Driving the future: A comprehensive review of automotive battery management system technologies, and future trends

Pegah Rahmani; Sajib Chakraborty; Omar Hegazy; Igor  Mele; Tomaž  Katrašnik; Stanje  Bernhard; Stephan Pruefling; Steven Wilkins

doi:10.1016/j.jpowsour.2024.235827

Driving the future: A comprehensive review of automotive battery management system technologies, and future trends

Pegah Rahmani, Sajib Chakraborty, Omar Hegazy, Igor Mele, Tomaž Katrašnik, Stanje Bernhard, Stephan Pruefling, Steven Wilkins

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

76 Downloads (Pure)

Abstract

To date, a variety of Battery Energy Storage Systems (BESS) have been utilized in the EV industry, with lithium-ion (Li-ion) batteries emerging as a dominant choice. Li-ion batteries have not only captured the automotive market but have also exponentially been used in stationary energy storage sectors, thanks to their extended service life, high power, and volumetric density. The surge in Li-ion battery demand, increasing by approximately 65 % from 330 GWh in 2021 to 550 GWh in 2022, is primarily attributed to the exponential growth in electric vehicles sales. However, despite extensive research in academia and industry on Battery Management Systems (BMS), several gaps persist. Challenges include optimizing battery utilization within real-world operational limits, adapting BMS concerning chemical changes within batteries, e.g., aging, addressing the complexities of cell balancing in future battery packs, restricting fast charging below room temperature, limitations in fault tolerance capabilities, and the tendency to oversize for safety margins. Furthermore, the integration of efficient models (i.e., physics/data) with cutting-edge sensing technology remains a challenge as current BMS are often isolated and disconnected, narrowing the operational limits of battery systems for EV and stationary energy storage applications. This paper conducts a comprehensive review covering all possible aspects of BMS soft- and hardware solutions for EV applications, focusing on technical performance, safety, and reliability. Topics covered physics- and data-based modelling approaches for edge and cloud, state-of-X (SoX) estimation methods, charging strategies, balancing techniques, fault diagnostics, safety considerations, warranty management, and Vehicle-to-Everything (V2X) capabilities. Additionally, the paper sheds light on emerging technologies and future opportunities in this related field.

Original language	English
Article number	235827
Number of pages	41
Journal	Journal of Power Sources
Volume	629
DOIs	https://doi.org/10.1016/j.jpowsour.2024.235827
Publication status	Published - 15 Feb 2025

Bibliographical note

Funding Information:
This research has received funding from the European Union\u2019s Horizon Europe research and innovation program under Grant Numbers 101103898 (NEXTBMS) and 101137975 (InnoBMS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Additional support was provided by the Slovenian Research Agency (research core funding No. P2-0401).

