Machine Learning-Enabled Battery Management System on FPGA for Electric Vehicles
This study presents an FPGA-based implementation of an adaptive power management unit (APMU) for electric vehicles (EVs), leveraging a hybrid bidirectional long short-term memory (Bi-LSTM) network and decision tree classifier to optimize power distribution in real time. Developed on the Zynq UltraSc...
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| Published in: | Programming and computer software Vol. 51; no. 6; pp. 373 - 384 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Moscow
Pleiades Publishing
01.12.2025
Springer Nature B.V |
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| ISSN: | 0361-7688, 1608-3261 |
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| Abstract | This study presents an FPGA-based implementation of an adaptive power management unit (APMU) for electric vehicles (EVs), leveraging a hybrid bidirectional long short-term memory (Bi-LSTM) network and decision tree classifier to optimize power distribution in real time. Developed on the Zynq UltraScale+ MPSoC platform, the proposed system estimates the state of charge (SoC) and classifies driving conditions to dynamically allocate power to onboard components. The FPGA testbed models a mid-range EV by simulating key parameters such as throttle position, vehicle speed, battery voltage/current, and GPS data at 30-second intervals. Experimental results demonstrate significant improvements in power efficiency and computational latency compared to conventional battery management units (BMUs). The proposed system consumes approximately 0.98 W, achieves a latency of 5.6 µs, and operates at 181.6 operations per watt-far surpassing traditional microcontroller or DSP-based BMUs. Range estimation shows up to a 25% increase under highway conditions using the Bi-LSTM + decision tree model, validating the effectiveness of the adaptive strategy for intelligent energy management in EVs. |
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| AbstractList | This study presents an FPGA-based implementation of an adaptive power management unit (APMU) for electric vehicles (EVs), leveraging a hybrid bidirectional long short-term memory (Bi-LSTM) network and decision tree classifier to optimize power distribution in real time. Developed on the Zynq UltraScale+ MPSoC platform, the proposed system estimates the state of charge (SoC) and classifies driving conditions to dynamically allocate power to onboard components. The FPGA testbed models a mid-range EV by simulating key parameters such as throttle position, vehicle speed, battery voltage/current, and GPS data at 30-second intervals. Experimental results demonstrate significant improvements in power efficiency and computational latency compared to conventional battery management units (BMUs). The proposed system consumes approximately 0.98 W, achieves a latency of 5.6 µs, and operates at 181.6 operations per watt-far surpassing traditional microcontroller or DSP-based BMUs. Range estimation shows up to a 25% increase under highway conditions using the Bi-LSTM + decision tree model, validating the effectiveness of the adaptive strategy for intelligent energy management in EVs. |
| Author | Saravana Ram. R Daisy Merina. R Lordwin Cecil Prabhaker Micheal |
| Author_xml | – sequence: 1 surname: Daisy Merina. R fullname: Daisy Merina. R email: rdaisymerina@gmail.com organization: Department of Artificial Intelligence and Data Science, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College – sequence: 2 surname: Saravana Ram. R fullname: Saravana Ram. R email: saravanaramkrishnan@gmail.com organization: Department of Electronics and Communication Engineering, Anna University – Regional Campus – sequence: 3 surname: Lordwin Cecil Prabhaker Micheal fullname: Lordwin Cecil Prabhaker Micheal email: drlordwin@veltech.edu.in organization: Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology |
