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