Funding Information:
Significant advancements in Battery Management Systems (BMS) are being driven by cutting-edge technologies such as multi-model co-estimation, digital twins, Software Defined Vehicles [349], the integration of IoT and cloud computing, and smart power electronics [350]. These technologies aim to enhance the performance, lifetime, reliability, and safety of battery systems for both transport and stationary applications, facilitating predictive maintenance, end-of-life management and potential second-life applications. Several EU-funded projects, such as NEXTBMS, BATMAX, NEMO, ENERGETIC, BATSS and InnoBMS, under Horizon Europe calls HORIZON-CL5-2022-D2-01-09, HORIZON-CL5-2022-D2-01-05 and HORIZON-CL5-2023-D5-01-02, focus on utilizing cutting-edge innovations and technologies to develop next-generation Battery Management Systems (BMS). These initiatives aim to advance BMS capabilities setting the stage for highly efficient, scalable, and sustainable BMS solutions crucial to the future of electric vehicles and renewable energy systems.Fig. 20 illustrates the roadmap for BMS technology development, spanning from 2025 to 2050, and shows various phases of transition - short-term (2025), mid-term (2035), and long-term (2050). Short-Term Transition (2025): early innovations will focus on improving co-estimation methods, smart power electronics, and proof-of-concept of Electrochemical Impedance Spectroscopy (EIS) measurements. EU projects in this phase prioritize enhancing battery diagnostics and the readiness for future battery materials, aligning with the short-term goals outlined in EU battery initiatives. Mid-Term Transition (2035): research efforts will focus on real-time EIS measurements at a battery module level, physicochemical models as virtual sensors integrated with cloud connectivity to perform detailed diagnostics and IoT. Vehicle-to-Grid (V2G) technology, digital twin models, and the Battery Passport initiative are critical in this phase. These developments aim to provide scalability for new battery chemistries and contribute to data-driven decision-making algorithms in the BMS, supporting EU goals for sustainable energy and mobility solutions. Long-Term Transition (2050): Future BMS trends will prioritize advanced SoX diagnostics using physicochemically consistent models, enhanced cyber-secured BMS systems, battery swapping technologies, and V2V (Vehicle-to-Vehicle) communication. These advancements will be further supported by EU-funded projects focusing on battery safety, circular economy, and cyber-physical integration within energy grids.The widely useable model for battery systems in conventional BMS is the electrical model. However, the impact of electrochemical, mechanical, and optical characteristics on battery status is inevitable. Although developing a high-fidelity battery model improves the accuracy of state estimation, complexity will be increased. The next generation of BMS will not only focus on electrical measurement but also on electrochemical, mechanical, and optical characteristics taken into consideration [351]. Lin M. et al. [352] proposed a multi-feature-based multi-model so as to estimate SoH. First seven factors of the SoH are extracted by multiple sources, then by applying multiple linear regression, support vector regression, and Gaussian process regression model, the preliminary predictions of SoH are produced. Lastly, at last, a random forest is utilized to fuse the predictions of the SoH.Although, in recent decades, the EV industry has attracted considerable public attention, there are still some fundamental issues/barriers limiting the adoption of EVs on a large scale, including charge duration, low driving range of a single charge (range anxiety), and high cost of battery [363\u2013368].To tackle the charging problems/issues and further deployment of EVs, the construction of an electric vehicle supply is a prerequisite [369]. Battery swapping as an alternative time-efficient and cost\u2013effective approach can tackle the aforementioned problems to a great extent. In this method, a depleted battery substitutes with the fully-charged battery at the battery-swapping system. Then, the BSS transferred the drained battery to the battery charging station to recharge it. After that, the recharged battery back to BSS for swapping in EVs [368,370]. This BSS concept was developed by Better Place company. According to their idea, car ownership was separate from battery ownership. Despite the strong start, the company went bankrupt in 2013 because of financial issues and the imitation of infrastructure. Tesla's Motor Company introduced another business model in which the core of the business model, instead of battery swapping, is car production. In fact, in this model, Tesla offers supercharging instead of leasing the battery, which does not provide Tesla owners with financial benefit, which results in showing no real willingness for battery swapping technology [366]. There are some companies, namely NIO and BAIC, which explore/examine the feasibility and possibility of large-scale deployment of BSS. Renault offered a new concept of BSS in which a 40 kWh battery suitable for daily urban travel fixed to the rare of the vehicle and a swappable 50 kWh battery in front of the vehicle support long-distance trips. The swappable battery can be uninstalled in support of smart/intelligent charging and B2G. In a BSS, EV users, BSS operators, and grid operators are the main contributors/participants. BSS can play a pivotal role in the development of smart cities and smart grids in terms of energy storage and energy regulation. Actually, in addition to some advantages such as lower initial purchase cost, short time for swapping compared to recharging time, and lengthened lifecycle in centralized battery management, BSSs cannot only be used/utilized as a virtual power plant for grid load shifting but also can reduce the burden of the uncontrolled EV charging on power grid [363,368].To be more specific, if a large number/a great deal of EVs are charged in the course of peak electricity consumption, it can lead to increased network loss, peak-on-peak phenomenon, reduced power quality, and transformer overload; however, the use of BSS can be of great assistance in solving/tackling power quality problems/issues including harmonics caused by EV charging [368].This research has received funding from the European Union's Horizon Europe research and innovation program under Grant Numbers 101103898 (NEXTBMS) and 101137975 (InnoBMS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Additional support was provided by the Slovenian Research Agency (research core funding No. P2-0401).The authors acknowledge all the project partners involved in the NEXTBMS and INNOBMS projects. The authors also acknowledge Flanders Make for the support to our research group.

