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Battery management systems (BMSs) are critical to ensure the efficiency and safety of high-power battery energy storage systems (BESSs) in vehicular and stationary applications. Recently, the proliferation of battery big data and cloud computing advancements has led to the development of a new generation of BMSs, named Cloud BMS (CBMS), aiming to improve the performance and safety of BESSs. The CBMS is a cyber-physical system with connectivity between the physical BMS and a cloud-based virtual BMS, which is realized through a communication channel such as Internet of Things. Compared to the traditional BMS, the CBMS offers significantly higher computational resources, leveraging the implementation of advanced digital twin models and best-in-class algorithms in the BMS software, which will provide superior performances. However, as for any other CPS, the CBMS creates vulnerabilities against cyberattacks and if not properly secured, could end up damaging the BESS and/or causing dangerous, expensive, and life-threatening situations. Cybersecurity of the CBMSs has thus become a trending topic and several works have been published in this area in recent years. This paper conducts a scoping review to address different topics related to BMS cybersecurity. The CBMS architecture is presented, and the potential cyberattack surfaces are identified. Different possible attack scenarios, including attack points, attack types, and their impact at the component level (BMS and BESS) and system level (vehicle or grid), are discussed. In addition, the paper provides a review of potential countermeasures to protect the CBMS against cyberattacks. The paper also includes a review of the applicable standards and regulations that relate to this trending topic. Finally, based on the reviewed gaps, potential future research domains on BMS cybersecurity topics are identified and presented at the end of the paper.

Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy Optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.

Design of the Electric Vehicle (EV) battery pack involves different requirements related to the driving range, acceleration, fast-charging, lifetime, weight, volume, etc. Therefore, sizing of the EV battery pack necessitates a multi-objective optimization study to achieve the right trade-off considering the aforementioned factors. This “trade-off” can vary depending on the type and size of the EV, as well as use cases. In this regard, a nice solution is to use a hybridized battery pack consisting of both High-Energy (HE) and High-Power (HP) battery cells, which will help to meet a wider range of customer requirements. Hybridization decouples energy and power and thus increases design flexibility to achieve a better trade-off for a wider range of EV applications. This paper proposes an effective framework for optimal sizing of such hybridized battery packs for a typical EV, namely the Mitsubishi MiEV. Lithium-ion cells with Nickel Manganese Cobalt Oxide (NMC) and Lithium Titanate Oxide (LTO) chemistry are chosen as HE and HP cells, respectively. A detailed analysis is fulfilled to determine the best hybridization topology, e.g. voltage profile, interface with DC-link, etc. The genetic algorithm is used to solve the multi-objective optimization problem considering two different driving cycles, namely the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicle Test Procedure (WLTP). The results favor the usefulness of the hybrid battery pack to simultaneously achieve lifetime and charge power requirements compared to mono battery systems. The hybrid pack offers >+40,000km improvement in the achievable driving when an end-of-life criterion of 70% for the cell capacity is considered. Remarkably, the optimized hybrid pack can also be fast charged up to 70% within 6min.