Smart and secure battery management: The role of artificial intelligence and edge computing in the next generation of electric vehicles

The increasing adoption of lithium-ion (Li-ion) batteries in electric vehicles (EVs) has intensified the need for secure battery management systems (BMSs). It is crucial to secure the BMS. BMS is exposed to cybersecurity risks due to its dependency on communication technologies. It is vulnerable to cyberattacks such as data modification, interception, and denial-of-service (DoS) attacks, which may result in overcharging, undercharging, or thermal hazards of the battery. The aim of this paper is to develop and experimentally validate a secure local monitoring architecture for BMS data using encryption-based protection mechanisms. A password-protected encryption and decryption framework is used to protect important battery parameters like voltage, current, state of charge (SoC), temperature, and humidity. The proposed system utilizes AES-128 encryption deployed on a NodeMCU 32-bit microcontroller-based local web server. The process of encryption achieves an execution time of 1.2 ms with 811 transactions per second (TPS), while decryption requires 1.5 ms with 660 TPS. A 6S Li-ion battery bank (14.4 Wh, 600 mAh per cell) is used to validate system performance. This paper also presents an overview of cybersecurity threats in EV BMS, cell-balancing methods, SoC estimation methods, and the application of blockchain technology to put the proposed secure monitoring framework in context. • This article reviews BMS technologies, focusing on battery cell modelling and balancing methods. • It discusses EV cyber-attacks, communication systems, SoC estimation, and blockchain security. • A web server–based BMS with encryption, decryption, and message-layer security is implemented. • NodeMCU monitors Li-ion battery voltage, current, SoC, temperature, and humidity. • A 14.4 Wh power bank with a 6S Li-ion battery (600 mAh per cell) is integrated with the system.