Publisher Copyright:
© 2024 The Author(s)

Keywords

Advanced battery management system
Battery balancing system
Digital Twin

Access to Document

10.1016/j.jpowsour.2024.235827Licence: CC BY

1-s2.0-S0378775324017798-mainFinal published version, 22.5 MBLicence: CC BY

Cite this

@article{845d6e13eed74dd3aff3b48e35dfab45,

title = "Driving the future: A comprehensive review of automotive battery management system technologies, and future trends",

abstract = "To date, a variety of Battery Energy Storage Systems (BESS) have been utilized in the EV industry, with lithium-ion (Li-ion) batteries emerging as a dominant choice. Li-ion batteries have not only captured the automotive market but have also exponentially been used in stationary energy storage sectors, thanks to their extended service life, high power, and volumetric density. The surge in Li-ion battery demand, increasing by approximately 65 % from 330 GWh in 2021 to 550 GWh in 2022, is primarily attributed to the exponential growth in electric vehicles sales. However, despite extensive research in academia and industry on Battery Management Systems (BMS), several gaps persist. Challenges include optimizing battery utilization within real-world operational limits, adapting BMS concerning chemical changes within batteries, e.g., aging, addressing the complexities of cell balancing in future battery packs, restricting fast charging below room temperature, limitations in fault tolerance capabilities, and the tendency to oversize for safety margins. Furthermore, the integration of efficient models (i.e., physics/data) with cutting-edge sensing technology remains a challenge as current BMS are often isolated and disconnected, narrowing the operational limits of battery systems for EV and stationary energy storage applications. This paper conducts a comprehensive review covering all possible aspects of BMS soft- and hardware solutions for EV applications, focusing on technical performance, safety, and reliability. Topics covered physics- and data-based modelling approaches for edge and cloud, state-of-X (SoX) estimation methods, charging strategies, balancing techniques, fault diagnostics, safety considerations, warranty management, and Vehicle-to-Everything (V2X) capabilities. Additionally, the paper sheds light on emerging technologies and future opportunities in this related field.",

keywords = "Advanced battery management system, Battery balancing system, Digital Twin",

author = "Pegah Rahmani and Sajib Chakraborty and Omar Hegazy and Igor Mele and Toma{\v z} Katra{\v s}nik and Stanje Bernhard and Stephan Pruefling and Steven Wilkins",