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| Cites_doi | 10.24846/v27i1y201808 10.1142/s0218126623500470 10.1016/j.jpowsour.2018.10.04 10.1007/s10470-025-02370-8 10.1016/j.segan.2024.101348 10.1016/j.jpowsour.2018.05.078 10.1016/j.physd.2019.132306 10.1016/j.heliyon.2024.e35183 10.18100/ijamec.1233451 10.3390/electronics12122647 10.1016/j.est.2023.108876 10.3390/en18051188 10.1142/s0218126621500432 10.3390/en10030264 10.1007/978-981-19-4502-1_9 10.1007/s00521-021-06247-5 10.4271/12-07-03-0016 10.1109/ACCESS.2023.3250221 10.1109/access.2018.2824559 10.3390/wevj15110484 10.1016/j.microrel.2018.03.015 |
| ContentType | Journal Article |
| Copyright | Pleiades Publishing, Ltd. 2025 ISSN 0361-7688, Programming and Computer Software, 2025, Vol. 51, No. 6, pp. 373–384. © Pleiades Publishing, Ltd., 2025.Russian Text © The Author(s), 2025, published in Proceedings of ISP RAS, 2025, Vol. 37, No. 4. Pleiades Publishing, Ltd. 2025. |
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| Keywords | driving pattern recognition using machine learning battery management system (BMS) for electric vehicles long short-term memory (LSTM) networks bidirectional LSTM (Bi-LSTM) architecture decision tree classification state of charge (SoC) estimation algorithms FPGA-based embedded implementation |
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| References_xml | – volume: 27 start-page: 73 year: 2018 ident: 3956_CR20 publication-title: Stud. Inf. Control doi: 10.24846/v27i1y201808 – ident: 3956_CR21 doi: 10.1142/s0218126623500470 – volume: 407 start-page: 92 year: 2018 ident: 3956_CR3 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.10.04 – volume: 123 start-page: 42 year: 2025 ident: 3956_CR14 publication-title: Analog Integr. Circuits Signal Process. doi: 10.1007/s10470-025-02370-8 – ident: 3956_CR2 doi: 10.1016/j.segan.2024.101348 – volume: 395 start-page: 262 year: 2018 ident: 3956_CR4 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.05.078 – volume-title: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Phys. D year: 2020 ident: 3956_CR5 doi: 10.1016/j.physd.2019.132306 – ident: 3956_CR6 doi: 10.1016/j.heliyon.2024.e35183 – volume: 11 start-page: 55 year: 2023 ident: 3956_CR18 publication-title: International Journal of Applied Mathematics Electronics and Computers doi: 10.18100/ijamec.1233451 – volume: 12 start-page: 2647 year: 2023 ident: 3956_CR17 publication-title: Electronics doi: 10.3390/electronics12122647 – ident: 3956_CR12 doi: 10.1016/j.est.2023.108876 – ident: 3956_CR8 doi: 10.3390/en18051188 – ident: 3956_CR13 doi: 10.1142/s0218126621500432 – volume: 10 start-page: 264 year: 2017 ident: 3956_CR15 publication-title: Energies doi: 10.3390/en10030264 – ident: 3956_CR10 doi: 10.1007/978-981-19-4502-1_9 – volume: 33 start-page: 16095 year: 2021 ident: 3956_CR16 publication-title: Neural Computing and Applications doi: 10.1007/s00521-021-06247-5 – volume: 7 start-page: 261 year: 2024 ident: 3956_CR1 publication-title: SAE Int. J. Connected Autom. Veh. doi: 10.4271/12-07-03-0016 – volume: 11 start-page: 20994 year: 2023 ident: 3956_CR9 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3250221 – volume: 6 start-page: 20868 year: 2018 ident: 3956_CR11 publication-title: IEEE Access doi: 10.1109/access.2018.2824559 – volume: 15 start-page: 484 year: 2024 ident: 3956_CR7 publication-title: World Electr. Veh. J. doi: 10.3390/wevj15110484 – volume: 84 start-page: 66 year: 2018 ident: 3956_CR19 publication-title: Microelectron. Reliab. doi: 10.1016/j.microrel.2018.03.015 |
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| SubjectTerms | Accuracy Artificial Intelligence Batteries Computer Science Cost control Decision trees Driving conditions Electric vehicles Emissions Energy consumption Energy efficiency Energy management Field programmable gate arrays Genetic algorithms Lithium Machine learning Neural networks Operating Systems Power efficiency Power management Software Engineering Software Engineering/Programming and Operating Systems State of charge Traffic speed |
| Title | Machine Learning-Enabled Battery Management System on FPGA for Electric Vehicles |
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