancy that aids to achieve a more accurate SOC estimation. In this sense, the algorithm can be described as a model-adaptive extended Kalman estimator. Two variants of a polynomial EKF are investigated: polynomial EKF with output and with state nonlinearity models. Their performances are compared in real experiments using a dedicated test bench based on a Samsung ICR18650-26F cell, demonstrating the effectiveness of the algorithms. The variant that includes the terminal voltage in the state vector, i.e. the state nonlinearity variant of the EKF, shows better result, also compared to the existing literature.

Abstract

Typically, the unique objective pursued in either active or passive balancing is equalization of single cell charge. However, a balancing circuit may offer more control features, like virtual equalization of single cell internal resistance or thermal balancing. Such control features for balancing systems are evaluated in this paper by means of convex optimization. More than one hundred cases in a pure EV application are evaluated. Balancing circuits' efficiency models are implemented and realistic cell-to-cell parameter distributions are considered based on experimental data. Different battery sizes and driving cycles are considered. Balancing circuit topology is taken into account by selecting a specific category of energy transfer: cell-to-heat, bypass, cell-to-pack, pack-to-cell, cell-to-cell shared, cell-to-cell distributed or cell-to-pack-to-cell. In general, better results in terms of energy losses, available capacity or temperature are obtained for the last three categories, even for moderate balancing currents. In particular, remarkable improvements are observed under conditions of high power demand with high variability, i.e., smaller battery sizes and more demanding driving cycles.

Abstract

Battery thermal management is essential to efficienct and safe operation of Li-ion batteries, particularly in challenging applications such as fast charging. This work proposes a thermal management control framework based on MPC. The novelty of this approach resides in the focus on incorporating disturbance prediction into the control decision. Simulations show that this methodology allows for (1) lower peak and mean battery temperatures during fast charging for a similarly sized battery thermal management system; or (2) battery thermal management system downsizing for similar thermal performance.

Abstract

Model based development methodologies have strong potential in reducing development effort of complex automotive systems. This paper presents a model based development methodology used to develop battery state estimation algorithms, and steps through all the steps required for this. The benefits of the mixed simulation and testing methodology are outlined using a State of Charge estimation algorithm development example. Overall, this development methodology leads to safer, more efficient and more affordable battery packs, enabling faster market update of electric vehicles.