Energy Aware Solutions for Battery Management Systems

Dear Colleagues,

Battery Management Systems (BMSs) play a crucial role in monitoring, controlling, and protecting the battery pack, and its design can significantly impact overall power efficiency. Minimizing power consumption in the BMS is a critical aspect because it has significant consequences on the managed system and applications. For instance, in Electric Vehicles (EVs) the power consumed by the BMS directly affects the vehicle's range, therefore optimizing the BMS for low power consumption contributes to increased mileage per charge, making electric vehicles more practical and attractive to consumers. In embedded systems and Internet of Things (IoT) devices, minimizing power consumption is crucial for extending the operational life of these devices. Low-power design is especially important for remote and sensor-based applications. Accordingly, in scenarios where batteries are used in remote or inaccessible locations, minimizing power consumption becomes critical for applications in which energy harvesting is employed to recharge the battery, and power efficiency ensures prolonged operation between charging cycles. Efforts are ongoing in the research and development of BMS technologies to optimize power consumption while maintaining or enhancing the performance and safety aspects. This includes the use of low-power microcontrollers, efficient algorithms, and advancements in hardware design to strike a balance between effective battery management and minimal energy usage. We expect your contributions in the following sub-topics:

• Battery monitoring and diagnostics: techniques for the real-time monitoring of battery health, state of charge (SoC), state of health (SoH), state of power (SoP), etc.;
• Energy-efficient charging and discharging: strategies to enhance the charging and discharging processes to minimize energy loss, such as smart charging algorithms, fast charging technologies, and efficient power conversion techniques;
• Battery balancing and cell management: methods to ensure uniform cell charging and discharging within a battery pack, including active/passive balancing techniques, cell equalization, and thermal management to prevent hot spots and improve overall performance;
• Predictive analytics and machine learning: using data analytics and machine learning algorithms to predict battery performance, degradation, and failure, enabling proactive maintenance and optimization strategies;
• Optimization algorithms: developing algorithms to optimize the usage of stored energy, considering factors like load requirements, temperature, and battery condition to maximize efficiency and prolong battery life;
• Safety and reliability: ensuring safety standards, fault detection, and fail-safe mechanisms within BMS to prevent hazardous situations and maintain reliable operation;
• Security and resilience against cyber attacks: methods, strategies, and solutions to prevent or mitigate security attacks and reduce the security vulnerabilities for data authentication and integrity, access control and authorization, intrusion detection and prevention, secure firmware, secure software updates, vendor and supply chain security, etc.;
• Energy-aware oriented emerging technologies: exploring new materials, designs, or technologies that could revolutionize the energy awareness of BMSs;

Dr. Luca Crocetti
Prof. Dr. Roberto Saletti
Guest Editors

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• energy-efficient embedded systems
• tiny machine learning
• low-power computing
• ultra-low-power processors
• low-power distributed computing
• low-power security
• energy-efficient hardware security
• energy harvesting circuits
• ultra-low-power sensors