Periodic Reporting for period 1 - InnoBMS (Situationally aware innovative battery management system for next generation vehicles)
Periodo di rendicontazione: 2024-01-01 al 2025-06-30
Conventional BMSs rely on static algorithms and fixed parameters established at the outset of battery life. This conventional paradigm limits adaptability to evolving conditions, leading to imprecise state-of-charge (SoC) estimations and diminished battery longevity.
InnoBMS integrates dynamic situational awareness in the BMS, empowering batteries to operate optimally across diverse scenarios. Continuously analyzing real-time data, our BMS dynamically adjusts charging and discharging strategies, ensuring accurate SoC estimations and maximizing battery efficiency.
Key Advantages:
1) Adaptive Responsiveness: Real-time adjustments optimize energy utilization, prolonging battery lifespan and enhancing operational performance.
2) Precise SoC Estimation: Accurate calculations improve range predictions and eliminate uncertainty.
3) Efficient Resource Allocation: Tailored energy utilization minimizes wastage based on environmental variables and usage patterns.
4) Enhanced Safety and Reliability: Proactive identification and mitigation of safety hazards ensure system integrity and user trust.
5) Cost and Size Optimization: Reduced reliance on oversized batteries lowers procurement expenses and simplifies system design.
The core objective of InnoBMS is to develop and demonstrate the best-in-class BMS hard- and software solution that maximizes battery performance for the user without negatively affecting battery life, even in extreme conditions, whilst maintaining durability and safety at all times. Concretely, InnoBMS proposal will deliver a 12% higher effective battery pack volumetric density, a 33% longer battery lifetime and a demonstrated lifetime of up to 15 years. The results will be demonstrated in two use cases, one light commercial vehicle (Fiat Doblo Electric) with a 400V system and one medium-duty van (IVECO eDaily) with an 800V system, using novel testing methods that give a 36% reduction of the time needed for calibrating SoX algorithms. See Figure 1 and Table 1 for more details, including the reference baselines. These outcomes will enable a cost reduction of 12% and 9.7% for passenger cars and light-duty vehicles, respectively.
The targeted project objectives as defined in §1.1 of Annex 1 of the Grant Agreement are:
1. Models and algorithms to enable a flexible BMS that is suitable for a range of batteries, and that is fully informed to take balanced decisions for safe and optimal battery use in every driving condition
2. Software for a wireless BMS with reliable and secure connections between all actors in the battery system
3. A scalable, fully wireless, self-testing BMS hardware that enables OEMs to use different battery sizes (energy content) at different operating voltage levels (e.g. 400 and 1000V), and sensor integration
4. Better use and exploitation of the battery using on cloud-informed strategies and procedures, including full diagnostics for data-informed non-primary use (predictive maintenance, 2nd life, V2X capabilities)
5. Multiple demonstrations of these innovations integrated in a vehicle by using new simulation tools and automated test methods to develop a more efficient BMS in a shorter time (virtual & physical use cases)
6. Communication, dissemination and exploitation of the InnoBMS results
InnoBMS integrates dynamic situational awareness in the BMS, empowering batteries to operate optimally across diverse scenarios. Continuously analyzing real-time data, our BMS dynamically adjusts charging and discharging strategies, ensuring accurate SoC estimations and maximizing battery efficiency.
Key Advantages:
1) Adaptive Responsiveness: Real-time adjustments optimize energy utilization, prolonging battery lifespan and enhancing operational performance.
2) Precise SoC Estimation: Accurate calculations improve range predictions and eliminate uncertainty.
3) Efficient Resource Allocation: Tailored energy utilization minimizes wastage based on environmental variables and usage patterns.
4) Enhanced Safety and Reliability: Proactive identification and mitigation of safety hazards ensure system integrity and user trust.
5) Cost and Size Optimization: Reduced reliance on oversized batteries lowers procurement expenses and simplifies system design.
The core objective of InnoBMS is to develop and demonstrate the best-in-class BMS hard- and software solution that maximizes battery performance for the user without negatively affecting battery life, even in extreme conditions, whilst maintaining durability and safety at all times. Concretely, InnoBMS proposal will deliver a 12% higher effective battery pack volumetric density, a 33% longer battery lifetime and a demonstrated lifetime of up to 15 years. The results will be demonstrated in two use cases, one light commercial vehicle (Fiat Doblo Electric) with a 400V system and one medium-duty van (IVECO eDaily) with an 800V system, using novel testing methods that give a 36% reduction of the time needed for calibrating SoX algorithms. See Figure 1 and Table 1 for more details, including the reference baselines. These outcomes will enable a cost reduction of 12% and 9.7% for passenger cars and light-duty vehicles, respectively.
The targeted project objectives as defined in §1.1 of Annex 1 of the Grant Agreement are:
1. Models and algorithms to enable a flexible BMS that is suitable for a range of batteries, and that is fully informed to take balanced decisions for safe and optimal battery use in every driving condition
2. Software for a wireless BMS with reliable and secure connections between all actors in the battery system
3. A scalable, fully wireless, self-testing BMS hardware that enables OEMs to use different battery sizes (energy content) at different operating voltage levels (e.g. 400 and 1000V), and sensor integration
4. Better use and exploitation of the battery using on cloud-informed strategies and procedures, including full diagnostics for data-informed non-primary use (predictive maintenance, 2nd life, V2X capabilities)
5. Multiple demonstrations of these innovations integrated in a vehicle by using new simulation tools and automated test methods to develop a more efficient BMS in a shorter time (virtual & physical use cases)
6. Communication, dissemination and exploitation of the InnoBMS results
In this first reporting period (RP1), significant progress has been made towards the project objectives. Currently achieved project technical results include (reported per linked Objectives):
1. Models and algorithms to enable a flexible BMS that is suitable for a range of batteries, and that is fully informed to take balanced decisions for safe and optimal battery use in every driving condition
a) Completed. The Definition of data collection and flow management process, considering advanced models and algorithms (WP2)
b) Completed. Interface (TI/O) definitions of advanced functionalities (WP2)
` c) In progress. Ddevelopment of advanced functionalities is ongoing (WP2)
2. Software for a wireless BMS with reliable and secure connections between all actors in the battery system
a) Completed. Early versions of embedded software interfaces have been designed for integration with edge controllers and cloud platforms’ algorithms (WP3).
