Periodic Reporting for period 3 - EVERLASTING (Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management)
Okres sprawozdawczy: 2019-09-01 do 2021-02-28
State-of-the-art BMS predominantly react to ‘symptoms’. They have only a rudimentary knowledge of the battery state and use rules-of-the thumb to manage the battery state. Due to this approximate and reactive way of working, the safe operating range of the battery has to be significantly narrowed down to ensure safe and reliable operation. This leads to inefficient use of the battery and still doesn’t give a 100% guarantee to prevent unsafe situations or circumstances damaging to the battery.
The overall objective of EVERLASTING is to develop model-based, flexible, modular, cost-effective battery management technologies based on an in-depth understanding of the internal state of the battery enabling pro-active control at the cell-level. This “next-generation” model-based and pro-active BMS will help in solving the issues of range anxiety, cost, reliability and safety of EV battery systems. Developing techniques to cost effectively monitor batteries and manage performance measures while predicting battery aging and failure remains a key technological challenge. No single innovation will completely solve these challenges. However, comprehensive solutions that combine data from a multitude of novel sensors with advanced models and efficient thermal and load management techniques can drastically enhance the utilization and rate capabilities of battery systems within safe limits while extending their lifetime and reliability. A management system that successfully addresses all these needs would be a game changer.
Combining the more accurate battery information with road, vehicle and driver data, leads to a more accurate driver feedback. This will increase trust and leads to a lower range anxiety.
The second objective of EVERLASTING is to develop a standardized BMS architecture (hardware, software and interfaces) and develop reliability testing procedures for BMS to further increase reliability. This information will be publicly accessible, so that the whole EV community can benefit from this pre-standardization work. The information on the BMS architecture and the BMS reliability testing procedures will be shared with appropriate standardization working groups outside of EVERLASTING, so that this can be the starting base for further standardization work to the benefit of the whole EV community.
The overall objective of EVERLASTING is to develop model-based, flexible, modular, cost-effective battery management technologies based on an in-depth understanding of the internal state of the battery enabling pro-active control at the cell-level. This “next-generation” model-based and pro-active BMS will help in solving the issues of range anxiety, cost, reliability and safety of EV battery systems. Developing techniques to cost effectively monitor batteries and manage performance measures while predicting battery aging and failure remains a key technological challenge. No single innovation will completely solve these challenges. However, comprehensive solutions that combine data from a multitude of novel sensors with advanced models and efficient thermal and load management techniques can drastically enhance the utilization and rate capabilities of battery systems within safe limits while extending their lifetime and reliability. A management system that successfully addresses all these needs would be a game changer.
Combining the more accurate battery information with road, vehicle and driver data, leads to a more accurate driver feedback. This will increase trust and leads to a lower range anxiety.
The second objective of EVERLASTING is to develop a standardized BMS architecture (hardware, software and interfaces) and develop reliability testing procedures for BMS to further increase reliability. This information will be publicly accessible, so that the whole EV community can benefit from this pre-standardization work. The information on the BMS architecture and the BMS reliability testing procedures will be shared with appropriate standardization working groups outside of EVERLASTING, so that this can be the starting base for further standardization work to the benefit of the whole EV community.
Objective 1: develop model-based, flexible, modular, cost-effective battery management technologies based on an in-depth understanding of the internal state of the battery enabling pro-active control at the cell-level.
The EVERLASTING partners selected a Li-ion battery cell (18650 NMC chemistry) characterized in detail via performance/ageing tests executed by VITO and TUV SUD. Ageing tests (life/drive cycling & calendar ageing) using representative charge/discharge profiles, provided data supporting battery modeling & BMS algorithms research.
TUM and TU/e developed reduced electrochemical models taking into account modelling errors vs calculation time. VITO studied machine learning techniques to improve the accuracy of SOC/SOH algorithms.
Siemens made significant progress on virtual test benches development within Simcenter Amesim supporting advanced BMS research & development. This tool allows a fast & efficient way to consider battery ageing when developing BMS strategies, deal with subsystems faults and ways to protect the battery.
LION Smart developed a new BMS (hardware-software-interfaces) based on the needs of the EVERLASTING technologies. A BMS master-slave configuration was designed, tested and implemented in a full-size 40 kWh demonstrator battery pack, installed in VOLTIA’s electric van. The demonstrator battery pack was integrated using a battery swap procedure.
The VOLTIA battery pack demonstrator allowed validation of LION Smart Standardized BMS architecture, RWTH battery Flexible operating range and Online-EIS, CEA thermal runaway multiphysics sensors and ALGOLiON safety diagnostic algorithms predicting safety hazards.
Thermal runaway precursor detection is a key point to improve safety. The capacity to detect those precursors was evaluated & demonstrated at early stage (ALGOLiON’s early warning diagnostic algorithm based on battery voltage and current measurements) and at middle and late stage (CEA strain sensors). To support this development, TUV SUD defined a safety testing toolset & conducted lab safety tests. Some test methods were improved e.g. the abuse method to create a soft internal short circuit.
To improve battery lifetime, thermal and load management strategies were studied. For the thermal management a combined active/passive cooling solution was evaluated on a lab test setup by CEA. For the load management strategies, evaluation by TUM focused on dissipative balancing with promising results.
EVERLASTING aimed to extend driving range at same battery size without impacting user comfort by expanding battery’s operational envelope within the safety limits combined with driveline energy management. VDL ETS, with TU/e support, developed & validated models using a VDL Citea SLFe-120 electric city-bus via extreme climate & aerodynamic tests, achieving good results on lowering errors on range estimation and on increasing driving range.
Objective 2: develop standardized BMS architecture & BMS reliability testing procedures.