note = "Funding Information: This research has received funding from the European Union\u2019s Horizon Europe research and innovation program under Grant Numbers 101103898 (NEXTBMS) and 101137975 (InnoBMS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Additional support was provided by the Slovenian Research Agency (research core funding No. P2-0401). Funding Information: Significant advancements in Battery Management Systems (BMS) are being driven by cutting-edge technologies such as multi-model co-estimation, digital twins, Software Defined Vehicles [349], the integration of IoT and cloud computing, and smart power electronics [350]. These technologies aim to enhance the performance, lifetime, reliability, and safety of battery systems for both transport and stationary applications, facilitating predictive maintenance, end-of-life management and potential second-life applications. Several EU-funded projects, such as NEXTBMS, BATMAX, NEMO, ENERGETIC, BATSS and InnoBMS, under Horizon Europe calls HORIZON-CL5-2022-D2-01-09, HORIZON-CL5-2022-D2-01-05 and HORIZON-CL5-2023-D5-01-02, focus on utilizing cutting-edge innovations and technologies to develop next-generation Battery Management Systems (BMS). These initiatives aim to advance BMS capabilities setting the stage for highly efficient, scalable, and sustainable BMS solutions crucial to the future of electric vehicles and renewable energy systems.Fig. 20 illustrates the roadmap for BMS technology development, spanning from 2025 to 2050, and shows various phases of transition - short-term (2025), mid-term (2035), and long-term (2050). Short-Term Transition (2025): early innovations will focus on improving co-estimation methods, smart power electronics, and proof-of-concept of Electrochemical Impedance Spectroscopy (EIS) measurements. EU projects in this phase prioritize enhancing battery diagnostics and the readiness for future battery materials, aligning with the short-term goals outlined in EU battery initiatives. Mid-Term Transition (2035): research efforts will focus on real-time EIS measurements at a battery module level, physicochemical models as virtual sensors integrated with cloud connectivity to perform detailed diagnostics and IoT. Vehicle-to-Grid (V2G) technology, digital twin models, and the Battery Passport initiative are critical in this phase. These developments aim to provide scalability for new battery chemistries and contribute to data-driven decision-making algorithms in the BMS, supporting EU goals for sustainable energy and mobility solutions. Long-Term Transition (2050): Future BMS trends will prioritize advanced SoX diagnostics using physicochemically consistent models, enhanced cyber-secured BMS systems, battery swapping technologies, and V2V (Vehicle-to-Vehicle) communication. These advancements will be further supported by EU-funded projects focusing on battery safety, circular economy, and cyber-physical integration within energy grids.The widely useable model for battery systems in conventional BMS is the electrical model. However, the impact of electrochemical, mechanical, and optical characteristics on battery status is inevitable. Although developing a high-fidelity battery model improves the accuracy of state estimation, complexity will be increased. The next generation of BMS will not only focus on electrical measurement but also on electrochemical, mechanical, and optical characteristics taken into consideration [351]. Lin M. et al. [352] proposed a multi-feature-based multi-model so as to estimate SoH. First seven factors of the SoH are extracted by multiple sources, then by applying multiple linear regression, support vector regression, and Gaussian process regression model, the preliminary predictions of SoH are produced. Lastly, at last, a random forest is utilized to fuse the predictions of the SoH.Although, in recent decades, the EV industry has attracted considerable public attention, there are still some fundamental issues/barriers limiting the adoption of EVs on a large scale, including charge duration, low driving range of a single charge (range anxiety), and high cost of battery [363\u2013368].To tackle the charging problems/issues and further deployment of EVs, the construction of an electric vehicle supply is a prerequisite [369]. Battery swapping as an alternative time-efficient and cost\u2013effective approach can tackle the aforementioned problems to a great extent. In this method, a depleted battery substitutes with the fully-charged battery at the battery-swapping system. Then, the BSS transferred the drained battery to the battery charging station to recharge it. After that, the recharged battery back to BSS for swapping in EVs [368,370]. This BSS concept was developed by Better Place company. According to their idea, car ownership was separate from battery ownership. Despite the strong start, the company went bankrupt in 2013 because of financial issues and the imitation of infrastructure. Tesla's Motor Company introduced another business model in which the core of the business model, instead of battery swapping, is car production. In fact, in this model, Tesla offers supercharging instead of leasing the battery, which does not provide Tesla owners with financial benefit, which results in showing no real willingness for battery swapping technology [366]. There are some companies, namely NIO and BAIC, which explore/examine the feasibility and possibility of large-scale deployment of BSS. Renault offered a new concept of BSS in which a 40 kWh battery suitable for daily urban travel fixed to the rare of the vehicle and a swappable 50 kWh battery in front of the vehicle support long-distance trips. The swappable battery can be uninstalled in support of smart/intelligent charging and B2G. In a BSS, EV users, BSS operators, and grid operators are the main contributors/participants. BSS can play a pivotal role in the development of smart cities and smart grids in terms of energy storage and energy regulation. Actually, in addition to some advantages such as lower initial purchase cost, short time for swapping compared to recharging time, and lengthened lifecycle in centralized battery management, BSSs cannot only be used/utilized as a virtual power plant for grid load shifting but also can reduce the burden of the uncontrolled EV charging on power grid [363,368].To be more specific, if a large number/a great deal of EVs are charged in the course of peak electricity consumption, it can lead to increased network loss, peak-on-peak phenomenon, reduced power quality, and transformer overload; however, the use of BSS can be of great assistance in solving/tackling power quality problems/issues including harmonics caused by EV charging [368].This research has received funding from the European Union's Horizon Europe research and innovation program under Grant Numbers 101103898 (NEXTBMS) and 101137975 (InnoBMS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Additional support was provided by the Slovenian Research Agency (research core funding No. P2-0401).The authors acknowledge all the project partners involved in the NEXTBMS and INNOBMS projects. The authors also acknowledge Flanders Make for the support to our research group. Publisher Copyright: {\textcopyright} 2024 The Author(s)",

