Battery Control Algorithms
Control algorithms and sensing and cell-actuation circuitry can extend the lifetime and enhance the performance of battery systems. NREL's battery management research focuses in areas of:
- Extending lifespan managing temperatures, charging, and electrical duty cycles to optimize lifetime
- Enhancing performance reducing conservatism in operating limits using real-time knowledge of internal electrochemical dynamics
- Improving safety detecting and actively accommodating battery pack internal shorts.
Researchers evaluate, demonstrate, and validate control strategies by employing hardware-in-the-loop strategies at the cell and pack levels. As the nation's most credible and complete clearinghouse for validated and up-to-date transportation energy efficiency statistics, data analysis, and tools, NREL provides the accurate information on battery state of health needed to facilitate fleet electric-drive vehicle (EDV) management and adoption.
NREL's algorithms are unique in their application of physics-based models for battery control and estimation. Compared to rule-based algorithms, physics reference models simplify the development of controls, and ensure that controls will be applicable to the full range of possible operating conditions and environments. This permits reuse of algorithms across battery technologies and mitigates risk of incidents due to unsafe battery operation.
Electric Vehicle Charge Control
NREL has partnered with University of Colorado at Boulder to develop an optimal control strategy for all-electric vehicle (EV) chargers that minimizes the main factors in battery calendar life degradation time spent at high states of charge and peak temperatures caused by high C-rate charging. Simulations have demonstrated potential for optimal charge control to extend battery life. This technology also allows EV chargers to intelligently respond to variable pricing of grid-fed electricity, automatically adjusting charging times to lower-cost, non-peak periods.
NREL has partnered with University of Washington to develop fast-running models of charge and ion transport processes governing battery electrochemical dynamics. These models have been compiled and implemented on real-time controllers. Combined with estimation algorithms and model predictive control, the electrochemical control algorithms make it possible, for example, to intelligently manage charging for the longest possible battery life. These algorithms also provide a wider range of charge/discharge operation, particularly at cold temperatures, where battery performance often suffers.
This model-based method enables a supervisory controller to dynamically adjust control limits over the course of a battery's lifetime, giving the owner the best possible performance from a long-lasting battery. The prognostic-based control accommodates differential battery aging pathways that are dependent on environment and driver behavior. Cell- and pack-level versions of the algorithms are being analyzed by NREL in partnership with Eaton Corporation.
Active Cell Balancing
NREL is working with Utah State University, Ford, and University of Colorado to develop an active cell electrical management system enabling cell differential control. This technology:
- Provides access to the pack's entire energy, rather than being limited by the weakest cell in a series string
- Extends pack lifetime by differential control of strong versus weak cells
- Reduces lifetime cell imbalance growth that can be caused by factors such as pack temperature gradients and slight manufacturing variances amongst cells
- Allows partial pack functionality with a failed cell.
This technology may extend the lifetime of an automotive pack well beyond 1015 years, including in a second-use environment. It is expected that the active cell management system will reduce the cost of today's EDV battery packs by replacing today's centralized DC-DC converters with cell-level converters.
NREL has created analysis tools for managing "big data" from fleets, including algorithms that trend battery health changes over time using raw current, voltage, and temperature measurements from battery packs in the field. The toolset enables fleet managers to determine the impact of factors such as charging and route selection on battery health and lifetime. These algorithms have been validated with controlled measurements of battery evaluation activities.
- University of Colorado at Boulder
- University of Washington
- Utah State University
- Ford Motor Company
- Eaton Corporation
- Smith Electric Vehicles
- BAE Systems
- ARPA-E AMPED
- University of Colorado at Colorado Springs
Learn more about NREL's battery control research in these publications.
- Accounting for Lithium-Ion Battery Degradation in Electric Vehicle Charging Optimization
- Advanced Models and Controls for Prediction and Extension of Battery Lifetime
- Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation
- Fail-Safe Design for Large Capacity Lithium-Ion Battery Systems