b) Completed. Cloud layout (WP3)
c) In progress. Interaction between the edge device and the cloud and deployment of advanced functionalities in the cloud (yet to be started) (WP3).
3. A scalable, fully wireless, self-testing BMS hardware that enables OEMs to use different battery sizes (energy content) at different operating voltage levels (e.g. 400 and 1000V), and sensor integration
a) BMS Hardware design freeze: Fully wireless CMB layout is completed; BMS component selection is completed; Wireless Network manager is selected; Edge device is selected (WP4).
b) BMS Hardware implementation: PCB and Bill of Materials for the BMS are completed;
c) In progress. Advanced sensors integration with battery pack, PCB soldering, BOM acquisition (WP4).
4. Better use and exploitation of the battery using on cloud-informed strategies and procedures, including full diagnostics for data-informed non-primary use (predictive maintenance, 2nd life, V2X capabilities)
a) Completed. The project defined a total of 111 feature-level, 82 system-level, and 14 component-level requirements (WP1).
b) Completed. 2 types of target vehicles and operational use-cases with V2G capabilities (WP1).
5. Multiple demonstrations of these innovations integrated in a vehicle by using new simulation tools and automated test methods to develop a more efficient BMS in a shorter time (virtual & physical use cases)
a) Two separate demos have been agreed and are under preparation: TOFAS (400V) and FMF (800V) (WP5).
b) Completed. The deployment of a hybrid test infrastructure combining software-in-the-loop and hardware-in-the-loop platforms (WP5).
6. Communication, dissemination and exploitation of the InnoBMS results
a) Project website
b) 110 LinkedIn Members
c) 1 AB meeting with industry partners
d) 2 Q1 Journals and 1 conference
e) Actively participate in 10 dissemination activities
1. Models and algorithms to enable a flexible BMS that is suitable for a range of batteries, and that is fully informed to take balanced decisions for safe and optimal battery use in every driving condition
a) Completed. The Definition of data collection and flow management process, considering advanced models and algorithms (WP2)
b) Completed. Interface (TI/O) definitions of advanced functionalities (WP2)
` c) In progress. Ddevelopment of advanced functionalities is ongoing (WP2)
2. Software for a wireless BMS with reliable and secure connections between all actors in the battery system
a) Completed. Early versions of embedded software interfaces have been designed for integration with edge controllers and cloud platforms’ algorithms (WP3).
b) Completed. Cloud layout (WP3)
c) In progress. Interaction between the edge device and the cloud and deployment of advanced functionalities in the cloud (yet to be started) (WP3).
3. A scalable, fully wireless, self-testing BMS hardware that enables OEMs to use different battery sizes (energy content) at different operating voltage levels (e.g. 400 and 1000V), and sensor integration
a) BMS Hardware design freeze: Fully wireless CMB layout is completed; BMS component selection is completed; Wireless Network manager is selected; Edge device is selected (WP4).
b) BMS Hardware implementation: PCB and Bill of Materials for the BMS are completed;
c) In progress. Advanced sensors integration with battery pack, PCB soldering, BOM acquisition (WP4).
4. Better use and exploitation of the battery using on cloud-informed strategies and procedures, including full diagnostics for data-informed non-primary use (predictive maintenance, 2nd life, V2X capabilities)
a) Completed. The project defined a total of 111 feature-level, 82 system-level, and 14 component-level requirements (WP1).
b) Completed. 2 types of target vehicles and operational use-cases with V2G capabilities (WP1).
5. Multiple demonstrations of these innovations integrated in a vehicle by using new simulation tools and automated test methods to develop a more efficient BMS in a shorter time (virtual & physical use cases)
a) Two separate demos have been agreed and are under preparation: TOFAS (400V) and FMF (800V) (WP5).
b) Completed. The deployment of a hybrid test infrastructure combining software-in-the-loop and hardware-in-the-loop platforms (WP5).
6. Communication, dissemination and exploitation of the InnoBMS results
a) Project website
b) 110 LinkedIn Members
c) 1 AB meeting with industry partners
d) 2 Q1 Journals and 1 conference
e) Actively participate in 10 dissemination activities
In the first 18 months, InnoBMS has already begun delivering impacts through early results. Through Deliverables D1.1 and D1.2 the consortium defined over 200 detailed requirements and developed real-world EV use cases (Passenger Cars and Light Commcercial vehicles), including urban delivery and vehicle-to-grid scenarios. This work ensured that development across partners is aligned with practical constraints and user needs. On the modelling front, the project has successfully developed and tested an early version of the SOC/SOH estimation algorithm. Delivered by IDIADA and validated on real cell data, the model tracks SOC with less than 3% error and provides meaningful SOH trends. BMS Hardware design freeze: Fully wireless CMB layout is completed; BMS component selection is completed; Wireless Network manager is selected; Edge device is selected (WP4).