year = "2025",

month = feb,

day = "15",

doi = "10.1016/j.jpowsour.2024.235827",

language = "English",

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T1 - Driving the future: A comprehensive review of automotive battery management system technologies, and future trends

AU - Rahmani, Pegah

AU - Chakraborty, Sajib

AU - Hegazy, Omar

AU - Mele, Igor

AU - Katrašnik, Tomaž

AU - Bernhard, Stanje

AU - Pruefling, Stephan

AU - Wilkins, Steven

N1 - Funding Information: This research has received funding from the European Union\u2019s Horizon Europe research and innovation program under Grant Numbers 101103898 (NEXTBMS) and 101137975 (InnoBMS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Additional support was provided by the Slovenian Research Agency (research core funding No. P2-0401). Funding Information: Significant advancements in Battery Management Systems (BMS) are being driven by cutting-edge technologies such as multi-model co-estimation, digital twins, Software Defined Vehicles [349], the integration of IoT and cloud computing, and smart power electronics [350]. These technologies aim to enhance the performance, lifetime, reliability, and safety of battery systems for both transport and stationary applications, facilitating predictive maintenance, end-of-life management and potential second-life applications. Several EU-funded projects, such as NEXTBMS, BATMAX, NEMO, ENERGETIC, BATSS and InnoBMS, under Horizon Europe calls HORIZON-CL5-2022-D2-01-09, HORIZON-CL5-2022-D2-01-05 and HORIZON-CL5-2023-D5-01-02, focus on utilizing cutting-edge innovations and technologies to develop next-generation Battery Management Systems (BMS). These initiatives aim to advance BMS capabilities setting the stage for highly efficient, scalable, and sustainable BMS solutions crucial to the future of electric vehicles and renewable energy systems.Fig. 20 illustrates the roadmap for BMS technology development, spanning from 2025 to 2050, and shows various phases of transition - short-term (2025), mid-term (2035), and long-term (2050). Short-Term Transition (2025): early innovations will focus on improving co-estimation methods, smart power electronics, and proof-of-concept of Electrochemical Impedance Spectroscopy (EIS) measurements. EU projects in this phase prioritize enhancing battery diagnostics and the readiness for future battery materials, aligning with the short-term goals outlined in EU battery initiatives. Mid-Term Transition (2035): research efforts will focus on real-time EIS measurements at a battery module level, physicochemical models as virtual sensors integrated with cloud connectivity to perform detailed diagnostics and IoT. Vehicle-to-Grid (V2G) technology, digital twin models, and the Battery Passport initiative are critical in this phase. These developments aim to provide scalability for new battery chemistries and contribute to data-driven decision-making algorithms in the BMS, supporting EU goals for sustainable energy and mobility solutions. Long-Term Transition (2050): Future BMS trends will prioritize advanced SoX diagnostics using physicochemically consistent models, enhanced cyber-secured BMS systems, battery swapping technologies, and V2V (Vehicle-to-Vehicle) communication. These advancements will be further supported by EU-funded projects focusing on battery safety, circular economy, and cyber-physical integration within energy grids.The widely useable model for battery systems in conventional BMS is the electrical model. However, the impact of electrochemical, mechanical, and optical characteristics on battery status is inevitable. Although developing a high-fidelity battery model improves the accuracy of state estimation, complexity will be increased. The next generation of BMS will not only focus on electrical measurement but also on electrochemical, mechanical, and optical