Existing BMS standards & standardization potential of EVERLASTING BMS architectures were studied. Information is shared via a standardization workshop, contacts with standardization bodies and the EVERLASTING website (https://everlasting-project.eu/results/).
EVERLASTING Dissemination: 37 journal publications, 20 peer reviewed & 33 non-peer reviewed conference publications, 2 PhD thesis, 14 public events & 33 public deliverables (of which 13 white papers). EVERLASTING participated in the Open Research Data Pilot (ORDP) & 27 open datasets were shared on 4TU.ResearchData repository.
The EVERLASTING partners selected a Li-ion battery cell (18650 NMC chemistry) characterized in detail via performance/ageing tests executed by VITO and TUV SUD. Ageing tests (life/drive cycling & calendar ageing) using representative charge/discharge profiles, provided data supporting battery modeling & BMS algorithms research.
TUM and TU/e developed reduced electrochemical models taking into account modelling errors vs calculation time. VITO studied machine learning techniques to improve the accuracy of SOC/SOH algorithms.
Siemens made significant progress on virtual test benches development within Simcenter Amesim supporting advanced BMS research & development. This tool allows a fast & efficient way to consider battery ageing when developing BMS strategies, deal with subsystems faults and ways to protect the battery.
LION Smart developed a new BMS (hardware-software-interfaces) based on the needs of the EVERLASTING technologies. A BMS master-slave configuration was designed, tested and implemented in a full-size 40 kWh demonstrator battery pack, installed in VOLTIA’s electric van. The demonstrator battery pack was integrated using a battery swap procedure.
The VOLTIA battery pack demonstrator allowed validation of LION Smart Standardized BMS architecture, RWTH battery Flexible operating range and Online-EIS, CEA thermal runaway multiphysics sensors and ALGOLiON safety diagnostic algorithms predicting safety hazards.
Thermal runaway precursor detection is a key point to improve safety. The capacity to detect those precursors was evaluated & demonstrated at early stage (ALGOLiON’s early warning diagnostic algorithm based on battery voltage and current measurements) and at middle and late stage (CEA strain sensors). To support this development, TUV SUD defined a safety testing toolset & conducted lab safety tests. Some test methods were improved e.g. the abuse method to create a soft internal short circuit.
To improve battery lifetime, thermal and load management strategies were studied. For the thermal management a combined active/passive cooling solution was evaluated on a lab test setup by CEA. For the load management strategies, evaluation by TUM focused on dissipative balancing with promising results.
EVERLASTING aimed to extend driving range at same battery size without impacting user comfort by expanding battery’s operational envelope within the safety limits combined with driveline energy management. VDL ETS, with TU/e support, developed & validated models using a VDL Citea SLFe-120 electric city-bus via extreme climate & aerodynamic tests, achieving good results on lowering errors on range estimation and on increasing driving range.
Objective 2: develop standardized BMS architecture & BMS reliability testing procedures.
Existing BMS standards & standardization potential of EVERLASTING BMS architectures were studied. Information is shared via a standardization workshop, contacts with standardization bodies and the EVERLASTING website (https://everlasting-project.eu/results/).
EVERLASTING Dissemination: 37 journal publications, 20 peer reviewed & 33 non-peer reviewed conference publications, 2 PhD thesis, 14 public events & 33 public deliverables (of which 13 white papers). EVERLASTING participated in the Open Research Data Pilot (ORDP) & 27 open datasets were shared on 4TU.ResearchData repository.
EVERLASTING had impact on commercial exploitation, high-quality scientific research & education. In the exploitation plan, 38 Technology Components with clear exploitation potential were identified, most of them reaching TRL 7-9 & included in partner's portfolio of commercial products/services.
Innovations going beyond state of the art:
- safer batteries: diagnostic methods predicting thermal runaway events much earlier than existing methods (improvement from seconds to days before event). This extends the state of the art not only in terms of providing the earliest detection of precursors to hazards while the battery is still operating safely, but also enables the
ultimate goal of preventing fire & explosion events. The algorithm output can be used to trigger measures for avoiding thermal runaway. New standards are calling for 5 minutes early warning of thermal runaway in EV batteries. Thanks to EVERLASTING innovations EV OEMS can exceed this requirement.
- more performant batteries and driveline energy management increasing driving range up to 5.6% & reduce average range estimation error to 3.2%
- improved tool chain for design/testing of BMS leading to quicker & less expensive testing & more reliable products
- intelligent BMS slaves: precise monitoring leads to safer & more reliable batteries at lower costs due to longer lifetime
On a wider societal level EVERLASTING results contribute to more safe, reliable and performant batteries ultimately resulting in a further uptake of electric mobility.
Innovations going beyond state of the art:
- safer batteries: diagnostic methods predicting thermal runaway events much earlier than existing methods (improvement from seconds to days before event). This extends the state of the art not only in terms of providing the earliest detection of precursors to hazards while the battery is still operating safely, but also enables the
ultimate goal of preventing fire & explosion events. The algorithm output can be used to trigger measures for avoiding thermal runaway. New standards are calling for 5 minutes early warning of thermal runaway in EV batteries. Thanks to EVERLASTING innovations EV OEMS can exceed this requirement.
- more performant batteries and driveline energy management increasing driving range up to 5.6% & reduce average range estimation error to 3.2%
- improved tool chain for design/testing of BMS leading to quicker & less expensive testing & more reliable products
- intelligent BMS slaves: precise monitoring leads to safer & more reliable batteries at lower costs due to longer lifetime
On a wider societal level EVERLASTING results contribute to more safe, reliable and performant batteries ultimately resulting in a further uptake of electric mobility.