characteristics taken into consideration [351]. Lin M. et al. [352] proposed a multi-feature-based multi-model so as to estimate SoH. First seven factors of the SoH are extracted by multiple sources, then by applying multiple linear regression, support vector regression, and Gaussian process regression model, the preliminary predictions of SoH are produced. Lastly, at last, a random forest is utilized to fuse the predictions of the SoH.Although, in recent decades, the EV industry has attracted considerable public attention, there are still some fundamental issues/barriers limiting the adoption of EVs on a large scale, including charge duration, low driving range of a single charge (range anxiety), and high cost of battery [363\u2013368].To tackle the charging problems/issues and further deployment of EVs, the construction of an electric vehicle supply is a prerequisite [369]. Battery swapping as an alternative time-efficient and cost\u2013effective approach can tackle the aforementioned problems to a great extent. In this method, a depleted battery substitutes with the fully-charged battery at the battery-swapping system. Then, the BSS transferred the drained battery to the battery charging station to recharge it. After that, the recharged battery back to BSS for swapping in EVs [368,370]. This BSS concept was developed by Better Place company. According to their idea, car ownership was separate from battery ownership. Despite the strong start, the company went bankrupt in 2013 because of financial issues and the imitation of infrastructure. Tesla's Motor Company introduced another business model in which the core of the business model, instead of battery swapping, is car production. In fact, in this model, Tesla offers supercharging instead of leasing the battery, which does not provide Tesla owners with financial benefit, which results in showing no real willingness for battery swapping technology [366]. There are some companies, namely NIO and BAIC, which explore/examine the feasibility and possibility of large-scale deployment of BSS. Renault offered a new concept of BSS in which a 40 kWh battery suitable for daily urban travel fixed to the rare of the vehicle and a swappable 50 kWh battery in front of the vehicle support long-distance trips. The swappable battery can be uninstalled in support of smart/intelligent charging and B2G. In a BSS, EV users, BSS operators, and grid operators are the main contributors/participants. BSS can play a pivotal role in the development of smart cities and smart grids in terms of energy storage and energy regulation. Actually, in addition to some advantages such as lower initial purchase cost, short time for swapping compared to recharging time, and lengthened lifecycle in centralized battery management, BSSs cannot only be used/utilized as a virtual power plant for grid load shifting but also can reduce the burden of the uncontrolled EV charging on power grid [363,368].To be more specific, if a large number/a great deal of EVs are charged in the course of peak electricity consumption, it can lead to increased network loss, peak-on-peak phenomenon, reduced power quality, and transformer overload; however, the use of BSS can be of great assistance in solving/tackling power quality problems/issues including harmonics caused by EV charging [368].This research has received funding from the European Union's Horizon Europe research and innovation program under Grant Numbers 101103898 (NEXTBMS) and 101137975 (InnoBMS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Additional support was provided by the Slovenian Research Agency (research core funding No. P2-0401).The authors acknowledge all the project partners involved in the NEXTBMS and INNOBMS projects. The authors also acknowledge Flanders Make for the support to our research group. Publisher Copyright: © 2024 The Author(s)

PY - 2025/2/15

Y1 - 2025/2/15

N2 - To date, a variety of Battery Energy Storage Systems (BESS) have been utilized in the EV industry, with lithium-ion (Li-ion) batteries emerging as a dominant choice. Li-ion batteries have not only captured the automotive market but have also exponentially been used in stationary energy storage sectors, thanks to their extended service life, high power, and volumetric density. The surge in Li-ion battery demand, increasing by approximately 65 % from 330 GWh in 2021 to 550 GWh in 2022, is primarily attributed to the exponential growth in electric vehicles sales. However, despite extensive research in academia and industry on Battery Management Systems (BMS), several gaps persist. Challenges include optimizing battery utilization within real-world operational limits, adapting BMS concerning chemical changes within batteries, e.g., aging, addressing the complexities of cell balancing in future battery packs, restricting fast charging below room temperature, limitations in fault tolerance capabilities, and the tendency to oversize for safety margins. Furthermore, the integration of efficient models (i.e., physics/data) with cutting-edge sensing technology remains a challenge as current BMS are often isolated and disconnected, narrowing the operational limits of battery systems for EV and stationary energy storage applications. This paper conducts a comprehensive review covering all possible aspects of BMS soft- and hardware solutions for EV applications, focusing on technical performance, safety, and reliability. Topics covered physics- and data-based modelling approaches for edge and cloud, state-of-X (SoX) estimation methods, charging strategies, balancing techniques, fault diagnostics, safety considerations, warranty management, and Vehicle-to-Everything (V2X) capabilities. Additionally, the paper sheds light on emerging technologies and future opportunities in this related field.

AB - To date, a variety of Battery Energy Storage Systems (BESS) have been utilized in the EV industry, with lithium-ion (Li-ion) batteries emerging as a dominant choice. Li-ion batteries have not only captured the automotive market but have also exponentially been used in stationary energy storage sectors, thanks to their extended service life, high power, and volumetric density. The surge in Li-ion battery demand, increasing by approximately 65 % from 330 GWh in 2021 to 550 GWh in 2022, is primarily attributed to the exponential growth in electric vehicles sales. However, despite extensive research in academia and industry on Battery Management Systems (BMS), several gaps persist. Challenges include optimizing battery utilization within real-world operational limits, adapting BMS concerning chemical changes within batteries, e.g., aging, addressing the complexities of cell balancing in future battery packs, restricting fast charging below room temperature, limitations in fault tolerance capabilities, and the tendency to oversize for safety margins. Furthermore, the integration of efficient models (i.e., physics/data) with cutting-edge sensing technology remains a challenge as current BMS are often isolated and disconnected, narrowing the operational limits of battery systems for EV and stationary energy storage applications. This paper conducts a comprehensive review covering all possible aspects of BMS soft- and hardware solutions for EV applications, focusing on technical performance, safety, and reliability. Topics covered physics- and data-based modelling approaches for edge and cloud, state-of-X (SoX) estimation methods, charging strategies, balancing techniques, fault diagnostics, safety considerations, warranty management, and Vehicle-to-Everything (V2X) capabilities. Additionally, the paper sheds light on emerging technologies and future opportunities in this related field.

KW - Advanced battery management system

KW - Battery balancing system

KW - Digital Twin

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U2 - 10.1016/j.jpowsour.2024.235827

DO - 10.1016/j.jpowsour.2024.235827

M3 - Article

SN - 0378-7753

VL - 629

JO - Journal of Power Sources

JF - Journal of Power Sources

M1 - 235827

ER -

Driving the future: A comprehensive review of automotive battery management system technologies, and future trends

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

EUAR168: InnoBMS : Situationally aware innovative battery management system for next generation vehicles

EUAR140: NEXTBMS : NEXT-generation physics and data-based Battery Management Systems for optimised battery utilization

Cite this

Driving the future: A comprehensive review of automotive battery management system technologies, and future trends

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Projects

EUAR168: InnoBMS : Situationally aware innovative battery management system for next generation vehicles

EUAR140: NEXTBMS : NEXT-generation physics and data-based Battery Management Systems for optimised battery utilization

Cite this